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Peng Y, Yao Y, Pang J, Di T, Du G, Chen B. Genetic Diversity and Virulence Variation of Metarhizium rileyi from Infected Spodoptera frugiperda in Corn Fields. Microorganisms 2024; 12:264. [PMID: 38399668 PMCID: PMC10892884 DOI: 10.3390/microorganisms12020264] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 01/10/2024] [Accepted: 01/22/2024] [Indexed: 02/25/2024] Open
Abstract
Metarhizium rileyi is an entomopathogenic fungus that naturally infects the larvae of Spodoptera frugiperda, and has biocontrol potential. To explore more natural entomopathogenic fungi resources, a total of 31 strains were isolated from 13 prefectures in Yunnan Province. All the strains were identified using morphology and molecular biology. The genetic diversity of the 31 isolates of M. rileyi was analyzed using inter-simple sequence repeat (ISSR) techniques. Seven primers with good polymorphism were selected, and fifty-four distinct amplification sites were obtained by polymerase chain reaction amplification. Among them, 50 were polymorphic sites, and the percentage of polymorphic sites was 94.44%. The thirty-one strains were divided into eight subpopulations according to the regions. The Nei's gene diversity was 0.2945, and the Shannon information index was 0.4574, indicating that M. rileyi had rich genetic diversity. The average total genetic diversity of the subpopulations in the different regions was 0.2962, the gene diversity within the populations was 0.1931, the genetic differentiation coefficient was 0.3482 (>0.25), and the gene flow was 0.9360 (<1). The individual cluster analysis showed that there was no obvious correlation between the genetic diversity of the strains and their geographical origin, which also indicated that the virulence of the strains was not related to their phylogeny. Thus, the genetic distance of the different populations of M. rileyi in Yunnan Province was not related to the geographical distance. The virulence of those 32 strains against the 3rd-instar larvae of S. frugiperda were varied with the differences in geographical locations. On the 10th day of inoculation, seventeen strains had an insect mortality rate of 70.0%, and seven strains had an insect mortality rate of 100%. The half-lethal times of the M. rileyi SZCY201010, XSBN200920, and MDXZ200803 strains against the S. frugiperda larvae were less than 4 d. Thus, they have the potential to be developed into fungal insecticidal agents.
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Affiliation(s)
| | | | | | | | | | - Bin Chen
- Yunnan State Key Laboratory of Conservation and Utilization of Biological Resources, College of Plant Protection, Yunnan Agricultural University, Kunming 650201, China; (Y.P.); (Y.Y.); (J.P.); (T.D.); (G.D.)
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Walker KA, Chen J, Shi L, Yang Y, Fornage M, Zhou L, Schlosser P, Surapaneni A, Grams ME, Duggan MR, Peng Z, Gomez GT, Tin A, Hoogeveen RC, Sullivan KJ, Ganz P, Lindbohm JV, Kivimaki M, Nevado-Holgado AJ, Buckley N, Gottesman RF, Mosley TH, Boerwinkle E, Ballantyne CM, Coresh J. Proteomics analysis of plasma from middle-aged adults identifies protein markers of dementia risk in later life. Sci Transl Med 2023; 15:eadf5681. [PMID: 37467317 PMCID: PMC10665113 DOI: 10.1126/scitranslmed.adf5681] [Citation(s) in RCA: 24] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 06/28/2023] [Indexed: 07/21/2023]
Abstract
A diverse set of biological processes have been implicated in the pathophysiology of Alzheimer's disease (AD) and related dementias. However, there is limited understanding of the peripheral biological mechanisms relevant in the earliest phases of the disease. Here, we used a large-scale proteomics platform to examine the association of 4877 plasma proteins with 25-year dementia risk in 10,981 middle-aged adults. We found 32 dementia-associated plasma proteins that were involved in proteostasis, immunity, synaptic function, and extracellular matrix organization. We then replicated the association between 15 of these proteins and clinically relevant neurocognitive outcomes in two independent cohorts. We demonstrated that 12 of these 32 dementia-associated proteins were associated with cerebrospinal fluid (CSF) biomarkers of AD, neurodegeneration, or neuroinflammation. We found that eight of these candidate protein markers were abnormally expressed in human postmortem brain tissue from patients with AD, although some of the proteins that were most strongly associated with dementia risk, such as GDF15, were not detected in these brain tissue samples. Using network analyses, we found a protein signature for dementia risk that was characterized by dysregulation of specific immune and proteostasis/autophagy pathways in adults in midlife ~20 years before dementia onset, as well as abnormal coagulation and complement signaling ~10 years before dementia onset. Bidirectional two-sample Mendelian randomization genetically validated nine of our candidate proteins as markers of AD in midlife and inferred causality of SERPINA3 in AD pathogenesis. Last, we prioritized a set of candidate markers for AD and dementia risk prediction in midlife.
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Affiliation(s)
- Keenan A. Walker
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Intramural Research Program, Baltimore, MD 21224, USA
| | - Jingsha Chen
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21210, USA
| | - Liu Shi
- Novo Nordisk Research Centre Oxford (NNRCO), Oxford OX3 7FZ, UK
| | - Yunju Yang
- Brown Foundation Institute of Molecular Medicine, McGovern Medical School and Human Genetics Center, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Myriam Fornage
- Brown Foundation Institute of Molecular Medicine, McGovern Medical School and Human Genetics Center, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Linda Zhou
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21210, USA
| | - Pascal Schlosser
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21210, USA
| | - Aditya Surapaneni
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21210, USA
| | - Morgan E. Grams
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21210, USA
- Division of Nephrology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21210, USA
| | - Michael R. Duggan
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Intramural Research Program, Baltimore, MD 21224, USA
| | - Zhongsheng Peng
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Intramural Research Program, Baltimore, MD 21224, USA
| | - Gabriela T. Gomez
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21210, USA
| | - Adrienne Tin
- MIND Center and Division of Nephrology, University of Mississippi Medical Center, Jackson, MS 39216, USA
| | - Ron C. Hoogeveen
- Section of Cardiovascular Research, Department of Medicine, Baylor College of Medicine, Houston, TX 77030, USA
| | - Kevin J. Sullivan
- Department of Medicine, Division of Geriatrics, University of Mississippi Medical Center, Jackson, MS 39216, USA
| | - Peter Ganz
- Department of Medicine, University of California-San Francisco, San Francisco, CA 94115, USA
| | - Joni V. Lindbohm
- Broad Institute of the Massachusetts Institute of Technology and Harvard University, Cambridge, MA 02142, USA
| | - Mika Kivimaki
- Department of Mental Health of Older People, Faculty of Brain Sciences, University College London, London WC1E 6BT, UK
- Clinicum, Faculty of Medicine, University of Helsinki, Helsinki 00100, Finland
| | | | - Noel Buckley
- Department of Psychiatry, University of Oxford, Oxford OX1 2JD, UK
| | - Rebecca F. Gottesman
- National Institute of Neurological Disorders and Stroke, Intramural Research Program, Bethesda, MD 20892, USA
| | - Thomas H. Mosley
- Department of Medicine, Division of Geriatrics, University of Mississippi Medical Center, Jackson, MS 39216, USA
| | - Eric Boerwinkle
- Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, University of Texas Health Science Center at Houston; Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Christie M. Ballantyne
- Section of Cardiovascular Research, Department of Medicine, Baylor College of Medicine, Houston, TX 77030, USA
| | - Josef Coresh
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21210, USA
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Avery E, Sanelli PC, Aboian M, Payabvash S. Radiomics: A Primer on Processing Workflow and Analysis. Semin Ultrasound CT MR 2022; 43:142-146. [PMID: 35339254 PMCID: PMC8961004 DOI: 10.1053/j.sult.2022.02.003] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Quantitative analysis of medical images can provide objective tools for diagnosis, prognostication, and disease monitoring. Radiomics refers to automated extraction of a large number of quantitative features from medical images for characterization of underlying pathologies. In this review, we will discuss the principles of radiomics, image preprocessing, feature extraction workflow, and statistical analysis. We will also address the limitations and future directions of radiomics.
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Affiliation(s)
- Emily Avery
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT
| | - Pina C Sanelli
- Northwell Health, and Feinstein Institute for Medical Research, Manhasset, NY
| | - Mariam Aboian
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT
| | - Seyedmehdi Payabvash
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT.
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Chiu JKH, Ong RTH. Clustering biological sequences with dynamic sequence similarity threshold. BMC Bioinformatics 2022; 23:108. [PMID: 35354426 PMCID: PMC8969259 DOI: 10.1186/s12859-022-04643-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Accepted: 03/02/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Biological sequence clustering is a complicated data clustering problem owing to the high computation costs incurred for pairwise sequence distance calculations through sequence alignments, as well as difficulties in determining parameters for deriving robust clusters. While current approaches are successful in reducing the number of sequence alignments performed, the generated clusters are based on a single sequence identity threshold applied to every cluster. Poor choices of this identity threshold would thus lead to low quality clusters. There is however little support provided to users in selecting thresholds that are well matched with the input sequences. RESULTS We present a novel sequence clustering approach called ALFATClust that exploits rapid pairwise alignment-free sequence distance calculations and community detection in graph for clusters generation. Instead of a single threshold applied to every generated cluster, ALFATClust is capable of dynamically determining the cut-off threshold for each individual cluster by considering both cluster separation and intra-cluster sequence similarity. Benchmarking analysis shows that ALFATClust generally outperforms existing approaches by simultaneously maintaining cluster robustness and substantial cluster separation for the benchmark datasets. The software also provides an evaluation report for verifying the quality of the non-singleton clusters obtained. CONCLUSIONS ALFATClust is able to generate sequence clusters having high intra-cluster sequence similarity and substantial separation between clusters without having users to decide precise similarity cut-off thresholds.
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Affiliation(s)
- Jimmy Ka Ho Chiu
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, 117549, Singapore
| | - Rick Twee-Hee Ong
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, 117549, Singapore.
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Ertaş A, Yorulmaz B, Sukatar A. Comparative analysis of biotic indices for assessment of water quality of Balaban Stream in West Anatolia, Turkey. Biologia (Bratisl) 2022. [DOI: 10.1007/s11756-021-00992-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Assessment of population structure, genetic diversity and relationship of Mediterranean olive accessions using SSR markers and computational tools. Biotechnol Lett 2021; 44:113-127. [PMID: 34761348 DOI: 10.1007/s10529-021-03204-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Accepted: 10/09/2021] [Indexed: 01/09/2023]
Abstract
Olive tree is an emblematic crop of the Mediterranean region, mainly renowned for its fruit oil, although the species provides several industrial purposes. The Mediterranean basin constitutes the origin of olive species diversification and represents a valuable source of genetic variability of olive germplasm. Therefore, the evaluation of the diversity and the population structure of this Mediterranean germplasm is a challenge for olive species preservation, crop breeding and genetic improvement. In this context, our study aims to analyze the genetic diversity and the population structure of 79 Mediterranean olive accessions using 15 genomic SSRs and by applying computational model-based approaches. The used SSRs revealed a total number of 225 alleles with a mean of 15 alleles per locus. Observed and expected heterozygosity (Ho = 0.79, He = 0.805) with a Polymorphism Information Content value of 0.775 indicate high level of genetic diversity. All results of the Unweighted Pair Group Method with Arithmetic (UPGMA), Jaccard similarity index, Principal Coordinate Analysis (PCoA) and the Bayesian analyses supported the separation of the Mediterranean varieties in two sub-populations, one of which mainly composed by Spanish accessions.
