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Schmitz JE, Aggarwal N, Laufer L, Walter J, Salhab A, Rahmann S. EpiSegMix: a flexible distribution hidden Markov model with duration modeling for chromatin state discovery. Bioinformatics 2024; 40:btae178. [PMID: 38565260 PMCID: PMC11026141 DOI: 10.1093/bioinformatics/btae178] [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: 10/06/2023] [Revised: 03/07/2024] [Accepted: 04/01/2024] [Indexed: 04/04/2024] Open
Abstract
MOTIVATION Automated chromatin segmentation based on ChIP-seq (chromatin immunoprecipitation followed by sequencing) data reveals insights into the epigenetic regulation of chromatin accessibility. Existing segmentation methods are constrained by simplifying modeling assumptions, which may have a negative impact on the segmentation quality. RESULTS We introduce EpiSegMix, a novel segmentation method based on a hidden Markov model with flexible read count distribution types and state duration modeling, allowing for a more flexible modeling of both histone signals and segment lengths. In a comparison with existing tools, ChromHMM, Segway, and EpiCSeg, we show that EpiSegMix is more predictive of cell biology, such as gene expression. Its flexible framework enables it to fit an accurate probabilistic model, which has the potential to increase the biological interpretability of chromatin states. AVAILABILITY AND IMPLEMENTATION Source code: https://gitlab.com/rahmannlab/episegmix.
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Affiliation(s)
- Johanna Elena Schmitz
- Algorithmic Bioinformatics, Center for Bioinformatics Saar, Saarland Informatics Campus, 66123 Saarbrücken, Germany
- Fakultät MI, Saarland University, Saarland Informatics Campus, 66123 Saarbrücken, Germany
- Saarbrücken Graduate School of Computer Science, Saarland Informatics Campus, 66123 Saarbrücken, Germany
| | - Nihit Aggarwal
- Department of Genetics, Saarland University, 66123 Saarbrücken, Germany
| | - Lukas Laufer
- Department of Genetics, Saarland University, 66123 Saarbrücken, Germany
| | - Jörn Walter
- Department of Genetics, Saarland University, 66123 Saarbrücken, Germany
| | - Abdulrahman Salhab
- Department of Genetics, Saarland University, 66123 Saarbrücken, Germany
- Genomics Data Science Core, Integrated Genomics Services, Sidra Medicine, Doha, Qatar
| | - Sven Rahmann
- Algorithmic Bioinformatics, Center for Bioinformatics Saar, Saarland Informatics Campus, 66123 Saarbrücken, Germany
- Fakultät MI, Saarland University, Saarland Informatics Campus, 66123 Saarbrücken, Germany
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2
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Shokraneh N, Arab M, Libbrecht M. Integrative chromatin domain annotation through graph embedding of Hi-C data. Bioinformatics 2022; 39:6935783. [PMID: 36534827 PMCID: PMC9848054 DOI: 10.1093/bioinformatics/btac813] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 11/02/2022] [Accepted: 12/16/2022] [Indexed: 12/24/2022] Open
Abstract
MOTIVATION The organization of the genome into domains plays a central role in gene expression and other cellular activities. Researchers identify genomic domains mainly through two views: 1D functional assays such as ChIP-seq, and chromatin conformation assays such as Hi-C. Fully understanding domains requires integrative modeling that combines these two views. However, the predominant form of integrative modeling uses segmentation and genome annotation (SAGA) along with the rigid assumption that loci in contact are more likely to share the same domain type, which is not necessarily true for epigenomic domain types and genome-wide chromatin interactions. RESULTS Here, we present an integrative approach that annotates domains using both 1D functional genomic signals and Hi-C measurements of genome-wide 3D interactions without the use of a pairwise prior. We do so by using a graph embedding to learn structural features corresponding to each genomic region, then inputting learned structural features along with functional genomic signals to a SAGA algorithm. We show that our domain types recapitulate well-known subcompartments with an additional granularity that distinguishes a combination of the spatial and functional states of the genomic regions. In particular, we identified a division of the previously identified A2 subcompartment such that the divided domain types have significantly varying expression levels. AVAILABILITY AND IMPLEMENTATION https://github.com/nedashokraneh/IChDA. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Neda Shokraneh
- Computing Science Department, Simon Fraser University, Burnaby V5A 1S6, Canada
| | - Mariam Arab
- Computing Science Department, Simon Fraser University, Burnaby V5A 1S6, Canada
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3
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Orouji E, Raman AT. Computational methods to explore chromatin state dynamics. Brief Bioinform 2022; 23:6751148. [PMID: 36208178 PMCID: PMC9677473 DOI: 10.1093/bib/bbac439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Revised: 08/25/2022] [Accepted: 09/09/2022] [Indexed: 12/14/2022] Open
Abstract
The human genome is marked by several singular and combinatorial histone modifications that shape the different states of chromatin and its three-dimensional organization. Genome-wide mapping of these marks as well as histone variants and open chromatin regions is commonly carried out via profiling DNA-protein binding or via chromatin accessibility methods. After the generation of epigenomic datasets in a cell type, statistical models can be used to annotate the noncoding regions of DNA and infer the combinatorial histone marks or chromatin states (CS). These methods involve partitioning the genome and labeling individual segments based on their CS patterns. Chromatin labels enable the systematic discovery of genomic function and activity and can label the gene body, promoters or enhancers without using other genomic maps. CSs are dynamic and change under different cell conditions, such as in normal, preneoplastic or tumor cells. This review aims to explore the available computational tools that have been developed to capture CS alterations under two or more cellular conditions.
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Affiliation(s)
- Elias Orouji
- Corresponding author: Elias Orouji, Epigenomics Lab, Princess Margaret Cancer Centre, University Health Network (UHN), 101 College St., Toronto, ON M5G 1 L7, Canada. Tel: +1 (917) 647-2202; E-mail:
| | - Ayush T Raman
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA,Department of Biostatistics, Harvard T.H. Chan School of Public Health, Cambridge, Massachusetts, USA
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Dsouza KB, Li AY, Bhargava VK, Libbrecht MW. Latent Representation of the Human Pan-Celltype Epigenome Through a Deep Recurrent Neural Network. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:2313-2323. [PMID: 34043510 DOI: 10.1109/tcbb.2021.3084147] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
The availability of thousands of assays of epigenetic activity necessitates compressed representations of these data sets that summarize the epigenetic landscape of the genome. Until recently, most such representations were cell type-specific, applying to a single tissue or cell state. Recently, neural networks have made it possible to summarize data across tissues to produce a pan-cell type representation. In this work, we propose Epi-LSTM, a deep long short-term memory (LSTM) recurrent neural network autoencoder to capture the long-term dependencies in the epigenomic data. The latent representations from Epi-LSTM capture a variety of genomic phenomena, including gene-expression, promoter-enhancer interactions, replication timing, frequently interacting regions, and evolutionary conservation. These representations outperform existing methods in a majority of cell types while yielding smoother representations along the genomic axis due to their sequential nature.
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Hesami M, Alizadeh M, Jones AMP, Torkamaneh D. Machine learning: its challenges and opportunities in plant system biology. Appl Microbiol Biotechnol 2022; 106:3507-3530. [PMID: 35575915 DOI: 10.1007/s00253-022-11963-6] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 03/14/2022] [Accepted: 05/07/2022] [Indexed: 12/25/2022]
Abstract
Sequencing technologies are evolving at a rapid pace, enabling the generation of massive amounts of data in multiple dimensions (e.g., genomics, epigenomics, transcriptomic, metabolomics, proteomics, and single-cell omics) in plants. To provide comprehensive insights into the complexity of plant biological systems, it is important to integrate different omics datasets. Although recent advances in computational analytical pipelines have enabled efficient and high-quality exploration and exploitation of single omics data, the integration of multidimensional, heterogenous, and large datasets (i.e., multi-omics) remains a challenge. In this regard, machine learning (ML) offers promising approaches to integrate large datasets and to recognize fine-grained patterns and relationships. Nevertheless, they require rigorous optimizations to process multi-omics-derived datasets. In this review, we discuss the main concepts of machine learning as well as the key challenges and solutions related to the big data derived from plant system biology. We also provide in-depth insight into the principles of data integration using ML, as well as challenges and opportunities in different contexts including multi-omics, single-cell omics, protein function, and protein-protein interaction. KEY POINTS: • The key challenges and solutions related to the big data derived from plant system biology have been highlighted. • Different methods of data integration have been discussed. • Challenges and opportunities of the application of machine learning in plant system biology have been highlighted and discussed.
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Affiliation(s)
- Mohsen Hesami
- Department of Plant Agriculture, University of Guelph, Guelph, ON, N1G 2W1, Canada
| | - Milad Alizadeh
- Department of Botany, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada
| | | | - Davoud Torkamaneh
- Département de Phytologie, Université Laval, Québec City, QC, G1V 0A6, Canada. .,Institut de Biologie Intégrative Et Des Systèmes (IBIS), Université Laval, Québec City, QC, G1V 0A6, Canada.
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6
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Daneshpajouh H, Chen B, Shokraneh N, Masoumi S, Wiese KC, Libbrecht MW. Continuous chromatin state feature annotation of the human epigenome. Bioinformatics 2022; 38:3029-3036. [PMID: 35451453 PMCID: PMC9154241 DOI: 10.1093/bioinformatics/btac283] [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: 11/22/2021] [Revised: 02/18/2022] [Accepted: 04/18/2022] [Indexed: 12/02/2022] Open
Abstract
Motivation Segmentation and genome annotation (SAGA) algorithms are widely used to understand genome activity and gene regulation. These methods take as input a set of sequencing-based assays of epigenomic activity, such as ChIP-seq measurements of histone modification and transcription factor binding. They output an annotation of the genome that assigns a chromatin state label to each genomic position. Existing SAGA methods have several limitations caused by the discrete annotation framework: such annotations cannot easily represent varying strengths of genomic elements, and they cannot easily represent combinatorial elements that simultaneously exhibit multiple types of activity. To remedy these limitations, we propose an annotation strategy that instead outputs a vector of chromatin state features at each position rather than a single discrete label. Continuous modeling is common in other fields, such as in topic modeling of text documents. We propose a method, epigenome-ssm-nonneg, that uses a non-negative state space model to efficiently annotate the genome with chromatin state features. We also propose several measures of the quality of a chromatin state feature annotation and we compare the performance of several alternative methods according to these quality measures. Results We show that chromatin state features from epigenome-ssm-nonneg are more useful for several downstream applications than both continuous and discrete alternatives, including their ability to identify expressed genes and enhancers. Therefore, we expect that these continuous chromatin state features will be valuable reference annotations to be used in visualization and downstream analysis. Availability and implementation Source code for epigenome-ssm is available at https://github.com/habibdanesh/epigenome-ssm and Zenodo (DOI: 10.5281/zenodo.6507585). Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Habib Daneshpajouh
- School of Computing Science, Simon Fraser University, Burnaby, BC, V5A 1S6, Canada
| | - Bowen Chen
- School of Computing Science, Simon Fraser University, Burnaby, BC, V5A 1S6, Canada
| | - Neda Shokraneh
- School of Computing Science, Simon Fraser University, Burnaby, BC, V5A 1S6, Canada
| | - Shohre Masoumi
- School of Computing Science, Simon Fraser University, Burnaby, BC, V5A 1S6, Canada
| | - Kay C Wiese
- School of Computing Science, Simon Fraser University, Burnaby, BC, V5A 1S6, Canada
| | - Maxwell W Libbrecht
- School of Computing Science, Simon Fraser University, Burnaby, BC, V5A 1S6, Canada
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Caudai C, Galizia A, Geraci F, Le Pera L, Morea V, Salerno E, Via A, Colombo T. AI applications in functional genomics. Comput Struct Biotechnol J 2021; 19:5762-5790. [PMID: 34765093 PMCID: PMC8566780 DOI: 10.1016/j.csbj.2021.10.009] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Revised: 10/05/2021] [Accepted: 10/05/2021] [Indexed: 12/13/2022] Open
Abstract
We review the current applications of artificial intelligence (AI) in functional genomics. The recent explosion of AI follows the remarkable achievements made possible by "deep learning", along with a burst of "big data" that can meet its hunger. Biology is about to overthrow astronomy as the paradigmatic representative of big data producer. This has been made possible by huge advancements in the field of high throughput technologies, applied to determine how the individual components of a biological system work together to accomplish different processes. The disciplines contributing to this bulk of data are collectively known as functional genomics. They consist in studies of: i) the information contained in the DNA (genomics); ii) the modifications that DNA can reversibly undergo (epigenomics); iii) the RNA transcripts originated by a genome (transcriptomics); iv) the ensemble of chemical modifications decorating different types of RNA transcripts (epitranscriptomics); v) the products of protein-coding transcripts (proteomics); and vi) the small molecules produced from cell metabolism (metabolomics) present in an organism or system at a given time, in physiological or pathological conditions. After reviewing main applications of AI in functional genomics, we discuss important accompanying issues, including ethical, legal and economic issues and the importance of explainability.
