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Choi TJ, Malik A, Han SM, Kim CB. Differences in alternative splicing events in the adaptive strategies of two Daphnia galeata genotypes induced by fish kairomones. BMC Genomics 2024; 25:725. [PMID: 39060996 PMCID: PMC11282837 DOI: 10.1186/s12864-024-10643-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2024] [Accepted: 07/19/2024] [Indexed: 07/28/2024] Open
Abstract
BACKGROUND Daphnia galeata is a suitable model organism for investigating predator-induced defense. Genes and pathways exhibiting differential expression between fish kairomone-treated and untreated groups in D. galeata have been identified. However, understanding of the significance of alternative splicing, a crucial process of the regulation of gene expression in eukaryotes, to this mechanism remains limited. This study measured life-history traits and conducted short-read RNA sequencing and long-read isoform sequencing of two Korean D. galeata genotypes (KB1 and KE2) to uncover the genetic mechanism underlying their phenotypic plasticity under predation stress. RESULTS KB1 exhibited strategies to enhance fertility and decrease body length when exposed to fish kairomones, while KE2 deployed an adaptive strategy to increase body length. Full-length transcriptomes from KB1 and KE2 yielded 65,736 and 57,437 transcripts, respectively, of which 32 differentially expressed transcripts (DETs) were shared under predation stress across both genotypes. Prominent DETs common to both genotypes were related to energy metabolism and the immune system. Additionally, differential alternative splicing (DAS) events were detected in both genotypes in response to fish kairomones. DAS genes shared between both genotypes may indicate their significant role in the post-transcriptional stress response to fish predation. Calpain-3, involved in digestion and nutrient absorption, was identified as a DAS gene in both genotypes when exposed to fish kairomones. In addition, the gene encoding thymosin beta, which is related to growth, was found to be a statistically significant DAS only in KB1, while that encoding ultraspiracle protein, also associated with growth, was only identified in KE2. Moreover, transcripts encoding proteins such as EGF-like domain-containing protein, vitellogenin fused with superoxide dismutase, and others were identified overlapping between DAS events and DETs and potentially elucidating their association with the observed phenotypic variation in each genotype. CONCLUSIONS Our findings highlight the crucial role of alternative splicing in modulating transcriptome landscape under predation stress in D. galeata, emphasizing the requirement for integrating gene expression and splicing analyses in evolutionary adaptation studies.
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Affiliation(s)
- Tae-June Choi
- Department of Biotechnology, Sangmyung University, Seoul, 03016, Korea
| | - Adeel Malik
- Institute of Intelligence Informatics Technology, Sangmyung University, Seoul, 03016, Korea
| | - Seung-Min Han
- Department of Biotechnology, Sangmyung University, Seoul, 03016, Korea
| | - Chang-Bae Kim
- Department of Biotechnology, Sangmyung University, Seoul, 03016, Korea.
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2
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Garrido-Martín D, Calvo M, Reverter F, Guigó R. A fast non-parametric test of association for multiple traits. Genome Biol 2023; 24:230. [PMID: 37828616 PMCID: PMC10571397 DOI: 10.1186/s13059-023-03076-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Accepted: 09/27/2023] [Indexed: 10/14/2023] Open
Abstract
The increasing availability of multidimensional phenotypic data in large cohorts of genotyped individuals requires efficient methods to identify genetic effects on multiple traits. Permutational multivariate analysis of variance (PERMANOVA) offers a powerful non-parametric approach. However, it relies on permutations to assess significance, which hinders the analysis of large datasets. Here, we derive the limiting null distribution of the PERMANOVA test statistic, providing a framework for the fast computation of asymptotic p values. Our asymptotic test presents controlled type I error and high power, often outperforming parametric approaches. We illustrate its applicability in the context of QTL mapping and GWAS.
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Affiliation(s)
- Diego Garrido-Martín
- Department of Genetics, Microbiology and Statistics, Universitat de Barcelona (UB), Av. Diagonal 643, Barcelona, 08028, Spain.
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr. Aiguader 88, Barcelona, 08003, Catalonia, Spain.
| | - Miquel Calvo
- Department of Genetics, Microbiology and Statistics, Universitat de Barcelona (UB), Av. Diagonal 643, Barcelona, 08028, Spain
| | - Ferran Reverter
- Department of Genetics, Microbiology and Statistics, Universitat de Barcelona (UB), Av. Diagonal 643, Barcelona, 08028, Spain
| | - Roderic Guigó
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr. Aiguader 88, Barcelona, 08003, Catalonia, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Catalonia, Spain
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3
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Ebrahimie E, Rahimirad S, Tahsili M, Mohammadi-Dehcheshmeh M. Alternative RNA splicing in stem cells and cancer stem cells: Importance of transcript-based expression analysis. World J Stem Cells 2021; 13:1394-1416. [PMID: 34786151 PMCID: PMC8567453 DOI: 10.4252/wjsc.v13.i10.1394] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Revised: 06/21/2021] [Accepted: 09/14/2021] [Indexed: 02/06/2023] Open
Abstract
Alternative ribonucleic acid (RNA) splicing can lead to the assembly of different protein isoforms with distinctive functions. The outcome of alternative splicing (AS) can result in a complete loss of function or the acquisition of new functions. There is a gap in knowledge of abnormal RNA splice variants promoting cancer stem cells (CSCs), and their prospective contribution in cancer progression. AS directly regulates the self-renewal features of stem cells (SCs) and stem-like cancer cells. Notably, octamer-binding transcription factor 4A spliced variant of octamer-binding transcription factor 4 contributes to maintaining stemness properties in both SCs and CSCs. The epithelial to mesenchymal transition pathway regulates the AS events in CSCs to maintain stemness. The alternative spliced variants of CSCs markers, including cluster of differentiation 44, aldehyde dehydrogenase, and doublecortin-like kinase, α6β1 integrin, have pivotal roles in increasing self-renewal properties and maintaining the pluripotency of CSCs. Various splicing analysis tools are considered in this study. LeafCutter software can be considered as the best tool for differential splicing analysis and identification of the type of splicing events. Additionally, LeafCutter can be used for efficient mapping splicing quantitative trait loci. Altogether, the accumulating evidence re-enforces the fact that gene and protein expression need to be investigated in parallel with alternative splice variants.
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Affiliation(s)
- Esmaeil Ebrahimie
- School of Animal and Veterinary Sciences, The University of Adelaide, Adelaide 5005, South Australia, Australia
- La Trobe Genomics Research Platform, School of Life Sciences, College of Science, Health and Engineering, La Trobe University, Melbourne 3086, Australia
- School of Biosciences, The University of Melbourne, Melbourne 3010, Australia,
| | - Samira Rahimirad
- Department of Medical Genetics, National Institute of Genetic Engineering and Biotechnology, Tehran 1497716316, Iran
- Division of Urology, Department of Surgery, McGill University and the Research Institute of the McGill University Health Centre, Montreal H4A 3J1, Quebec, Canada
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4
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Jokar MH, Jafaripour S, Abdollahi N, Nazemipour M, Moradzadeh M, Mansournia MA. Serum lysyl oxidase concentration increases in long-standing systemic sclerosis: Can lysyl oxidase change over time? Arch Rheumatol 2021; 37:261-270. [PMID: 36017203 PMCID: PMC9377183 DOI: 10.46497/archrheumatol.2022.8977] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2021] [Accepted: 08/02/2021] [Indexed: 11/23/2022] Open
Abstract
Objectives
This study aims to investigate the association of serum lysyl oxidase (LOX) levels with systemic sclerosis (SSc), to examine the relationship between LOX and disease onset, and to evaluate the probable effects of hyperlipidemia on the circulating levels of LOX among patients with SSc. Patients and methods
Between May 2017 and November 2018, a total of 39 patients with SSc (2 males, 37 females; mean age: 46.6±12.3 years; range, 18 to 65 years) and 35 healthy controls (4 males, 31 females; mean age: 43.1±14.1 years; range, 18 to 65 years) were included. Serum LOX concentration was measured using the enzyme-linked immunoassay in triplicate. Results
We found higher levels of serum LOX in patients with SSc compared to healthy controls. There was a significant relationship between serum LOX levels and disease onset. Patients with long-standing disease demonstrated increased levels of LOX in the blood compared to the recent-onset group. Hyperlipidemia did not have a significant effect on circulating levels of LOX. There was a significant negative correlation between LOX levels and modified Rodnan Skin Score in the subgroup of patients with skin involvement only and in patients without gastrointestinal involvement. Conclusion
Our study findings show an increased level of LOX protein level in the blood of patients diagnosed with SSc. Hyperlipidemia seems not to affect the concentrations of LOX in the peripheral blood of patients with SSc.
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Affiliation(s)
- Mohammad Hassan Jokar
- Golestan Rheumatology Research Center, Sayad Shirazi Hospital, Golestan University of Medical Sciences, Gorgan, Iran
| | - Simin Jafaripour
- Department of Medical Genetics, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Nafiseh Abdollahi
- Golestan Rheumatology Research Center, Sayad Shirazi Hospital, Golestan University of Medical Sciences, Gorgan, Iran
| | - Maryam Nazemipour
- Psychosocial Health Research Institute, Iran University of Medical Sciences, Tehran, Iran
| | - Maliheh Moradzadeh
- Golestan Rheumatology Research Center, Sayad Shirazi Hospital, Golestan University of Medical Sciences, Gorgan, Iran
| | - Mohammad Ali Mansournia
- Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
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5
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Garrido-Martín D, Borsari B, Calvo M, Reverter F, Guigó R. Identification and analysis of splicing quantitative trait loci across multiple tissues in the human genome. Nat Commun 2021; 12:727. [PMID: 33526779 PMCID: PMC7851174 DOI: 10.1038/s41467-020-20578-2] [Citation(s) in RCA: 71] [Impact Index Per Article: 23.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2020] [Accepted: 12/02/2020] [Indexed: 12/13/2022] Open
Abstract
Alternative splicing (AS) is a fundamental step in eukaryotic mRNA biogenesis. Here, we develop an efficient and reproducible pipeline for the discovery of genetic variants that affect AS (splicing QTLs, sQTLs). We use it to analyze the GTEx dataset, generating a comprehensive catalog of sQTLs in the human genome. Downstream analysis of this catalog provides insight into the mechanisms underlying splicing regulation. We report that a core set of sQTLs is shared across multiple tissues. sQTLs often target the global splicing pattern of genes, rather than individual splicing events. Many also affect the expression of the same or other genes, uncovering regulatory loci that act through different mechanisms. sQTLs tend to be located in post-transcriptionally spliced introns, which would function as hotspots for splicing regulation. While many variants affect splicing patterns by altering the sequence of splice sites, many more modify the binding sites of RNA-binding proteins. Genetic variants affecting splicing can have a stronger phenotypic impact than those affecting gene expression.
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Affiliation(s)
- Diego Garrido-Martín
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr. Aiguader 88, Barcelona, 08003, Catalonia, Spain.
| | - Beatrice Borsari
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr. Aiguader 88, Barcelona, 08003, Catalonia, Spain
| | - Miquel Calvo
- Section of Statistics, Faculty of Biology, Universitat de Barcelona (UB), Av. Diagonal 643, Barcelona, 08028, Spain
| | - Ferran Reverter
- Section of Statistics, Faculty of Biology, Universitat de Barcelona (UB), Av. Diagonal 643, Barcelona, 08028, Spain
| | - Roderic Guigó
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr. Aiguader 88, Barcelona, 08003, Catalonia, Spain.
- Universitat Pompeu Fabra (UPF), Barcelona, Catalonia, Spain.
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6
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Andrade-Navarro MA, Mühlenberg K, Spruth EJ, Mah N, González-López A, Andreani T, Russ J, Huska MR, Muro EM, Fontaine JF, Amstislavskiy V, Soldatov A, Nietfeld W, Wanker EE, Priller J. RNA Sequencing of Human Peripheral Blood Cells Indicates Upregulation of Immune-Related Genes in Huntington's Disease. Front Neurol 2020; 11:573560. [PMID: 33329316 PMCID: PMC7731869 DOI: 10.3389/fneur.2020.573560] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2020] [Accepted: 11/06/2020] [Indexed: 11/13/2022] Open
Abstract
Huntington's disease (HD) is an autosomal dominantly inherited neurodegenerative disorder caused by a trinucleotide repeat expansion in the Huntingtin gene. As disease-modifying therapies for HD are being developed, peripheral blood cells may be used to indicate disease progression and to monitor treatment response. In order to investigate whether gene expression changes can be found in the blood of individuals with HD that distinguish them from healthy controls, we performed transcriptome analysis by next-generation sequencing (RNA-seq). We detected a gene expression signature consistent with dysregulation of immune-related functions and inflammatory response in peripheral blood from HD cases vs. controls, including induction of the interferon response genes, IFITM3, IFI6 and IRF7. Our results suggest that it is possible to detect gene expression changes in blood samples from individuals with HD, which may reflect the immune pathology associated with the disease.
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Affiliation(s)
- Miguel A Andrade-Navarro
- Faculty of Biology, Institute of Organismic and Molecular Evolution, Johannes Gutenberg University Mainz, Mainz, Germany
| | - Katja Mühlenberg
- Neuroproteomics, Max-Delbrück Center for Molecular Medicine, Berlin, Germany
| | - Eike J Spruth
- Department of Neuropsychiatry, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Nancy Mah
- Charité-Universitätsmedizin Berlin, Virchow-Klinikum, Berlin-Brandenburger Centrum für Regenerative Therapien, Berlin, Germany
| | - Adrián González-López
- Klinik f. Anästhesiologie m.S. operative Intensivmedizin, Virchow Klinikum, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Tommaso Andreani
- Faculty of Biology, Institute of Organismic and Molecular Evolution, Johannes Gutenberg University Mainz, Mainz, Germany
| | - Jenny Russ
- German Center for Neurodegenerative Diseases, Bonn, Germany
| | - Matthew R Huska
- Department for Computational Molecular Biology, Max Planck Institute for Molecular Genetics, Berlin, Germany
| | - Enrique M Muro
- Faculty of Biology, Institute of Organismic and Molecular Evolution, Johannes Gutenberg University Mainz, Mainz, Germany
| | - Jean-Fred Fontaine
- Faculty of Biology, Institute of Organismic and Molecular Evolution, Johannes Gutenberg University Mainz, Mainz, Germany
| | | | - Alexei Soldatov
- Max-Planck-Institute for Molecular Genetics, Berlin, Germany
| | | | - Erich E Wanker
- Neuroproteomics, Max-Delbrück Center for Molecular Medicine, Berlin, Germany.,German Centre for Neurodegenerative Diseases, Berlin Institute of Health, Berlin, Germany
| | - Josef Priller
- Department of Neuropsychiatry, Charité-Universitätsmedizin Berlin, Berlin, Germany.,German Centre for Neurodegenerative Diseases, Berlin Institute of Health, Berlin, Germany.,Centre for Clinical Brain Sciences, UK Dementia Research Institute, University of Edinburgh, Edinburgh, United Kingdom
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7
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Breschi A, Muñoz-Aguirre M, Wucher V, Davis CA, Garrido-Martín D, Djebali S, Gillis J, Pervouchine DD, Vlasova A, Dobin A, Zaleski C, Drenkow J, Danyko C, Scavelli A, Reverter F, Snyder MP, Gingeras TR, Guigó R. A limited set of transcriptional programs define major cell types. Genome Res 2020; 30:1047-1059. [PMID: 32759341 PMCID: PMC7397875 DOI: 10.1101/gr.263186.120] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2020] [Accepted: 04/29/2020] [Indexed: 12/12/2022]
Abstract
We have produced RNA sequencing data for 53 primary cells from different locations in the human body. The clustering of these primary cells reveals that most cells in the human body share a few broad transcriptional programs, which define five major cell types: epithelial, endothelial, mesenchymal, neural, and blood cells. These act as basic components of many tissues and organs. Based on gene expression, these cell types redefine the basic histological types by which tissues have been traditionally classified. We identified genes whose expression is specific to these cell types, and from these genes, we estimated the contribution of the major cell types to the composition of human tissues. We found this cellular composition to be a characteristic signature of tissues and to reflect tissue morphological heterogeneity and histology. We identified changes in cellular composition in different tissues associated with age and sex, and found that departures from the normal cellular composition correlate with histological phenotypes associated with disease.