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Schlosser P, Knaus J, Schmutz M, Dohner K, Plass C, Bullinger L, Claus R, Binder H, Lubbert M, Schumacher M. Netboost: Boosting-Supported Network Analysis Improves High-Dimensional Omics Prediction in Acute Myeloid Leukemia and Huntington's Disease. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:2635-2648. [PMID: 32365034 DOI: 10.1109/tcbb.2020.2983010] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
State-of-the art selection methods fail to identify weak but cumulative effects of features found in many high-dimensional omics datasets. Nevertheless, these features play an important role in certain diseases. We present Netboost, a three-step dimension reduction technique. First, a boosting-based filter is combined with the topological overlap measure to identify the essential edges of the network. Second, sparse hierarchical clustering is applied on the selected edges to identify modules and finally module information is aggregated by the first principal components. We demonstrate the application of the newly developed Netboost in combination with CoxBoost for survival prediction of DNA methylation and gene expression data from 180 acute myeloid leukemia (AML) patients and show, based on cross-validated prediction error curve estimates, its prediction superiority over variable selection on the full dataset as well as over an alternative clustering approach. The identified signature related to chromatin modifying enzymes was replicated in an independent dataset, the phase II AMLSG 12-09 study. In a second application we combine Netboost with Random Forest classification and improve the disease classification error in RNA-sequencing data of Huntington's disease mice. Netboost is a freely available Bioconductor R package for dimension reduction and hypothesis generation in high-dimensional omics applications.
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Ho HY, Chuang S, Dai NT, Cheng CH, Kao WF. Ranking hospitals' burn care capacity using cluster analysis on open government data. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 207:106166. [PMID: 34077867 DOI: 10.1016/j.cmpb.2021.106166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Accepted: 05/04/2021] [Indexed: 06/12/2023]
Abstract
BACKGROUND AND OBJECTIVE To deal with burn mass casualty incidents (BMCIs), various countries have established national or regional BMCI emergency response plans (ERPs). A burn care capacity ranking model for hospitals can play an integral role in ERPs by providing essential information to emergency medical services for distributing and handling mass burn patients. Ranking models vary across countries and contexts. However, Taiwan has had no such model. The study aims to develop a ranking model for classifying hospitals' burn care capacity in preparation for the development of a national BMCI ERP. METHODS Multiple methods were adopted. An expert panel provided consultations on data selection and clustering validation. Data on 116 variables from 535 hospitals were collected via open data platforms under the Ministry of Health and Welfare. Data selection and streamlining was conducted to determine 42 variables for cluster analysis. SAS 9.4 was used to analyze the data set -via a hierarchical cluster analysis using Ward's method, followed by a tree-based model analysis to identify the criteria for each cluster. Both internal and external cluster validation were performed. RESULTS Four clusters of burn care capacity were determined to be a suitable number of clusters. All hospitals were arranged into capacity levels accordingly. Results of the Kruskal-Wallis test showed that the difference between clusters were significant. Tree-based model analysis revealed four determining variables, among which the refined level of emergency care responsibility hospital was found to be most influential on the clustering process. Responses from the questionnaire were used as an external validation tool to corroborate with the cluster analysis results. CONCLUSION The use of open government data and cluster analysis was suitable for developing a ranking model to determine hospitals' burn care capacity levels in Taiwan. The proposed ranking model can be used to develop a BMCI emergency response plan and can also serve as a reference for using cluster analysis with open government data to rank care capacity or quality in other domains.
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Affiliation(s)
- Hui Yan Ho
- School of Health Care Administration, Taipei Medical University, Taipei, Taiwan
| | - Sheuwen Chuang
- Graduate Institute of Data Science, Taipei Medical University, Taipei, Taiwan; Health Policy and Care Research Center, Taipei Medical University, Taipei, Taiwan.
| | - Niann-Tzyy Dai
- Division of Plastic and Reconstructive Surgery, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
| | - Chia-Hsin Cheng
- Graduate Institute of Data Science, Taipei Medical University, Taipei, Taiwan
| | - Wei-Fong Kao
- Department of Emergency Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
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Guo X, Song Y, Liu S, Gao M, Qi Y, Shang X. Linking genotype to phenotype in multi-omics data of small sample. BMC Genomics 2021; 22:537. [PMID: 34256701 PMCID: PMC8278664 DOI: 10.1186/s12864-021-07867-w] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Accepted: 06/30/2021] [Indexed: 12/03/2022] Open
Abstract
BACKGROUND Genome-wide association studies (GWAS) that link genotype to phenotype represent an effective means to associate an individual genetic background with a disease or trait. However, single-omics data only provide limited information on biological mechanisms, and it is necessary to improve the accuracy for predicting the biological association between genotype and phenotype by integrating multi-omics data. Typically, gene expression data are integrated to analyze the effect of single nucleotide polymorphisms (SNPs) on phenotype. Such multi-omics data integration mainly follows two approaches: multi-staged analysis and meta-dimensional analysis, which respectively ignore intra-omics and inter-omics associations. Moreover, both approaches require omics data from a single sample set, and the large feature set of SNPs necessitates a large sample size for model establishment, but it is difficult to obtain multi-omics data from a single, large sample set. RESULTS To address this problem, we propose a method of genotype-phenotype association based on multi-omics data from small samples. The workflow of this method includes clustering genes using a protein-protein interaction network and gene expression data, screening gene clusters with group lasso, obtaining SNP clusters corresponding to the selected gene clusters through expression quantitative trait locus data, integrating SNP clusters and corresponding gene clusters and phenotypes into three-layer network blocks, analyzing and predicting based on each block, and obtaining the final prediction by taking the average. CONCLUSIONS We compare this method to others using two datasets and find that our method shows better results in both cases. Our method can effectively solve the prediction problem in multi-omics data of small sample, and provide valuable resources for further studies on the fusion of more omics data.
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Affiliation(s)
- Xinpeng Guo
- School of Computer Science, Northwestern Polytechnical University, Xi'an, 710072, People's Republic of China
- School of Air and Missile Defense, Air Force Engineering University, Xi'an, 710051, People's Republic of China
| | - Yafei Song
- School of Air and Missile Defense, Air Force Engineering University, Xi'an, 710051, People's Republic of China
| | - Shuhui Liu
- School of Computer Science, Northwestern Polytechnical University, Xi'an, 710072, People's Republic of China
| | - Meihong Gao
- School of Computer Science, Northwestern Polytechnical University, Xi'an, 710072, People's Republic of China
| | - Yang Qi
- School of Computer Science, Northwestern Polytechnical University, Xi'an, 710072, People's Republic of China
| | - Xuequn Shang
- School of Computer Science, Northwestern Polytechnical University, Xi'an, 710072, People's Republic of China.
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Stokes JM, Yang K, Swanson K, Jin W, Cubillos-Ruiz A, Donghia NM, MacNair CR, French S, Carfrae LA, Bloom-Ackermann Z, Tran VM, Chiappino-Pepe A, Badran AH, Andrews IW, Chory EJ, Church GM, Brown ED, Jaakkola TS, Barzilay R, Collins JJ. A Deep Learning Approach to Antibiotic Discovery. Cell 2020; 180:688-702.e13. [PMID: 32084340 DOI: 10.1016/j.cell.2020.01.021] [Citation(s) in RCA: 731] [Impact Index Per Article: 182.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2019] [Revised: 12/04/2019] [Accepted: 01/15/2020] [Indexed: 02/06/2023]
Abstract
Due to the rapid emergence of antibiotic-resistant bacteria, there is a growing need to discover new antibiotics. To address this challenge, we trained a deep neural network capable of predicting molecules with antibacterial activity. We performed predictions on multiple chemical libraries and discovered a molecule from the Drug Repurposing Hub-halicin-that is structurally divergent from conventional antibiotics and displays bactericidal activity against a wide phylogenetic spectrum of pathogens including Mycobacterium tuberculosis and carbapenem-resistant Enterobacteriaceae. Halicin also effectively treated Clostridioides difficile and pan-resistant Acinetobacter baumannii infections in murine models. Additionally, from a discrete set of 23 empirically tested predictions from >107 million molecules curated from the ZINC15 database, our model identified eight antibacterial compounds that are structurally distant from known antibiotics. This work highlights the utility of deep learning approaches to expand our antibiotic arsenal through the discovery of structurally distinct antibacterial molecules.