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Affiliation(s)
- Claudia Caudai
- CNR, Institute of Information Science and Technologies “A. Faedo” (ISTI), Pisa, Italy
| | - Antonella Galizia
- CNR, Institute of Applied Mathematics and Information Technologies (IMATI), Genoa, Italy
| | - Filippo Geraci
- CNR, Institute for Informatics and Telematics (IIT), Pisa, Italy
| | - Loredana Le Pera
- CNR, Institute of Biomembranes, Bioenergetics and Molecular Biotechnologies (IBIOM), Bari, Italy
- CNR, Institute of Molecular Biology and Pathology (IBPM), Rome, Italy
| | - Veronica Morea
- CNR, Institute of Molecular Biology and Pathology (IBPM), Rome, Italy
| | - Emanuele Salerno
- CNR, Institute of Information Science and Technologies “A. Faedo” (ISTI), Pisa, Italy
| | - Allegra Via
- CNR, Institute of Molecular Biology and Pathology (IBPM), Rome, Italy
| | - Teresa Colombo
- CNR, Institute of Molecular Biology and Pathology (IBPM), Rome, Italy
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8
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Libbrecht MW, Chan RCW, Hoffman MM. Segmentation and genome annotation algorithms for identifying chromatin state and other genomic patterns. PLoS Comput Biol 2021; 17:e1009423. [PMID: 34648491 PMCID: PMC8516206 DOI: 10.1371/journal.pcbi.1009423] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
Segmentation and genome annotation (SAGA) algorithms are widely used to understand genome activity and gene regulation. These algorithms take as input epigenomic datasets, such as chromatin immunoprecipitation-sequencing (ChIP-seq) measurements of histone modifications or transcription factor binding. They partition the genome and assign a label to each segment such that positions with the same label exhibit similar patterns of input data. SAGA algorithms discover categories of activity such as promoters, enhancers, or parts of genes without prior knowledge of known genomic elements. In this sense, they generally act in an unsupervised fashion like clustering algorithms, but with the additional simultaneous function of segmenting the genome. Here, we review the common methodological framework that underlies these methods, review variants of and improvements upon this basic framework, and discuss the outlook for future work. This review is intended for those interested in applying SAGA methods and for computational researchers interested in improving upon them.
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Affiliation(s)
| | - Rachel C. W. Chan
- Department of Computer Science, University of Toronto, Toronto, Canada
- Princess Margaret Cancer Centre, University Health Network, Toronto, Canada
| | - Michael M. Hoffman
- Department of Computer Science, University of Toronto, Toronto, Canada
- Princess Margaret Cancer Centre, University Health Network, Toronto, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Canada
- Vector Institute for Artificial Intelligence, Toronto, Canada
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9
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Fang K, Li T, Huang Y, Jin VX. NucHMM: a method for quantitative modeling of nucleosome organization identifying functional nucleosome states distinctly associated with splicing potentiality. Genome Biol 2021; 22:250. [PMID: 34446075 PMCID: PMC8390234 DOI: 10.1186/s13059-021-02465-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2020] [Accepted: 08/12/2021] [Indexed: 01/01/2023] Open
Abstract
We develop a novel computational method, NucHMM, to identify functional nucleosome states associated with cell type-specific combinatorial histone marks and nucleosome organization features such as phasing, spacing and positioning. We test it on publicly available MNase-seq and ChIP-seq data in MCF7, H1, and IMR90 cells and identify 11 distinct functional nucleosome states. We demonstrate these nucleosome states are distinctly associated with the splicing potentiality of skipping exons. This advances our understanding of the chromatin function at the nucleosome level and offers insights into the interplay between nucleosome organization and splicing processes.
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Affiliation(s)
- Kun Fang
- Department of Molecular Medicine, UTHSA-UTSA Joint Biomedical Engineering Program, San Antonio, TX, 78229, USA
| | - Tianbao Li
- Department of Molecular Medicine, University of Texas Health Science Center at San Antonio, San Antonio, TX, 78229, USA
| | - Yufei Huang
- Department of Medicine, UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, PA, 15232, USA
| | - Victor X Jin
- Department of Molecular Medicine, University of Texas Health Science Center at San Antonio, San Antonio, TX, 78229, USA.
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10
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Roberts BS, Partridge EC, Moyers BA, Agarwal V, Newberry KM, Martin BK, Shendure J, Myers RM, Cooper GM. Genome-wide strand asymmetry in massively parallel reporter activity favors genic strands. Genome Res 2021; 31:866-876. [PMID: 33879525 PMCID: PMC8092006 DOI: 10.1101/gr.270751.120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2020] [Accepted: 02/18/2021] [Indexed: 11/24/2022]
Abstract
Massively parallel reporter assays (MPRAs) are useful tools to characterize regulatory elements in human genomes. An aspect of MPRAs that is not typically the focus of analysis is their intrinsic ability to differentiate activity levels for a given sequence element when placed in both of its possible orientations relative to the reporter construct. Here, we describe pervasive strand asymmetry of MPRA signals in data sets from multiple reporter configurations in both published and newly reported data. These effects are reproducible across different cell types and in different treatments within a cell type and are observed both within and outside of annotated regulatory elements. From elements in gene bodies, MPRA strand asymmetry favors the sense strand, suggesting that function related to endogenous transcription is driving the phenomenon. Similarly, we find that within Alu mobile element insertions, strand asymmetry favors the transcribed strand of the ancestral retrotransposon. The effect is consistent across the multiplicity of Alu elements in human genomes and is more pronounced in less diverged Alu elements. We find sequence features driving MPRA strand asymmetry and show its prediction from sequence alone. We see some evidence for RNA stabilization and transcriptional activation mechanisms and hypothesize that the effect is driven by natural selection favoring efficient transcription. Our results indicate that strand asymmetry is a pervasive and reproducible feature in MPRA data. More importantly, the fact that MPRA asymmetry favors naturally transcribed strands suggests that it stems from preserved biological functions that have a substantial, global impact on gene and genome evolution.
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Affiliation(s)
- Brian S Roberts
- HudsonAlpha Institute for Biotechnology, Huntsville, Alabama 35806, USA.,Department of Biological Sciences, The University of Alabama in Huntsville, Huntsville, Alabama 35899, USA
| | | | - Bryan A Moyers
- HudsonAlpha Institute for Biotechnology, Huntsville, Alabama 35806, USA
| | - Vikram Agarwal
- Calico Life Sciences LLC, South San Francisco, California 94080, USA
| | | | - Beth K Martin
- Department of Genome Sciences, University of Washington, Seattle, Washington 98195, USA
| | - Jay Shendure
- Department of Genome Sciences, University of Washington, Seattle, Washington 98195, USA.,Howard Hughes Medical Institute, Seattle, Washington 98195, USA.,Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, Washington 98195, USA
| | - Richard M Myers
- HudsonAlpha Institute for Biotechnology, Huntsville, Alabama 35806, USA
| | - Gregory M Cooper
- HudsonAlpha Institute for Biotechnology, Huntsville, Alabama 35806, USA
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Tobias IC, Abatti LE, Moorthy SD, Mullany S, Taylor T, Khader N, Filice MA, Mitchell JA. Transcriptional enhancers: from prediction to functional assessment on a genome-wide scale. Genome 2020; 64:426-448. [PMID: 32961076 DOI: 10.1139/gen-2020-0104] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Enhancers are cis-regulatory sequences located distally to target genes. These sequences consolidate developmental and environmental cues to coordinate gene expression in a tissue-specific manner. Enhancer function and tissue specificity depend on the expressed set of transcription factors, which recognize binding sites and recruit cofactors that regulate local chromatin organization and gene transcription. Unlike other genomic elements, enhancers are challenging to identify because they function independently of orientation, are often distant from their promoters, have poorly defined boundaries, and display no reading frame. In addition, there are no defined genetic or epigenetic features that are unambiguously associated with enhancer activity. Over recent years there have been developments in both empirical assays and computational methods for enhancer prediction. We review genome-wide tools, CRISPR advancements, and high-throughput screening approaches that have improved our ability to both observe and manipulate enhancers in vitro at the level of primary genetic sequences, chromatin states, and spatial interactions. We also highlight contemporary animal models and their importance to enhancer validation. Together, these experimental systems and techniques complement one another and broaden our understanding of enhancer function in development, evolution, and disease.