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Affiliation(s)
- Alessandra Breschi
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, E-08003 Barcelona, Catalonia, Spain
- Universitat Pompeu Fabra (UPF), E-08003 Barcelona, Catalonia, Spain
- Department of Genetics, Stanford University, Stanford, California 94305, USA
| | - Manuel Muñoz-Aguirre
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, E-08003 Barcelona, Catalonia, Spain
- Universitat Politècnica de Catalunya. Departament d'Estadística i Investigació Operativa, 08034 Barcelona, Catalonia, Spain
| | - Valentin Wucher
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, E-08003 Barcelona, Catalonia, Spain
| | - Carrie A Davis
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York 11742, USA
| | - Diego Garrido-Martín
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, E-08003 Barcelona, Catalonia, Spain
- Universitat Pompeu Fabra (UPF), E-08003 Barcelona, Catalonia, Spain
| | - Sarah Djebali
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, E-08003 Barcelona, Catalonia, Spain
- Universitat Pompeu Fabra (UPF), E-08003 Barcelona, Catalonia, Spain
- Institut National de Recherche en Santé Digestive (IRSD), Université de Toulouse, Institut National de la Santé et de la Recherche Médicale (INSERM), Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (INRAE), École Nationale Vétérinaire de Toulouse (ENVT), Université Paul Sabatier (UPS), 31024 Toulouse, France
| | - Jesse Gillis
- Department of Genetics, Stanford University, Stanford, California 94305, USA
| | - Dmitri D Pervouchine
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, E-08003 Barcelona, Catalonia, Spain
- Skolkovo Institute for Science and Technology, Moscow, Russia 143025
| | - Anna Vlasova
- Research Institute of Molecular Pathology (IMP), Vienna Biocenter (VBC), 1030 Vienna, Austria
| | - Alexander Dobin
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York 11742, USA
| | - Chris Zaleski
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York 11742, USA
| | - Jorg Drenkow
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York 11742, USA
| | - Cassidy Danyko
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York 11742, USA
| | | | - Ferran Reverter
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, E-08003 Barcelona, Catalonia, Spain
- Universitat Pompeu Fabra (UPF), E-08003 Barcelona, Catalonia, Spain
| | - Michael P Snyder
- Department of Genetics, Stanford University, Stanford, California 94305, USA
| | - Thomas R Gingeras
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York 11742, USA
| | - Roderic Guigó
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, E-08003 Barcelona, Catalonia, Spain
- Universitat Pompeu Fabra (UPF), E-08003 Barcelona, Catalonia, Spain
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8
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Suresh S, Crease TJ, Cristescu ME, Chain FJJ. Alternative splicing is highly variable among Daphnia pulex lineages in response to acute copper exposure. BMC Genomics 2020; 21:433. [PMID: 32586292 PMCID: PMC7318467 DOI: 10.1186/s12864-020-06831-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2020] [Accepted: 06/15/2020] [Indexed: 01/02/2023] Open
Abstract
BACKGROUND Despite being one of the primary mechanisms of gene expression regulation in eukaryotes, alternative splicing is often overlooked in ecotoxicogenomic studies. The process of alternative splicing facilitates the production of multiple mRNA isoforms from a single gene thereby greatly increasing the diversity of the transcriptome and proteome. This process can be important in enabling the organism to cope with stressful conditions. Accurate identification of splice sites using RNA sequencing requires alignment to independent exonic positions within the genome, presenting bioinformatic challenges, particularly when using short read data. Although technological advances allow for the detection of splicing patterns on a genome-wide scale, very little is known about the extent of intraspecies variation in splicing patterns, particularly in response to environmental stressors. In this study, we used RNA-sequencing to study the molecular responses to acute copper exposure in three lineages of Daphnia pulex by focusing on the contribution of alternative splicing in addition to gene expression responses. RESULTS By comparing the overall gene expression and splicing patterns among all 15 copper-exposed samples and 6 controls, we identified 588 differentially expressed (DE) genes and 16 differentially spliced (DS) genes. Most of the DS genes (13) were not found to be DE, suggesting unique transcriptional regulation in response to copper that went unnoticed with conventional DE analysis. To understand the influence of genetic background on gene expression and alternative splicing responses to Cu, each of the three lineages was analyzed separately. In contrast to the overall analysis, each lineage had a higher proportion of unique DS genes than DE genes suggesting that genetic background has a larger influence on DS than on DE. Gene Ontology analysis revealed that some pathways involved in stress response were jointly regulated by DS and DE genes while others were regulated by only transcription or only splicing. CONCLUSIONS Our findings suggest an important role for alternative splicing in shaping transcriptome diversity in response to metal exposure in Daphnia, highlighting the importance of integrating splicing analyses with gene expression surveys to characterize molecular pathways in evolutionary and environmental studies.
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Affiliation(s)
- Sneha Suresh
- Department of Biological Sciences, University of Massachusetts Lowell, Lowell, MA, 01854, USA
- Present address: The Swire Institute of Marine Science and School of Biological Sciences, The University of Hong Kong, Pokfulam Road, Pok Fu Lam, Hong Kong SAR
| | - Teresa J Crease
- Department of Integrative Biology, University of Guelph, Guelph, ON, N1G 2W1, Canada
| | - Melania E Cristescu
- Department of Biology, McGill University, 1205 Docteur Penfield, Montreal, QC, H3A 1B1, Canada
| | - Frédéric J J Chain
- Department of Biological Sciences, University of Massachusetts Lowell, Lowell, MA, 01854, USA.
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9
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Bou Sleiman M, Frochaux MV, Andreani T, Osman D, Guigo R, Deplancke B. Enteric infection induces Lark-mediated intron retention at the 5' end of Drosophila genes. Genome Biol 2020; 21:4. [PMID: 31948480 PMCID: PMC6966827 DOI: 10.1186/s13059-019-1918-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2019] [Accepted: 12/09/2019] [Indexed: 01/15/2023] Open
Abstract
BACKGROUND RNA splicing is a key post-transcriptional mechanism that generates protein diversity and contributes to the fine-tuning of gene expression, which may facilitate adaptation to environmental challenges. Here, we employ a systems approach to study alternative splicing changes upon enteric infection in females from classical Drosophila melanogaster strains as well as 38 inbred lines. RESULTS We find that infection leads to extensive differences in isoform ratios, which results in a more diverse transcriptome with longer 5' untranslated regions (5'UTRs). We establish a role for genetic variation in mediating inter-individual splicing differences, with local splicing quantitative trait loci (local-sQTLs) being preferentially located at the 5' end of transcripts and directly upstream of splice donor sites. Moreover, local-sQTLs are more numerous in the infected state, indicating that acute stress unmasks a substantial number of silent genetic variants. We observe a general increase in intron retention concentrated at the 5' end of transcripts across multiple strains, whose prevalence scales with the degree of pathogen virulence. The length, GC content, and RNA polymerase II occupancy of these introns with increased retention suggest that they have exon-like characteristics. We further uncover that retained intron sequences are enriched for the Lark/RBM4 RNA binding motif. Interestingly, we find that lark is induced by infection in wild-type flies, its overexpression and knockdown alter survival, and tissue-specific overexpression mimics infection-induced intron retention. CONCLUSION Our collective findings point to pervasive and consistent RNA splicing changes, partly mediated by Lark/RBM4, as being an important aspect of the gut response to infection.
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Affiliation(s)
- Maroun Bou Sleiman
- Laboratory of Integrative Systems Physiology, Institue of Bioengineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Michael Vincent Frochaux
- Laboratory of System Biology and Genetics and Swiss Institute of Bioinformatics, Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Tommaso Andreani
- Computational Biology and Data Mining Group, Institute of Molecular Biology, Ackermannweg 4, 55128 Mainz, Germany
| | - Dani Osman
- Faculty of Sciences III and Azm Center for Research in Biotechnology and its Applications, LBA3B, EDST, Lebanese University, Tripoli, 1300 Lebanon
| | - Roderic Guigo
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, 08003 Barcelona, Catalonia Spain
| | - Bart Deplancke
- Laboratory of Integrative Systems Physiology, Institue of Bioengineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
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10
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Transcriptome variation in human populations and its potential application in forensics. J Appl Genet 2019; 60:319-328. [PMID: 31401728 PMCID: PMC6803616 DOI: 10.1007/s13353-019-00510-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2019] [Revised: 07/22/2019] [Accepted: 07/24/2019] [Indexed: 12/04/2022]
Abstract
This review presents the state-of-the-art in the forensic application of genetic methods driven by the research in population transcriptomics. In the first part of the review, the constraints of using classical genomic markers are shortly reviewed. In the second part, the developments in the field of inter-population diversity at the transcriptomic level are presented. Subsequently, a potential of population-specific transcriptomic markers in forensic science applications, including ascertaining population affiliation of human samples and cell mixtures separation, are presented.
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11
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de Jong TV, Moshkin YM, Guryev V. Gene expression variability: the other dimension in transcriptome analysis. Physiol Genomics 2019; 51:145-158. [DOI: 10.1152/physiolgenomics.00128.2018] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
Transcriptome sequencing is a powerful technique to study molecular changes that underlie the differences in physiological conditions and disease progression. A typical question that is posed in such studies is finding genes with significant changes between sample groups. In this respect expression variability is regarded as a nuisance factor that is primarily of technical origin and complicates the data analysis. However, it is becoming apparent that the biological variation in gene expression might be an important molecular phenotype that can affect physiological parameters. In this review we explore the recent literature on technical and biological variability in gene expression, sources of expression variability, (epi-)genetic hallmarks, and evolutionary constraints in genes with robust and variable gene expression. We provide an overview of recent findings on effects of external cues, such as diet and aging, on expression variability and on other biological phenomena that can be linked to it. We discuss metrics and tools that were developed for quantification of expression variability and highlight the importance of future studies in this direction. To assist the adoption of expression variability analysis, we also provide a detailed description and computer code, which can easily be utilized by other researchers. We also provide a reanalysis of recently published data to highlight the value of the analysis method.
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Affiliation(s)
- Tristan V. de Jong
- European Research Institute for the Biology of Ageing, University of Groningen, University Medical Centre Groningen, Groningen, The Netherlands
| | - Yuri M. Moshkin
- Institute of Cytology and Genetics, Siberian Branch of RAS, Novosibirsk, Russia
- Institute of Molecular and Cellular Biology, Siberian Branch of RAS, Novosibirsk, Russia
| | - Victor Guryev
- European Research Institute for the Biology of Ageing, University of Groningen, University Medical Centre Groningen, Groningen, The Netherlands
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12
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Xiong J, Jiang X, Ditsiou A, Gao Y, Sun J, Lowenstein ED, Huang S, Khaitovich P. Predominant patterns of splicing evolution on human, chimpanzee and macaque evolutionary lineages. Hum Mol Genet 2019; 27:1474-1485. [PMID: 29452398 DOI: 10.1093/hmg/ddy058] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2017] [Accepted: 02/12/2018] [Indexed: 11/14/2022] Open
Abstract
Although splicing is widespread and evolves rapidly among species, the mechanisms driving this evolution, as well as its functional implications, are not yet fully understood. We analyzed the evolution of splicing patterns based on transcriptome data from five tissues of humans, chimpanzees, rhesus macaques and mice. In total, 1526 exons and exon sets from 1236 genes showed significant splicing differences among primates. More than 60% of these differences represent constitutive-to-alternative exon transitions while an additional 25% represent changes in exon inclusion frequency. These two dominant evolutionary patterns have contrasting conservation, regulation and functional features. The sum of these features indicates that, despite their prevalence, constitutive-to-alternative exon transitions do not substantially contribute to long-term functional transcriptome changes. Conversely, changes in exon inclusion frequency appear to be functionally relevant, especially for changes taking place in the brain on the human evolutionary lineage.
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Affiliation(s)
- Jieyi Xiong
- Chinese Academy of Sciences-Max Planck Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 200031 Shanghai, China.,Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), 13125 Berlin, Germany
| | - Xi Jiang
- Chinese Academy of Sciences-Max Planck Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 200031 Shanghai, China
| | - Angeliki Ditsiou
- JBC/WTB Biocentre, University of Dundee, DD1 5EH Scotland, UK.,JMS Building, University of Sussex, BN1 9QG Brighton, UK
| | - Yang Gao
- Chinese Academy of Sciences-Max Planck Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 200031 Shanghai, China
| | - Jing Sun
- Chinese Academy of Sciences-Max Planck Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 200031 Shanghai, China
| | - Elijah D Lowenstein
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), 13125 Berlin, Germany.,Freie Universität Berlin, 14195 Berlin, Germany
| | - Shuyun Huang
- Chinese Academy of Sciences-Max Planck Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 200031 Shanghai, China.,School of Life Science and Technology, ShanghaiTech University, 201210 Shanghai, China
| | - Philipp Khaitovich
- Skolkovo Institute of Science and Technology, 143025 Skolkovo, Russia.,Max Planck Institute for Evolutionary Anthropology, 04103 Leipzig, Germany.,Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 200031 Shanghai, China
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13
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Uszczynska-Ratajczak B, Lagarde J, Frankish A, Guigó R, Johnson R. Towards a complete map of the human long non-coding RNA transcriptome. Nat Rev Genet 2018; 19:535-548. [PMID: 29795125 PMCID: PMC6451964 DOI: 10.1038/s41576-018-0017-y] [Citation(s) in RCA: 381] [Impact Index Per Article: 63.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Gene maps, or annotations, enable us to navigate the functional landscape of our genome. They are a resource upon which virtually all studies depend, from single-gene to genome-wide scales and from basic molecular biology to medical genetics. Yet present-day annotations suffer from trade-offs between quality and size, with serious but often unappreciated consequences for downstream studies. This is particularly true for long non-coding RNAs (lncRNAs), which are poorly characterized compared to protein-coding genes. Long-read sequencing technologies promise to improve current annotations, paving the way towards a complete annotation of lncRNAs expressed throughout a human lifetime.
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Affiliation(s)
| | - Julien Lagarde
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Catalonia, Spain
| | - Adam Frankish
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Roderic Guigó
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Catalonia, Spain
| | - Rory Johnson
- Department of Medical Oncology, Inselspital, University Hospital and University of Bern, Bern, Switzerland.
- Department of Biomedical Research (DBMR), University of Bern, Bern, Switzerland.
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14
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Alternatively spliced variants in Atlantic cod (Gadus morhua) support response to variable salinity environment. Sci Rep 2018; 8:11607. [PMID: 30072755 PMCID: PMC6072735 DOI: 10.1038/s41598-018-29723-w] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2017] [Accepted: 07/14/2018] [Indexed: 12/21/2022] Open
Abstract
Analysis of gill transcriptome of the Atlantic cod from the Baltic Sea demonstrated that alternatively spliced (AS) variants may be actively involved in the process of adaptation to altered salinity. Some AS variants of different genes, like phospholipase A2 group IVC (PLA2G4C), appeared only in fish exposed to altered salinity, while other isoforms of the same genes were present in all experimental groups. Novel sequence arrangements represent 89% of all AS in the Baltic cod compared to the Atlantic population. Profiles of modified pathways suggest that regulation by AS can afford specific changes of genes expressed in response to the environment. The AS variants appear to be involved in the response to stress by modifications of signalling in apoptosis pathways, an innate immunological response and pro-inflammatory process. Present results support the hypothesis that developing new AS variants could support genome complexity and reinforce the ability to fast adapt to local environments.