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Affiliation(s)
- Jonathan M Stokes
- Department of Biological Engineering, Synthetic Biology Center, Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Machine Learning for Pharmaceutical Discovery and Synthesis Consortium, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Kevin Yang
- Machine Learning for Pharmaceutical Discovery and Synthesis Consortium, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Kyle Swanson
- Machine Learning for Pharmaceutical Discovery and Synthesis Consortium, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Wengong Jin
- Machine Learning for Pharmaceutical Discovery and Synthesis Consortium, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Andres Cubillos-Ruiz
- Department of Biological Engineering, Synthetic Biology Center, Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA
| | - Nina M Donghia
- Department of Biological Engineering, Synthetic Biology Center, Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA
| | - Craig R MacNair
- Department of Biochemistry and Biomedical Sciences, Michael G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, ON L8N 3Z5, Canada
| | - Shawn French
- Department of Biochemistry and Biomedical Sciences, Michael G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, ON L8N 3Z5, Canada
| | - Lindsey A Carfrae
- Department of Biochemistry and Biomedical Sciences, Michael G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, ON L8N 3Z5, Canada
| | - Zohar Bloom-Ackermann
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Victoria M Tran
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Anush Chiappino-Pepe
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA; Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Ahmed H Badran
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Ian W Andrews
- Department of Biological Engineering, Synthetic Biology Center, Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA
| | - Emma J Chory
- Department of Biological Engineering, Synthetic Biology Center, Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - George M Church
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA; Department of Genetics, Harvard Medical School, Boston, MA 02115, USA; Harvard-MIT Program in Health Sciences and Technology, Cambridge, MA 02139, USA
| | - Eric D Brown
- Department of Biochemistry and Biomedical Sciences, Michael G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, ON L8N 3Z5, Canada
| | - Tommi S Jaakkola
- Machine Learning for Pharmaceutical Discovery and Synthesis Consortium, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Regina Barzilay
- Machine Learning for Pharmaceutical Discovery and Synthesis Consortium, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Abdul Latif Jameel Clinic for Machine Learning in Health, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
| | - James J Collins
- Department of Biological Engineering, Synthetic Biology Center, Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA; Harvard-MIT Program in Health Sciences and Technology, Cambridge, MA 02139, USA; Abdul Latif Jameel Clinic for Machine Learning in Health, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
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Genetic studies of urinary metabolites illuminate mechanisms of detoxification and excretion in humans. Nat Genet 2020; 52:167-176. [PMID: 31959995 DOI: 10.1038/s41588-019-0567-8] [Citation(s) in RCA: 77] [Impact Index Per Article: 19.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2019] [Accepted: 12/05/2019] [Indexed: 11/08/2022]
Abstract
The kidneys integrate information from continuous systemic processes related to the absorption, distribution, metabolism and excretion (ADME) of metabolites. To identify underlying molecular mechanisms, we performed genome-wide association studies of the urinary concentrations of 1,172 metabolites among 1,627 patients with reduced kidney function. The 240 unique metabolite-locus associations (metabolite quantitative trait loci, mQTLs) that were identified and replicated highlight novel candidate substrates for transport proteins. The identified genes are enriched in ADME-relevant tissues and cell types, and they reveal novel candidates for biotransformation and detoxification reactions. Fine mapping of mQTLs and integration with single-cell gene expression permitted the prioritization of causal genes, functional variants and target cell types. The combination of mQTLs with genetic and health information from 450,000 UK Biobank participants illuminated metabolic mediators, and hence, novel urinary biomarkers of disease risk. This comprehensive resource of genetic targets and their substrates is informative for ADME processes in humans and is relevant to basic science, clinical medicine and pharmaceutical research.
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Molecular Cavity Topological Representation for Pattern Analysis: A NLP Analogy-Based Word2Vec Method. Int J Mol Sci 2019; 20:ijms20236019. [PMID: 31795343 PMCID: PMC6928730 DOI: 10.3390/ijms20236019] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2019] [Revised: 11/23/2019] [Accepted: 11/26/2019] [Indexed: 12/31/2022] Open
Abstract
Cavity analysis in molecular dynamics is important for understanding molecular function. However, analyzing the dynamic pattern of molecular cavities remains a difficult task. In this paper, we propose a novel method to topologically represent molecular cavities by vectorization. First, a characterization of cavities is established through Word2Vec model, based on an analogy between the cavities and natural language processing (NLP) terms. Then, we use some techniques such as dimension reduction and clustering to conduct an exploratory analysis of the vectorized molecular cavity. On a real data set, we demonstrate that our approach is applicable to maintain the topological characteristics of the cavity and can find the change patterns from a large number of cavities.
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Azad A, Pavlopoulos GA, Ouzounis CA, Kyrpides NC, Buluç A. HipMCL: a high-performance parallel implementation of the Markov clustering algorithm for large-scale networks. Nucleic Acids Res 2019; 46:e33. [PMID: 29315405 PMCID: PMC5888241 DOI: 10.1093/nar/gkx1313] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2017] [Accepted: 01/02/2018] [Indexed: 11/13/2022] Open
Abstract
Biological networks capture structural or functional properties of relevant entities such as molecules, proteins or genes. Characteristic examples are gene expression networks or protein–protein interaction networks, which hold information about functional affinities or structural similarities. Such networks have been expanding in size due to increasing scale and abundance of biological data. While various clustering algorithms have been proposed to find highly connected regions, Markov Clustering (MCL) has been one of the most successful approaches to cluster sequence similarity or expression networks. Despite its popularity, MCL’s scalability to cluster large datasets still remains a bottleneck due to high running times and memory demands. Here, we present High-performance MCL (HipMCL), a parallel implementation of the original MCL algorithm that can run on distributed-memory computers. We show that HipMCL can efficiently utilize 2000 compute nodes and cluster a network of ∼70 million nodes with ∼68 billion edges in ∼2.4 h. By exploiting distributed-memory environments, HipMCL clusters large-scale networks several orders of magnitude faster than MCL and enables clustering of even bigger networks. HipMCL is based on MPI and OpenMP and is freely available under a modified BSD license.
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Affiliation(s)
- Ariful Azad
- Computational Research Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720-8150, USA
| | - Georgios A Pavlopoulos
- DOE Joint Genome Institute, Lawrence Berkeley National Laboratory, 2800 Mitchell Drive, Walnut Creek, CA 94598, USA
| | - Christos A Ouzounis
- Biological Computation & Process Laboratory, Chemical Process & Energy Resources Institute, Centre for Research & Technology Hellas, Thessalonica 57001, Greece
| | - Nikos C Kyrpides
- DOE Joint Genome Institute, Lawrence Berkeley National Laboratory, 2800 Mitchell Drive, Walnut Creek, CA 94598, USA
| | - Aydin Buluç
- Computational Research Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720-8150, USA.,Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA 94720, USA
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14
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Nayfach S, Shi ZJ, Seshadri R, Pollard KS, Kyrpides NC. New insights from uncultivated genomes of the global human gut microbiome. Nature 2019; 568:505-510. [PMID: 30867587 PMCID: PMC6784871 DOI: 10.1038/s41586-019-1058-x] [Citation(s) in RCA: 381] [Impact Index Per Article: 76.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2018] [Accepted: 03/06/2019] [Indexed: 12/22/2022]
Abstract
The genome sequences of many species of the human gut microbiome remain unknown, largely owing to challenges in cultivating microorganisms under laboratory conditions. Here we address this problem by reconstructing 60,664 draft prokaryotic genomes from 3,810 faecal metagenomes, from geographically and phenotypically diverse humans. These genomes provide reference points for 2,058 newly identified species-level operational taxonomic units (OTUs), which represents a 50% increase over the previously known phylogenetic diversity of sequenced gut bacteria. On average, the newly identified OTUs comprise 33% of richness and 28% of species abundance per individual, and are enriched in humans from rural populations. A meta-analysis of clinical gut-microbiome studies pinpointed numerous disease associations for the newly identified OTUs, which have the potential to improve predictive models. Finally, our analysis revealed that uncultured gut species have undergone genome reduction that has resulted in the loss of certain biosynthetic pathways, which may offer clues for improving cultivation strategies in the future.
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Affiliation(s)
- Stephen Nayfach
- United States Department of Energy Joint Genome Institute, Walnut Creek, CA, USA.
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
| | - Zhou Jason Shi
- Gladstone Institutes, San Francisco, CA, USA
- Chan-Zuckerberg Biohub, San Francisco, CA, USA
| | - Rekha Seshadri
- United States Department of Energy Joint Genome Institute, Walnut Creek, CA, USA
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Katherine S Pollard
- Gladstone Institutes, San Francisco, CA, USA
- Chan-Zuckerberg Biohub, San Francisco, CA, USA
- Institute for Human Genetics, University of California San Francisco, San Francisco, CA, USA
- Institute for Computational Health Sciences, University of California San Francisco, San Francisco, CA, USA
- Quantitative Biology Institute, University of California San Francisco, San Francisco, CA, USA
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA
| | - Nikos C Kyrpides
- United States Department of Energy Joint Genome Institute, Walnut Creek, CA, USA.
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
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15
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Abstract
With the likelihood that changes in global climate will adversely affect the soil C reservoir in the northern circumpolar permafrost zone, an understanding of the potential role of diazotrophic communities in enhancing biological N2 fixation, which constrains both plant production and microbial decomposition in tundra soils, is important in elucidating the responses of soil microbial communities to global climate change. A recent study showed that the composition of the diazotrophic community in a tundra soil exhibited no change under a short-term (1.5-year) winter warming experiment. However, it remains crucial to examine whether the lack of diazotrophic community responses to warming is persistent over a longer time period as a possibly important mechanism in stabilizing tundra soil C. Through a detailed characterization of the effects of winter warming on diazotrophic communities, we showed that a long-term (5-year) winter warming substantially enhanced diazotrophic abundance and altered community composition, though soil depth had a stronger influence on diazotrophic community composition than warming. These changes were best explained by changes in soil moisture, soil thaw duration, and plant biomass. These results provide crucial insights into the potential factors that may impact future C and N availability in tundra regions. Tundra ecosystems are typically carbon (C) rich but nitrogen (N) limited. Since biological N2 fixation is the major source of biologically available N, the soil N2-fixing (i.e., diazotrophic) community serves as an essential N supplier to the tundra ecosystem. Recent climate warming has induced deeper permafrost thaw and adversely affected C sequestration, which is modulated by N availability. Therefore, it is crucial to examine the responses of diazotrophic communities to warming across the depths of tundra soils. Herein, we carried out one of the deepest sequencing efforts of nitrogenase gene (nifH) to investigate how 5 years of experimental winter warming affects Alaskan soil diazotrophic community composition and abundance spanning both the organic and mineral layers. Although soil depth had a stronger influence on diazotrophic community composition than warming, warming significantly (P < 0.05) enhanced diazotrophic abundance by 86.3% and aboveground plant biomass by 25.2%. Diazotrophic composition in the middle and lower organic layers, detected by nifH sequencing and a microarray-based tool (GeoChip), was markedly altered, with an increase of α-diversity. Changes in diazotrophic abundance and composition significantly correlated with soil moisture, soil thaw duration, and plant biomass, as shown by structural equation modeling analyses. Therefore, more abundant diazotrophic communities induced by warming may potentially serve as an important mechanism for supplementing biologically available N in this tundra ecosystem.