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Affiliation(s)
- Ian C Tobias
- Department of Cell and Systems Biology, University of Toronto, Toronto, ON, M5S 3G5, Canada.,Department of Cell and Systems Biology, University of Toronto, Toronto, ON, M5S 3G5, Canada
| | - Luis E Abatti
- Department of Cell and Systems Biology, University of Toronto, Toronto, ON, M5S 3G5, Canada.,Department of Cell and Systems Biology, University of Toronto, Toronto, ON, M5S 3G5, Canada
| | - Sakthi D Moorthy
- Department of Cell and Systems Biology, University of Toronto, Toronto, ON, M5S 3G5, Canada.,Department of Cell and Systems Biology, University of Toronto, Toronto, ON, M5S 3G5, Canada
| | - Shanelle Mullany
- Department of Cell and Systems Biology, University of Toronto, Toronto, ON, M5S 3G5, Canada.,Department of Cell and Systems Biology, University of Toronto, Toronto, ON, M5S 3G5, Canada
| | - Tiegh Taylor
- Department of Cell and Systems Biology, University of Toronto, Toronto, ON, M5S 3G5, Canada.,Department of Cell and Systems Biology, University of Toronto, Toronto, ON, M5S 3G5, Canada
| | - Nawrah Khader
- Department of Cell and Systems Biology, University of Toronto, Toronto, ON, M5S 3G5, Canada.,Department of Cell and Systems Biology, University of Toronto, Toronto, ON, M5S 3G5, Canada
| | - Mario A Filice
- Department of Cell and Systems Biology, University of Toronto, Toronto, ON, M5S 3G5, Canada.,Department of Cell and Systems Biology, University of Toronto, Toronto, ON, M5S 3G5, Canada
| | - Jennifer A Mitchell
- Department of Cell and Systems Biology, University of Toronto, Toronto, ON, M5S 3G5, Canada.,Department of Cell and Systems Biology, University of Toronto, Toronto, ON, M5S 3G5, Canada
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12
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Yattah C, Hernandez M, Huang D, Park H, Liao W, Casaccia P. Dynamic Lamin B1-Gene Association During Oligodendrocyte Progenitor Differentiation. Neurochem Res 2020; 45:606-619. [PMID: 32020491 PMCID: PMC7060805 DOI: 10.1007/s11064-019-02941-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2019] [Revised: 12/04/2019] [Accepted: 12/19/2019] [Indexed: 12/27/2022]
Abstract
Differentiation of oligodendrocytes (OL) from progenitor cells (OPC) is the result of a unique program of gene expression, which is further regulated by the formation of topological domains of association with the nuclear lamina. In this study, we show that cultured OPC were characterized by progressively declining levels of endogenous Lamin B1 (LMNB1) during differentiation into OL. We then identify the genes dynamically associated to the nuclear lamina component LMNB1 during this transition, using a well established technique called DamID, which is based on the ability of a bacterially-derived deoxyadenosine methylase (Dam), to modify genomic regions in close proximity. We expressed a fusion protein containing Dam and LMNB1 in OPC (OPCLMNB1-Dam) and either kept them proliferating or differentiated them into OL (OLLMNB1-Dam) and identified genes that were dynamically associated to LMNB1 with differentiation. Importantly, we identified Lss, the gene encoding for lanosterol synthase, a key enzyme in cholesterol synthesis, as associated to the nuclear lamina in OLLMNB1-Dam. This finding could at least in part explain the lipid dysregulation previously reported for mouse models of ADLD characterized by persistent LMNB1 expression in oligodendrocytes.
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Affiliation(s)
- Camila Yattah
- Neuroscience Initiative at the Advanced Science Research Center of the Graduate Center of the City University of New York, 85 St. Nicholas Terrace, New York, NY, 10031, USA
- Graduate Program in Biochemistry, The Graduate Center of The City University of New York, 365 5th Avenue, New York, NY, 10016, USA
| | - Marylens Hernandez
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Dennis Huang
- Neuroscience Initiative at the Advanced Science Research Center of the Graduate Center of the City University of New York, 85 St. Nicholas Terrace, New York, NY, 10031, USA
- Graduate Program in Biochemistry, The Graduate Center of The City University of New York, 365 5th Avenue, New York, NY, 10016, USA
| | - HyeJin Park
- Neuroscience Initiative at the Advanced Science Research Center of the Graduate Center of the City University of New York, 85 St. Nicholas Terrace, New York, NY, 10031, USA
| | - Will Liao
- New York Genome Center, New York, NY, 10013, USA
| | - Patrizia Casaccia
- Neuroscience Initiative at the Advanced Science Research Center of the Graduate Center of the City University of New York, 85 St. Nicholas Terrace, New York, NY, 10031, USA.
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Graduate Program in Biochemistry and in Biology, The Graduate Center of The City University of New York, 365 5th Avenue, New York, NY, 10016, USA.
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13
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Zitnik M, Nguyen F, Wang B, Leskovec J, Goldenberg A, Hoffman MM. Machine Learning for Integrating Data in Biology and Medicine: Principles, Practice, and Opportunities. AN INTERNATIONAL JOURNAL ON INFORMATION FUSION 2019; 50:71-91. [PMID: 30467459 PMCID: PMC6242341 DOI: 10.1016/j.inffus.2018.09.012] [Citation(s) in RCA: 222] [Impact Index Per Article: 44.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
New technologies have enabled the investigation of biology and human health at an unprecedented scale and in multiple dimensions. These dimensions include myriad properties describing genome, epigenome, transcriptome, microbiome, phenotype, and lifestyle. No single data type, however, can capture the complexity of all the factors relevant to understanding a phenomenon such as a disease. Integrative methods that combine data from multiple technologies have thus emerged as critical statistical and computational approaches. The key challenge in developing such approaches is the identification of effective models to provide a comprehensive and relevant systems view. An ideal method can answer a biological or medical question, identifying important features and predicting outcomes, by harnessing heterogeneous data across several dimensions of biological variation. In this Review, we describe the principles of data integration and discuss current methods and available implementations. We provide examples of successful data integration in biology and medicine. Finally, we discuss current challenges in biomedical integrative methods and our perspective on the future development of the field.
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Affiliation(s)
- Marinka Zitnik
- Department of Computer Science, Stanford University,
Stanford, CA, USA
| | - Francis Nguyen
- Department of Medical Biophysics, University of Toronto,
Toronto, ON, Canada
- Princess Margaret Cancer Centre, Toronto, ON, Canada
| | - Bo Wang
- Hikvision Research Institute, Santa Clara, CA, USA
| | - Jure Leskovec
- Department of Computer Science, Stanford University,
Stanford, CA, USA
- Chan Zuckerberg Biohub, San Francisco, CA, USA
| | - Anna Goldenberg
- Genetics & Genome Biology, SickKids Research Institute,
Toronto, ON, Canada
- Department of Computer Science, University of Toronto,
Toronto, ON, Canada
- Vector Institute, Toronto, ON, Canada
| | - Michael M. Hoffman
- Department of Medical Biophysics, University of Toronto,
Toronto, ON, Canada
- Princess Margaret Cancer Centre, Toronto, ON, Canada
- Department of Computer Science, University of Toronto,
Toronto, ON, Canada
- Vector Institute, Toronto, ON, Canada
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14
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Fosslie M, Manaf A, Lerdrup M, Hansen K, Gilfillan GD, Dahl JA. Going low to reach high: Small-scale ChIP-seq maps new terrain. WILEY INTERDISCIPLINARY REVIEWS-SYSTEMS BIOLOGY AND MEDICINE 2019; 12:e1465. [PMID: 31478357 DOI: 10.1002/wsbm.1465] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/15/2019] [Revised: 07/02/2019] [Accepted: 07/25/2019] [Indexed: 12/20/2022]
Abstract
Chromatin immunoprecipitation (ChIP) enables mapping of specific histone modifications or chromatin-associated factors in the genome and represents a powerful tool in the study of chromatin and genome regulation. Importantly, recent technological advances that couple ChIP with whole-genome high-throughput sequencing (ChIP-seq) now allow the mapping of chromatin factors throughout the genome. However, the requirement for large amounts of ChIP-seq input material has long made it challenging to assess chromatin profiles of cell types only available in limited numbers. For many cell types, it is not feasible to reach high numbers when collecting them as homogeneous cell populations in vivo. Nonetheless, it is an advantage to work with pure cell populations to reach robust biological conclusions. Here, we review (a) how ChIP protocols have been scaled down for use with as little as a few hundred cells; (b) which considerations to be aware of when preparing small-scale ChIP-seq and analyzing data; and (c) the potential of small-scale ChIP-seq datasets for elucidating chromatin dynamics in various biological systems, including some examples such as oocyte maturation and preimplantation embryo development. This article is categorized under: Laboratory Methods and Technologies > Genetic/Genomic Methods Developmental Biology > Developmental Processes in Health and Disease Biological Mechanisms > Cell Fates.
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Affiliation(s)
| | - Adeel Manaf
- Department of Microbiology, Oslo University Hospital, Oslo, Norway
| | - Mads Lerdrup
- The Biotech Research and Innovation Centre, University of Copenhagen, Copenhagen, Denmark.,Centre for Epigenetics, University of Copenhagen, Copenhagen, Denmark
| | - Klaus Hansen
- The Biotech Research and Innovation Centre, University of Copenhagen, Copenhagen, Denmark.,Centre for Epigenetics, University of Copenhagen, Copenhagen, Denmark
| | - Gregor D Gilfillan
- Department of Medical Genetics, Oslo University Hospital and University of Oslo, Oslo, Norway
| | - John Arne Dahl
- Department of Microbiology, Oslo University Hospital, Oslo, Norway
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15
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Libbrecht MW, Rodriguez OL, Weng Z, Bilmes JA, Hoffman MM, Noble WS. A unified encyclopedia of human functional DNA elements through fully automated annotation of 164 human cell types. Genome Biol 2019; 20:180. [PMID: 31462275 PMCID: PMC6714098 DOI: 10.1186/s13059-019-1784-2] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2018] [Accepted: 08/05/2019] [Indexed: 12/31/2022] Open
Abstract
Semi-automated genome annotation methods such as Segway take as input a set of genome-wide measurements such as of histone modification or DNA accessibility and output an annotation of genomic activity in the target cell type. Here we present annotations of 164 human cell types using 1615 data sets. To produce these annotations, we automated the label interpretation step to produce a fully automated annotation strategy. Using these annotations, we developed a measure of the importance of each genomic position called the “conservation-associated activity score.” We further combined all annotations into a single, cell type-agnostic encyclopedia that catalogs all human regulatory elements.
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Affiliation(s)
| | - Oscar L Rodriguez
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Zhiping Weng
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Boston, USA
| | - Jeffrey A Bilmes
- Department of Electrical Engineering, University of Washington, Seattle, USA
| | - Michael M Hoffman
- Princess Margaret Cancer Centre, Toronto, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Canada.,Department of Computer Science, University of Toronto, Toronto, Canada
| | - William Stafford Noble
- Department of Genome Sciences, University of Washington, Seattle, USA. .,Department of Computer Science, University of Washington, Seattle, USA.
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16
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Banerjee S, Zhu H, Tang M, Feng WC, Wu X, Xie H. Identifying Transcriptional Regulatory Modules Among Different Chromatin States in Mouse Neural Stem Cells. Front Genet 2019; 9:731. [PMID: 30697231 PMCID: PMC6341026 DOI: 10.3389/fgene.2018.00731] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2018] [Accepted: 12/22/2018] [Indexed: 12/19/2022] Open
Abstract
Gene expression regulation is a complex process involving the interplay between transcription factors and chromatin states. Significant progress has been made toward understanding the impact of chromatin states on gene expression. Nevertheless, the mechanism of transcription factors binding combinatorially in different chromatin states to enable selective regulation of gene expression remains an interesting research area. We introduce a nonparametric Bayesian clustering method for inhomogeneous Poisson processes to detect heterogeneous binding patterns of multiple proteins including transcription factors to form regulatory modules in different chromatin states. We applied this approach on ChIP-seq data for mouse neural stem cells containing 21 proteins and observed different groups or modules of proteins clustered within different chromatin states. These chromatin-state-specific regulatory modules were found to have significant influence on gene expression. We also observed different motif preferences for certain TFs between different chromatin states. Our results reveal a degree of interdependency between chromatin states and combinatorial binding of proteins in the complex transcriptional regulatory process. The software package is available on Github at - https://github.com/BSharmi/DPM-LGCP.