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15
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Thurman M, van Doorn J, Danzer B, Webb TR, Stamm S. Changes in Alternative Splicing as Pharmacodynamic Markers for Sudemycin D6. Biomark Insights 2017; 12:1177271917730557. [PMID: 28932105 PMCID: PMC5598794 DOI: 10.1177/1177271917730557] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2017] [Accepted: 08/08/2017] [Indexed: 12/12/2022] Open
Abstract
OBJECTIVE The aim of the study was to define pharmacodynamic markers for sudemycin D6, an experimental cancer drug that changes alternative splicing in human blood. METHODS Blood samples from 12 donors were incubated with sudemycin D6 for up to 24 hours, and at several time points total RNA from lymphocytes was prepared and the pre-messenger RNA (mRNA) splicing patterns were analyzed with reverse transcription-polymerase chain reaction. RESULTS Similar to immortalized cells, blood lymphocytes change alternative splicing due to sudemycin D6 treatment. However, lymphocytes in blood respond slower than immortalized cultured cells. CONCLUSIONS Exon skipping in the DUSP11 and SRRM1 pre-mRNAs are pharmacodynamic markers for sudemycin D6 treatment and show effects beginning at 9 hours after treatment.
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16
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Nogueira Jorge NA, Wajnberg G, Ferreira CG, de Sa Carvalho B, Passetti F. snoRNA and piRNA expression levels modified by tobacco use in women with lung adenocarcinoma. PLoS One 2017; 12:e0183410. [PMID: 28817650 PMCID: PMC5560661 DOI: 10.1371/journal.pone.0183410] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2017] [Accepted: 08/03/2017] [Indexed: 12/22/2022] Open
Abstract
Lung cancer is one of the most frequent types of cancer worldwide. Most patients are diagnosed at advanced stage and thus have poor prognosis. Smoking is a risk factor for lung cancer, however most smokers do not develop lung cancer while 20% of women with lung adenocarcinoma are non-smokers. Therefore, it is possible that these two groups present differences besides the smoking status, including differences in their gene expression signature. The altered expression patterns of non-coding RNAs in complex diseases make them potential biomarkers for diagnosis and treatment. We analyzed data from differentially and constitutively expressed PIWI-interacting RNAs and small nucleolar RNAs from publicly available small RNA high-throughput sequencing data in search of an expression pattern of non-coding RNA that could differentiate these two groups. Here, we report two sets of differentially expressed small non-coding RNAs identified in normal and tumoral tissues of women with lung adenocarcinoma, that discriminate between smokers and non-smokers. Our findings may offer new insights on metabolic alterations caused by tobacco and may be used for early diagnosis of lung cancer.
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Affiliation(s)
- Natasha Andressa Nogueira Jorge
- Laboratory of Functional Genomics and Bioinformatics, Oswaldo Cruz Institute, Fundação Oswaldo Cruz, Rio de Janeiro, RJ, Brazil
| | - Gabriel Wajnberg
- Laboratory of Functional Genomics and Bioinformatics, Oswaldo Cruz Institute, Fundação Oswaldo Cruz, Rio de Janeiro, RJ, Brazil
| | | | | | - Fabio Passetti
- Laboratory of Functional Genomics and Bioinformatics, Oswaldo Cruz Institute, Fundação Oswaldo Cruz, Rio de Janeiro, RJ, Brazil
- * E-mail:
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17
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Evidence of selection on splicing-associated loci in human populations and relevance to disease loci mapping. Sci Rep 2017; 7:5980. [PMID: 28729732 PMCID: PMC5519721 DOI: 10.1038/s41598-017-05744-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2016] [Accepted: 06/14/2017] [Indexed: 12/27/2022] Open
Abstract
We performed a whole-genome scan of genetic variants in splicing regulatory elements (SREs) and evaluated the extent to which natural selection has shaped extant patterns of variation in SREs. We investigated the degree of differentiation of single nucleotide polymorphisms (SNPs) in SREs among human populations and applied long-range haplotype- and multilocus allelic differentiation-based methods to detect selection signatures. We describe an approach, sampling a large number of loci across the genome from functional classes and using the consensus from multiple tests, for identifying candidates for selection signals. SRE SNPs in various SNP functional classes show different patterns of population differentiation compared with their non-SRE counterparts. Intronic regions display a greater enrichment for extreme population differentiation among the potentially tissue-dependent transcript ratio quantitative trait loci (trQTLs) than SRE SNPs in general and includ outlier trQTLs for cross-population composite likelihood ratio, suggesting that incorporation of context annotation for regulatory variation may lead to improved detection of signature of selection on these loci. The proportion of extremely rare SNPs disrupting SREs is significantly higher in European than in African samples. The approach developed here will be broadly useful for studies of function and disease-associated variation in the human genome.
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18
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Castelo-Szekely V, Arpat AB, Janich P, Gatfield D. Translational contributions to tissue specificity in rhythmic and constitutive gene expression. Genome Biol 2017. [PMID: 28622766 PMCID: PMC5473967 DOI: 10.1186/s13059-017-1222-2] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
Background The daily gene expression oscillations that underlie mammalian circadian rhythms show striking differences between tissues and involve post-transcriptional regulation. Both aspects remain poorly understood. We have used ribosome profiling to explore the contribution of translation efficiency to temporal gene expression in kidney and contrasted our findings with liver data available from the same mice. Results Rhythmic translation of constantly abundant messenger RNAs (mRNAs) affects largely non-overlapping transcript sets with distinct phase clustering in the two organs. Moreover, tissue differences in translation efficiency modulate the timing and amount of protein biosynthesis from rhythmic mRNAs, consistent with organ specificity in clock output gene repertoires and rhythmicity parameters. Our comprehensive datasets provided insights into translational control beyond temporal regulation. Between tissues, many transcripts show differences in translation efficiency, which are, however, of markedly smaller scale than mRNA abundance differences. Tissue-specific changes in translation efficiency are associated with specific transcript features and, intriguingly, globally counteracted and compensated transcript abundance variations, leading to higher similarity at the level of protein biosynthesis between both tissues. Conclusions We show that tissue specificity in rhythmic gene expression extends to the translatome and contributes to define the identities, the phases and the expression levels of rhythmic protein biosynthesis. Moreover, translational compensation of transcript abundance divergence leads to overall higher similarity at the level of protein production across organs. The unique resources provided through our study will serve to address fundamental questions of post-transcriptional control and differential gene expression in vivo. Electronic supplementary material The online version of this article (doi:10.1186/s13059-017-1222-2) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Violeta Castelo-Szekely
- Center for Integrative Genomics, University of Lausanne, Génopode, 1015, Lausanne, Switzerland
| | - Alaaddin Bulak Arpat
- Center for Integrative Genomics, University of Lausanne, Génopode, 1015, Lausanne, Switzerland.,Vital-IT, Swiss Institute of Bioinformatics, Génopode, 1015, Lausanne, Switzerland
| | - Peggy Janich
- Center for Integrative Genomics, University of Lausanne, Génopode, 1015, Lausanne, Switzerland
| | - David Gatfield
- Center for Integrative Genomics, University of Lausanne, Génopode, 1015, Lausanne, Switzerland.
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19
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Johnson M, Purdom E. Clustering of mRNA-Seq data based on alternative splicing patterns. Biostatistics 2017; 18:295-307. [PMID: 27780810 PMCID: PMC6415726 DOI: 10.1093/biostatistics/kxw044] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2016] [Revised: 05/16/2016] [Accepted: 08/12/2016] [Indexed: 01/18/2023] Open
Abstract
Sequencing of messenger RNA (mRNA) can provide estimates of the levels of individual isoforms within the cell. It remains to adapt many standard statistical methods commonly used for analyzing gene expression levels to take advantage of this additional information. One novel question is whether we can find clusters of samples that are distinguished not by their gene expression but by their isoform usage. We propose a novel approach for clustering mRNA-Seq data that identifies such clusters. We show via simulation that our methods are more sensitive to finding clusters based on isoform usage than standard clustering techniques. We demonstrate its performance by finding a technical artifact that resulted in different batches having different isoform usage patterns, and illustrate its usage on several The Cancer Genome Atlas datasets.
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20
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Nachshon A, Abu-Toamih Atamni HJ, Steuerman Y, Sheikh-Hamed R, Dorman A, Mott R, Dohm JC, Lehrach H, Sultan M, Shamir R, Sauer S, Himmelbauer H, Iraqi FA, Gat-Viks I. Dissecting the Effect of Genetic Variation on the Hepatic Expression of Drug Disposition Genes across the Collaborative Cross Mouse Strains. Front Genet 2016; 7:172. [PMID: 27761138 PMCID: PMC5050206 DOI: 10.3389/fgene.2016.00172] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2016] [Accepted: 09/09/2016] [Indexed: 12/26/2022] Open
Abstract
A central challenge in pharmaceutical research is to investigate genetic variation in response to drugs. The Collaborative Cross (CC) mouse reference population is a promising model for pharmacogenomic studies because of its large amount of genetic variation, genetic reproducibility, and dense recombination sites. While the CC lines are phenotypically diverse, their genetic diversity in drug disposition processes, such as detoxification reactions, is still largely uncharacterized. Here we systematically measured RNA-sequencing expression profiles from livers of 29 CC lines under baseline conditions. We then leveraged a reference collection of metabolic biotransformation pathways to map potential relations between drugs and their underlying expression quantitative trait loci (eQTLs). By applying this approach on proximal eQTLs, including eQTLs acting on the overall expression of genes and on the expression of particular transcript isoforms, we were able to construct the organization of hepatic eQTL-drug connectivity across the CC population. The analysis revealed a substantial impact of genetic variation acting on drug biotransformation, allowed mapping of potential joint genetic effects in the context of individual drugs, and demonstrated crosstalk between drug metabolism and lipid metabolism. Our findings provide a resource for investigating drug disposition in the CC strains, and offer a new paradigm for integrating biotransformation reactions to corresponding variations in DNA sequences.
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Affiliation(s)
- Aharon Nachshon
- Department of Cell Research and Immunology, Faculty of Life Sciences, Tel-Aviv University Tel-Aviv, Israel
| | - Hanifa J Abu-Toamih Atamni
- Department of Clinical Microbiology and Immunology, Sackler Faculty of Medicine, Tel- Aviv University Tel-Aviv, Israel
| | - Yael Steuerman
- Department of Cell Research and Immunology, Faculty of Life Sciences, Tel-Aviv University Tel-Aviv, Israel
| | - Roa'a Sheikh-Hamed
- Department of Clinical Microbiology and Immunology, Sackler Faculty of Medicine, Tel- Aviv University Tel-Aviv, Israel
| | - Alexandra Dorman
- Department of Clinical Microbiology and Immunology, Sackler Faculty of Medicine, Tel- Aviv University Tel-Aviv, Israel
| | - Richard Mott
- Genetics Institute, University College of London London, UK
| | - Juliane C Dohm
- Genomics Unit, Center for Genomic RegulationBarcelona, Spain; Universitat Pompeu FabraBarcelona, Spain; Department of Biotechnology, University of Natural Resources and Life Sciences Vienna (BOKU)Vienna, Austria
| | - Hans Lehrach
- Department of Vertebrate Genomics, Max Planck Institute for Molecular Genetics Berlin, Germany
| | - Marc Sultan
- Department of Vertebrate Genomics, Max Planck Institute for Molecular Genetics Berlin, Germany
| | - Ron Shamir
- The Blavatnik School of Computer Science, Tel Aviv University Tel Aviv, Israel
| | - Sascha Sauer
- Department of Vertebrate Genomics, Max Planck Institute for Molecular GeneticsBerlin, Germany; CU Systems Medicine, University of WürzburgWürzburg, Germany
| | - Heinz Himmelbauer
- Genomics Unit, Center for Genomic RegulationBarcelona, Spain; Universitat Pompeu FabraBarcelona, Spain; Department of Biotechnology, University of Natural Resources and Life Sciences Vienna (BOKU)Vienna, Austria
| | - Fuad A Iraqi
- Department of Clinical Microbiology and Immunology, Sackler Faculty of Medicine, Tel- Aviv University Tel-Aviv, Israel
| | - Irit Gat-Viks
- Department of Cell Research and Immunology, Faculty of Life Sciences, Tel-Aviv University Tel-Aviv, Israel
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21
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Long non-coding RNAs display higher natural expression variation than protein-coding genes in healthy humans. Genome Biol 2016; 17:14. [PMID: 26821746 PMCID: PMC4731934 DOI: 10.1186/s13059-016-0873-8] [Citation(s) in RCA: 102] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2015] [Accepted: 01/06/2016] [Indexed: 02/06/2023] Open
Abstract
Background Long non-coding RNAs (lncRNAs) are increasingly implicated as gene regulators and may ultimately be more numerous than protein-coding genes in the human genome. Despite large numbers of reported lncRNAs, reference annotations are likely incomplete due to their lower and tighter tissue-specific expression compared to mRNAs. An unexplored factor potentially confounding lncRNA identification is inter-individual expression variability. Here, we characterize lncRNA natural expression variability in human primary granulocytes. Results We annotate granulocyte lncRNAs and mRNAs in RNA-seq data from 10 healthy individuals, identifying multiple lncRNAs absent from reference annotations, and use this to investigate three known features (higher tissue-specificity, lower expression, and reduced splicing efficiency) of lncRNAs relative to mRNAs. Expression variability was examined in seven individuals sampled three times at 1- or more than 1-month intervals. We show that lncRNAs display significantly more inter-individual expression variability compared to mRNAs. We confirm this finding in two independent human datasets by analyzing multiple tissues from the GTEx project and lymphoblastoid cell lines from the GEUVADIS project. Using the latter dataset we also show that including more human donors into the transcriptome annotation pipeline allows identification of an increasing number of lncRNAs, but minimally affects mRNA gene number. Conclusions A comprehensive annotation of lncRNAs is known to require an approach that is sensitive to low and tight tissue-specific expression. Here we show that increased inter-individual expression variability is an additional general lncRNA feature to consider when creating a comprehensive annotation of human lncRNAs or proposing their use as prognostic or disease markers. Electronic supplementary material The online version of this article (doi:10.1186/s13059-016-0873-8) contains supplementary material, which is available to authorized users.
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22
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Mirsafian H, Manda SS, Mitchell CJ, Sreenivasamurthy S, Ripen AM, Mohamad SB, Merican AF, Pandey A. Long non-coding RNA expression in primary human monocytes. Genomics 2016; 108:37-45. [PMID: 26778813 DOI: 10.1016/j.ygeno.2016.01.002] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2015] [Revised: 12/27/2015] [Accepted: 01/01/2016] [Indexed: 12/23/2022]
Abstract
Long non-coding RNAs (lncRNAs) have been shown to possess a wide range of functions in both cellular and developmental processes including cancers. Although some of the lncRNAs have been implicated in the regulation of the immune response, the exact function of the large majority of lncRNAs still remains unknown. In this study, we characterized the lncRNAs in human primary monocytes, an essential component of the innate immune system. We performed RNA sequencing of monocytes from four individuals and combined our data with eleven other publicly available datasets. Our analysis led to identification of ~8000 lncRNAs of which >1000 have not been previously reported in monocytes. PCR-based validation of a subset of the identified novel long intergenic noncoding RNAs (lincRNAs) revealed distinct expression patterns. Our study provides a landscape of lncRNAs in monocytes, which could facilitate future experimental studies to characterize the functions of these molecules in the innate immune system.