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16
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Harrison MC, Arning N, Kremer LPM, Ylla G, Belles X, Bornberg‐Bauer E, Huylmans AK, Jongepier E, Piulachs M, Richards S, Schal C. Expansions of key protein families in the German cockroach highlight the molecular basis of its remarkable success as a global indoor pest. JOURNAL OF EXPERIMENTAL ZOOLOGY PART B-MOLECULAR AND DEVELOPMENTAL EVOLUTION 2018; 330:254-264. [DOI: 10.1002/jez.b.22824] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/25/2018] [Revised: 06/08/2018] [Accepted: 06/20/2018] [Indexed: 01/04/2023]
Affiliation(s)
- Mark C. Harrison
- Institute for Evolution and Biodiversity University of Münster Münster Germany
| | - Nicolas Arning
- Institute for Evolution and Biodiversity University of Münster Münster Germany
| | - Lukas P. M. Kremer
- Institute for Evolution and Biodiversity University of Münster Münster Germany
| | - Guillem Ylla
- Institut de Biologia Evolutiva CSIC‐University Pompeu Fabra Barcelona Spain
| | - Xavier Belles
- Institut de Biologia Evolutiva CSIC‐University Pompeu Fabra Barcelona Spain
| | | | | | - Evelien Jongepier
- Institute for Evolution and Biodiversity University of Münster Münster Germany
| | | | - Stephen Richards
- Human Genome Sequencing Center, Department of Human and Molecular Genetics Baylor College of Medicine Houston Texas
| | - Coby Schal
- Department of Entomology and Plant Pathology North Carolina State University Raleigh North Carolina
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17
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Hemimetabolous genomes reveal molecular basis of termite eusociality. Nat Ecol Evol 2018; 2:557-566. [PMID: 29403074 PMCID: PMC6482461 DOI: 10.1038/s41559-017-0459-1] [Citation(s) in RCA: 173] [Impact Index Per Article: 28.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2017] [Accepted: 12/19/2017] [Indexed: 11/25/2022]
Abstract
Around 150 million years ago, eusocial termites evolved from within the cockroaches, 50 million years before eusocial Hymenoptera, such as bees and ants, appeared. Here, we report the 2-Gb genome of the German cockroach, Blattella germanica, and the 1.3-Gb genome of the drywood termite Cryptotermes secundus. We show evolutionary signatures of termite eusociality by comparing the genomes and transcriptomes of three termites and the cockroach against the background of 16 other eusocial and non-eusocial insects. Dramatic adaptive changes in genes underlying the production and perception of pheromones confirm the importance of chemical communication in the termites. These are accompanied by major changes in gene regulation and the molecular evolution of caste determination. Many of these results parallel molecular mechanisms of eusocial evolution in Hymenoptera. However, the specific solutions are remarkably different, thus revealing a striking case of convergence in one of the major evolutionary transitions in biological complexity.
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18
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Abstract
The significant expansion in protein sequence and structure data that we are now witnessing brings with it a pressing need to bring order to the protein world. Such order enables us to gain insights into the evolution of proteins, their function and the extent to which the functional repertoire can vary across the three kingdoms of life. This has lead to the creation of a wide range of protein family classifications that aim to group proteins based upon their evolutionary relationships.In this chapter we discuss the approaches and methods that are frequently used in the classification of proteins, with a specific emphasis on the classification of protein domains. The construction of both domain sequence and domain structure databases is considered and we show how the use of domain family annotations to assign structural and functional information is enhancing our understanding of genomes.
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19
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Ponesakki V, Paul S, Mani DKS, Rajendiran V, Kanniah P, Sivasubramaniam S. Annotation of nerve cord transcriptome in earthworm Eisenia fetida. GENOMICS DATA 2017; 14:91-105. [PMID: 29204349 PMCID: PMC5688751 DOI: 10.1016/j.gdata.2017.10.002] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/09/2017] [Revised: 09/02/2017] [Accepted: 10/07/2017] [Indexed: 11/26/2022]
Abstract
In annelid worms, the nerve cord serves as a crucial organ to control the sensory and behavioral physiology. The inadequate genome resource of earthworms has prioritized the comprehensive analysis of their transcriptome dataset to monitor the genes express in the nerve cord and predict their role in the neurotransmission and sensory perception of the species. The present study focuses on identifying the potential transcripts and predicting their functional features by annotating the transcriptome dataset of nerve cord tissues prepared by Gong et al., 2010 from the earthworm Eisenia fetida. Totally 9762 transcripts were successfully annotated against the NCBI nr database using the BLASTX algorithm and among them 7680 transcripts were assigned to a total of 44,354 GO terms. The conserve domain analysis indicated the over representation of P-loop NTPase domain and calcium binding EF-hand domain. The COG functional annotation classified 5860 transcript sequences into 25 functional categories. Further, 4502 contig sequences were found to map with 124 KEGG pathways. The annotated contig dataset exhibited 22 crucial neuropeptides having considerable matches to the marine annelid Platynereis dumerilii, suggesting their possible role in neurotransmission and neuromodulation. In addition, 108 human stem cell marker homologs were identified including the crucial epigenetic regulators, transcriptional repressors and cell cycle regulators, which may contribute to the neuronal and segmental regeneration. The complete functional annotation of this nerve cord transcriptome can be further utilized to interpret genetic and molecular mechanisms associated with neuronal development, nervous system regeneration and nerve cord function.
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Affiliation(s)
| | | | | | | | | | - Sudhakar Sivasubramaniam
- Department of Biotechnology, Manonmaniam Sundaranar University, Tirunelveli, Tamilnadu 627012, India
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20
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Simões T, Lopes D, Dias S, Fernandes F, Pereira J, Jorge J, Bajaj C, Gomes A. Geometric Detection Algorithms for Cavities on Protein Surfaces in Molecular Graphics: A Survey. COMPUTER GRAPHICS FORUM : JOURNAL OF THE EUROPEAN ASSOCIATION FOR COMPUTER GRAPHICS 2017; 36:643-683. [PMID: 29520122 PMCID: PMC5839519 DOI: 10.1111/cgf.13158] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/17/2023]
Abstract
Detecting and analyzing protein cavities provides significant information about active sites for biological processes (e.g., protein-protein or protein-ligand binding) in molecular graphics and modeling. Using the three-dimensional structure of a given protein (i.e., atom types and their locations in 3D) as retrieved from a PDB (Protein Data Bank) file, it is now computationally viable to determine a description of these cavities. Such cavities correspond to pockets, clefts, invaginations, voids, tunnels, channels, and grooves on the surface of a given protein. In this work, we survey the literature on protein cavity computation and classify algorithmic approaches into three categories: evolution-based, energy-based, and geometry-based. Our survey focuses on geometric algorithms, whose taxonomy is extended to include not only sphere-, grid-, and tessellation-based methods, but also surface-based, hybrid geometric, consensus, and time-varying methods. Finally, we detail those techniques that have been customized for GPU (Graphics Processing Unit) computing.
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Affiliation(s)
- Tiago Simões
- Instituto de Telecomunicações, Portugal
- Universidade da Beira Interior, Portugal
| | | | - Sérgio Dias
- Instituto de Telecomunicações, Portugal
- Universidade da Beira Interior, Portugal
| | | | - João Pereira
- INESC-ID Lisboa, Portugal
- Instituto Superior Técnico, Universidade de Lisboa, Portugal
| | - Joaquim Jorge
- INESC-ID Lisboa, Portugal
- Instituto Superior Técnico, Universidade de Lisboa, Portugal
| | | | - Abel Gomes
- Instituto de Telecomunicações, Portugal
- Universidade da Beira Interior, Portugal
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21
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Paul AK, Shill PC. Incorporating gene ontology into fuzzy relational clustering of microarray gene expression data. Biosystems 2017; 163:1-10. [PMID: 29113811 DOI: 10.1016/j.biosystems.2017.09.017] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2017] [Revised: 09/26/2017] [Accepted: 09/27/2017] [Indexed: 12/28/2022]
Abstract
The product of gene expression works together in the cell for each living organism in order to achieve different biological processes. Many proteins are involved in different roles depending on the environment of the organism for the functioning of the cell. In this paper, we propose gene ontology (GO) annotations based semi-supervised clustering algorithm called GO fuzzy relational clustering (GO-FRC) where one gene is allowed to be assigned to multiple clusters which are the most biologically relevant behavior of genes. In the clustering process, GO-FRC utilizes useful biological knowledge which is available in the form of a gene ontology, as a prior knowledge along with the gene expression data. The prior knowledge helps to improve the coherence of the groups concerning the knowledge field. The proposed GO-FRC has been tested on the two yeast (Saccharomyces cerevisiae) expression profiles datasets (Eisen and Dream5 yeast datasets) and compared with other state-of-the-art clustering algorithms. Experimental results imply that GO-FRC is able to produce more biologically relevant clusters with the use of the small amount of GO annotations.
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Affiliation(s)
- Animesh Kumar Paul
- Department of Computer Science and Engineering, Khulna University of Engineering & Technology, Khulna, Bangladesh.
| | - Pintu Chandra Shill
- Department of Computer Science and Engineering, Khulna University of Engineering & Technology, Khulna, Bangladesh
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22
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Wang YY, Bai H, Zhang RZ, Yan H, Ning K, Zhao XM. Predicting new indications of compounds with a network pharmacology approach: Liuwei Dihuang Wan as a case study. Oncotarget 2017; 8:93957-93968. [PMID: 29212201 PMCID: PMC5706847 DOI: 10.18632/oncotarget.21398] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2017] [Accepted: 09/05/2017] [Indexed: 01/15/2023] Open
Abstract
With the ever increasing cost and time required for drug development, new strategies for drug development are highly demanded, whereas repurposing old drugs has attracted much attention in drug discovery. In this paper, we introduce a new network pharmacology approach, namely PINA, to predict potential novel indications of old drugs based on the molecular networks affected by drugs and associated with diseases. Benchmark results on FDA approved drugs have shown the superiority of PINA over traditional computational approaches in identifying new indications of old drugs. We further extend PINA to predict the novel indications of Traditional Chinese Medicines (TCMs) with Liuwei Dihuang Wan (LDW) as a case study. The predicted indications, including immune system disorders and tumor, are validated by expert knowledge and evidences from literature, demonstrating the effectiveness of our proposed computational approach.
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Affiliation(s)
- Yin-Ying Wang
- Institute of Science and Technology for Brain-Inspired Intelligence (ISTBI), Fudan University, Shanghai 200433, China.,Department of Computer Science and Technology, Tongji University, Shanghai 201804, China.,Department of Electronic Engineering, City University of Hong Kong, Kowloon 999077, Hong Kong
| | - Hong Bai
- Key Laboratory of Molecular Biophysics of the Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Run-Zhi Zhang
- Key Laboratory of Molecular Biophysics of the Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Hong Yan
- Department of Electronic Engineering, City University of Hong Kong, Kowloon 999077, Hong Kong
| | - Kang Ning
- Key Laboratory of Molecular Biophysics of the Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Xing-Ming Zhao
- Institute of Science and Technology for Brain-Inspired Intelligence (ISTBI), Fudan University, Shanghai 200433, China
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23
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Schmidt M, Kutzner A, Heese K. A novel specialized single-linkage clustering algorithm for taxonomically ordered data. J Theor Biol 2017; 427:1-7. [PMID: 28522359 DOI: 10.1016/j.jtbi.2017.05.008] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2016] [Revised: 04/17/2017] [Accepted: 05/04/2017] [Indexed: 11/26/2022]
Abstract
Similarities among ortholog genes for a given set of species S can be expressed by alignment matrices, where each matrix cell results from aligning a gene transcript against the genome of a species within S. Gene clusters can be computed by using single-linkage clustering in time n × m, where n denotes the number of ortholog genes and m denotes the number of inspected assemblies. Our approach can break the O(n × m) complexity of single-linkage clustering by exploiting an order among species that results from an in-order traversal of a given phylogenetic tree. The order among species allows the reduction of the inspected scope of the matrix to taxonomically related combinations of assemblies and genes, thus lowering the computational efforts necessary for creating the alignment matrix without affecting cluster quality. We present two novel approaches for clustering. First, we introduce a hierarchical clustering with, omitting the initial sorting of |S| elements, amortized O(|S|) time behavior, where it holds |S|≤n+m. Then, we propose a consecutive clustering having a linear time complexity O(|S|). Both approaches compute identical clusters, whereas dendrograms can only be obtained from the hierarchical one. We prove that our approaches deliver higher cluster densities than single linkage clustering. Additionally, we show that we compute clusters of superior quality, which ensures that our approaches are generally less error prone.