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Affiliation(s)
- Sharmi Banerjee
- Bradley Department of Electrical and Computer Engineering, Virginia Tech, Blacksburg, VA, United States.,Biocomplexity Institute of Virginia Tech, Blacksburg, VA, United States
| | - Hongxiao Zhu
- Department of Statistics, Virginia Tech, Blacksburg, VA, United States
| | - Man Tang
- Department of Statistics, Virginia Tech, Blacksburg, VA, United States
| | - Wu-Chun Feng
- Department of Computer Science, Virginia Tech, Blacksburg, VA, United States
| | - Xiaowei Wu
- Department of Statistics, Virginia Tech, Blacksburg, VA, United States
| | - Hehuang Xie
- Biocomplexity Institute of Virginia Tech, Blacksburg, VA, United States.,Department of Biomedical Sciences and Pathobiology, Virginia-Maryland College of Veterinary Medicine, Blacksburg, VA, United States.,Department of Biological Sciences, Virginia Tech, Blacksburg, VA, United States.,School of Neuroscience, Virginia Tech, Blacksburg, VA, United States
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17
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Wang C, Zhang S. Reveal cell type-specific regulatory elements and their characterized histone code classes via a hidden Markov model. BMC Genomics 2018; 19:903. [PMID: 30598107 PMCID: PMC6311906 DOI: 10.1186/s12864-018-5274-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
BACKGROUND With the maturity of next generation sequencing technology, a huge amount of epigenomic data have been generated by several large consortia in the last decade. These plenty resources leave us the opportunity about sufficiently utilizing those data to explore biological problems. RESULTS Here we developed an integrative and comparative method, CsreHMM, which is based on a hidden Markov model, to systematically reveal cell type-specific regulatory elements (CSREs) along the whole genome, and simultaneously recognize the histone codes (mark combinations) charactering them. This method also reveals the subclasses of CSREs and explicitly label those shared by a few cell types. We applied this method to a data set of 9 cell types and 9 chromatin marks to demonstrate its effectiveness and found that the revealed CSREs relates to different kinds of functional regulatory regions significantly. Their proximal genes have consistent expression and are likely to participate in cell type-specific biological functions. CONCLUSIONS These results suggest CsreHMM has the potential to help understand cell identity and the diverse mechanisms of gene regulation.
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Affiliation(s)
- Can Wang
- Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China
- School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Shihua Zhang
- Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China.
- School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China.
- Center for Excel-lence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, 650223, China.
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18
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Yang Y, Gu Q, Zhang Y, Sasaki T, Crivello J, O'Neill RJ, Gilbert DM, Ma J. Continuous-Trait Probabilistic Model for Comparing Multi-species Functional Genomic Data. Cell Syst 2018; 7:208-218.e11. [PMID: 29936186 PMCID: PMC6107375 DOI: 10.1016/j.cels.2018.05.022] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2018] [Revised: 05/17/2018] [Accepted: 05/29/2018] [Indexed: 01/22/2023]
Abstract
A large amount of multi-species functional genomic data from high-throughput assays are becoming available to help understand the molecular mechanisms for phenotypic diversity across species. However, continuous-trait probabilistic models, which are key to such comparative analysis, remain under-explored. Here we develop a new model, called phylogenetic hidden Markov Gaussian processes (Phylo-HMGP), to simultaneously infer heterogeneous evolutionary states of functional genomic features in a genome-wide manner. Both simulation studies and real data application demonstrate the effectiveness of Phylo-HMGP. Importantly, we applied Phylo-HMGP to analyze a new cross-species DNA replication timing (RT) dataset from the same cell type in five primate species (human, chimpanzee, orangutan, gibbon, and green monkey). We demonstrate that our Phylo-HMGP model enables discovery of genomic regions with distinct evolutionary patterns of RT. Our method provides a generic framework for comparative analysis of multi-species continuous functional genomic signals to help reveal regions with conserved or lineage-specific regulatory roles.
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Affiliation(s)
- Yang Yang
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Quanquan Gu
- Department of Computer Science, University of Virginia, Charlottesville, VA 22904, USA
| | - Yang Zhang
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Takayo Sasaki
- Department of Biological Science, Florida State University, Tallahassee, FL 32306, USA
| | - Julianna Crivello
- Institute for Systems Genomics, Department of Molecular & Cell Biology, University of Connecticut, Storrs, CT 06269, USA
| | - Rachel J O'Neill
- Institute for Systems Genomics, Department of Molecular & Cell Biology, University of Connecticut, Storrs, CT 06269, USA
| | - David M Gilbert
- Department of Biological Science, Florida State University, Tallahassee, FL 32306, USA
| | - Jian Ma
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
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19
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Abstract
Abstract
Next Generation Sequencing (NGS) or deep sequencing technology enables parallel reading of multiple individual DNA fragments, thereby enabling the identification of millions of base pairs in several hours. Recent research has clearly shown that machine learning technologies can efficiently analyse large sets of genomic data and help to identify novel gene functions and regulation regions. A deep artificial neural network consists of a group of artificial neurons that mimic the properties of living neurons. These mathematical models, termed Artificial Neural Networks (ANN), can be used to solve artificial intelligence engineering problems in several different technological fields (e.g., biology, genomics, proteomics, and metabolomics). In practical terms, neural networks are non-linear statistical structures that are organized as modelling tools and are used to simulate complex genomic relationships between inputs and outputs. To date, Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNN) have been demonstrated to be the best tools for improving performance in problem solving tasks within the genomic field.
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20
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Recurrent structural variation, clustered sites of selection, and disease risk for the complement factor H ( CFH) gene family. Proc Natl Acad Sci U S A 2018; 115:E4433-E4442. [PMID: 29686068 DOI: 10.1073/pnas.1717600115] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Structural variation and single-nucleotide variation of the complement factor H (CFH) gene family underlie several complex genetic diseases, including age-related macular degeneration (AMD) and atypical hemolytic uremic syndrome (AHUS). To understand its diversity and evolution, we performed high-quality sequencing of this ∼360-kbp locus in six primate lineages, including multiple human haplotypes. Comparative sequence analyses reveal two distinct periods of gene duplication leading to the emergence of four CFH-related (CFHR) gene paralogs (CFHR2 and CFHR4 ∼25-35 Mya and CFHR1 and CFHR3 ∼7-13 Mya). Remarkably, all evolutionary breakpoints share a common ∼4.8-kbp segment corresponding to an ancestral CFHR gene promoter that has expanded independently throughout primate evolution. This segment is recurrently reused and juxtaposed with a donor duplication containing exons 8 and 9 from ancestral CFH, creating four CFHR fusion genes that include lineage-specific members of the gene family. Combined analysis of >5,000 AMD cases and controls identifies a significant burden of a rare missense mutation that clusters at the N terminus of CFH [P = 5.81 × 10-8, odds ratio (OR) = 9.8 (3.67-Infinity)]. A bipolar clustering pattern of rare nonsynonymous mutations in patients with AMD (P < 10-3) and AHUS (P = 0.0079) maps to functional domains that show evidence of positive selection during primate evolution. Our structural variation analysis in >2,400 individuals reveals five recurrent rearrangement breakpoints that show variable frequency among AMD cases and controls. These data suggest a dynamic and recurrent pattern of mutation critical to the emergence of new CFHR genes but also in the predisposition to complex human genetic disease phenotypes.
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21
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Girimurugan SB, Liu Y, Lung PY, Vera DL, Dennis JH, Bass HW, Zhang J. iSeg: an efficient algorithm for segmentation of genomic and epigenomic data. BMC Bioinformatics 2018; 19:131. [PMID: 29642840 PMCID: PMC5896135 DOI: 10.1186/s12859-018-2140-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2017] [Accepted: 03/26/2018] [Indexed: 11/16/2022] Open
Abstract
Background Identification of functional elements of a genome often requires dividing a sequence of measurements along a genome into segments where adjacent segments have different properties, such as different mean values. Despite dozens of algorithms developed to address this problem in genomics research, methods with improved accuracy and speed are still needed to effectively tackle both existing and emerging genomic and epigenomic segmentation problems. Results We designed an efficient algorithm, called iSeg, for segmentation of genomic and epigenomic profiles. iSeg first utilizes dynamic programming to identify candidate segments and test for significance. It then uses a novel data structure based on two coupled balanced binary trees to detect overlapping significant segments and update them simultaneously during searching and refinement stages. Refinement and merging of significant segments are performed at the end to generate the final set of segments. By using an objective function based on the p-values of the segments, the algorithm can serve as a general computational framework to be combined with different assumptions on the distributions of the data. As a general segmentation method, it can segment different types of genomic and epigenomic data, such as DNA copy number variation, nucleosome occupancy, nuclease sensitivity, and differential nuclease sensitivity data. Using simple t-tests to compute p-values across multiple datasets of different types, we evaluate iSeg using both simulated and experimental datasets and show that it performs satisfactorily when compared with some other popular methods, which often employ more sophisticated statistical models. Implemented in C++, iSeg is also very computationally efficient, well suited for large numbers of input profiles and data with very long sequences. Conclusions We have developed an efficient general-purpose segmentation tool and showed that it had comparable or more accurate results than many of the most popular segment-calling algorithms used in contemporary genomic data analysis. iSeg is capable of analyzing datasets that have both positive and negative values. Tunable parameters allow users to readily adjust the statistical stringency to best match the biological nature of individual datasets, including widely or sparsely mapped genomic datasets or those with non-normal distributions. Electronic supplementary material The online version of this article (10.1186/s12859-018-2140-3) contains supplementary material, which is available to authorized users.
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Affiliation(s)
| | - Yuhang Liu
- Department of Statistics, Florida State University, Tallahassee, FL, USA
| | - Pei-Yau Lung
- Department of Statistics, Florida State University, Tallahassee, FL, USA
| | - Daniel L Vera
- Center for Genomics and Personalized Medicine, Florida State University, Tallahassee, FL, USA
| | - Jonathan H Dennis
- Department of Biological Science, Florida State University, Tallahassee, FL, USA
| | - Hank W Bass
- Department of Biological Science, Florida State University, Tallahassee, FL, USA
| | - Jinfeng Zhang
- Department of Statistics, Florida State University, Tallahassee, FL, USA.
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22
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Abstract
Transcription is regulated by transcription factor (TF) binding at promoters and distal regulatory elements and histone modifications that control the accessibility of these elements. Chromatin immunoprecipitation followed by sequencing (ChIP-seq) has become the standard assay for identifying genome-wide protein-DNA interactions in vitro and in vivo. As large-scale ChIP-seq data sets have been collected for different TFs and histone modifications, their potential to predict gene expression can be used to test hypotheses about the mechanisms of gene regulation. In addition, complementary functional genomics assays provide a global view of chromatin accessibility and long-range cis-regulatory interactions that are being combined with TF binding and histone remodeling to study the regulation of gene expression. Thus, ChIP-seq analysis is now widely integrated with other functional genomics assays to better understand gene regulatory mechanisms. In this review, we discuss advances and challenges in integrating ChIP-seq data to identify context-specific chromatin states associated with gene activity. We describe the overall computational design of integrating ChIP-seq data with other functional genomics assays. We also discuss the challenges of extending these methods to low-input ChIP-seq assays and related single-cell assays.