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Affiliation(s)
- Hoda Mirsafian
- Institute of Biological Sciences, Faculty of Science, University of Malaya, 50603 Kuala Lumpur, Malaysia
| | - Srinivas Srikanth Manda
- Institute of Bioinformatics, International Technology Park, Bangalore, 560066, India; McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Centre of Bioinformatics, Bioinformatics Centre, School of Life Sciences, Pondicherry University, Puducherry 605 014, India
| | - Christopher J Mitchell
- McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Sreelakshmi Sreenivasamurthy
- Institute of Bioinformatics, International Technology Park, Bangalore, 560066, India; McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Adiratna Mat Ripen
- Allergy and Immunology Research Centre, Institute for Medical Research, Jalan Pahang, 50588 Kuala Lumpur, Malaysia
| | - Saharuddin Bin Mohamad
- Institute of Biological Sciences, Faculty of Science, University of Malaya, 50603 Kuala Lumpur, Malaysia; Centre of Research for Computational Sciences and Informatics in Biology, Bioindustry, Environment, Agriculture and Healthcare (CRYSTAL), University of Malaya, 50603 Kuala Lumpur, Malaysia
| | - Amir Feisal Merican
- Institute of Biological Sciences, Faculty of Science, University of Malaya, 50603 Kuala Lumpur, Malaysia; Centre of Research for Computational Sciences and Informatics in Biology, Bioindustry, Environment, Agriculture and Healthcare (CRYSTAL), University of Malaya, 50603 Kuala Lumpur, Malaysia.
| | - Akhilesh Pandey
- Institute of Bioinformatics, International Technology Park, Bangalore, 560066, India; McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Department of Biological Chemistry, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
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23
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Hestand MS, Zeng Z, Coleman SJ, Liu J, MacLeod JN. Tissue Restricted Splice Junctions Originate Not Only from Tissue-Specific Gene Loci, but Gene Loci with a Broad Pattern of Expression. PLoS One 2015; 10:e0144302. [PMID: 26713731 PMCID: PMC4695084 DOI: 10.1371/journal.pone.0144302] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2015] [Accepted: 11/16/2015] [Indexed: 12/29/2022] Open
Abstract
Cellular mechanisms that achieve protein diversity in eukaryotes are multifaceted, including transcriptional components such as RNA splicing. Through alternative splicing, a single protein-coding gene can generate multiple mRNA transcripts and protein isoforms, some of which are tissue-specific. We have conducted qualitative and quantitative analyses of the Bodymap 2.0 messenger RNA-sequencing data from 16 human tissue samples and identified 209,363 splice junctions. Of these, 22,231 (10.6%) were not previously annotated and 21,650 (10.3%) were expressed in a tissue-restricted pattern. Tissue-restricted alternative splicing was found to be widespread, with approximately 65% of expressed multi-exon genes containing at least one tissue-specific splice junction. Interestingly, we observed many tissue-specific splice junctions not only in genes expressed in one or a few tissues, but also from gene loci with a broad pattern of expression.
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Affiliation(s)
- Matthew S. Hestand
- Gluck Equine Research Center, Department of Veterinary Science, University of Kentucky, Lexington, KY, United States of America
| | - Zheng Zeng
- Department of Computer Science, University of Kentucky, Lexington, KY, United States of America
| | - Stephen J. Coleman
- Gluck Equine Research Center, Department of Veterinary Science, University of Kentucky, Lexington, KY, United States of America
| | - Jinze Liu
- Department of Computer Science, University of Kentucky, Lexington, KY, United States of America
| | - James N. MacLeod
- Gluck Equine Research Center, Department of Veterinary Science, University of Kentucky, Lexington, KY, United States of America
- * E-mail:
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24
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Leigh-Brown S, Goncalves A, Thybert D, Stefflova K, Watt S, Flicek P, Brazma A, Marioni JC, Odom DT. Regulatory Divergence of Transcript Isoforms in a Mammalian Model System. PLoS One 2015; 10:e0137367. [PMID: 26339903 PMCID: PMC4560434 DOI: 10.1371/journal.pone.0137367] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2015] [Accepted: 08/15/2015] [Indexed: 11/24/2022] Open
Abstract
Phenotypic differences between species are driven by changes in gene expression and, by extension, by modifications in the regulation of the transcriptome. Investigation of mammalian transcriptome divergence has been restricted to analysis of bulk gene expression levels and gene-internal splicing. Using allele-specific expression analysis in inter-strain hybrids of Mus musculus, we determined the contribution of multiple cellular regulatory systems to transcriptome divergence, including: alternative promoter usage, transcription start site selection, cassette exon usage, alternative last exon usage, and alternative polyadenylation site choice. Between mouse strains, a fifth of genes have variations in isoform usage that contribute to transcriptomic changes, half of which alter encoded amino acid sequence. Virtually all divergence in isoform usage altered the post-transcriptional regulatory instructions in gene UTRs. Furthermore, most genes with isoform differences between strains contain changes originating from multiple regulatory systems. This result indicates widespread cross-talk and coordination exists among different regulatory systems. Overall, isoform usage diverges in parallel with and independently to gene expression evolution, and the cis and trans regulatory contribution to each differs significantly.
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Affiliation(s)
- Sarah Leigh-Brown
- University of Cambridge, Cancer Research UK - Cambridge Institute, Li Ka Shing Centre, Cambridge, United Kingdom
| | - Angela Goncalves
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom
| | - David Thybert
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom
| | - Klara Stefflova
- California Institute of Technology, Division of Biology, Pasadena, California, United States of America
| | - Stephen Watt
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom
| | - Paul Flicek
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom
| | - Alvis Brazma
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom
| | - John C. Marioni
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom
| | - Duncan T. Odom
- University of Cambridge, Cancer Research UK - Cambridge Institute, Li Ka Shing Centre, Cambridge, United Kingdom
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom
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25
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Melé M, Ferreira PG, Reverter F, DeLuca DS, Monlong J, Sammeth M, Young TR, Goldmann JM, Pervouchine DD, Sullivan TJ, Johnson R, Segrè AV, Djebali S, Niarchou A, Wright FA, Lappalainen T, Calvo M, Getz G, Dermitzakis ET, Ardlie KG, Guigó R. Human genomics. The human transcriptome across tissues and individuals. Science 2015; 348:660-5. [PMID: 25954002 PMCID: PMC4547472 DOI: 10.1126/science.aaa0355] [Citation(s) in RCA: 859] [Impact Index Per Article: 95.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Transcriptional regulation and posttranscriptional processing underlie many cellular and organismal phenotypes. We used RNA sequence data generated by Genotype-Tissue Expression (GTEx) project to investigate the patterns of transcriptome variation across individuals and tissues. Tissues exhibit characteristic transcriptional signatures that show stability in postmortem samples. These signatures are dominated by a relatively small number of genes—which is most clearly seen in blood—though few are exclusive to a particular tissue and vary more across tissues than individuals. Genes exhibiting high interindividual expression variation include disease candidates associated with sex, ethnicity, and age. Primary transcription is the major driver of cellular specificity, with splicing playing mostly a complementary role; except for the brain, which exhibits a more divergent splicing program. Variation in splicing, despite its stochasticity, may play in contrast a comparatively greater role in defining individual phenotypes.
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Affiliation(s)
- Marta Melé
- Center for Genomic Regulation (CRG), Barcelona, Catalonia, Spain. Harvard Department of stem cell and regenerative biology, Harvard University, Cambridge, MA, USA
| | - Pedro G Ferreira
- Center for Genomic Regulation (CRG), Barcelona, Catalonia, Spain. Department of Genetic Medicine and Development, University of Geneva, Geneva, Switzerland. Institute for Genetics and Genomics in Geneva (iGE3), University of Geneva, Geneva, Switzerland. Swiss Institute of Bioinformatics, Geneva, Switzerland
| | - Ferran Reverter
- Center for Genomic Regulation (CRG), Barcelona, Catalonia, Spain. Facultat de Biologia, Universitat de Barcelona (UB), Barcelona, Catalonia, Spain. Universitat Pompeu Fabra (UPF), Barcelona, Catalonia, Spain
| | | | - Jean Monlong
- Center for Genomic Regulation (CRG), Barcelona, Catalonia, Spain. Universitat Pompeu Fabra (UPF), Barcelona, Catalonia, Spain. McGill University, Montreal, Canada
| | - Michael Sammeth
- Center for Genomic Regulation (CRG), Barcelona, Catalonia, Spain. Universitat Pompeu Fabra (UPF), Barcelona, Catalonia, Spain. National Institute for Scientific Computing (LNCC), Petropolis, Rio de Janeiro, Brazil
| | | | - Jakob M Goldmann
- Center for Genomic Regulation (CRG), Barcelona, Catalonia, Spain. Universitat Pompeu Fabra (UPF), Barcelona, Catalonia, Spain. Radboud University, Nijmegen, Netherlands
| | - Dmitri D Pervouchine
- Center for Genomic Regulation (CRG), Barcelona, Catalonia, Spain. Universitat Pompeu Fabra (UPF), Barcelona, Catalonia, Spain. Faculty of Bioengineering and Bioinformatics, Moscow State University, Leninskie Gory 1-73, 119992 Moscow, Russia
| | | | - Rory Johnson
- Center for Genomic Regulation (CRG), Barcelona, Catalonia, Spain. Universitat Pompeu Fabra (UPF), Barcelona, Catalonia, Spain
| | | | - Sarah Djebali
- Center for Genomic Regulation (CRG), Barcelona, Catalonia, Spain. Universitat Pompeu Fabra (UPF), Barcelona, Catalonia, Spain
| | - Anastasia Niarchou
- Department of Genetic Medicine and Development, University of Geneva, Geneva, Switzerland. Institute for Genetics and Genomics in Geneva (iGE3), University of Geneva, Geneva, Switzerland. Swiss Institute of Bioinformatics, Geneva, Switzerland
| | | | - Tuuli Lappalainen
- Department of Genetic Medicine and Development, University of Geneva, Geneva, Switzerland. Institute for Genetics and Genomics in Geneva (iGE3), University of Geneva, Geneva, Switzerland. Swiss Institute of Bioinformatics, Geneva, Switzerland. New York Genome Center, New York, NY, USA. Department of Systems Biology, Columbia University, New York, NY, USA
| | - Miquel Calvo
- Facultat de Biologia, Universitat de Barcelona (UB), Barcelona, Catalonia, Spain
| | - Gad Getz
- Broad Institute of MIT and Harvard, Cambridge, MA, USA. Cancer Center and Department of Pathology, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Emmanouil T Dermitzakis
- Department of Genetic Medicine and Development, University of Geneva, Geneva, Switzerland. Institute for Genetics and Genomics in Geneva (iGE3), University of Geneva, Geneva, Switzerland. Swiss Institute of Bioinformatics, Geneva, Switzerland
| | | | - Roderic Guigó
- Center for Genomic Regulation (CRG), Barcelona, Catalonia, Spain. Universitat Pompeu Fabra (UPF), Barcelona, Catalonia, Spain. Institut Hospital del Mar d'Investigacions Mèdiques (IMIM), Barcelona, Catalonia, Spain. Joint CRG-Barcelona Super Computing Center (BSC)-Institut de Recerca Biomedica (IRB) Program in Computational Biology, Barcelona, Catalonia, Spain.
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26
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Farkas MH, Au ED, Sousa ME, Pierce EA. RNA-Seq: Improving Our Understanding of Retinal Biology and Disease. Cold Spring Harb Perspect Med 2015; 5:a017152. [PMID: 25722474 PMCID: PMC4561396 DOI: 10.1101/cshperspect.a017152] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
Over the past several years, rapid technological advances have allowed for a dramatic increase in our knowledge and understanding of the transcriptional landscape, because of the ability to study gene expression in greater depth and with more detail than previously possible. To this end, RNA-Seq has quickly become one of the most widely used methods for studying transcriptomes of tissues and individual cells. Unlike previously favored analysis methods, RNA-Seq is extremely high-throughput, and is not dependent on an annotated transcriptome, laying the foundation for novel genetic discovery. Additionally, RNA-Seq derived transcriptomes provide a basis for widening the scope of research to identify potential targets in the treatment of retinal disease.
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Affiliation(s)
- Michael H Farkas
- Ocular Genomics Institute, Department of Ophthalmology, Massachusetts Eye and Ear Infirmary, Harvard Medical School, Boston, Massachusetts 02114
| | - Elizabeth D Au
- Ocular Genomics Institute, Department of Ophthalmology, Massachusetts Eye and Ear Infirmary, Harvard Medical School, Boston, Massachusetts 02114
| | - Maria E Sousa
- Ocular Genomics Institute, Department of Ophthalmology, Massachusetts Eye and Ear Infirmary, Harvard Medical School, Boston, Massachusetts 02114
| | - Eric A Pierce
- Ocular Genomics Institute, Department of Ophthalmology, Massachusetts Eye and Ear Infirmary, Harvard Medical School, Boston, Massachusetts 02114
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27
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Zhu D, Deng N, Bai C. A generalized dSpliceType framework to detect differential splicing and differential expression events using RNA-Seq. IEEE Trans Nanobioscience 2015; 14:192-202. [PMID: 25680210 DOI: 10.1109/tnb.2015.2388593] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Transcriptomes are routinely compared in term of a list of differentially expressed genes followed by functional enrichment analysis. Due to the technology limitations of microarray, the molecular mechanisms of differential expression is poorly understood. Using RNA-seq data, we propose a generalized dSpliceType framework to systematically investigate the synergistic and antagonistic effects of differential splicing and differential expression. We applied the method to two public RNA-seq data sets and compared the transcriptomes between treatment and control conditions. The generalized dSpliceType detects and prioritizes a list of genes that are differentially expressed and/or spliced. In particular, the multivariate dSpliceType is among the fist to utilize sequential dependency of normalized base-wise read coverage signals and capture biological variability among replicates using a multivariate statistical model. We compared dSpliceType with two other methods in terms of five most common types of differential splicing events between two conditions using RNA-Seq. dSpliceType is free, available from http://dsplicetype.sourceforge.net/.
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28
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Tsoi LC, Iyer MK, Stuart PE, Swindell WR, Gudjonsson JE, Tejasvi T, Sarkar MK, Li B, Ding J, Voorhees JJ, Kang HM, Nair RP, Chinnaiyan AM, Abecasis GR, Elder JT. Analysis of long non-coding RNAs highlights tissue-specific expression patterns and epigenetic profiles in normal and psoriatic skin. Genome Biol 2015; 16:24. [PMID: 25723451 PMCID: PMC4311508 DOI: 10.1186/s13059-014-0570-4] [Citation(s) in RCA: 177] [Impact Index Per Article: 19.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2014] [Accepted: 12/11/2014] [Indexed: 01/09/2023] Open
Abstract
BACKGROUND Although analysis pipelines have been developed to use RNA-seq to identify long non-coding RNAs (lncRNAs), inference of their biological and pathological relevance remains a challenge. As a result, most transcriptome studies of autoimmune disease have only assessed protein-coding transcripts. RESULTS We used RNA-seq data from 99 lesional psoriatic, 27 uninvolved psoriatic, and 90 normal skin biopsies, and applied computational approaches to identify and characterize expressed lncRNAs. We detect 2,942 previously annotated and 1,080 novel lncRNAs which are expected to be skin specific. Notably, over 40% of the novel lncRNAs are differentially expressed and the proportions of differentially expressed transcripts among protein-coding mRNAs and previously-annotated lncRNAs are lower in psoriasis lesions versus uninvolved or normal skin. We find that many lncRNAs, in particular those that are differentially expressed, are co-expressed with genes involved in immune related functions, and that novel lncRNAs are enriched for localization in the epidermal differentiation complex. We also identify distinct tissue-specific expression patterns and epigenetic profiles for novel lncRNAs, some of which are shown to be regulated by cytokine treatment in cultured human keratinocytes. CONCLUSIONS Together, our results implicate many lncRNAs in the immunopathogenesis of psoriasis, and our results provide a resource for lncRNA studies in other autoimmune diseases.