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Affiliation(s)
- Markus Schmidt
- Department of Computer Science, Friedrich-Alexander University, Martensstr. 3, Erlangen-Nürnberg, Germany
| | - Arne Kutzner
- Department of Information Systems, College of Engineering, Hanyang University, 222 Wangsimni-ro, Seongdong-gu, Seoul 133-791, Republic of Korea.
| | - Klaus Heese
- Graduate School of Biomedical Science and Engineering, Hanyang University, 222 Wangsimni-ro, Seongdong-gu, Seoul 133-791, Republic of Korea.
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24
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Henriques AC, Azevedo RMS, De Marco P. Metagenomic survey of methanesulfonic acid (MSA) catabolic genes in an Atlantic Ocean surface water sample and in a partial enrichment. PeerJ 2016; 4:e2498. [PMID: 27761315 PMCID: PMC5068391 DOI: 10.7717/peerj.2498] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2016] [Accepted: 08/28/2016] [Indexed: 11/23/2022] Open
Abstract
Methanesulfonic acid (MSA) is a relevant intermediate of the biogeochemical cycle of sulfur and environmental microorganisms assume an important role in the mineralization of this compound. Several methylotrophic bacterial strains able to grow on MSA have been isolated from soil or marine water and two conserved operons, msmABCD coding for MSA monooxygenase and msmEFGH coding for a transport system, have been repeatedly encountered in most of these strains. Homologous sequences have also been amplified directly from the environment or observed in marine metagenomic data, but these showed a base composition (G + C content) very different from their counterparts from cultivated bacteria. The aim of this study was to understand which microorganisms within the coastal surface oceanic microflora responded to MSA as a nutrient and how the community evolved in the early phases of an enrichment by means of metagenome and gene-targeted amplicon sequencing. From the phylogenetic point of view, the community shifted significantly with the disappearance of all signals related to the Archaea, the Pelagibacteraceae and phylum SAR406, and the increase in methylotroph-harboring taxa, accompanied by other groups so far not known to comprise methylotrophs such as the Hyphomonadaceae. At the functional level, the abundance of several genes related to sulfur metabolism and methylotrophy increased during the enrichment and the allelic distribution of gene msmA diagnostic for MSA monooxygenase altered considerably. Even more dramatic was the disappearance of MSA import-related gene msmE, which suggests that alternative transporters must be present in the enriched community and illustrate the inadequacy of msmE as an ecofunctional marker for MSA degradation at sea.
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Affiliation(s)
- Ana C Henriques
- Instituto de Investigação e Formação Avançada em Ciências e Tecnologias da Saúde (IINFACTS), CESPU , Gandra PRD , Portugal
| | - Rui M S Azevedo
- Instituto de Investigação e Formação Avançada em Ciências e Tecnologias da Saúde (IINFACTS), CESPU , Gandra PRD , Portugal
| | - Paolo De Marco
- Instituto de Investigação e Formação Avançada em Ciências e Tecnologias da Saúde (IINFACTS), CESPU , Gandra PRD , Portugal
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25
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Nayfach S, Rodriguez-Mueller B, Garud N, Pollard KS. An integrated metagenomics pipeline for strain profiling reveals novel patterns of bacterial transmission and biogeography. Genome Res 2016; 26:1612-1625. [PMID: 27803195 PMCID: PMC5088602 DOI: 10.1101/gr.201863.115] [Citation(s) in RCA: 285] [Impact Index Per Article: 35.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2015] [Accepted: 09/08/2016] [Indexed: 01/19/2023]
Abstract
We present the Metagenomic Intra-species Diversity Analysis System (MIDAS), which is an integrated computational pipeline for quantifying bacterial species abundance and strain-level genomic variation, including gene content and single-nucleotide polymorphisms (SNPs), from shotgun metagenomes. Our method leverages a database of more than 30,000 bacterial reference genomes that we clustered into species groups. These cover the majority of abundant species in the human microbiome but only a small proportion of microbes in other environments, including soil and seawater. We applied MIDAS to stool metagenomes from 98 Swedish mothers and their infants over one year and used rare SNPs to track strains between hosts. Using this approach, we found that although species compositions of mothers and infants converged over time, strain-level similarity diverged. Specifically, early colonizing bacteria were often transmitted from an infant’s mother, while late colonizing bacteria were often transmitted from other sources in the environment and were enriched for spore-formation genes. We also applied MIDAS to 198 globally distributed marine metagenomes and used gene content to show that many prevalent bacterial species have population structure that correlates with geographic location. Strain-level genetic variants present in metagenomes clearly reveal extensive structure and dynamics that are obscured when data are analyzed at a coarser taxonomic resolution.
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Affiliation(s)
- Stephen Nayfach
- Integrative Program in Quantitative Biology, University of California, San Francisco, San Francisco, California 94158, USA.,Gladstone Institutes, San Francisco, California 94158, USA
| | | | - Nandita Garud
- Gladstone Institutes, San Francisco, California 94158, USA
| | - Katherine S Pollard
- Integrative Program in Quantitative Biology, University of California, San Francisco, San Francisco, California 94158, USA.,Gladstone Institutes, San Francisco, California 94158, USA.,Institute for Human Genetics, Institute for Computational Health Sciences, and Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, California 94158, USA
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26
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Szilágyi L, Szilágyi SM. A modified two-stage Markov clustering algorithm for large and sparse networks. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2016; 135:15-26. [PMID: 27586476 DOI: 10.1016/j.cmpb.2016.07.007] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/06/2016] [Revised: 05/27/2016] [Accepted: 07/01/2016] [Indexed: 06/06/2023]
Abstract
BACKGROUND Graph-based hierarchical clustering algorithms become prohibitively costly in both execution time and storage space, as the number of nodes approaches the order of millions. OBJECTIVE A fast and highly memory efficient Markov clustering algorithm is proposed to perform the classification of huge sparse networks using an ordinary personal computer. METHODS Improvements compared to previous versions are achieved through adequately chosen data structures that facilitate the efficient handling of symmetric sparse matrices. Clustering is performed in two stages: the initial connected network is processed in a sparse matrix until it breaks into isolated, small, and relatively dense subgraphs, which are then processed separately until convergence is obtained. An intelligent stopping criterion is also proposed to quit further processing of a subgraph that tends toward completeness with equal edge weights. The main advantage of this algorithm is that the necessary number of iterations is separately decided for each graph node. RESULTS The proposed algorithm was tested using the SCOP95 and large synthetic protein sequence data sets. The validation process revealed that the proposed method can reduce 3-6 times the processing time of huge sequence networks compared to previous Markov clustering solutions, without losing anything from the partition quality. CONCLUSIONS A one-million-node and one-billion-edge protein sequence network defined by a BLAST similarity matrix can be processed with an upper-class personal computer in 100 minutes. Further improvement in speed is possible via parallel data processing, while the extension toward several million nodes needs intermediary data storage, for example on solid state drives.
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Affiliation(s)
- László Szilágyi
- Faculty of Technical and Human Sciences, Sapientia University of Transylvania,Şoseaua Sighişoarei 1/C, 540485 Tîrgu Mureş, Romania; Department of Informatics, Petru Maior University, Str. N. Iorga Nr. 1, 540088 Tîrgu Mureş, Romania
| | - Sándor M Szilágyi
- Budapest University of Technology and Economics, Department of Control Engineering and Information Technology, Magyar tudósok krt. 2, H-1117 Budapest, Hungary; Department of Informatics, Petru Maior University, Str. N. Iorga Nr. 1, 540088 Tîrgu Mureş, Romania.
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27
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Zaslavsky L, Ciufo S, Fedorov B, Tatusova T. Clustering analysis of proteins from microbial genomes at multiple levels of resolution. BMC Bioinformatics 2016; 17 Suppl 8:276. [PMID: 27586436 PMCID: PMC5009818 DOI: 10.1186/s12859-016-1112-8] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022] Open
Abstract
Background Microbial genomes at the National Center for Biotechnology Information (NCBI) represent a large collection of more than 35,000 assemblies. There are several complexities associated with the data: a great variation in sampling density since human pathogens are densely sampled while other bacteria are less represented; different protein families occur in annotations with different frequencies; and the quality of genome annotation varies greatly. In order to extract useful information from these sophisticated data, the analysis needs to be performed at multiple levels of phylogenomic resolution and protein similarity, with an adequate sampling strategy. Results Protein clustering is used to construct meaningful and stable groups of similar proteins to be used for analysis and functional annotation. Our approach is to create protein clusters at three levels. First, tight clusters in groups of closely-related genomes (species-level clades) are constructed using a combined approach that takes into account both sequence similarity and genome context. Second, clustroids of conservative in-clade clusters are organized into seed global clusters. Finally, global protein clusters are built around the the seed clusters. We propose filtering strategies that allow limiting the protein set included in global clustering. The in-clade clustering procedure, subsequent selection of clustroids and organization into seed global clusters provides a robust representation and high rate of compression. Seed protein clusters are further extended by adding related proteins. Extended seed clusters include a significant part of the data and represent all major known cell machinery. The remaining part, coming from either non-conservative (unique) or rapidly evolving proteins, from rare genomes, or resulting from low-quality annotation, does not group together well. Processing these proteins requires significant computational resources and results in a large number of questionable clusters. Conclusion The developed filtering strategies allow to identify and exclude such peripheral proteins limiting the protein dataset in global clustering. Overall, the proposed methodology allows the relevant data at different levels of details to be obtained and data redundancy eliminated while keeping biologically interesting variations. Electronic supplementary material The online version of this article (doi:10.1186/s12859-016-1112-8) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Leonid Zaslavsky
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, 20894, MD, USA.
| | - Stacy Ciufo
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, 20894, MD, USA
| | - Boris Fedorov
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, 20894, MD, USA
| | - Tatiana Tatusova
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, 20894, MD, USA
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Pavelka A, Sebestova E, Kozlikova B, Brezovsky J, Sochor J, Damborsky J. CAVER: Algorithms for Analyzing Dynamics of Tunnels in Macromolecules. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2016; 13:505-517. [PMID: 27295634 DOI: 10.1109/tcbb.2015.2459680] [Citation(s) in RCA: 81] [Impact Index Per Article: 10.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
The biological function of a macromolecule often requires that a small molecule or ion is transported through its structure. The transport pathway often leads through void spaces in the structure. The properties of transport pathways change significantly in time; therefore, the analysis of a trajectory from molecular dynamics rather than of a single static structure is needed for understanding the function of pathways. The identification and analysis of transport pathways are challenging because of the high complexity and diversity of macromolecular shapes, the thermal motion of their atoms, and the large amount of conformations needed to properly describe conformational space of protein structure. In this paper, we describe the principles of the CAVER 3.0 algorithms for the identification and analysis of properties of transport pathways both in static and dynamic structures. Moreover, we introduce the improved clustering solution for finding tunnels in macromolecules, which is included in the latest CAVER 3.02 version. Voronoi diagrams are used to identify potential pathways in each snapshot of a molecular dynamics trajectory and clustering is then used to find the correspondence between tunnels from different snapshots. Furthermore, the geometrical properties of pathways and their evolution in time are computed and visualized.