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Affiliation(s)
| | - Ali Mortazavi
- Corresponding author: Ali Mortazavi, Department of Developmental and Cell Biology, 2300 Biological Sciences 3, University of California, Irvine, CA 92697, USA. Tel: (949)824-6762; E-mail:
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23
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Abstract
Noncoding DNA regions have central roles in human biology, evolution, and disease. ChromHMM helps to annotate the noncoding genome using epigenomic information across one or multiple cell types. It combines multiple genome-wide epigenomic maps, and uses combinatorial and spatial mark patterns to infer a complete annotation for each cell type. ChromHMM learns chromatin-state signatures using a multivariate hidden Markov model (HMM) that explicitly models the combinatorial presence or absence of each mark. ChromHMM uses these signatures to generate a genome-wide annotation for each cell type by calculating the most probable state for each genomic segment. ChromHMM provides an automated enrichment analysis of the resulting annotations to facilitate the functional interpretations of each chromatin state. ChromHMM is distinguished by its modeling emphasis on combinations of marks, its tight integration with downstream functional enrichment analyses, its speed, and its ease of use. Chromatin states are learned, annotations are produced, and enrichments are computed within 1 d.
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24
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ChromBiSim: Interactive chromatin biclustering using a simple approach. Genomics 2017; 109:353-361. [PMID: 28579515 DOI: 10.1016/j.ygeno.2017.05.010] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2017] [Revised: 05/21/2017] [Accepted: 05/31/2017] [Indexed: 11/21/2022]
Abstract
Combinatorial patterns of histone modifications sketch the epigenomic locale. Specific positions of these modifications in the genome are marked by the presence of such signals. Various methods highlight such patterns on global scale hence missing the local patterns which are the actual hidden combinatorics. We present ChromBiSim, an interactive tool for mining subsets of modifications from epigenomic profiles. ChromBiSim efficiently extracts biclusters with their genomic locations. It is the very first user interface based and multiple cell type handling tool for decoding the interplay of subsets of histone modifications combinations along their genomic locations. It displays the results in the forms of charts and heat maps in accordance with saving them in files which could be used for post analysis. ChromBiSim tested on multiple cell types produced in total 803 combinatorial patterns. It could be used to highlight variations among diseased versus normal cell types of any species. AVAILABILITY ChromBiSim is available at (http://sourceforge.net/projects/chrombisim) in C-sharp and python languages.
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25
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Liu Q, Bonneville R, Li T, Jin VX. Transcription factor-associated combinatorial epigenetic pattern reveals higher transcriptional activity of TCF7L2-regulated intragenic enhancers. BMC Genomics 2017; 18:375. [PMID: 28499350 PMCID: PMC5429574 DOI: 10.1186/s12864-017-3764-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2017] [Accepted: 05/03/2017] [Indexed: 01/24/2023] Open
Abstract
Background Recent studies have suggested that combinations of multiple epigenetic modifications are essential for controlling gene expression. Despite numerous computational approaches have been developed to decipher the combinatorial epigenetic patterns or “epigenetic code”, none of them has explicitly addressed the relationship between a specific transcription factor (TF) and the patterns. Methods Here, we developed a novel computational method, T-cep, for annotating chromatin states associated with a specific TF. T-cep is composed of three key consecutive modules: (i) Data preprocessing, (ii) HMM training, and (iii) Potential TF-states calling. Results We evaluated T-cep on a TCF7L2-omics data. Unexpectedly, our method has uncovered a novel set of TCF7L2-regulated intragenic enhancers missed by other software tools, where the associated genes exert the highest gene expression. We further used siRNA knockdown, Co-transfection, RT-qPCR and Luciferase Reporter Assay not only to validate the accuracy and efficiency of prediction by T-cep, but also to confirm the functionality of TCF7L2-regulated enhancers in both MCF7 and PANC1 cells respectively. Conclusions Our study for the first time at a genome-wide scale reveals the enhanced transcriptional activity of cell-type-specific TCF7L2 intragenic enhancers in regulating gene expression. Electronic supplementary material The online version of this article (doi:10.1186/s12864-017-3764-9) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Qi Liu
- Department of Molecular Medicine, University of Texas Health Science Center, 8403 Floyd Curl, San Antonio, TX, 78229, USA.,College of Life Science, Jilin University, Changchun, 130012, China
| | - Russell Bonneville
- Biomedical Sciences Graduate Program, The Ohio State University, Columbus, OH, 43210, USA
| | - Tianbao Li
- Department of Molecular Medicine, University of Texas Health Science Center, 8403 Floyd Curl, San Antonio, TX, 78229, USA.,College of Life Science, Jilin University, Changchun, 130012, China
| | - Victor X Jin
- Department of Molecular Medicine, University of Texas Health Science Center, 8403 Floyd Curl, San Antonio, TX, 78229, USA.
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26
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Wei K, Libbrecht MW, Bilmes JA, Noble WS. Choosing panels of genomics assays using submodular optimization. Genome Biol 2016; 17:229. [PMID: 27846892 PMCID: PMC5111315 DOI: 10.1186/s13059-016-1089-7] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2016] [Accepted: 10/24/2016] [Indexed: 01/23/2023] Open
Abstract
Due to the high cost of sequencing-based genomics assays such as ChIP-seq and DNase-seq, the epigenomic characterization of a cell type is typically carried out using a small panel of assay types. Deciding a priori which assays to perform is, thus, a critical step in many studies. We present the submodular selection of assays (SSA), a method for choosing a diverse panel of genomic assays that leverages methods from submodular optimization. More generally, this application serves as a model for how submodular optimization can be applied to other discrete problems in biology.
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Affiliation(s)
- Kai Wei
- Department of Electrical Engineering, University of Washington, Seattle, WA, USA
| | - Maxwell W Libbrecht
- Department of Computer Science and Engineering, University of Washington, Seattle, WA, USA
| | - Jeffrey A Bilmes
- Department of Electrical Engineering, University of Washington, Seattle, WA, USA
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27
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Zhan X, Girirajan S, Zhao N, Wu MC, Ghosh D. A novel copy number variants kernel association test with application to autism spectrum disorders studies. Bioinformatics 2016; 32:3603-3610. [PMID: 27497442 DOI: 10.1093/bioinformatics/btw500] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2016] [Revised: 06/28/2016] [Accepted: 07/22/2016] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION Copy number variants (CNVs) have been implicated in a variety of neurodevelopmental disorders, including autism spectrum disorders, intellectual disability and schizophrenia. Recent advances in high-throughput genomic technologies have enabled rapid discovery of many genetic variants including CNVs. As a result, there is increasing interest in studying the role of CNVs in the etiology of many complex diseases. Despite the availability of an unprecedented wealth of CNV data, methods for testing association between CNVs and disease-related traits are still under-developed due to the low prevalence and complicated multi-scale features of CNVs. RESULTS We propose a novel CNV kernel association test (CKAT) in this paper. To address the low prevalence, CNVs are first grouped into CNV regions (CNVR). Then, taking into account the multi-scale features of CNVs, we first design a single-CNV kernel which summarizes the similarity between two CNVs, and next aggregate the single-CNV kernel to a CNVR kernel which summarizes the similarity between two CNVRs. Finally, association between CNVR and disease-related traits is assessed by comparing the kernel-based similarity with the similarity in the trait using a score test for variance components in a random effect model. We illustrate the proposed CKAT using simulations and show that CKAT is more powerful than existing methods, while always being able to control the type I error. We also apply CKAT to a real dataset examining the association between CNV and autism spectrum disorders, which demonstrates the potential usefulness of the proposed method. AVAILABILITY AND IMPLEMENTATION A R package to implement the proposed CKAT method is available at http://works.bepress.com/debashis_ghosh/ CONTACTS: xzhan@fhcrc.org or debashis.ghosh@ucdenver.eduSupplementary information: Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Xiang Zhan
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Santhosh Girirajan
- Department of Biochemistry and Molecular Biology, Pennsylvania State University, University Park, PA 16802, USA.,Department of Anthropology, Pennsylvania State University, University Park, PA 16802, USA and
| | - Ni Zhao
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Michael C Wu
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Debashis Ghosh
- Department of Biostatistics and Informatics, University of Colorado, Aurora, CO 80045, USA
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28
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Glas J, Dümcke S, Zacher B, Poron D, Gagneur J, Tresch A. Simultaneous characterization of sense and antisense genomic processes by the double-stranded hidden Markov model. Nucleic Acids Res 2016; 44:e44. [PMID: 26578558 PMCID: PMC4797261 DOI: 10.1093/nar/gkv1184] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2015] [Accepted: 10/24/2015] [Indexed: 11/14/2022] Open
Abstract
Hidden Markov models (HMMs) have been extensively used to dissect the genome into functionally distinct regions using data such as RNA expression or DNA binding measurements. It is a challenge to disentangle processes occurring on complementary strands of the same genomic region. We present the double-stranded HMM (dsHMM), a model for the strand-specific analysis of genomic processes. We applied dsHMM to yeast using strand specific transcription data, nucleosome data, and protein binding data for a set of 11 factors associated with the regulation of transcription.The resulting annotation recovers the mRNA transcription cycle (initiation, elongation, termination) while correctly predicting strand-specificity and directionality of the transcription process. We find that pre-initiation complex formation is an essentially undirected process, giving rise to a large number of bidirectional promoters and to pervasive antisense transcription. Notably, 12% of all transcriptionally active positions showed simultaneous activity on both strands. Furthermore, dsHMM reveals that antisense transcription is specifically suppressed by Nrd1, a yeast termination factor.