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Affiliation(s)
- Lam C Tsoi
- />Department of Biostatistics, Center for Statistical Genetics, School of Public Health, M4614 SPH I, University of Michigan, Box 2029, Ann Arbor, MI 48109-2029 USA
| | - Matthew K Iyer
- />Michigan Center for Translational Pathology, University of Michigan Medical School, Ann Arbor, MI USA
| | - Philip E Stuart
- />Department of Dermatology, University of Michigan, Ann Arbor, MI USA
| | | | | | - Trilokraj Tejasvi
- />Department of Dermatology, University of Michigan, Ann Arbor, MI USA
- />Ann Arbor Veterans Affairs Hospital, University of Michigan, Ann Arbor, MI USA
| | - Mrinal K Sarkar
- />Department of Dermatology, University of Michigan, Ann Arbor, MI USA
| | - Bingshan Li
- />Department of Biostatistics, Center for Statistical Genetics, School of Public Health, M4614 SPH I, University of Michigan, Box 2029, Ann Arbor, MI 48109-2029 USA
- />Department of Molecular Physiology and Biophysics, Center for Quantitative Sciences, Vanderbilt University, Nashville, TN USA
| | - Jun Ding
- />Department of Biostatistics, Center for Statistical Genetics, School of Public Health, M4614 SPH I, University of Michigan, Box 2029, Ann Arbor, MI 48109-2029 USA
- />Laboratory of Genetics, National Institute on Aging, National Institutes of Health, Baltimore, MD USA
| | - John J Voorhees
- />Department of Dermatology, University of Michigan, Ann Arbor, MI USA
| | - Hyun M Kang
- />Department of Biostatistics, Center for Statistical Genetics, School of Public Health, M4614 SPH I, University of Michigan, Box 2029, Ann Arbor, MI 48109-2029 USA
| | - Rajan P Nair
- />Department of Dermatology, University of Michigan, Ann Arbor, MI USA
| | - Arul M Chinnaiyan
- />Michigan Center for Translational Pathology, University of Michigan Medical School, Ann Arbor, MI USA
- />Department of Pathology, University of Michigan Medical School, Ann Arbor, MI USA
- />Department of Urology, University of Michigan Medical School, Ann Arbor, MI USA
| | - Goncalo R Abecasis
- />Department of Biostatistics, Center for Statistical Genetics, School of Public Health, M4614 SPH I, University of Michigan, Box 2029, Ann Arbor, MI 48109-2029 USA
| | - James T Elder
- />Department of Dermatology, University of Michigan, Ann Arbor, MI USA
- />Ann Arbor Veterans Affairs Hospital, University of Michigan, Ann Arbor, MI USA
- />University of Michigan Medical School, 7412 Medical Sciences Building 1, 1301 E. Catherine, Ann Arbor, MI 48109-5675 USA
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29
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Enhanced transcriptome maps from multiple mouse tissues reveal evolutionary constraint in gene expression. Nat Commun 2015; 6:5903. [PMID: 25582907 PMCID: PMC4308717 DOI: 10.1038/ncomms6903] [Citation(s) in RCA: 58] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2014] [Accepted: 11/18/2014] [Indexed: 12/13/2022] Open
Abstract
Mice have been a long-standing model for human biology and disease. Here we characterize, by RNA sequencing, the transcriptional profiles of a large and heterogeneous collection of mouse tissues, augmenting the mouse transcriptome with thousands of novel transcript candidates. Comparison with transcriptome profiles in human cell lines reveals substantial conservation of transcriptional programmes, and uncovers a distinct class of genes with levels of expression that have been constrained early in vertebrate evolution. This core set of genes captures a substantial fraction of the transcriptional output of mammalian cells, and participates in basic functional and structural housekeeping processes common to all cell types. Perturbation of these constrained genes is associated with significant phenotypes including embryonic lethality and cancer. Evolutionary constraint in gene expression levels is not reflected in the conservation of the genomic sequences, but is associated with conserved epigenetic marking, as well as with characteristic post-transcriptional regulatory programme, in which sub-cellular localization and alternative splicing play comparatively large roles. The analysis of mammalian transcriptomes could provide new insights into human biology. Here the authors carry out RNA sequencing in a large collection of mouse tissues and compare these data to human transcriptome profiles, identifying a set of constrained genes that carry out basic cellular functions with remarkably constant expression levels across tissues and species.
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30
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Scelo G, Riazalhosseini Y, Greger L, Letourneau L, Gonzàlez-Porta M, Wozniak MB, Bourgey M, Harnden P, Egevad L, Jackson SM, Karimzadeh M, Arseneault M, Lepage P, How-Kit A, Daunay A, Renault V, Blanché H, Tubacher E, Sehmoun J, Viksna J, Celms E, Opmanis M, Zarins A, Vasudev NS, Seywright M, Abedi-Ardekani B, Carreira C, Selby PJ, Cartledge JJ, Byrnes G, Zavadil J, Su J, Holcatova I, Brisuda A, Zaridze D, Moukeria A, Foretova L, Navratilova M, Mates D, Jinga V, Artemov A, Nedoluzhko A, Mazur A, Rastorguev S, Boulygina E, Heath S, Gut M, Bihoreau MT, Lechner D, Foglio M, Gut IG, Skryabin K, Prokhortchouk E, Cambon-Thomsen A, Rung J, Bourque G, Brennan P, Tost J, Banks RE, Brazma A, Lathrop GM. Variation in genomic landscape of clear cell renal cell carcinoma across Europe. Nat Commun 2014; 5:5135. [PMID: 25351205 DOI: 10.1038/ncomms6135] [Citation(s) in RCA: 138] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2014] [Accepted: 09/03/2014] [Indexed: 12/31/2022] Open
Abstract
The incidence of renal cell carcinoma (RCC) is increasing worldwide, and its prevalence is particularly high in some parts of Central Europe. Here we undertake whole-genome and transcriptome sequencing of clear cell RCC (ccRCC), the most common form of the disease, in patients from four different European countries with contrasting disease incidence to explore the underlying genomic architecture of RCC. Our findings support previous reports on frequent aberrations in the epigenetic machinery and PI3K/mTOR signalling, and uncover novel pathways and genes affected by recurrent mutations and abnormal transcriptome patterns including focal adhesion, components of extracellular matrix (ECM) and genes encoding FAT cadherins. Furthermore, a large majority of patients from Romania have an unexpected high frequency of A:T>T:A transversions, consistent with exposure to aristolochic acid (AA). These results show that the processes underlying ccRCC tumorigenesis may vary in different populations and suggest that AA may be an important ccRCC carcinogen in Romania, a finding with major public health implications.
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Affiliation(s)
- Ghislaine Scelo
- International Agency for Research on Cancer (IARC), 150 cours Albert Thomas, 69008 Lyon, France
| | - Yasser Riazalhosseini
- 1] Department of Human Genetics, McGill University, 1205 Dr Penfield Avenue, Montreal, Quebec, Canada H3A 1B1 [2] McGill University and Genome Quebec Innovation Centre, 740 Doctor Penfield Avenue, Montreal, Quebec, Canada H3A 0G1
| | - Liliana Greger
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Wellcome Trust Genome Campus, Hinxton CB10 1SD, UK
| | - Louis Letourneau
- McGill University and Genome Quebec Innovation Centre, 740 Doctor Penfield Avenue, Montreal, Quebec, Canada H3A 0G1
| | - Mar Gonzàlez-Porta
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Wellcome Trust Genome Campus, Hinxton CB10 1SD, UK
| | - Magdalena B Wozniak
- International Agency for Research on Cancer (IARC), 150 cours Albert Thomas, 69008 Lyon, France
| | - Mathieu Bourgey
- McGill University and Genome Quebec Innovation Centre, 740 Doctor Penfield Avenue, Montreal, Quebec, Canada H3A 0G1
| | - Patricia Harnden
- Leeds Institute of Cancer and Pathology, University of Leeds, Cancer Research Building, St James's University Hospital, Leeds LS9 7TF, UK
| | - Lars Egevad
- Department of Pathology, Karolinska Institutet, SE-171 77 Stockholm, Sweden
| | - Sharon M Jackson
- Leeds Institute of Cancer and Pathology, University of Leeds, Cancer Research Building, St James's University Hospital, Leeds LS9 7TF, UK
| | - Mehran Karimzadeh
- 1] Department of Human Genetics, McGill University, 1205 Dr Penfield Avenue, Montreal, Quebec, Canada H3A 1B1 [2] McGill University and Genome Quebec Innovation Centre, 740 Doctor Penfield Avenue, Montreal, Quebec, Canada H3A 0G1
| | - Madeleine Arseneault
- 1] Department of Human Genetics, McGill University, 1205 Dr Penfield Avenue, Montreal, Quebec, Canada H3A 1B1 [2] McGill University and Genome Quebec Innovation Centre, 740 Doctor Penfield Avenue, Montreal, Quebec, Canada H3A 0G1
| | - Pierre Lepage
- McGill University and Genome Quebec Innovation Centre, 740 Doctor Penfield Avenue, Montreal, Quebec, Canada H3A 0G1
| | - Alexandre How-Kit
- Fondation Jean Dausset - Centre d'Etude du Polymorphisme Humain, 27 rue Juliette Dodu, 75010 Paris, France
| | - Antoine Daunay
- Fondation Jean Dausset - Centre d'Etude du Polymorphisme Humain, 27 rue Juliette Dodu, 75010 Paris, France
| | - Victor Renault
- Fondation Jean Dausset - Centre d'Etude du Polymorphisme Humain, 27 rue Juliette Dodu, 75010 Paris, France
| | - Hélène Blanché
- Fondation Jean Dausset - Centre d'Etude du Polymorphisme Humain, 27 rue Juliette Dodu, 75010 Paris, France
| | - Emmanuel Tubacher
- Fondation Jean Dausset - Centre d'Etude du Polymorphisme Humain, 27 rue Juliette Dodu, 75010 Paris, France
| | - Jeremy Sehmoun
- Fondation Jean Dausset - Centre d'Etude du Polymorphisme Humain, 27 rue Juliette Dodu, 75010 Paris, France
| | - Juris Viksna
- Institute of Mathematics and Computer Science, University of Latvia, 29 Rainis Boulevard, Riga LV-1459, Latvia
| | - Edgars Celms
- Institute of Mathematics and Computer Science, University of Latvia, 29 Rainis Boulevard, Riga LV-1459, Latvia
| | - Martins Opmanis
- Institute of Mathematics and Computer Science, University of Latvia, 29 Rainis Boulevard, Riga LV-1459, Latvia
| | - Andris Zarins
- Institute of Mathematics and Computer Science, University of Latvia, 29 Rainis Boulevard, Riga LV-1459, Latvia
| | - Naveen S Vasudev
- Leeds Institute of Cancer and Pathology, University of Leeds, Cancer Research Building, St James's University Hospital, Leeds LS9 7TF, UK
| | - Morag Seywright
- Department of Pathology, The Beatson Institute for Cancer Research, Switchback Road, Bearsden, Glasgow G61 1BD, UK
| | - Behnoush Abedi-Ardekani
- International Agency for Research on Cancer (IARC), 150 cours Albert Thomas, 69008 Lyon, France
| | - Christine Carreira
- International Agency for Research on Cancer (IARC), 150 cours Albert Thomas, 69008 Lyon, France
| | - Peter J Selby
- Leeds Institute of Cancer and Pathology, University of Leeds, Cancer Research Building, St James's University Hospital, Leeds LS9 7TF, UK
| | - Jon J Cartledge
- Leeds Teaching Hospitals NHS Trust, Pyrah Department of Urology, Lincoln Wing, St James's University Hospital, Leeds LS9 7TF, UK
| | - Graham Byrnes
- International Agency for Research on Cancer (IARC), 150 cours Albert Thomas, 69008 Lyon, France
| | - Jiri Zavadil
- International Agency for Research on Cancer (IARC), 150 cours Albert Thomas, 69008 Lyon, France
| | - Jing Su
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Wellcome Trust Genome Campus, Hinxton CB10 1SD, UK
| | - Ivana Holcatova
- First Faculty of Medicine, Institute of Hygiene and Epidemiology, Charles University in Prague, Studničkova 7, Praha 2, 128 00 Prague, Czech Republic
| | - Antonin Brisuda
- University Hospital Motol, V Úvalu 84, 150 06 Prague, Czech Republic
| | - David Zaridze
- Russian N.N. Blokhin Cancer Research Centre, Kashirskoye shosse 24, Moscow 115478, Russian Federation
| | - Anush Moukeria
- Russian N.N. Blokhin Cancer Research Centre, Kashirskoye shosse 24, Moscow 115478, Russian Federation
| | - Lenka Foretova
- Department of Cancer Epidemiology and Genetics, Masaryk Memorial Cancer Institute and MF MU, Zluty Kopec 7, 656 53 Brno, Czech Republic
| | - Marie Navratilova
- Department of Cancer Epidemiology and Genetics, Masaryk Memorial Cancer Institute and MF MU, Zluty Kopec 7, 656 53 Brno, Czech Republic
| | - Dana Mates
- National Institute of Public Health, Dr Leonte Anastasievici 1-3, sector 5, Bucuresti 050463, Romania
| | - Viorel Jinga
- Carol Davila University of Medicine and Pharmacy, Th. Burghele Hospital, 20 Panduri Street, 050659 Bucharest, Romania
| | - Artem Artemov
- Centre 'Bioengineering', The Russian Academy of Sciences, Moscow 117312, Russian Federation
| | - Artem Nedoluzhko
- National Research Centre 'Kurchatov Institute', 1 Akademika Kurchatova pl., Moscow 123182, Russia
| | - Alexander Mazur
- Centre 'Bioengineering', The Russian Academy of Sciences, Moscow 117312, Russian Federation
| | - Sergey Rastorguev
- National Research Centre 'Kurchatov Institute', 1 Akademika Kurchatova pl., Moscow 123182, Russia
| | - Eugenia Boulygina
- National Research Centre 'Kurchatov Institute', 1 Akademika Kurchatova pl., Moscow 123182, Russia
| | - Simon Heath
- Centro Nacional de Análisis Genómico, Baldiri Reixac, 4, Barcleona Science Park - Tower I, 08028 Barcelona, Spain
| | - Marta Gut
- Centro Nacional de Análisis Genómico, Baldiri Reixac, 4, Barcleona Science Park - Tower I, 08028 Barcelona, Spain
| | - Marie-Therese Bihoreau
- Centre National de Génotypage, CEA - Institute de Génomique, 2 rue Gaston Crémieux, 91000 Evry, France
| | - Doris Lechner
- Centre National de Génotypage, CEA - Institute de Génomique, 2 rue Gaston Crémieux, 91000 Evry, France
| | - Mario Foglio
- Centre National de Génotypage, CEA - Institute de Génomique, 2 rue Gaston Crémieux, 91000 Evry, France
| | - Ivo G Gut
- Centro Nacional de Análisis Genómico, Baldiri Reixac, 4, Barcleona Science Park - Tower I, 08028 Barcelona, Spain
| | - Konstantin Skryabin
- 1] Centre 'Bioengineering', The Russian Academy of Sciences, Moscow 117312, Russian Federation [2] National Research Centre 'Kurchatov Institute', 1 Akademika Kurchatova pl., Moscow 123182, Russia
| | - Egor Prokhortchouk
- 1] Centre 'Bioengineering', The Russian Academy of Sciences, Moscow 117312, Russian Federation [2] National Research Centre 'Kurchatov Institute', 1 Akademika Kurchatova pl., Moscow 123182, Russia
| | - Anne Cambon-Thomsen
- Faculty of Medicine, Institut National de la Santé et de la Recherche Medicale (INSERM) and University Toulouse III-Paul Sabatier, UMR 1027, 37 allées Jules Guesde, 31000 Toulouse, France
| | - Johan Rung
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Wellcome Trust Genome Campus, Hinxton CB10 1SD, UK
| | - Guillaume Bourque
- 1] Department of Human Genetics, McGill University, 1205 Dr Penfield Avenue, Montreal, Quebec, Canada H3A 1B1 [2] McGill University and Genome Quebec Innovation Centre, 740 Doctor Penfield Avenue, Montreal, Quebec, Canada H3A 0G1
| | - Paul Brennan
- International Agency for Research on Cancer (IARC), 150 cours Albert Thomas, 69008 Lyon, France
| | - Jörg Tost
- Centre National de Génotypage, CEA - Institute de Génomique, 2 rue Gaston Crémieux, 91000 Evry, France
| | - Rosamonde E Banks
- Leeds Institute of Cancer and Pathology, University of Leeds, Cancer Research Building, St James's University Hospital, Leeds LS9 7TF, UK
| | - Alvis Brazma
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Wellcome Trust Genome Campus, Hinxton CB10 1SD, UK
| | - G Mark Lathrop
- 1] Department of Human Genetics, McGill University, 1205 Dr Penfield Avenue, Montreal, Quebec, Canada H3A 1B1 [2] Fondation Jean Dausset - Centre d'Etude du Polymorphisme Humain, 27 rue Juliette Dodu, 75010 Paris, France [3] Centre National de Génotypage, CEA - Institute de Génomique, 2 rue Gaston Crémieux, 91000 Evry, France
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31
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Abstract
A paragraph from the highlights of “Transcriptomics: Throwing light on dark matter” by L. Flintoft (Nature Reviews Genetics 11, 455, 2010), says: “Reports over the past few years of extensive transcription throughout eukaryotic genomes have led to considerable excitement. However, doubts have been raised about the methods that have detected this pervasive transcription and about how much of it is functional.” Since the appearance of the ENCODE project and due to follow-up work, a shift from the pervasive transcription observed from RNA-Seq data to its functional validation is gradually occurring. However, much less attention has been turned to the problem of deciphering the complexity of transcriptome data, which determines uncertainty with regard to identification, quantification and differential expression of genes and non-coding RNAs. The aim of this mini-review is to emphasize transcriptome-related problems of direct and inverse nature for which novel inference approaches are needed.