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Kuznetsov IB. Identification of non-random sequence properties in groups of signature peptides obtained in random sequence peptide microarray experiments. Biopolymers 2016; 106:318-29. [PMID: 27037995 DOI: 10.1002/bip.22845] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2015] [Revised: 02/16/2016] [Accepted: 03/28/2016] [Indexed: 11/09/2022]
Abstract
Immunosignaturing is an emerging experimental technique that uses random sequence peptide microarrays to detect antibodies produced by the immune system in response to a particular disease. Two important questions regarding immunosignaturing are "Do microarray peptides that exhibit a strong affinity to a given type of antibodies share common sequence properties?" and "If so, what are those properties?" In this work, three statistical tests designed to detect non-random patterns in the amino acid makeup of a group of microarray peptides are presented. One test detects patterns of significantly biased amino acid usage, whereas the other two detect patterns of significant bias in the biochemical properties. These tests do not require a large number of peptides per group. The tests were applied to analyze 19 groups of peptides identified in immunosignaturing experiments as being specific for antibodies produced in response to various types of cancer and other diseases. The positional distribution of the biochemical properties of the amino acids in these 19 peptide groups was also studied. Remarkably, despite the random nature of the sequence libraries used to design the microarrays, a unique group-specific non-random pattern was identified in the majority of the peptide groups studied. © 2016 Wiley Periodicals, Inc. Biopolymers (Pept Sci) 106: 318-329, 2016.
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Affiliation(s)
- Igor B Kuznetsov
- Cancer Research Center and Department of Epidemiology and Biostatistics, University at Albany, State University of New York, One Discovery Drive, Rensselaer, NY, 12144
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Abstract
SummaryClustering is a long-standing problem in computer science and is applied in virtually any scientific field for exploring the inherent structure of datasets. In biomedical research, clustering tools have been utilized in manifold areas, among many others in expression analysis, disease subtyping or protein research. A plethora of different approaches have been developed but there is only little guideline what approach is the optimal in what particular situation. Furthermore, a typical cluster analysis is an entire process with several highly interconnected steps; from preprocessing, proximity calculation, the actual clustering to evaluation and optimization. Only when all steps seamlessly work together, an optimal result can be achieved. This renders a cluster analyses tiresome and error-prone especially for non-experts. A mere trial-and-error approach renders increasingly infeasible when considering the tremendous growth of available datasets; thus, a strategic and thoughtful course of action is crucial for a cluster analysis. This manuscript provides an overview of the crucial steps and the most common techniques involved in conducting a state-of-the-art cluster analysis of biomedical datasets.
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Affiliation(s)
- Richard Röttger
- 1Department of Mathematics and Computer Science, University of Southern Denmark, Campusvej 55, 5230 Odense, http://imada.sdu.dk/˜roettger/ Denmark
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The M, Käll L. MaRaCluster: A Fragment Rarity Metric for Clustering Fragment Spectra in Shotgun Proteomics. J Proteome Res 2016; 15:713-20. [PMID: 26653874 DOI: 10.1021/acs.jproteome.5b00749] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Shotgun proteomics experiments generate large amounts of fragment spectra as primary data, normally with high redundancy between and within experiments. Here, we have devised a clustering technique to identify fragment spectra stemming from the same species of peptide. This is a powerful alternative method to traditional search engines for analyzing spectra, specifically useful for larger scale mass spectrometry studies. As an aid in this process, we propose a distance calculation relying on the rarity of experimental fragment peaks, following the intuition that peaks shared by only a few spectra offer more evidence than peaks shared by a large number of spectra. We used this distance calculation and a complete-linkage scheme to cluster data from a recent large-scale mass spectrometry-based study. The clusterings produced by our method have up to 40% more identified peptides for their consensus spectra compared to those produced by the previous state-of-the-art method. We see that our method would advance the construction of spectral libraries as well as serve as a tool for mining large sets of fragment spectra. The source code and Ubuntu binary packages are available at https://github.com/statisticalbiotechnology/maracluster (under an Apache 2.0 license).
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Affiliation(s)
- Matthew The
- Science for Life Laboratory, School of Biotechnology, Royal Institute of Technology - KTH , Box 1031, 17121 Solna, Sweden
| | - Lukas Käll
- Science for Life Laboratory, School of Biotechnology, Royal Institute of Technology - KTH , Box 1031, 17121 Solna, Sweden
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Ahmad M, Jung LT, Bhuiyan MAA. On fuzzy semantic similarity measure for DNA coding. Comput Biol Med 2015; 69:144-51. [PMID: 26773936 DOI: 10.1016/j.compbiomed.2015.12.017] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2015] [Revised: 12/22/2015] [Accepted: 12/23/2015] [Indexed: 11/28/2022]
Abstract
A coding measure scheme numerically translates the DNA sequence to a time domain signal for protein coding regions identification. A number of coding measure schemes based on numerology, geometry, fixed mapping, statistical characteristics and chemical attributes of nucleotides have been proposed in recent decades. Such coding measure schemes lack the biologically meaningful aspects of nucleotide data and hence do not significantly discriminate coding regions from non-coding regions. This paper presents a novel fuzzy semantic similarity measure (FSSM) coding scheme centering on FSSM codons׳ clustering and genetic code context of nucleotides. Certain natural characteristics of nucleotides i.e. appearance as a unique combination of triplets, preserving special structure and occurrence, and ability to own and share density distributions in codons have been exploited in FSSM. The nucleotides׳ fuzzy behaviors, semantic similarities and defuzzification based on the center of gravity of nucleotides revealed a strong correlation between nucleotides in codons. The proposed FSSM coding scheme attains a significant enhancement in coding regions identification i.e. 36-133% as compared to other existing coding measure schemes tested over more than 250 benchmarked and randomly taken DNA datasets of different organisms.
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Affiliation(s)
- Muneer Ahmad
- College of Computer Sciences, King Faisal University, Saudi Arabia.
| | - Low Tang Jung
- Department of Computer Sciences, University Technology PETRONAS, Malaysia.
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Wong YH, Sun J, He LS, Chen LG, Qiu JW, Qian PY. High-throughput transcriptome sequencing of the cold seep mussel Bathymodiolus platifrons. Sci Rep 2015; 5:16597. [PMID: 26593439 PMCID: PMC4655397 DOI: 10.1038/srep16597] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2014] [Accepted: 10/08/2015] [Indexed: 12/29/2022] Open
Abstract
Bathymodiolid mussels dominate hydrothermal vents, cold methane/sulfide-hydrocarbon seeps, and other sites of organic enrichment. Here, we aimed to explore the innate immune system and detoxification mechanism of the deep sea mussel Bathymodiolus platifrons collected from a methane seep in the South China Sea. We sequenced the transcriptome of the mussels’ gill, foot and mantle tissues and generated a transcriptomic database containing 96,683 transcript sequences. Based on GO and KEGG annotations, we reported transcripts that were related to the innate immune system, heavy metal detoxification and sulfide metabolic genes. Our in-depth analysis on the isoforms of peptidoglycan recognition protein (PGRP) that have different cellular location and potentially differential selectivity towards peptidoglycan (PGN) from gram-positive and gram-negative bacteria were differentially expressed in different tissues. We also reported a potentially novel form of metallothionein and the production of phytochelatin in B. platifrons, which has not been reported in any of its coastal relative Mytilus mussel species. Overall, the present study provided new insights into heavy metal and sulfide metabolism in B. platifrons and can be served as the basis for future molecular studies on host-symbiont interactions in cold seep mussels.
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Affiliation(s)
- Yue Him Wong
- Division of Life Science, School of Science, the Hong Kong University of Science and Technology, Hong Kong S.A.R
| | - Jin Sun
- Department of Biology, Hong Kong Baptist University, Hong Kong S.A.R
| | - Li Sheng He
- Sanya Institute of Deep-sea Science and Engineering, Chinese Academy of Sciences, Hainan, People Republic of China
| | - Lian Guo Chen
- Division of Life Science, School of Science, the Hong Kong University of Science and Technology, Hong Kong S.A.R
| | - Jian-Wen Qiu
- Department of Biology, Hong Kong Baptist University, Hong Kong S.A.R
| | - Pei-Yuan Qian
- Division of Life Science, School of Science, the Hong Kong University of Science and Technology, Hong Kong S.A.R
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Lerato L, Niesler T. Clustering Acoustic Segments Using Multi-Stage Agglomerative Hierarchical Clustering. PLoS One 2015; 10:e0141756. [PMID: 26517376 PMCID: PMC4627777 DOI: 10.1371/journal.pone.0141756] [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: 06/23/2015] [Accepted: 10/13/2015] [Indexed: 11/18/2022] Open
Abstract
Agglomerative hierarchical clustering becomes infeasible when applied to large datasets due to its O(N2) storage requirements. We present a multi-stage agglomerative hierarchical clustering (MAHC) approach aimed at large datasets of speech segments. The algorithm is based on an iterative divide-and-conquer strategy. The data is first split into independent subsets, each of which is clustered separately. Thus reduces the storage required for sequential implementations, and allows concurrent computation on parallel computing hardware. The resultant clusters are merged and subsequently re-divided into subsets, which are passed to the following iteration. We show that MAHC can match and even surpass the performance of the exact implementation when applied to datasets of speech segments.