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Affiliation(s)
- Julia Glas
- Gene Center Munich and Department of Biochemistry, Ludwig-Maximilians-Universität München, Feodor-Lynen-Straße 25, 81377 Munich, Germany
| | - Sebastian Dümcke
- Department of Plant Breeding and Genetics, Max Planck Institute for Plant Breeding Research, Carl-von-Linné-Weg 10, 50829 Cologne, Germany Institute for Genetics, University of Cologne, Zülpicher Str. 47b, 50674 Cologne, Germany
| | - Benedikt Zacher
- Gene Center Munich and Department of Biochemistry, Ludwig-Maximilians-Universität München, Feodor-Lynen-Straße 25, 81377 Munich, Germany Department of Plant Breeding and Genetics, Max Planck Institute for Plant Breeding Research, Carl-von-Linné-Weg 10, 50829 Cologne, Germany
| | - Don Poron
- Institute for Genetics, University of Cologne, Zülpicher Str. 47b, 50674 Cologne, Germany
| | - Julien Gagneur
- Gene Center Munich and Department of Biochemistry, Ludwig-Maximilians-Universität München, Feodor-Lynen-Straße 25, 81377 Munich, Germany
| | - Achim Tresch
- Gene Center Munich and Department of Biochemistry, Ludwig-Maximilians-Universität München, Feodor-Lynen-Straße 25, 81377 Munich, Germany Department of Plant Breeding and Genetics, Max Planck Institute for Plant Breeding Research, Carl-von-Linné-Weg 10, 50829 Cologne, Germany Institute for Genetics, University of Cologne, Zülpicher Str. 47b, 50674 Cologne, Germany
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29
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Noureen N, Touseef M, Fazal S, Qadir MA. ChromClust: A semi-supervised chromatin clustering toolkit for mining histone modifications interplay. Genomics 2015; 106:355-9. [DOI: 10.1016/j.ygeno.2015.11.002] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2015] [Revised: 10/30/2015] [Accepted: 11/04/2015] [Indexed: 10/22/2022]
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30
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Liu F, Ren C, Li H, Zhou P, Bo X, Shu W. De novo identification of replication-timing domains in the human genome by deep learning. Bioinformatics 2015; 32:641-9. [PMID: 26545821 PMCID: PMC4795613 DOI: 10.1093/bioinformatics/btv643] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2015] [Accepted: 10/27/2015] [Indexed: 12/31/2022] Open
Abstract
Motivation: The de novo identification of the initiation and termination zones—regions that replicate earlier or later than their upstream and downstream neighbours, respectively—remains a key challenge in DNA replication. Results: Building on advances in deep learning, we developed a novel hybrid architecture combining a pre-trained, deep neural network and a hidden Markov model (DNN-HMM) for the de novo identification of replication domains using replication timing profiles. Our results demonstrate that DNN-HMM can significantly outperform strong, discriminatively trained Gaussian mixture model–HMM (GMM-HMM) systems and other six reported methods that can be applied to this challenge. We applied our trained DNN-HMM to identify distinct replication domain types, namely the early replication domain (ERD), the down transition zone (DTZ), the late replication domain (LRD) and the up transition zone (UTZ), using newly replicated DNA sequencing (Repli-Seq) data across 15 human cells. A subsequent integrative analysis revealed that these replication domains harbour unique genomic and epigenetic patterns, transcriptional activity and higher-order chromosomal structure. Our findings support the ‘replication-domain’ model, which states (1) that ERDs and LRDs, connected by UTZs and DTZs, are spatially compartmentalized structural and functional units of higher-order chromosomal structure, (2) that the adjacent DTZ-UTZ pairs form chromatin loops and (3) that intra-interactions within ERDs and LRDs tend to be short-range and long-range, respectively. Our model reveals an important chromatin organizational principle of the human genome and represents a critical step towards understanding the mechanisms regulating replication timing. Availability and implementation: Our DNN-HMM method and three additional algorithms can be freely accessed at https://github.com/wenjiegroup/DNN-HMM. The replication domain regions identified in this study are available in GEO under the accession ID GSE53984. Contact:shuwj@bmi.ac.cn or boxc@bmi.ac.cn Supplementary information:Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Feng Liu
- Department of Biotechnology, Beijing Institute of Radiation Medicine and
| | - Chao Ren
- Department of Biotechnology, Beijing Institute of Radiation Medicine and
| | - Hao Li
- Department of Biotechnology, Beijing Institute of Radiation Medicine and
| | - Pingkun Zhou
- Department of Radiation Toxicology and Oncology, Beijing Institute of Radiation Medicine, Beijing 100850, China
| | - Xiaochen Bo
- Department of Biotechnology, Beijing Institute of Radiation Medicine and
| | - Wenjie Shu
- Department of Biotechnology, Beijing Institute of Radiation Medicine and
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31
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Walker LC, Wiggins GAR, Pearson JF. The Role of Constitutional Copy Number Variants in Breast Cancer. ACTA ACUST UNITED AC 2015; 4:407-23. [PMID: 27600231 PMCID: PMC4996380 DOI: 10.3390/microarrays4030407] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2015] [Revised: 08/26/2015] [Accepted: 09/01/2015] [Indexed: 01/16/2023]
Abstract
Constitutional copy number variants (CNVs) include inherited and de novo deviations from a diploid state at a defined genomic region. These variants contribute significantly to genetic variation and disease in humans, including breast cancer susceptibility. Identification of genetic risk factors for breast cancer in recent years has been dominated by the use of genome-wide technologies, such as single nucleotide polymorphism (SNP)-arrays, with a significant focus on single nucleotide variants. To date, these large datasets have been underutilised for generating genome-wide CNV profiles despite offering a massive resource for assessing the contribution of these structural variants to breast cancer risk. Technical challenges remain in determining the location and distribution of CNVs across the human genome due to the accuracy of computational prediction algorithms and resolution of the array data. Moreover, better methods are required for interpreting the functional effect of newly discovered CNVs. In this review, we explore current and future application of SNP array technology to assess rare and common CNVs in association with breast cancer risk in humans.
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Affiliation(s)
- Logan C Walker
- Mackenzie Cancer Research Group, Department of Pathology, University of Otago, Christchurch 8140, New Zealand.
| | - George A R Wiggins
- Mackenzie Cancer Research Group, Department of Pathology, University of Otago, Christchurch 8140, New Zealand.
| | - John F Pearson
- Biostatistics and Computational Biology Unit, University of Otago, Christchurch 8140, New Zealand.
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32
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Ay F, Noble WS. Analysis methods for studying the 3D architecture of the genome. Genome Biol 2015; 16:183. [PMID: 26328929 PMCID: PMC4556012 DOI: 10.1186/s13059-015-0745-7] [Citation(s) in RCA: 96] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2015] [Accepted: 08/10/2015] [Indexed: 11/10/2022] Open
Abstract
The rapidly increasing quantity of genome-wide chromosome conformation capture data presents great opportunities and challenges in the computational modeling and interpretation of the three-dimensional genome. In particular, with recent trends towards higher-resolution high-throughput chromosome conformation capture (Hi-C) data, the diversity and complexity of biological hypotheses that can be tested necessitates rigorous computational and statistical methods as well as scalable pipelines to interpret these datasets. Here we review computational tools to interpret Hi-C data, including pipelines for mapping, filtering, and normalization, and methods for confidence estimation, domain calling, visualization, and three-dimensional modeling.
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Affiliation(s)
- Ferhat Ay
- Department of Genome Sciences, University of Washington, Seattle, WA, 98195, USA. .,Feinberg School of Medicine, Northwestern University, Chicago, 60661, IL, USA.
| | - William S Noble
- Department of Genome Sciences, University of Washington, Seattle, WA, 98195, USA. .,Department of Computer Science and Engineering, University of Washington, Seattle, 98195, WA, USA.
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33
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Yen A, Kellis M. Systematic chromatin state comparison of epigenomes associated with diverse properties including sex and tissue type. Nat Commun 2015; 6:7973. [PMID: 26282110 PMCID: PMC4557131 DOI: 10.1038/ncomms8973] [Citation(s) in RCA: 48] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2015] [Accepted: 07/01/2015] [Indexed: 01/06/2023] Open
Abstract
Epigenomic data sets provide critical information about the dynamic role of chromatin states in gene regulation, but a key question of how chromatin state segmentations vary under different conditions across the genome has remained unaddressed. Here we present ChromDiff, a group-wise chromatin state comparison method that generates an information-theoretic representation of epigenomes and corrects for external covariate factors to better isolate relevant chromatin state changes. By applying ChromDiff to the 127 epigenomes from the Roadmap Epigenomics and ENCODE projects, we provide novel group-wise comparative analyses across sex, tissue type, state and developmental age. Remarkably, we find that distinct sets of epigenomic features are maximally discriminative for different group-wise comparisons, in each case revealing distinct enriched pathways, many of which do not show gene expression differences. Our methodology should be broadly applicable for epigenomic comparisons and provides a powerful new tool for studying chromatin state differences at the genome scale.
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Affiliation(s)
- Angela Yen
- 1] Electrical Engineering and Computer Science Department, Computer Science and Artificial Intelligence Laboratory, MIT, 32 Vassar Street, 32D-524, Cambridge, Massachusetts 02139, USA [2] Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA
| | - Manolis Kellis
- 1] Electrical Engineering and Computer Science Department, Computer Science and Artificial Intelligence Laboratory, MIT, 32 Vassar Street, 32D-524, Cambridge, Massachusetts 02139, USA [2] Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA
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34
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Abstract
The field of machine learning, which aims to develop computer algorithms that improve with experience, holds promise to enable computers to assist humans in the analysis of large, complex data sets. Here, we provide an overview of machine learning applications for the analysis of genome sequencing data sets, including the annotation of sequence elements and epigenetic, proteomic or metabolomic data. We present considerations and recurrent challenges in the application of supervised, semi-supervised and unsupervised machine learning methods, as well as of generative and discriminative modelling approaches. We provide general guidelines to assist in the selection of these machine learning methods and their practical application for the analysis of genetic and genomic data sets.
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Affiliation(s)
- Maxwell W Libbrecht
- Department of Computer Science and Engineering, University of Washington, 185 Stevens Way, Seattle, Washington 98195-2350, USA
| | - William Stafford Noble
- 1] Department of Computer Science and Engineering, University of Washington, 185 Stevens Way, Seattle, Washington 98195-2350, USA. [2] Department of Genome Sciences, University of Washington, 3720 15th Ave NE Seattle, Washington 98195-5065, USA
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35
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Glusman G, Severson A, Dhankani V, Robinson M, Farrah T, Mauldin DE, Stittrich AB, Ament SA, Roach JC, Brunkow ME, Bodian DL, Vockley JG, Shmulevich I, Niederhuber JE, Hood L. Identification of copy number variants in whole-genome data using Reference Coverage Profiles. Front Genet 2015; 6:45. [PMID: 25741365 PMCID: PMC4330915 DOI: 10.3389/fgene.2015.00045] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2014] [Accepted: 01/30/2015] [Indexed: 12/20/2022] Open
Abstract
The identification of DNA copy numbers from short-read sequencing data remains a challenge for both technical and algorithmic reasons. The raw data for these analyses are measured in tens to hundreds of gigabytes per genome; transmitting, storing, and analyzing such large files is cumbersome, particularly for methods that analyze several samples simultaneously. We developed a very efficient representation of depth of coverage (150–1000× compression) that enables such analyses. Current methods for analyzing variants in whole-genome sequencing (WGS) data frequently miss copy number variants (CNVs), particularly hemizygous deletions in the 1–100 kb range. To fill this gap, we developed a method to identify CNVs in individual genomes, based on comparison to joint profiles pre-computed from a large set of genomes. We analyzed depth of coverage in over 6000 high quality (>40×) genomes. The depth of coverage has strong sequence-specific fluctuations only partially explained by global parameters like %GC. To account for these fluctuations, we constructed multi-genome profiles representing the observed or inferred diploid depth of coverage at each position along the genome. These Reference Coverage Profiles (RCPs) take into account the diverse technologies and pipeline versions used. Normalization of the scaled coverage to the RCP followed by hidden Markov model (HMM) segmentation enables efficient detection of CNVs and large deletions in individual genomes. Use of pre-computed multi-genome coverage profiles improves our ability to analyze each individual genome. We make available RCPs and tools for performing these analyses on personal genomes. We expect the increased sensitivity and specificity for individual genome analysis to be critical for achieving clinical-grade genome interpretation.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | - Dale L Bodian
- Inova Translational Medicine Institute, Inova Health System Falls Church, VA, USA
| | - Joseph G Vockley
- Inova Translational Medicine Institute, Inova Health System Falls Church, VA, USA
| | | | - John E Niederhuber
- Inova Translational Medicine Institute, Inova Health System Falls Church, VA, USA
| | - Leroy Hood
- Institute for Systems Biology Seattle, WA, USA
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36
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Libbrecht MW, Ay F, Hoffman MM, Gilbert DM, Bilmes JA, Noble WS. Joint annotation of chromatin state and chromatin conformation reveals relationships among domain types and identifies domains of cell-type-specific expression. Genome Res 2015; 25:544-57. [PMID: 25677182 PMCID: PMC4381526 DOI: 10.1101/gr.184341.114] [Citation(s) in RCA: 61] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2014] [Accepted: 02/06/2015] [Indexed: 11/24/2022]
Abstract
The genomic neighborhood of a gene influences its activity, a behavior that is attributable in part to domain-scale regulation. Previous genomic studies have identified many types of regulatory domains. However, due to the difficulty of integrating genomics data sets, the relationships among these domain types are poorly understood. Semi-automated genome annotation (SAGA) algorithms facilitate human interpretation of heterogeneous collections of genomics data by simultaneously partitioning the human genome and assigning labels to the resulting genomic segments. However, existing SAGA methods cannot integrate inherently pairwise chromatin conformation data. We developed a new computational method, called graph-based regularization (GBR), for expressing a pairwise prior that encourages certain pairs of genomic loci to receive the same label in a genome annotation. We used GBR to exploit chromatin conformation information during genome annotation by encouraging positions that are close in 3D to occupy the same type of domain. Using this approach, we produced a model of chromatin domains in eight human cell types, thereby revealing the relationships among known domain types. Through this model, we identified clusters of tightly regulated genes expressed in only a small number of cell types, which we term “specific expression domains.” We found that domain boundaries marked by promoters and CTCF motifs are consistent between cell types even when domain activity changes. Finally, we showed that GBR can be used to transfer information from well-studied cell types to less well-characterized cell types during genome annotation, making it possible to produce high-quality annotations of the hundreds of cell types with limited available data.