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Affiliation(s)
- Enrico Capobianco
- Center for Computational Science, University of Miami, Miami, FL, USA
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32
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Identification of genetic variants associated with alternative splicing using sQTLseekeR. Nat Commun 2014; 5:4698. [PMID: 25140736 PMCID: PMC4143934 DOI: 10.1038/ncomms5698] [Citation(s) in RCA: 87] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2014] [Accepted: 07/11/2014] [Indexed: 02/05/2023] Open
Abstract
Identification of genetic variants affecting splicing in RNA sequencing population studies is still in its infancy. Splicing phenotype is more complex than gene expression and ought to be treated as a multivariate phenotype to be recapitulated completely. Here we represent the splicing pattern of a gene as the distribution of the relative abundances of a gene’s alternative transcript isoforms. We develop a statistical framework that uses a distance-based approach to compute the variability of splicing ratios across observations, and a non-parametric analogue to multivariate analysis of variance. We implement this approach in the R package sQTLseekeR and use it to analyze RNA-Seq data from the Geuvadis project in 465 individuals. We identify hundreds of single nucleotide polymorphisms (SNPs) as splicing QTLs (sQTLs), including some falling in genome-wide association study SNPs. By developing the appropriate metrics, we show that sQTLseekeR compares favorably with existing methods that rely on univariate approaches, predicting variants that behave as expected from mutations affecting splicing. RNA sequencing has enabled the global analysis of both gene expression levels and splicing events. Here, the authors develop a multivariate approach that is able to identify SNPs that influence splicing, and investigate the overlap of these with functional domains across the genome, including previously identified GWAS signals.
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33
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Martin AR, Costa HA, Lappalainen T, Henn BM, Kidd JM, Yee MC, Grubert F, Cann HM, Snyder M, Montgomery SB, Bustamante CD. Transcriptome sequencing from diverse human populations reveals differentiated regulatory architecture. PLoS Genet 2014; 10:e1004549. [PMID: 25121757 PMCID: PMC4133153 DOI: 10.1371/journal.pgen.1004549] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2013] [Accepted: 06/18/2014] [Indexed: 12/30/2022] Open
Abstract
Large-scale sequencing efforts have documented extensive genetic variation within the human genome. However, our understanding of the origins, global distribution, and functional consequences of this variation is far from complete. While regulatory variation influencing gene expression has been studied within a handful of populations, the breadth of transcriptome differences across diverse human populations has not been systematically analyzed. To better understand the spectrum of gene expression variation, alternative splicing, and the population genetics of regulatory variation in humans, we have sequenced the genomes, exomes, and transcriptomes of EBV transformed lymphoblastoid cell lines derived from 45 individuals in the Human Genome Diversity Panel (HGDP). The populations sampled span the geographic breadth of human migration history and include Namibian San, Mbuti Pygmies of the Democratic Republic of Congo, Algerian Mozabites, Pathan of Pakistan, Cambodians of East Asia, Yakut of Siberia, and Mayans of Mexico. We discover that approximately 25.0% of the variation in gene expression found amongst individuals can be attributed to population differences. However, we find few genes that are systematically differentially expressed among populations. Of this population-specific variation, 75.5% is due to expression rather than splicing variability, and we find few genes with strong evidence for differential splicing across populations. Allelic expression analyses indicate that previously mapped common regulatory variants identified in eight populations from the International Haplotype Map Phase 3 project have similar effects in our seven sampled HGDP populations, suggesting that the cellular effects of common variants are shared across diverse populations. Together, these results provide a resource for studies analyzing functional differences across populations by estimating the degree of shared gene expression, alternative splicing, and regulatory genetics across populations from the broadest points of human migration history yet sampled. Previous gene expression studies have identified factors influencing population-level variation in gene regulation. However, these efforts have been limited to a small set of well-studied populations. By leveraging the high resolution of RNA sequencing and broad population sampling, we survey the landscape of transcriptome variation across a globally distributed set of seven populations that span a breadth of human genetic variation and major dispersal events. We assess differences in gene expression, transcript structure, and regulatory variation. We find only 44 transcripts that show significant differences in expression, likely as a result of the small sample size, but we find that 25% of the variance in gene expression is due to population differences. This is a larger fraction than previously observed, and it is likely due to the greater breadth of human diversity assayed in this study. We also find that population-specific variance is mostly due to transcription variability rather than the configuration of expressed gene products. Additionally, known common regulatory variants have similar effects across populations including those we study here. These data and results serve as a resource cataloging the wide array of gene expression regulation affecting population variation among diverse groups, improving our understanding of transcriptional diversity.
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Affiliation(s)
- Alicia R. Martin
- Stanford University School of Medicine, Department of Genetics, Stanford, California, United States of America
| | - Helio A. Costa
- Stanford University School of Medicine, Department of Genetics, Stanford, California, United States of America
| | - Tuuli Lappalainen
- Stanford University School of Medicine, Department of Genetics, Stanford, California, United States of America
| | - Brenna M. Henn
- Stony Brook University, SUNY, Department of Ecology and Evolution, Stony Brook, New York, United States of America
| | - Jeffrey M. Kidd
- University of Michigan School of Medicine, Department of Human Genetics, Ann Arbor, Michigan, United States of America
| | - Muh-Ching Yee
- Stanford University School of Medicine, Department of Genetics, Stanford, California, United States of America
| | - Fabian Grubert
- Stanford University School of Medicine, Department of Genetics, Stanford, California, United States of America
| | - Howard M. Cann
- Foundation Jean Dausset, Centre d'Etude du Polymorphisme Humain, Paris, France
| | - Michael Snyder
- Stanford University School of Medicine, Department of Genetics, Stanford, California, United States of America
| | - Stephen B. Montgomery
- Stanford University School of Medicine, Department of Genetics, Stanford, California, United States of America
- Stanford University School of Medicine, Department of Pathology, Stanford, California, United States of America
| | - Carlos D. Bustamante
- Stanford University School of Medicine, Department of Genetics, Stanford, California, United States of America
- * E-mail:
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Das SK, Sharma NK. Expression quantitative trait analyses to identify causal genetic variants for type 2 diabetes susceptibility. World J Diabetes 2014; 5:97-114. [PMID: 24748924 PMCID: PMC3990322 DOI: 10.4239/wjd.v5.i2.97] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/27/2013] [Revised: 02/21/2014] [Accepted: 03/14/2014] [Indexed: 02/05/2023] Open
Abstract
Type 2 diabetes (T2D) is a common metabolic disorder which is caused by multiple genetic perturbations affecting different biological pathways. Identifying genetic factors modulating the susceptibility of this complex heterogeneous metabolic phenotype in different ethnic and racial groups remains challenging. Despite recent success, the functional role of the T2D susceptibility variants implicated by genome-wide association studies (GWAS) remains largely unknown. Genetic dissection of transcript abundance or expression quantitative trait (eQTL) analysis unravels the genomic architecture of regulatory variants. Availability of eQTL information from tissues relevant for glucose homeostasis in humans opens a new avenue to prioritize GWAS-implicated variants that may be involved in triggering a causal chain of events leading to T2D. In this article, we review the progress made in the field of eQTL research and knowledge gained from those studies in understanding transcription regulatory mechanisms in human subjects. We highlight several novel approaches that can integrate eQTL analysis with multiple layers of biological information to identify ethnic-specific causal variants and gene-environment interactions relevant to T2D pathogenesis. Finally, we discuss how the eQTL analysis mediated search for “missing heritability” may lead us to novel biological and molecular mechanisms involved in susceptibility to T2D.
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35
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Schunter C, Vollmer SV, Macpherson E, Pascual M. Transcriptome analyses and differential gene expression in a non-model fish species with alternative mating tactics. BMC Genomics 2014; 15:167. [PMID: 24581002 PMCID: PMC4029132 DOI: 10.1186/1471-2164-15-167] [Citation(s) in RCA: 70] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2013] [Accepted: 02/20/2014] [Indexed: 12/22/2022] Open
Abstract
Background Social dominance is important for the reproductive success of males in many species. In the black-faced blenny (Tripterygion delaisi) during the reproductive season, some males change color and invest in nest making and defending a territory, whereas others do not change color and ‘sneak’ reproductions when females lay their eggs. Using RNAseq, we profiled differential gene expression between the brains of territorial males, sneaker males, and females to study the molecular signatures of male dimorphism. Results We found that more genes were differentially expressed between the two male phenotypes than between males and females, suggesting that during the reproductive period phenotypic plasticity is a more important factor in differential gene expression than sexual dimorphism. The territorial male overexpresses genes related to synaptic plasticity and the sneaker male overexpresses genes involved in differentiation and development. Conclusions Previously suggested candidate genes for social dominance in the context of alternative mating strategies seem to be predominantly species-specific. We present a list of novel genes which are differentially expressed in Tripterygion delaisi. This is the first genome-wide study for a molecular non-model species in the context of alternative mating strategies and provides essential information for further studies investigating the molecular basis of social dominance.
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Affiliation(s)
- Celia Schunter
- Centre d'Estudis Avançats de Blanes (CEAB-CSIC), Car, Acc, Cala St, Francesc 14 Blanes 17300 Girona, Spain.
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36
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Zaghlool A, Ameur A, Cavelier L, Feuk L. Splicing in the human brain. INTERNATIONAL REVIEW OF NEUROBIOLOGY 2014; 116:95-125. [PMID: 25172473 DOI: 10.1016/b978-0-12-801105-8.00005-9] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
It has become increasingly clear over the past decade that RNA has important functions in human cells beyond its role as an intermediate translator of DNA to protein. It is now known that RNA plays highly specific roles in pathways involved in regulatory, structural, and catalytic functions. The complexity of RNA production and regulation has become evident with the advent of high-throughput methods to study the transcriptome. Deep sequencing has revealed an enormous diversity of RNA types and transcript isoforms in human cells. The transcriptome of the human brain is particularly interesting as it contains more expressed genes than other tissues and also displays an extreme diversity of transcript isoforms, indicating that highly complex regulatory pathways are present in the brain. Several of these regulatory proteins are now identified, including RNA-binding proteins that are neuron specific. RNA-binding proteins also play important roles in regulating the splicing process and the temporal and spatial isoform production. While significant progress has been made in understanding the human transcriptome, many questions still remain regarding the basic mechanisms of splicing and subcellular localization of RNA. A long-standing question is to what extent the splicing of pre-mRNA is cotranscriptional and posttranscriptional, respectively. Recent data, including studies of the human brain, indicate that splicing is primarily cotranscriptional in human cells. This chapter describes the current understanding of splicing and splicing regulation in the human brain and discusses the recent global sequence-based analyses of transcription and splicing.
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Affiliation(s)
- Ammar Zaghlool
- Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden; Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Adam Ameur
- Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden; Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Lucia Cavelier
- Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden; Uppsala University Hospital, Uppsala, Sweden
| | - Lars Feuk
- Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden; Science for Life Laboratory, Uppsala University, Uppsala, Sweden.
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37
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Ferreira PG, Jares P, Rico D, Gómez-López G, Martínez-Trillos A, Villamor N, Ecker S, González-Pérez A, Knowles DG, Monlong J, Johnson R, Quesada V, Djebali S, Papasaikas P, López-Guerra M, Colomer D, Royo C, Cazorla M, Pinyol M, Clot G, Aymerich M, Rozman M, Kulis M, Tamborero D, Gouin A, Blanc J, Gut M, Gut I, Puente XS, Pisano DG, Martin-Subero JI, López-Bigas N, López-Guillermo A, Valencia A, López-Otín C, Campo E, Guigó R. Transcriptome characterization by RNA sequencing identifies a major molecular and clinical subdivision in chronic lymphocytic leukemia. Genome Res 2013; 24:212-26. [PMID: 24265505 PMCID: PMC3912412 DOI: 10.1101/gr.152132.112] [Citation(s) in RCA: 153] [Impact Index Per Article: 13.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Chronic lymphocytic leukemia (CLL) has heterogeneous clinical and biological behavior. Whole-genome and -exome sequencing has contributed to the characterization of the mutational spectrum of the disease, but the underlying transcriptional profile is still poorly understood. We have performed deep RNA sequencing in different subpopulations of normal B-lymphocytes and CLL cells from a cohort of 98 patients, and characterized the CLL transcriptional landscape with unprecedented resolution. We detected thousands of transcriptional elements differentially expressed between the CLL and normal B cells, including protein-coding genes, noncoding RNAs, and pseudogenes. Transposable elements are globally derepressed in CLL cells. In addition, two thousand genes—most of which are not differentially expressed—exhibit CLL-specific splicing patterns. Genes involved in metabolic pathways showed higher expression in CLL, while genes related to spliceosome, proteasome, and ribosome were among the most down-regulated in CLL. Clustering of the CLL samples according to RNA-seq derived gene expression levels unveiled two robust molecular subgroups, C1 and C2. C1/C2 subgroups and the mutational status of the immunoglobulin heavy variable (IGHV) region were the only independent variables in predicting time to treatment in a multivariate analysis with main clinico-biological features. This subdivision was validated in an independent cohort of patients monitored through DNA microarrays. Further analysis shows that B-cell receptor (BCR) activation in the microenvironment of the lymph node may be at the origin of the C1/C2 differences.
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Affiliation(s)
- Pedro G Ferreira
- Bioinformatics and Genomics Programme, Centre for Genomic Regulation (CRG), 08003 Barcelona, Catalonia, Spain
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38
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Abstract
The last decade has seen tremendous effort committed to the annotation of the human genome sequence, most notably perhaps in the form of the ENCODE project. One of the major findings of ENCODE, and other genome analysis projects, is that the human transcriptome is far larger and more complex than previously thought. This complexity manifests, for example, as alternative splicing within protein-coding genes, as well as in the discovery of thousands of long noncoding RNAs. It is also possible that significant numbers of human transcripts have not yet been described by annotation projects, while existing transcript models are frequently incomplete. The question as to what proportion of this complexity is truly functional remains open, however, and this ambiguity presents a serious challenge to genome scientists. In this article, we will discuss the current state of human transcriptome annotation, drawing on our experience gained in generating the GENCODE gene annotation set. We highlight the gaps in our knowledge of transcript functionality that remain, and consider the potential computational and experimental strategies that can be used to help close them. We propose that an understanding of the true overlap between transcriptional complexity and functionality will not be gained in the short term. However, significant steps toward obtaining this knowledge can now be taken by using an integrated strategy, combining all of the experimental resources at our disposal.