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Affiliation(s)
- Lerato Lerato
- Department of Electrical and Electronic Engineering, University of Stellenbosch, Western Cape, South Africa
| | - Thomas Niesler
- Department of Electrical and Electronic Engineering, University of Stellenbosch, Western Cape, South Africa
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Microbial Community Analysis with Ribosomal Gene Fragments from Shotgun Metagenomes. Appl Environ Microbiol 2015; 82:157-66. [PMID: 26475107 DOI: 10.1128/aem.02772-15] [Citation(s) in RCA: 41] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2015] [Accepted: 10/13/2015] [Indexed: 01/22/2023] Open
Abstract
Shotgun metagenomic sequencing does not depend on gene-targeted primers or PCR amplification; thus, it is not affected by primer bias or chimeras. However, searching rRNA genes from large shotgun Illumina data sets is computationally expensive, and no approach exists for unsupervised community analysis of small-subunit (SSU) rRNA gene fragments retrieved from shotgun data. We present a pipeline, SSUsearch, to achieve the faster identification of short-subunit rRNA gene fragments and enabled unsupervised community analysis with shotgun data. It also includes classification and copy number correction, and the output can be used by traditional amplicon analysis platforms. Shotgun metagenome data using this pipeline yielded higher diversity estimates than amplicon data but retained the grouping of samples in ordination analyses. We applied this pipeline to soil samples with paired shotgun and amplicon data and confirmed bias against Verrucomicrobia in a commonly used V6-V8 primer set, as well as discovering likely bias against Actinobacteria and for Verrucomicrobia in a commonly used V4 primer set. This pipeline can utilize all variable regions in SSU rRNA and also can be applied to large-subunit (LSU) rRNA genes for confirmation of community structure. The pipeline can scale to handle large amounts of soil metagenomic data (5 Gb memory and 5 central processing unit hours to process 38 Gb [1 lane] of trimmed Illumina HiSeq2500 data) and is freely available at https://github.com/dib-lab/SSUsearch under a BSD license.
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Lee S, Min H, Yoon S. Will solid-state drives accelerate your bioinformatics? In-depth profiling, performance analysis and beyond. Brief Bioinform 2015; 17:713-27. [PMID: 26330577 DOI: 10.1093/bib/bbv073] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2015] [Indexed: 11/12/2022] Open
Abstract
A wide variety of large-scale data have been produced in bioinformatics. In response, the need for efficient handling of biomedical big data has been partly met by parallel computing. However, the time demand of many bioinformatics programs still remains high for large-scale practical uses because of factors that hinder acceleration by parallelization. Recently, new generations of storage devices have emerged, such as NAND flash-based solid-state drives (SSDs), and with the renewed interest in near-data processing, they are increasingly becoming acceleration methods that can accompany parallel processing. In certain cases, a simple drop-in replacement of hard disk drives by SSDs results in dramatic speedup. Despite the various advantages and continuous cost reduction of SSDs, there has been little review of SSD-based profiling and performance exploration of important but time-consuming bioinformatics programs. For an informative review, we perform in-depth profiling and analysis of 23 key bioinformatics programs using multiple types of devices. Based on the insight we obtain from this research, we further discuss issues related to design and optimize bioinformatics algorithms and pipelines to fully exploit SSDs. The programs we profile cover traditional and emerging areas of importance, such as alignment, assembly, mapping, expression analysis, variant calling and metagenomics. We explain how acceleration by parallelization can be combined with SSDs for improved performance and also how using SSDs can expedite important bioinformatics pipelines, such as variant calling by the Genome Analysis Toolkit and transcriptome analysis using RNA sequencing. We hope that this review can provide useful directions and tips to accompany future bioinformatics algorithm design procedures that properly consider new generations of powerful storage devices.
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Trends in genome dynamics among major orders of insects revealed through variations in protein families. BMC Genomics 2015; 16:583. [PMID: 26251035 PMCID: PMC4528696 DOI: 10.1186/s12864-015-1771-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2014] [Accepted: 07/13/2015] [Indexed: 01/22/2023] Open
Abstract
Background Insects belong to a class that accounts for the majority of animals on earth. With over one million identified species, insects display a huge diversity and occupy extreme environments. At present, there are dozens of fully sequenced insect genomes that cover a range of habitats, social behavior and morphologies. In view of such diverse collection of genomes, revealing evolutionary trends and charting functional relationships of proteins remain challenging. Results We analyzed the relatedness of 17 complete proteomes representative of proteomes from insects including louse, bee, beetle, ants, flies and mosquitoes, as well as an out-group from the crustaceans. The analyzed proteomes mostly represented the orders of Hymenoptera and Diptera. The 287,405 protein sequences from the 18 proteomes were automatically clustered into 20,933 families, including 799 singletons. A comprehensive analysis based on statistical considerations identified the families that were significantly expanded or reduced in any of the studied organisms. Among all the tested species, ants are characterized by an exceptionally high rate of family gain and loss. By assigning annotations to hundreds of species-specific families, the functional diversity among species and between the major clades (Diptera and Hymenoptera) is revealed. We found that many species-specific families are associated with receptor signaling, stress-related functions and proteases. The highest variability among insects associates with the function of transposition and nucleic acids processes (collectively coined TNAP). Specifically, the wasp and ants have an order of magnitude more TNAP families and proteins relative to species that belong to Diptera (mosquitoes and flies). Conclusions An unsupervised clustering methodology combined with a comparative functional analysis unveiled proteomic signatures in the major clades of winged insects. We propose that the expansion of TNAP families in Hymenoptera potentially contributes to the accelerated genome dynamics that characterize the wasp and ants. Electronic supplementary material The online version of this article (doi:10.1186/s12864-015-1771-2) contains supplementary material, which is available to authorized users.
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Rappoport N, Linial M. ProtoBug: functional families from the complete proteomes of insects. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2015; 2015:bau122. [PMID: 25911153 PMCID: PMC4408594 DOI: 10.1093/database/bau122] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/09/2014] [Accepted: 11/30/2014] [Indexed: 11/30/2022]
Abstract
ProtoBug (http://www.protobug.cs.huji.ac.il) is a database and resource of protein families in Arthropod genomes. ProtoBug platform presents the relatedness of complete proteomes from 17 insects as well as a proteome of the crustacean, Daphnia pulex. The represented proteomes from insects include louse, bee, beetle, ants, flies and mosquitoes. Based on an unsupervised clustering method, protein sequences were clustered into a hierarchical tree, called ProtoBug. ProtoBug covers about 300 000 sequences that are partitioned to families. At the default setting, all sequences are partitioned to ∼20 000 families (excluding singletons). From the species perspective, each of the 18 analysed proteomes is composed of 5000–8000 families. In the regime of the advanced operational mode, the ProtoBug provides rich navigation capabilities for touring the hierarchy of the families at any selected resolution. A proteome viewer shows the composition of sequences from any of the 18 analysed proteomes. Using functional annotation from an expert system (Pfam) we assigned domains, families and repeats by 4400 keywords that cover 73% of the sequences. A strict inference protocol is applied for expanding the functional knowledge. Consequently, secured annotations were associated with 81% of the proteins, and with 70% of the families (≥10 proteins each). ProtoBug is a database and webtool with rich visualization and navigation tools. The properties of each family in relation to other families in the ProtoBug tree, and in view of the taxonomy composition are reported. Furthermore, the user can paste its own sequences to find relatedness to any of the ProtoBug families. The database and the navigation tools are the basis for functional discoveries that span 350 million years of evolution of Arthropods. ProtoBug is available with no restriction at: www.protobug.cs.huji.ac.il. Database URL:www.protobug.cs.huji.ac.il.
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Affiliation(s)
- Nadav Rappoport
- School of Computer Science and Engineering and Department of Biological Chemistry, Institute of Life Sciences, The Hebrew University of Jerusalem, Givat Ram Campus, Jerusalem, 91904 Israel
| | - Michal Linial
- School of Computer Science and Engineering and Department of Biological Chemistry, Institute of Life Sciences, The Hebrew University of Jerusalem, Givat Ram Campus, Jerusalem, 91904 Israel
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Suzek BE, Wang Y, Huang H, McGarvey PB, Wu CH. UniRef clusters: a comprehensive and scalable alternative for improving sequence similarity searches. Bioinformatics 2014; 31:926-32. [PMID: 25398609 PMCID: PMC4375400 DOI: 10.1093/bioinformatics/btu739] [Citation(s) in RCA: 943] [Impact Index Per Article: 94.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
MOTIVATION UniRef databases provide full-scale clustering of UniProtKB sequences and are utilized for a broad range of applications, particularly similarity-based functional annotation. Non-redundancy and intra-cluster homogeneity in UniRef were recently improved by adding a sequence length overlap threshold. Our hypothesis is that these improvements would enhance the speed and sensitivity of similarity searches and improve the consistency of annotation within clusters. RESULTS Intra-cluster molecular function consistency was examined by analysis of Gene Ontology terms. Results show that UniRef clusters bring together proteins of identical molecular function in more than 97% of the clusters, implying that clusters are useful for annotation and can also be used to detect annotation inconsistencies. To examine coverage in similarity results, BLASTP searches against UniRef50 followed by expansion of the hit lists with cluster members demonstrated advantages compared with searches against UniProtKB sequences; the searches are concise (∼7 times shorter hit list before expansion), faster (∼6 times) and more sensitive in detection of remote similarities (>96% recall at e-value <0.0001). Our results support the use of UniRef clusters as a comprehensive and scalable alternative to native sequence databases for similarity searches and reinforces its reliability for use in functional annotation.