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Affiliation(s)
- Maxwell W Libbrecht
- Department of Computer Science and Engineering, University of Washington, Seattle, Washington 98195, USA
| | - Ferhat Ay
- Department of Genome Sciences, University of Washington, Seattle, Washington 98195, USA
| | - Michael M Hoffman
- Princess Margaret Cancer Centre, University of Toronto, ON M5G 1L7, Canada Department of Medical Biophysics, University of Toronto, ON M5G 1L7, Canada
| | - David M Gilbert
- Department of Biological Science, The Florida State University, Tallahassee, Florida 32304, USA
| | - Jeffrey A Bilmes
- Department of Electrical Engineering, University of Washington, Seattle, Washington 98195, USA
| | - William Stafford Noble
- Department of Computer Science and Engineering, University of Washington, Seattle, Washington 98195, USA Department of Genome Sciences, University of Washington, Seattle, Washington 98195, USA
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37
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Shim H, Stephens M. WAVELET-BASED GENETIC ASSOCIATION ANALYSIS OF FUNCTIONAL PHENOTYPES ARISING FROM HIGH-THROUGHPUT SEQUENCING ASSAYS. Ann Appl Stat 2015; 9:655-686. [PMID: 29399242 PMCID: PMC5795621 DOI: 10.1214/14-aoas776] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
Understanding how genetic variants influence cellular-level processes is an important step toward understanding how they influence important organismal-level traits, or "phenotypes," including human disease susceptibility. To this end, scientists are undertaking large-scale genetic association studies that aim to identify genetic variants associated with molecular and cellular phenotypes, such as gene expression, transcription factor binding, or chromatin accessibility. These studies use high-throughput sequencing assays (e.g., RNA-seq, ChIP-seq, DNase-seq) to obtain high-resolution data on how the traits vary along the genome in each sample. However, typical association analyses fail to exploit these high-resolution measurements, instead aggregating the data at coarser resolutions, such as genes, or windows of fixed length. Here we develop and apply statistical methods that better exploit the high-resolution data. The key idea is to treat the sequence data as measuring an underlying "function" that varies along the genome, and then, building on wavelet-based methods for functional data analysis, test for association between genetic variants and the underlying function. Applying these methods to identify genetic variants associated with chromatin accessibility (dsQTLs), we find that they identify substantially more associations than a simpler window-based analysis, and in total we identify 772 novel dsQTLs not identified by the original analysis.
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38
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Zacher B, Lidschreiber M, Cramer P, Gagneur J, Tresch A. Annotation of genomics data using bidirectional hidden Markov models unveils variations in Pol II transcription cycle. Mol Syst Biol 2014; 10:768. [PMID: 25527639 PMCID: PMC4300491 DOI: 10.15252/msb.20145654] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023] Open
Abstract
DNA replication, transcription and repair involve the recruitment of protein complexes that change their composition as they progress along the genome in a directed or strand-specific manner. Chromatin immunoprecipitation in conjunction with hidden Markov models (HMMs) has been instrumental in understanding these processes, as they segment the genome into discrete states that can be related to DNA-associated protein complexes. However, current HMM-based approaches are not able to assign forward or reverse direction to states or properly integrate strand-specific (e.g., RNA expression) with non-strand-specific (e.g., ChIP) data, which is indispensable to accurately characterize directed processes. To overcome these limitations, we introduce bidirectional HMMs which infer directed genomic states from occupancy profiles de novo. Application to RNA polymerase II-associated factors in yeast and chromatin modifications in human T cells recovers the majority of transcribed loci, reveals gene-specific variations in the yeast transcription cycle and indicates the existence of directed chromatin state patterns at transcribed, but not at repressed, regions in the human genome. In yeast, we identify 32 new transcribed loci, a regulated initiation–elongation transition, the absence of elongation factors Ctk1 and Paf1 from a class of genes, a distinct transcription mechanism for highly expressed genes and novel DNA sequence motifs associated with transcription termination. We anticipate bidirectional HMMs to significantly improve the analyses of genome-associated directed processes.
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Affiliation(s)
- Benedikt Zacher
- Gene Center and Department of Biochemistry, Center for Integrated Protein Science CIPSM, Ludwig-Maximilians-Universität München, Munich, Germany Institute for Genetics, University of Cologne, Cologne, Germany
| | - Michael Lidschreiber
- Gene Center and Department of Biochemistry, Center for Integrated Protein Science CIPSM, Ludwig-Maximilians-Universität München, Munich, Germany Department of Molecular Biology, Max Planck Institute for Biophysical Chemistry, Göttingen, Germany
| | - Patrick Cramer
- Gene Center and Department of Biochemistry, Center for Integrated Protein Science CIPSM, Ludwig-Maximilians-Universität München, Munich, Germany Department of Molecular Biology, Max Planck Institute for Biophysical Chemistry, Göttingen, Germany
| | - Julien Gagneur
- Gene Center and Department of Biochemistry, Center for Integrated Protein Science CIPSM, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Achim Tresch
- Gene Center and Department of Biochemistry, Center for Integrated Protein Science CIPSM, Ludwig-Maximilians-Universität München, Munich, Germany Institute for Genetics, University of Cologne, Cologne, Germany Max Planck Institute for Plant Breeding Research, Cologne, Germany
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39
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Qiao Y, Quinlan AR, Jazaeri AA, Verhaak RG, Wheeler DA, Marth GT. SubcloneSeeker: a computational framework for reconstructing tumor clone structure for cancer variant interpretation and prioritization. Genome Biol 2014; 15:443. [PMID: 25160522 PMCID: PMC4180956 DOI: 10.1186/s13059-014-0443-x] [Citation(s) in RCA: 41] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2014] [Accepted: 07/31/2014] [Indexed: 01/03/2023] Open
Abstract
Many tumors are composed of genetically divergent cell subpopulations. We report SubcloneSeeker, a package capable of exhaustive identification of subclone structures and evolutionary histories with bulk somatic variant allele frequency measurements from tumor biopsies. We present a statistical framework to elucidate whether specific sets of mutations are present within the same subclones, and the order in which they occur. We demonstrate how subclone reconstruction provides crucial information about tumorigenesis and relapse mechanisms; guides functional study by variant prioritization, and has the potential as a rational basis for informed therapeutic strategies for the patient. SubcloneSeeker is available at: https://github.com/yiq/SubcloneSeeker.
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40
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Nguyen N, Vo A, Won KJ. A wavelet approach to detect enriched regions and explore epigenomic landscapes. J Comput Biol 2014; 21:846-54. [PMID: 25072902 DOI: 10.1089/cmb.2014.0095] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023] Open
Abstract
Epigenetic landscapes represent how cells regulate gene activity. To understand their effect on gene regulation, it is important to detect their occupancy in the genome. Unlike transcription factors whose binding regions are limited to narrow regions, histone modification marks are enriched over broader areas. The stochastic characteristics unique to each mark make it hard to detect their enrichment. Classically, a predefined window has been used to detect their enrichment. However, these approaches heavily rely on the predetermined parameters. Also, the window-based approaches cannot handle the enrichment of multiple marks. We propose a novel algorithm, called SeqW, to detect enrichment of multiple histone modification marks. SeqW applies a zooming approach to detect a broadly enriched domain. The zooming approach helps domain detection by increasing signal-to-noise ratio. The borders of the domains are detected by studying the characteristics of signals in the wavelet domain. We show that SeqW outperformed previous predictors in detecting broad peaks. Also, we applied SeqW in studying spatial combinations of histone modification patterns.
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Affiliation(s)
- Nha Nguyen
- 1 Department of Genetics, School of Medicine, University of Pennsylvania , Philadelphia, Pennsylvania
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41
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Veloso A, Kirkconnell KS, Magnuson B, Biewen B, Paulsen MT, Wilson TE, Ljungman M. Rate of elongation by RNA polymerase II is associated with specific gene features and epigenetic modifications. Genome Res 2014; 24:896-905. [PMID: 24714810 PMCID: PMC4032854 DOI: 10.1101/gr.171405.113] [Citation(s) in RCA: 195] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
The rate of transcription elongation plays an important role in the timing of expression of full-length transcripts as well as in the regulation of alternative splicing. In this study, we coupled Bru-seq technology with 5,6-dichlorobenzimidazole 1-β-D-ribofuranoside (DRB) to estimate the elongation rates of over 2000 individual genes in human cells. This technique, BruDRB-seq, revealed gene-specific differences in elongation rates with a median rate of around 1.5 kb/min. We found that genes with rapid elongation rates showed higher densities of H3K79me2 and H4K20me1 histone marks compared to slower elongating genes. Furthermore, high elongation rates had a positive correlation with gene length, low complexity DNA sequence, and distance from the nearest active transcription unit. Features that negatively correlated with elongation rate included the density of exons, long terminal repeats, GC content of the gene, and DNA methylation density in the bodies of genes. Our results suggest that some static gene features influence transcription elongation rates and that cells may alter elongation rates by epigenetic regulation. The BruDRB-seq technique offers new opportunities to interrogate mechanisms of regulation of transcription elongation.