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Affiliation(s)
- Jonathan M Mudge
- Department of Informatics, Wellcome Trust Sanger Institute, Hinxton CB10 1SA, United Kingdom
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39
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Battle A, Mostafavi S, Zhu X, Potash JB, Weissman MM, McCormick C, Haudenschild CD, Beckman KB, Shi J, Mei R, Urban AE, Montgomery SB, Levinson DF, Koller D. Characterizing the genetic basis of transcriptome diversity through RNA-sequencing of 922 individuals. Genome Res 2013. [PMID: 24092820 DOI: 10.1101/gr.155192] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
Understanding the consequences of regulatory variation in the human genome remains a major challenge, with important implications for understanding gene regulation and interpreting the many disease-risk variants that fall outside of protein-coding regions. Here, we provide a direct window into the regulatory consequences of genetic variation by sequencing RNA from 922 genotyped individuals. We present a comprehensive description of the distribution of regulatory variation--by the specific expression phenotypes altered, the properties of affected genes, and the genomic characteristics of regulatory variants. We detect variants influencing expression of over ten thousand genes, and through the enhanced resolution offered by RNA-sequencing, for the first time we identify thousands of variants associated with specific phenotypes including splicing and allelic expression. Evaluating the effects of both long-range intra-chromosomal and trans (cross-chromosomal) regulation, we observe modularity in the regulatory network, with three-dimensional chromosomal configuration playing a particular role in regulatory modules within each chromosome. We also observe a significant depletion of regulatory variants affecting central and critical genes, along with a trend of reduced effect sizes as variant frequency increases, providing evidence that purifying selection and buffering have limited the deleterious impact of regulatory variation on the cell. Further, generalizing beyond observed variants, we have analyzed the genomic properties of variants associated with expression and splicing and developed a Bayesian model to predict regulatory consequences of genetic variants, applicable to the interpretation of individual genomes and disease studies. Together, these results represent a critical step toward characterizing the complete landscape of human regulatory variation.
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Affiliation(s)
- Alexis Battle
- Department of Computer Science, Stanford University, Stanford, California 94305, USA
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40
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Characterizing the genetic basis of transcriptome diversity through RNA-sequencing of 922 individuals. Genome Res 2013; 24:14-24. [PMID: 24092820 PMCID: PMC3875855 DOI: 10.1101/gr.155192.113] [Citation(s) in RCA: 381] [Impact Index Per Article: 34.6] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
Understanding the consequences of regulatory variation in the human genome remains a major challenge, with important implications for understanding gene regulation and interpreting the many disease-risk variants that fall outside of protein-coding regions. Here, we provide a direct window into the regulatory consequences of genetic variation by sequencing RNA from 922 genotyped individuals. We present a comprehensive description of the distribution of regulatory variation—by the specific expression phenotypes altered, the properties of affected genes, and the genomic characteristics of regulatory variants. We detect variants influencing expression of over ten thousand genes, and through the enhanced resolution offered by RNA-sequencing, for the first time we identify thousands of variants associated with specific phenotypes including splicing and allelic expression. Evaluating the effects of both long-range intra-chromosomal and trans (cross-chromosomal) regulation, we observe modularity in the regulatory network, with three-dimensional chromosomal configuration playing a particular role in regulatory modules within each chromosome. We also observe a significant depletion of regulatory variants affecting central and critical genes, along with a trend of reduced effect sizes as variant frequency increases, providing evidence that purifying selection and buffering have limited the deleterious impact of regulatory variation on the cell. Further, generalizing beyond observed variants, we have analyzed the genomic properties of variants associated with expression and splicing and developed a Bayesian model to predict regulatory consequences of genetic variants, applicable to the interpretation of individual genomes and disease studies. Together, these results represent a critical step toward characterizing the complete landscape of human regulatory variation.
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41
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Alternative splicing of mutually exclusive exons—A review. Biosystems 2013; 114:31-8. [DOI: 10.1016/j.biosystems.2013.07.003] [Citation(s) in RCA: 47] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2013] [Accepted: 07/03/2013] [Indexed: 12/16/2022]
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42
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Lappalainen T, Sammeth M, Friedländer MR, ‘t Hoen PAC, Monlong J, Rivas MA, Gonzàlez-Porta M, Kurbatova N, Griebel T, Ferreira PG, Barann M, Wieland T, Greger L, van Iterson M, Almlöf J, Ribeca P, Pulyakhina I, Esser D, Giger T, Tikhonov A, Sultan M, Bertier G, MacArthur DG, Lek M, Lizano E, Buermans HPJ, Padioleau I, Schwarzmayr T, Karlberg O, Ongen H, Kilpinen H, Beltran S, Gut M, Kahlem K, Amstislavskiy V, Stegle O, Pirinen M, Montgomery SB, Donnelly P, McCarthy MI, Flicek P, Strom TM, Lehrach H, Schreiber S, Sudbrak R, Carracedo Á, Antonarakis SE, Häsler R, Syvänen AC, van Ommen GJ, Brazma A, Meitinger T, Rosenstiel P, Guigó R, Gut IG, Estivill X, Dermitzakis ET. Transcriptome and genome sequencing uncovers functional variation in humans. Nature 2013; 501:506-11. [PMID: 24037378 PMCID: PMC3918453 DOI: 10.1038/nature12531] [Citation(s) in RCA: 1363] [Impact Index Per Article: 123.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2013] [Accepted: 08/05/2013] [Indexed: 02/06/2023]
Abstract
Genome sequencing projects are discovering millions of genetic variants in humans, and interpretation of their functional effects is essential for understanding the genetic basis of variation in human traits. Here we report sequencing and deep analysis of messenger RNA and microRNA from lymphoblastoid cell lines of 462 individuals from the 1000 Genomes Project--the first uniformly processed high-throughput RNA-sequencing data from multiple human populations with high-quality genome sequences. We discover extremely widespread genetic variation affecting the regulation of most genes, with transcript structure and expression level variation being equally common but genetically largely independent. Our characterization of causal regulatory variation sheds light on the cellular mechanisms of regulatory and loss-of-function variation, and allows us to infer putative causal variants for dozens of disease-associated loci. Altogether, this study provides a deep understanding of the cellular mechanisms of transcriptome variation and of the landscape of functional variants in the human genome.
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Affiliation(s)
- Tuuli Lappalainen
- Department of Genetic Medicine and Development, University of Geneva Medical School, 1211 Geneva, Switzerland
- Institute for Genetics and Genomics in Geneva (iGE3), University of Geneva, 1211 Geneva, Switzerland
- Swiss Institute of Bioinformatics, 1211 Geneva, Switzerland
| | - Michael Sammeth
- Centro Nacional d'Anàlisi Genòmica, 08028 Barcelona, Catalonia, Spain
- Center for Genomic Regulation (CRG), 08003 Barcelona, Catalonia, Spain
| | - Marc R Friedländer
- Center for Genomic Regulation (CRG), 08003 Barcelona, Catalonia, Spain
- Pompeu Fabra University (UPF), 08003 Barcelona, Catalonia, Spain
| | - Peter AC ‘t Hoen
- Department for Human and Clinical Genetics, Leiden University Medical Center, 2300 RC Leiden, the Netherlands
| | - Jean Monlong
- Center for Genomic Regulation (CRG), 08003 Barcelona, Catalonia, Spain
| | - Manuel A Rivas
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, United Kingdom
| | | | - Natalja Kurbatova
- European Bioinformatics Institute, EMBL-EBI, Hinxton, United Kingdom
| | - Thasso Griebel
- Centro Nacional d'Anàlisi Genòmica, 08028 Barcelona, Catalonia, Spain
| | - Pedro G Ferreira
- Center for Genomic Regulation (CRG), 08003 Barcelona, Catalonia, Spain
- Pompeu Fabra University (UPF), 08003 Barcelona, Catalonia, Spain
| | - Matthias Barann
- Institute of Clinical Molecular Biology, Christian-Albrechts-University Kiel, D-24105 Kiel, Germany
| | - Thomas Wieland
- Department of Genetic Medicine and Development, University of Geneva Medical School, 1211 Geneva, Switzerland
| | - Liliana Greger
- European Bioinformatics Institute, EMBL-EBI, Hinxton, United Kingdom
| | - Maarten van Iterson
- Department for Human and Clinical Genetics, Leiden University Medical Center, 2300 RC Leiden, the Netherlands
| | - Jonas Almlöf
- Department of Medical Sciences, Molecular Medicine and Science for Life Laboratory, Uppsala University, 751 85 Uppsala, Sweden
| | - Paolo Ribeca
- Centro Nacional d'Anàlisi Genòmica, 08028 Barcelona, Catalonia, Spain
| | - Irina Pulyakhina
- Department for Human and Clinical Genetics, Leiden University Medical Center, 2300 RC Leiden, the Netherlands
| | - Daniela Esser
- Institute of Clinical Molecular Biology, Christian-Albrechts-University Kiel, D-24105 Kiel, Germany
| | - Thomas Giger
- Department of Genetic Medicine and Development, University of Geneva Medical School, 1211 Geneva, Switzerland
| | - Andrew Tikhonov
- European Bioinformatics Institute, EMBL-EBI, Hinxton, United Kingdom
| | - Marc Sultan
- Max Planck Institute for Molecular Genetics, 14195 Berlin, Germany
| | - Gabrielle Bertier
- Center for Genomic Regulation (CRG), 08003 Barcelona, Catalonia, Spain
- Pompeu Fabra University (UPF), 08003 Barcelona, Catalonia, Spain
| | - Daniel G MacArthur
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge MA 02142, USA
| | - Monkol Lek
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge MA 02142, USA
| | - Esther Lizano
- Center for Genomic Regulation (CRG), 08003 Barcelona, Catalonia, Spain
- Pompeu Fabra University (UPF), 08003 Barcelona, Catalonia, Spain
| | - Henk PJ Buermans
- Department for Human and Clinical Genetics, Leiden University Medical Center, 2300 RC Leiden, the Netherlands
- Leiden Genome Technology Center, 2300 RC Leiden, the Netherlands
| | - Ismael Padioleau
- Department of Genetic Medicine and Development, University of Geneva Medical School, 1211 Geneva, Switzerland
- Institute for Genetics and Genomics in Geneva (iGE3), University of Geneva, 1211 Geneva, Switzerland
- Swiss Institute of Bioinformatics, 1211 Geneva, Switzerland
| | - Thomas Schwarzmayr
- Institute of Human Genetics, Helmholtz Zentrum München, 85764 Neuherberg, Germany
| | - Olof Karlberg
- Department of Medical Sciences, Molecular Medicine and Science for Life Laboratory, Uppsala University, 751 85 Uppsala, Sweden
| | - Halit Ongen
- Department of Genetic Medicine and Development, University of Geneva Medical School, 1211 Geneva, Switzerland
- Institute for Genetics and Genomics in Geneva (iGE3), University of Geneva, 1211 Geneva, Switzerland
- Swiss Institute of Bioinformatics, 1211 Geneva, Switzerland
| | - Helena Kilpinen
- Department of Genetic Medicine and Development, University of Geneva Medical School, 1211 Geneva, Switzerland
- Institute for Genetics and Genomics in Geneva (iGE3), University of Geneva, 1211 Geneva, Switzerland
- Swiss Institute of Bioinformatics, 1211 Geneva, Switzerland
| | - Sergi Beltran
- Centro Nacional d'Anàlisi Genòmica, 08028 Barcelona, Catalonia, Spain
| | - Marta Gut
- Centro Nacional d'Anàlisi Genòmica, 08028 Barcelona, Catalonia, Spain
| | - Katja Kahlem
- Centro Nacional d'Anàlisi Genòmica, 08028 Barcelona, Catalonia, Spain
| | | | - Oliver Stegle
- European Bioinformatics Institute, EMBL-EBI, Hinxton, United Kingdom
| | - Matti Pirinen
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, United Kingdom
| | - Stephen B Montgomery
- Department of Genetic Medicine and Development, University of Geneva Medical School, 1211 Geneva, Switzerland
| | - Peter Donnelly
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, United Kingdom
| | - Mark I McCarthy
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, United Kingdom
- Oxford Centre for Diabetes Endocrinology and Metabolism, University of Oxford, Oxford OX3 7BN, United Kingdom
| | - Paul Flicek
- European Bioinformatics Institute, EMBL-EBI, Hinxton, United Kingdom
| | - Tim M Strom
- Institute of Human Genetics, Helmholtz Zentrum München, 85764 Neuherberg, Germany
- Institute of Human Genetics, Technische Universität München, 81675 Munich, Germany
| | | | - Hans Lehrach
- Max Planck Institute for Molecular Genetics, 14195 Berlin, Germany
- Dahlem Centre for Genome Research and Medical Systems Biology, 14195 Berlin, Germany
| | - Stefan Schreiber
- Institute of Clinical Molecular Biology, Christian-Albrechts-University Kiel, D-24105 Kiel, Germany
| | - Ralf Sudbrak
- Max Planck Institute for Molecular Genetics, 14195 Berlin, Germany
- Dahlem Centre for Genome Research and Medical Systems Biology, 14195 Berlin, Germany
| | - Ángel Carracedo
- Fundacion Publica Galega de Medicina Xenomica SERGAS, Genomic Medicine Group CIBERER, Universidade de Santiago de Compostela, Santiago de Compostela, Spain
| | - Stylianos E Antonarakis
- Department of Genetic Medicine and Development, University of Geneva Medical School, 1211 Geneva, Switzerland
- Institute for Genetics and Genomics in Geneva (iGE3), University of Geneva, 1211 Geneva, Switzerland
| | - Robert Häsler
- Institute of Clinical Molecular Biology, Christian-Albrechts-University Kiel, D-24105 Kiel, Germany
| | - Ann-Christine Syvänen
- Department of Medical Sciences, Molecular Medicine and Science for Life Laboratory, Uppsala University, 751 85 Uppsala, Sweden
| | - Gert-Jan van Ommen
- Department for Human and Clinical Genetics, Leiden University Medical Center, 2300 RC Leiden, the Netherlands
| | - Alvis Brazma
- European Bioinformatics Institute, EMBL-EBI, Hinxton, United Kingdom
| | - Thomas Meitinger
- Institute of Human Genetics, Helmholtz Zentrum München, 85764 Neuherberg, Germany
- Institute of Human Genetics, Technische Universität München, 81675 Munich, Germany
| | - Philip Rosenstiel
- Institute of Clinical Molecular Biology, Christian-Albrechts-University Kiel, D-24105 Kiel, Germany
| | - Roderic Guigó
- Center for Genomic Regulation (CRG), 08003 Barcelona, Catalonia, Spain
- Pompeu Fabra University (UPF), 08003 Barcelona, Catalonia, Spain
| | - Ivo G Gut
- Centro Nacional d'Anàlisi Genòmica, 08028 Barcelona, Catalonia, Spain
| | - Xavier Estivill
- Center for Genomic Regulation (CRG), 08003 Barcelona, Catalonia, Spain
- Pompeu Fabra University (UPF), 08003 Barcelona, Catalonia, Spain
| | - Emmanouil T Dermitzakis
- Department of Genetic Medicine and Development, University of Geneva Medical School, 1211 Geneva, Switzerland
- Institute for Genetics and Genomics in Geneva (iGE3), University of Geneva, 1211 Geneva, Switzerland
- Swiss Institute of Bioinformatics, 1211 Geneva, Switzerland
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Florea L, Song L, Salzberg SL. Thousands of exon skipping events differentiate among splicing patterns in sixteen human tissues. F1000Res 2013; 2:188. [PMID: 24555089 DOI: 10.12688/f1000research.2-188.v1] [Citation(s) in RCA: 248] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 09/13/2013] [Indexed: 01/01/2023] Open
Abstract
Alternative splicing is widely recognized for its roles in regulating genes and creating gene diversity. However, despite many efforts, the repertoire of gene splicing variation is still incompletely characterized, even in humans. Here we describe a new computational system, ASprofile, and its application to RNA-seq data from Illumina's Human Body Map project (>2.5 billion reads). Using the system, we identified putative alternative splicing events in 16 different human tissues, which provide a dynamic picture of splicing variation across the tissues. We detected 26,989 potential exon skipping events representing differences in splicing patterns among the tissues. A large proportion of the events (>60%) were novel, involving new exons (~3000), new introns (~16000), or both. When tracing these events across the sixteen tissues, only a small number (4-7%) appeared to be differentially expressed ('switched') between two tissues, while 30-45% showed little variation, and the remaining 50-65% were not present in one or both tissues compared. Novel exon skipping events appeared to be slightly less variable than known events, but were more tissue-specific. Our study represents the first effort to build a comprehensive catalog of alternative splicing in normal human tissues from RNA-seq data, while providing insights into the role of alternative splicing in shaping tissue transcriptome differences. The catalog of events and the ASprofile software are freely available from the Zenodo repository ( http://zenodo.org/record/7068; doi: 10.5281/zenodo.7068) and from our web site http://ccb.jhu.edu/software/ASprofile.