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Affiliation(s)
- Artur Veloso
- Department of Radiation Oncology, University of Michigan Comprehensive Cancer Center and Translational Oncology Program, University of Michigan, Ann Arbor, Michigan 48109, USA; Bioinformatics Program, Department of Computational Medicine and Bioinformatics and Department of Statistics, University of Michigan, Ann Arbor, Michigan 48109, USA
| | - Killeen S Kirkconnell
- Department of Radiation Oncology, University of Michigan Comprehensive Cancer Center and Translational Oncology Program, University of Michigan, Ann Arbor, Michigan 48109, USA; Human Genetics Training Program, Department of Human Genetics, University of Michigan, Ann Arbor, Michigan 48109, USA
| | - Brian Magnuson
- Department of Radiation Oncology, University of Michigan Comprehensive Cancer Center and Translational Oncology Program, University of Michigan, Ann Arbor, Michigan 48109, USA
| | - Benjamin Biewen
- Department of Radiation Oncology, University of Michigan Comprehensive Cancer Center and Translational Oncology Program, University of Michigan, Ann Arbor, Michigan 48109, USA; Gustavus Adolphus College, St. Peter, Minnesota 56082, USA
| | - Michelle T Paulsen
- Department of Radiation Oncology, University of Michigan Comprehensive Cancer Center and Translational Oncology Program, University of Michigan, Ann Arbor, Michigan 48109, USA
| | - Thomas E Wilson
- Department of Pathology, University of Michigan, Ann Arbor, Michigan 48109, USA; Department of Human Genetics, University of Michigan, Ann Arbor, Michigan 48109, USA
| | - Mats Ljungman
- Department of Radiation Oncology, University of Michigan Comprehensive Cancer Center and Translational Oncology Program, University of Michigan, Ann Arbor, Michigan 48109, USA; Department of Environmental Health Sciences, School of Public Health, University of Michigan, Ann Arbor, Michigan 48109, USA
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42
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Sun Y, Shi N, Lu H, Zhang J, Ma Y, Qiao Y, Mao Y, Jia K, Han L, Liu F, Li H, Lin Z, Li X, Zhao X. ABCC4copy number variation is associated with susceptibility to esophageal squamous cell carcinoma. Carcinogenesis 2014; 35:1941-50. [DOI: 10.1093/carcin/bgu043] [Citation(s) in RCA: 39] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
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43
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Identifying and mapping cell-type-specific chromatin programming of gene expression. Proc Natl Acad Sci U S A 2014; 111:E645-54. [PMID: 24469817 DOI: 10.1073/pnas.1312523111] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
A problem of substantial interest is to systematically map variation in chromatin structure to gene-expression regulation across conditions, environments, or differentiated cell types. We developed and applied a quantitative framework for determining the existence, strength, and type of relationship between high-resolution chromatin structure in terms of DNaseI hypersensitivity and genome-wide gene-expression levels in 20 diverse human cell types. We show that ∼25% of genes show cell-type-specific expression explained by alterations in chromatin structure. We find that distal regions of chromatin structure (e.g., ±200 kb) capture more genes with this relationship than local regions (e.g., ±2.5 kb), yet the local regions show a more pronounced effect. By exploiting variation across cell types, we were capable of pinpointing the most likely hypersensitive sites related to cell-type-specific expression, which we show have a range of contextual uses. This quantitative framework is likely applicable to other settings aimed at relating continuous genomic measurements to gene-expression variation.
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Vandeweyer G, Kooy RF. Detection and interpretation of genomic structural variation in health and disease. Expert Rev Mol Diagn 2014; 13:61-82. [DOI: 10.1586/erm.12.119] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
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Lu J, Bushel PR. Dynamic expression of 3' UTRs revealed by Poisson hidden Markov modeling of RNA-Seq: implications in gene expression profiling. Gene 2013; 527:616-23. [PMID: 23845781 DOI: 10.1016/j.gene.2013.06.052] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2013] [Revised: 06/11/2013] [Accepted: 06/19/2013] [Indexed: 10/26/2022]
Abstract
RNA sequencing (RNA-Seq) allows for the identification of novel exon-exon junctions and quantification of gene expression levels. We show that from RNA-Seq data one may also detect utilization of alternative polyadenylation (APA) in 3' untranslated regions (3' UTRs) known to play a critical role in the regulation of mRNA stability, cellular localization and translation efficiency. Given the dynamic nature of APA, it is desirable to examine the APA on a sample by sample basis. We used a Poisson hidden Markov model (PHMM) of RNA-Seq data to identify potential APA in human liver and brain cortex tissues leading to shortened 3' UTRs. Over three hundred transcripts with shortened 3' UTRs were detected with sensitivity >75% and specificity >60%. Tissue-specific 3' UTR shortening was observed for 32 genes with a q-value ≤ 0.1. When compared to alternative isoforms detected by Cufflinks or MISO, our PHMM method agreed on over 100 transcripts with shortened 3' UTRs. Given the increasing usage of RNA-Seq for gene expression profiling, using PHMM to investigate sample-specific 3' UTR shortening could be an added benefit from this emerging technology.
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Affiliation(s)
- Jun Lu
- Microarray and Genome Informatics Group, National Institute of Environmental Health Sciences, Research Triangle Park, NC 27709, USA.
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Alonso A, Marsal S, Tortosa R, Canela-Xandri O, Julià A. GStream: improving SNP and CNV coverage on genome-wide association studies. PLoS One 2013; 8:e68822. [PMID: 23844243 PMCID: PMC3700900 DOI: 10.1371/journal.pone.0068822] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2013] [Accepted: 06/03/2013] [Indexed: 11/22/2022] Open
Abstract
We present GStream, a method that combines genome-wide SNP and CNV genotyping in the Illumina microarray platform with unprecedented accuracy. This new method outperforms previous well-established SNP genotyping software. More importantly, the CNV calling algorithm of GStream dramatically improves the results obtained by previous state-of-the-art methods and yields an accuracy that is close to that obtained by purely CNV-oriented technologies like Comparative Genomic Hybridization (CGH). We demonstrate the superior performance of GStream using microarray data generated from HapMap samples. Using the reference CNV calls generated by the 1000 Genomes Project (1KGP) and well-known studies on whole genome CNV characterization based either on CGH or genotyping microarray technologies, we show that GStream can increase the number of reliably detected variants up to 25% compared to previously developed methods. Furthermore, the increased genome coverage provided by GStream allows the discovery of CNVs in close linkage disequilibrium with SNPs, previously associated with disease risk in published Genome-Wide Association Studies (GWAS). These results could provide important insights into the biological mechanism underlying the detected disease risk association. With GStream, large-scale GWAS will not only benefit from the combined genotyping of SNPs and CNVs at an unprecedented accuracy, but will also take advantage of the computational efficiency of the method.
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Affiliation(s)
- Arnald Alonso
- Rheumatology Research Group, Vall d'Hebron Hospital Research Institute, Barcelona, Spain
- Department of ESAII, Polytechnical University of Catalonia, Barcelona, Spain
| | - Sara Marsal
- Rheumatology Research Group, Vall d'Hebron Hospital Research Institute, Barcelona, Spain
| | - Raül Tortosa
- Rheumatology Research Group, Vall d'Hebron Hospital Research Institute, Barcelona, Spain
| | - Oriol Canela-Xandri
- Rheumatology Research Group, Vall d'Hebron Hospital Research Institute, Barcelona, Spain
| | - Antonio Julià
- Rheumatology Research Group, Vall d'Hebron Hospital Research Institute, Barcelona, Spain
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The genomic landscape of the somatic linker histone subtypes H1.1 to H1.5 in human cells. Cell Rep 2013; 3:2142-54. [PMID: 23746450 DOI: 10.1016/j.celrep.2013.05.003] [Citation(s) in RCA: 95] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2012] [Revised: 02/19/2013] [Accepted: 05/03/2013] [Indexed: 12/27/2022] Open
Abstract
Human cells contain five canonical, replication-dependent somatic histone H1 subtypes (H1.1, H1.2, H1.3, H1.4, and H1.5). Although they are key chromatin components, the genomic distribution of the H1 subtypes is still unknown, and their role in chromatin processes has thus far remained elusive. Here, we map the genomic localization of all somatic replication-dependent H1 subtypes in human lung fibroblasts using an integrative DNA adenine methyltransferase identification (DamID) analysis. We find in general that H1.2 to H1.5 are depleted from CpG-dense regions and active regulatory regions. H1.1 shows a DamID binding profile distinct from the other subtypes, suggesting a unique function. H1 subtypes can mark specific domains and repressive regions, pointing toward a role for H1 in three-dimensional genome organization. Our work integrates H1 subtypes into the epigenome maps of human cells and provides a valuable resource to refine our understanding of the significance of H1 and its heterogeneity in the control of genome function.
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A tiered hidden Markov model characterizes multi-scale chromatin states. Genomics 2013; 102:1-7. [PMID: 23570996 DOI: 10.1016/j.ygeno.2013.03.009] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2012] [Revised: 03/28/2013] [Accepted: 03/31/2013] [Indexed: 11/21/2022]
Abstract
Precise characterization of chromatin states is an important but difficult task for understanding the regulatory role of chromatin. A number of computational methods have been developed with varying levels of success. However, a remaining challenge is to model epigenomic patterns over multi-scales, as each histone mark is distributed with its own characteristic length scale. We developed a tiered hidden Markov model and applied it to analyze a ChIP-seq dataset in human embryonic stem cells. We identified a two-tier structure containing 15 distinct bin-level chromatin states grouped into three domain-level states. Whereas the bin-level states capture the local variation of histone marks, the domain-level states detect large-scale variations. Compared to bin-level states, the domain-level states are more robust and coherent. We also found active regions in intergenic regions that upon closer examination were expressed non-coding RNAs and pseudogenes. These results provide insights into an additional layer of complexity in chromatin organization.
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Hatsuda H. Finding differentially expressed regions of arbitrary length in quantitative genomic data based on marked point process model. Bioinformatics 2012; 28:i633-i639. [PMID: 22962492 PMCID: PMC3436798 DOI: 10.1093/bioinformatics/bts371] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
Motivation: High-throughput nucleotide sequencing technologies provide large amounts of quantitative genomic data at nucleotide resolution, which are important for the present and future biomedical researches; for example differential analysis of base-level RNA expression data will improve our understanding of transcriptome, including both coding and non-coding genes. However, most studies of these data have relied on existing genome annotations and thus are limited to the analysis of known transcripts. Results: In this article, we propose a novel method based on a marked point process model to find differentially expressed genomic regions of arbitrary length without using genome annotations. The presented method conducts a statistical test for differential analysis in regions of various lengths at each nucleotide and searches the optimal configuration of the regions by using a Monte Carlo simulation. We applied the proposed method to both synthetic and real genomic data, and their results demonstrate the effectiveness of our method. Availability: The program used in this study is available at https://sites.google.com/site/hiroshihatsuda/. Contact:H.Hatsuda@warwick.ac.uk
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Affiliation(s)
- Hiroshi Hatsuda
- Department of Statistics, the University of Warwick, Coventry CV4 7AL, UK.
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