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Affiliation(s)
- Liliana Florea
- Center for Computational Biology, McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA ; Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA
| | - Li Song
- Center for Computational Biology, McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA ; Department of Computer Science, Johns Hopkins University, Baltimore, MD, 21205, USA
| | - Steven L Salzberg
- Center for Computational Biology, McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA ; Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA ; Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, 21205, USA
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Florea L, Song L, Salzberg SL. Thousands of exon skipping events differentiate among splicing patterns in sixteen human tissues. F1000Res 2013; 2:188. [PMID: 24555089 PMCID: PMC3892928 DOI: 10.12688/f1000research.2-188.v2] [Citation(s) in RCA: 185] [Impact Index Per Article: 16.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 11/20/2013] [Indexed: 11/20/2022] Open
Abstract
Alternative splicing is widely recognized for its roles in regulating genes and creating gene diversity. However, despite many efforts, the repertoire of gene splicing variation is still incompletely characterized, even in humans. Here we describe a new computational system, ASprofile, and its application to RNA-seq data from Illumina’s Human Body Map project (>2.5 billion reads). Using the system, we identified putative alternative splicing events in 16 different human tissues, which provide a dynamic picture of splicing variation across the tissues. We detected 26,989 potential exon skipping events representing differences in splicing patterns among the tissues. A large proportion of the events (>60%) were novel, involving new exons (~3000), new introns (~16000), or both. When tracing these events across the sixteen tissues, only a small number (4-7%) appeared to be differentially expressed (‘switched’) between two tissues, while 30-45% showed little variation, and the remaining 50-65% were not present in one or both tissues compared. Novel exon skipping events appeared to be slightly less variable than known events, but were more tissue-specific. Our study represents the first effort to build a comprehensive catalog of alternative splicing in normal human tissues from RNA-seq data, while providing insights into the role of alternative splicing in shaping tissue transcriptome differences. The catalog of events and the ASprofile software are freely available from the Zenodo repository (
http://zenodo.org/record/7068; doi:
10.5281/zenodo.7068) and from our web site
http://ccb.jhu.edu/software/ASprofile.
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Affiliation(s)
- Liliana Florea
- Center for Computational Biology, McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA ; Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA
| | - Li Song
- Center for Computational Biology, McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA ; Department of Computer Science, Johns Hopkins University, Baltimore, MD, 21205, USA
| | - Steven L Salzberg
- Center for Computational Biology, McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA ; Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA ; Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, 21205, USA
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Farkas MH, Grant GR, White JA, Sousa ME, Consugar MB, Pierce EA. Transcriptome analyses of the human retina identify unprecedented transcript diversity and 3.5 Mb of novel transcribed sequence via significant alternative splicing and novel genes. BMC Genomics 2013; 14:486. [PMID: 23865674 PMCID: PMC3924432 DOI: 10.1186/1471-2164-14-486] [Citation(s) in RCA: 136] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2013] [Accepted: 07/15/2013] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND The retina is a complex tissue comprised of multiple cell types that is affected by a diverse set of diseases that are important causes of vision loss. Characterizing the transcripts, both annotated and novel, that are expressed in a given tissue has become vital for understanding the mechanisms underlying the pathology of disease. RESULTS We sequenced RNA prepared from three normal human retinas and characterized the retinal transcriptome at an unprecedented level due to the increased depth of sampling provided by the RNA-seq approach. We used a non-redundant reference transcriptome from all of the empirically-determined human reference tracks to identify annotated and novel sequences expressed in the retina. We detected 79,915 novel alternative splicing events, including 29,887 novel exons, 21,757 3' and 5' alternate splice sites, and 28,271 exon skipping events. We also identified 116 potential novel genes. These data represent a significant addition to the annotated human transcriptome. For example, the novel exons detected increase the number of identified exons by 3%. Using a high-throughput RNA capture approach to validate 14,696 of these novel transcriptome features we found that 99% of the putative novel events can be reproducibly detected. Further, 15-36% of the novel splicing events maintain an open reading frame, suggesting they produce novel protein products. CONCLUSIONS To our knowledge, this is the first application of RNA capture to perform large-scale validation of novel transcriptome features. In total, these analyses provide extensive detail about a previously uncharacterized level of transcript diversity in the human retina.
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Affiliation(s)
- Michael H Farkas
- Ocular Genomics Institute, Department of Ophthalmology, Massachusetts Eye and Ear Infirmary, Harvard Medical School, Boston, MA, USA
- Berman-Gund Laboratory for the Study of Retinal Degenerations, Department of Ophthalmology, Massachusetts Eye and Ear Infirmary, Harvard Medical School, Boston, MA, USA
| | - Gregory R Grant
- Penn Center for Bioinformatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Joseph A White
- Ocular Genomics Institute, Department of Ophthalmology, Massachusetts Eye and Ear Infirmary, Harvard Medical School, Boston, MA, USA
- Berman-Gund Laboratory for the Study of Retinal Degenerations, Department of Ophthalmology, Massachusetts Eye and Ear Infirmary, Harvard Medical School, Boston, MA, USA
| | - Maria E Sousa
- Ocular Genomics Institute, Department of Ophthalmology, Massachusetts Eye and Ear Infirmary, Harvard Medical School, Boston, MA, USA
- Berman-Gund Laboratory for the Study of Retinal Degenerations, Department of Ophthalmology, Massachusetts Eye and Ear Infirmary, Harvard Medical School, Boston, MA, USA
| | - Mark B Consugar
- Ocular Genomics Institute, Department of Ophthalmology, Massachusetts Eye and Ear Infirmary, Harvard Medical School, Boston, MA, USA
- Berman-Gund Laboratory for the Study of Retinal Degenerations, Department of Ophthalmology, Massachusetts Eye and Ear Infirmary, Harvard Medical School, Boston, MA, USA
| | - Eric A Pierce
- Ocular Genomics Institute, Department of Ophthalmology, Massachusetts Eye and Ear Infirmary, Harvard Medical School, Boston, MA, USA
- Berman-Gund Laboratory for the Study of Retinal Degenerations, Department of Ophthalmology, Massachusetts Eye and Ear Infirmary, Harvard Medical School, Boston, MA, USA
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Gonzàlez-Porta M, Frankish A, Rung J, Harrow J, Brazma A. Transcriptome analysis of human tissues and cell lines reveals one dominant transcript per gene. Genome Biol 2013; 14:R70. [PMID: 23815980 PMCID: PMC4053754 DOI: 10.1186/gb-2013-14-7-r70] [Citation(s) in RCA: 183] [Impact Index Per Article: 16.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2013] [Accepted: 07/01/2013] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND RNA sequencing has opened new avenues for the study of transcriptome composition. Significant evidence has accumulated showing that the human transcriptome contains in excess of a hundred thousand different transcripts. However, it is still not clear to what extent this diversity prevails when considering the relative abundances of different transcripts from the same gene. RESULTS Here we show that, in a given condition, most protein coding genes have one major transcript expressed at significantly higher level than others, that in human tissues the major transcripts contribute almost 85 percent to the total mRNA from protein coding loci, and that often the same major transcript is expressed in many tissues. We detect a high degree of overlap between the set of major transcripts and a recently published set of alternatively spliced transcripts that are predicted to be translated utilizing proteomic data. Thus, we hypothesize that although some minor transcripts may play a functional role, the major ones are likely to be the main contributors to the proteome. However, we still detect a non-negligible fraction of protein coding genes for which the major transcript does not code a protein. CONCLUSIONS Overall, our findings suggest that the transcriptome from protein coding loci is dominated by one transcript per gene and that not all the transcripts that contribute to transcriptome diversity are equally likely to contribute to protein diversity. This observation can help to prioritize candidate targets in proteomics research and to predict the functional impact of the detected changes in variation studies.
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Li Y, Li-Byarlay H, Burns P, Borodovsky M, Robinson GE, Ma J. TrueSight: a new algorithm for splice junction detection using RNA-seq. Nucleic Acids Res 2013; 41:e51. [PMID: 23254332 PMCID: PMC3575843 DOI: 10.1093/nar/gks1311] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2012] [Revised: 11/15/2012] [Accepted: 11/16/2012] [Indexed: 01/21/2023] Open
Abstract
RNA-seq has proven to be a powerful technique for transcriptome profiling based on next-generation sequencing (NGS) technologies. However, due to the short length of NGS reads, it is challenging to accurately map RNA-seq reads to splice junctions (SJs), which is a critically important step in the analysis of alternative splicing (AS) and isoform construction. In this article, we describe a new method, called TrueSight, which for the first time combines RNA-seq read mapping quality and coding potential of genomic sequences into a unified model. The model is further utilized in a machine-learning approach to precisely identify SJs. Both simulations and real data evaluations showed that TrueSight achieved higher sensitivity and specificity than other methods. We applied TrueSight to new high coverage honey bee RNA-seq data to discover novel splice forms. We found that 60.3% of honey bee multi-exon genes are alternatively spliced. By utilizing gene models improved by TrueSight, we characterized AS types in honey bee transcriptome. We believe that TrueSight will be highly useful to comprehensively study the biology of alternative splicing.
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Affiliation(s)
- Yang Li
- Department of Bioengineering, Institute for Genomic Biology, Department of Entomology, University of Illinois at Urbana-Champaign, IL 61801, USA, Wallace H. Coulter Department of Biomedical Engineering, School of Computational Science & Engineering, Georgia Institute of Technology, Atlanta 30332, GA, USA, Department of Molecular and Biological Physics, Moscow Institute for Physics and Technology, Dolgoprudny, 141700, Moscow Region, Russia and Neuroscience Program, University of Illinois at Urbana-Champaign, IL 61801, USA
| | - Hongmei Li-Byarlay
- Department of Bioengineering, Institute for Genomic Biology, Department of Entomology, University of Illinois at Urbana-Champaign, IL 61801, USA, Wallace H. Coulter Department of Biomedical Engineering, School of Computational Science & Engineering, Georgia Institute of Technology, Atlanta 30332, GA, USA, Department of Molecular and Biological Physics, Moscow Institute for Physics and Technology, Dolgoprudny, 141700, Moscow Region, Russia and Neuroscience Program, University of Illinois at Urbana-Champaign, IL 61801, USA
| | - Paul Burns
- Department of Bioengineering, Institute for Genomic Biology, Department of Entomology, University of Illinois at Urbana-Champaign, IL 61801, USA, Wallace H. Coulter Department of Biomedical Engineering, School of Computational Science & Engineering, Georgia Institute of Technology, Atlanta 30332, GA, USA, Department of Molecular and Biological Physics, Moscow Institute for Physics and Technology, Dolgoprudny, 141700, Moscow Region, Russia and Neuroscience Program, University of Illinois at Urbana-Champaign, IL 61801, USA
| | - Mark Borodovsky
- Department of Bioengineering, Institute for Genomic Biology, Department of Entomology, University of Illinois at Urbana-Champaign, IL 61801, USA, Wallace H. Coulter Department of Biomedical Engineering, School of Computational Science & Engineering, Georgia Institute of Technology, Atlanta 30332, GA, USA, Department of Molecular and Biological Physics, Moscow Institute for Physics and Technology, Dolgoprudny, 141700, Moscow Region, Russia and Neuroscience Program, University of Illinois at Urbana-Champaign, IL 61801, USA
| | - Gene E. Robinson
- Department of Bioengineering, Institute for Genomic Biology, Department of Entomology, University of Illinois at Urbana-Champaign, IL 61801, USA, Wallace H. Coulter Department of Biomedical Engineering, School of Computational Science & Engineering, Georgia Institute of Technology, Atlanta 30332, GA, USA, Department of Molecular and Biological Physics, Moscow Institute for Physics and Technology, Dolgoprudny, 141700, Moscow Region, Russia and Neuroscience Program, University of Illinois at Urbana-Champaign, IL 61801, USA
| | - Jian Ma
- Department of Bioengineering, Institute for Genomic Biology, Department of Entomology, University of Illinois at Urbana-Champaign, IL 61801, USA, Wallace H. Coulter Department of Biomedical Engineering, School of Computational Science & Engineering, Georgia Institute of Technology, Atlanta 30332, GA, USA, Department of Molecular and Biological Physics, Moscow Institute for Physics and Technology, Dolgoprudny, 141700, Moscow Region, Russia and Neuroscience Program, University of Illinois at Urbana-Champaign, IL 61801, USA
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Barbosa-Morais NL, Irimia M, Pan Q, Xiong HY, Gueroussov S, Lee LJ, Slobodeniuc V, Kutter C, Watt S, Colak R, Kim T, Misquitta-Ali CM, Wilson MD, Kim PM, Odom DT, Frey BJ, Blencowe BJ. The evolutionary landscape of alternative splicing in vertebrate species. Science 2013; 338:1587-93. [PMID: 23258890 DOI: 10.1126/science.1230612] [Citation(s) in RCA: 699] [Impact Index Per Article: 63.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
How species with similar repertoires of protein-coding genes differ so markedly at the phenotypic level is poorly understood. By comparing organ transcriptomes from vertebrate species spanning ~350 million years of evolution, we observed significant differences in alternative splicing complexity between vertebrate lineages, with the highest complexity in primates. Within 6 million years, the splicing profiles of physiologically equivalent organs diverged such that they are more strongly related to the identity of a species than they are to organ type. Most vertebrate species-specific splicing patterns are cis-directed. However, a subset of pronounced splicing changes are predicted to remodel protein interactions involving trans-acting regulators. These events likely further contributed to the diversification of splicing and other transcriptomic changes that underlie phenotypic differences among vertebrate species.
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Affiliation(s)
- Nuno L Barbosa-Morais
- Banting and Best Department of Medical Research, Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
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Abstract
Our understanding of gene expression has changed dramatically over the past decade, largely catalysed by technological developments. High-throughput experiments - microarrays and next-generation sequencing - have generated large amounts of genome-wide gene expression data that are collected in public archives. Added-value databases process, analyse and annotate these data further to make them accessible to every biologist. In this Review, we discuss the utility of the gene expression data that are in the public domain and how researchers are making use of these data. Reuse of public data can be very powerful, but there are many obstacles in data preparation and analysis and in the interpretation of the results. We will discuss these challenges and provide recommendations that we believe can improve the utility of such data.
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Affiliation(s)
- Johan Rung
- EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
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Chen L. Statistical and Computational Methods for High-Throughput Sequencing Data Analysis of Alternative Splicing. STATISTICS IN BIOSCIENCES 2012; 5:138-155. [PMID: 24058384 DOI: 10.1007/s12561-012-9064-7] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
The burgeoning field of high-throughput sequencing significantly improves our ability to understand the complexity of transcriptomes. Alternative splicing, as one of the most important driving forces for transcriptome diversity, can now be studied at an unprecedent resolution. Efficient and powerful computational and statistical methods are in urgent need to facilitate the characterization and quantification of alternative splicing events. Here we discuss methods in splice junction read mapping, and methods in exon-centric or isoform-centric quantification of alternative splicing. In addition, we discuss HITS-CLIP and splicing QTL analyses which are novel high-throughput sequencing based approaches in the dissection of splicing regulation.
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Affiliation(s)
- Liang Chen
- Molecular and Computational Biology, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA
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