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Affiliation(s)
- Baris E Suzek
- Protein Information Resource, Georgetown University Medical Center, Washington, DC 20007, USA, Department of Computer Engineering, Muğla Sıtkı Koçman University, Muğla 48000, Turkey, Center for Bioinformatics and Computational Biology and Protein Information Resource, University of Delaware, Newark, DE 19711, USA, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK and Swiss Institute of Bioinformatics, Centre Medical Universitaire, 1 rue Michel Servet, 1211 Geneva 4, Switzerland Protein Information Resource, Georgetown University Medical Center, Washington, DC 20007, USA, Department of Computer Engineering, Muğla Sıtkı Koçman University, Muğla 48000, Turkey, Center for Bioinformatics and Computational Biology and Protein Information Resource, University of Delaware, Newark, DE 19711, USA, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK and Swiss Institute of Bioinformatics, Centre Medical Universitaire, 1 rue Michel Servet, 1211 Geneva 4, Switzerland
| | - Yuqi Wang
- Protein Information Resource, Georgetown University Medical Center, Washington, DC 20007, USA, Department of Computer Engineering, Muğla Sıtkı Koçman University, Muğla 48000, Turkey, Center for Bioinformatics and Computational Biology and Protein Information Resource, University of Delaware, Newark, DE 19711, USA, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK and Swiss Institute of Bioinformatics, Centre Medical Universitaire, 1 rue Michel Servet, 1211 Geneva 4, Switzerland
| | - Hongzhan Huang
- Protein Information Resource, Georgetown University Medical Center, Washington, DC 20007, USA, Department of Computer Engineering, Muğla Sıtkı Koçman University, Muğla 48000, Turkey, Center for Bioinformatics and Computational Biology and Protein Information Resource, University of Delaware, Newark, DE 19711, USA, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK and Swiss Institute of Bioinformatics, Centre Medical Universitaire, 1 rue Michel Servet, 1211 Geneva 4, Switzerland
| | - Peter B McGarvey
- Protein Information Resource, Georgetown University Medical Center, Washington, DC 20007, USA, Department of Computer Engineering, Muğla Sıtkı Koçman University, Muğla 48000, Turkey, Center for Bioinformatics and Computational Biology and Protein Information Resource, University of Delaware, Newark, DE 19711, USA, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK and Swiss Institute of Bioinformatics, Centre Medical Universitaire, 1 rue Michel Servet, 1211 Geneva 4, Switzerland
| | - Cathy H Wu
- Protein Information Resource, Georgetown University Medical Center, Washington, DC 20007, USA, Department of Computer Engineering, Muğla Sıtkı Koçman University, Muğla 48000, Turkey, Center for Bioinformatics and Computational Biology and Protein Information Resource, University of Delaware, Newark, DE 19711, USA, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK and Swiss Institute of Bioinformatics, Centre Medical Universitaire, 1 rue Michel Servet, 1211 Geneva 4, Switzerland Protein Information Resource, Georgetown University Medical Center, Washington, DC 20007, USA, Department of Computer Engineering, Muğla Sıtkı Koçman University, Muğla 48000, Turkey, Center for Bioinformatics and Computational Biology and Protein Information Resource, University of Delaware, Newark, DE 19711, USA, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK and Swiss Institute of Bioinformatics, Centre Medical Universitaire, 1 rue Michel Servet, 1211 Geneva 4, Switzerland
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Bekal S, Domier LL, Gonfa B, McCoppin NK, Lambert KN, Bhalerao K. A novel flavivirus in the soybean cyst nematode. J Gen Virol 2014; 95:1272-1280. [PMID: 24643877 DOI: 10.1099/vir.0.060889-0] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023] Open
Abstract
Heterodera glycines, the soybean cyst nematode (SCN), is a subterranean root pathogen that causes the most damaging disease of soybean in the USA. A novel nematode virus genome, soybean cyst nematode virus 5 (SbCNV-5), was identified in RNA sequencing data from SCN eggs and second-stage juveniles. The SbCNV-5 RNA-dependent RNA polymerase and RNA helicase domains had homology to pestiviruses in the family Flaviviridae, suggesting that SbCNV-5 is a positive-polarity ssRNA virus. SbCNV-5 RNA was present in all nematode developmental stages, indicating a transovarial mode of transmission, but is also potentially sexually transmitted via the male. SbCNV-5 was common in SCN laboratory cultures and in nematode populations isolated from the field. Transmission electron microscopy of sections from a female SCN showed virus particles budding from the endoplasmic reticulum and in endosomes. The size of the viral genome was 19 191 nt, which makes it much larger than other known pestiviruses. Additionally, the presence of a methyltransferase in the SbCNV-5 genome is atypical for a pestivirus. When cDNA sequences were mapped to the genome of SbCNV-5, a disproportionate number aligned to the 3' NTR, suggesting that SbCNV-5 produces a subgenomic RNA, which was confirmed by RNA blot analysis. As subgenomic RNAs and methyltransferases do not occur in pestiviruses, we conclude that SbCNV-5 is a new flavivirus infecting SCNs.
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Affiliation(s)
- Sadia Bekal
- Department of Agricultural and Biological Engineering, University of Illinois, Urbana, IL 61810, USA
| | - Leslie L Domier
- US Department of Agriculture, Agricultural Research Service, University of Illinois, Urbana, IL 61801, USA
- Department of Crop Sciences, University of Illinois, Urbana, IL 61810, USA
| | - Biruk Gonfa
- Department of Crop Sciences, University of Illinois, Urbana, IL 61810, USA
| | - Nancy K McCoppin
- US Department of Agriculture, Agricultural Research Service, University of Illinois, Urbana, IL 61801, USA
- Department of Crop Sciences, University of Illinois, Urbana, IL 61810, USA
| | - Kris N Lambert
- Department of Crop Sciences, University of Illinois, Urbana, IL 61810, USA
| | - Kaustubh Bhalerao
- Department of Agricultural and Biological Engineering, University of Illinois, Urbana, IL 61810, USA
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Ghaffari HR, Sadoghi Yazdi H. Multiclass classifier based on boundary complexity. Neural Comput Appl 2014. [DOI: 10.1007/s00521-012-1303-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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Cole JR, Wang Q, Fish JA, Chai B, McGarrell DM, Sun Y, Brown CT, Porras-Alfaro A, Kuske CR, Tiedje JM. Ribosomal Database Project: data and tools for high throughput rRNA analysis. Nucleic Acids Res 2013; 42:D633-42. [PMID: 24288368 PMCID: PMC3965039 DOI: 10.1093/nar/gkt1244] [Citation(s) in RCA: 2603] [Impact Index Per Article: 236.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
Ribosomal Database Project (RDP; http://rdp.cme.msu.edu/) provides the research community with aligned and annotated rRNA gene sequence data, along with tools to allow researchers to analyze their own rRNA gene sequences in the RDP framework. RDP data and tools are utilized in fields as diverse as human health, microbial ecology, environmental microbiology, nucleic acid chemistry, taxonomy and phylogenetics. In addition to aligned and annotated collections of bacterial and archaeal small subunit rRNA genes, RDP now includes a collection of fungal large subunit rRNA genes. RDP tools, including Classifier and Aligner, have been updated to work with this new fungal collection. The use of high-throughput sequencing to characterize environmental microbial populations has exploded in the past several years, and as sequence technologies have improved, the sizes of environmental datasets have increased. With release 11, RDP is providing an expanded set of tools to facilitate analysis of high-throughput data, including both single-stranded and paired-end reads. In addition, most tools are now available as open source packages for download and local use by researchers with high-volume needs or who would like to develop custom analysis pipelines.
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Affiliation(s)
- James R Cole
- Center for Microbial Ecology, Michigan State University, East Lansing, MI 48824, USA, Computer Science and Engineering, Michigan State University, East Lansing, MI 48824, USA, Microbiology and Molecular Genetics, Michigan State University, East Lansing, MI 48824, USA, Biological Sciences, Western Illinois University, Malcomb, IL 61455, USA and Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA
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Fish JA, Chai B, Wang Q, Sun Y, Brown CT, Tiedje JM, Cole JR. FunGene: the functional gene pipeline and repository. Front Microbiol 2013; 4:291. [PMID: 24101916 PMCID: PMC3787254 DOI: 10.3389/fmicb.2013.00291] [Citation(s) in RCA: 332] [Impact Index Per Article: 30.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2013] [Accepted: 09/10/2013] [Indexed: 11/29/2022] Open
Abstract
Ribosomal RNA genes have become the standard molecular markers for microbial community analysis for good reasons, including universal occurrence in cellular organisms, availability of large databases, and ease of rRNA gene region amplification and analysis. As markers, however, rRNA genes have some significant limitations. The rRNA genes are often present in multiple copies, unlike most protein-coding genes. The slow rate of change in rRNA genes means that multiple species sometimes share identical 16S rRNA gene sequences, while many more species share identical sequences in the short 16S rRNA regions commonly analyzed. In addition, the genes involved in many important processes are not distributed in a phylogenetically coherent manner, potentially due to gene loss or horizontal gene transfer. While rRNA genes remain the most commonly used markers, key genes in ecologically important pathways, e.g., those involved in carbon and nitrogen cycling, can provide important insights into community composition and function not obtainable through rRNA analysis. However, working with ecofunctional gene data requires some tools beyond those required for rRNA analysis. To address this, our Functional Gene Pipeline and Repository (FunGene; http://fungene.cme.msu.edu/) offers databases of many common ecofunctional genes and proteins, as well as integrated tools that allow researchers to browse these collections and choose subsets for further analysis, build phylogenetic trees, test primers and probes for coverage, and download aligned sequences. Additional FunGene tools are specialized to process coding gene amplicon data. For example, FrameBot produces frameshift-corrected protein and DNA sequences from raw reads while finding the most closely related protein reference sequence. These tools can help provide better insight into microbial communities by directly studying key genes involved in important ecological processes.
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Affiliation(s)
- Jordan A Fish
- Center for Microbial Ecology, Michigan State University East Lansing, MI, USA ; Department of Computer Science and Engineering, Michigan State University East Lansing, MI, USA
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Frech C, Chen N. Variant surface antigens of malaria parasites: functional and evolutionary insights from comparative gene family classification and analysis. BMC Genomics 2013; 14:427. [PMID: 23805789 PMCID: PMC3747859 DOI: 10.1186/1471-2164-14-427] [Citation(s) in RCA: 60] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2013] [Accepted: 06/19/2013] [Indexed: 11/10/2022] Open
Abstract
Background Plasmodium parasites, the causative agents of malaria, express many variant antigens on cell surfaces. Variant surface antigens (VSAs) are typically organized into large subtelomeric gene families that play critical roles in virulence and immune evasion. Many important aspects of VSA function and evolution remain obscure, impeding our understanding of virulence mechanisms and vaccine development. To gain further insights into VSA function and evolution, we comparatively classified and examined known VSA gene families across seven Plasmodium species. Results We identified a set of ultra-conserved orthologs within the largest Plasmodium gene family pir, which should be considered as high-priority targets for experimental functional characterization and vaccine development. Furthermore, we predict a lipid-binding domain in erythrocyte surface-expressed PYST-A proteins, suggesting a role of this second largest rodent parasite gene family in host cholesterol salvage. Additionally, it was found that PfMC-2TM proteins carry a novel and putative functional domain named MC-TYR, which is conserved in other P. falciparum gene families and rodent parasites. Finally, we present new conclusive evidence that the major Plasmodium VSAs PfEMP1, SICAvar, and SURFIN are evolutionarily linked through a modular and structurally conserved intracellular domain. Conclusion Our comparative analysis of Plasmodium VSA gene families revealed important functional and evolutionary insights, which can now serve as starting points for further experimental studies.
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Affiliation(s)
- Christian Frech
- Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, BC, Canada
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Guo L, Tsai SQ, Hardison NE, James AH, Motsinger-Reif AA, Thames B, Stone EA, Deng C, Piedrahita JA. Differentially expressed microRNAs and affected biological pathways revealed by modulated modularity clustering (MMC) analysis of human preeclamptic and IUGR placentas. Placenta 2013; 34:599-605. [PMID: 23639576 DOI: 10.1016/j.placenta.2013.04.007] [Citation(s) in RCA: 52] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/05/2012] [Revised: 03/25/2013] [Accepted: 04/05/2013] [Indexed: 01/12/2023]
Abstract
INTRODUCTION This study focuses on the implementation of modulated modularity clustering (MMC) a new cluster algorithm for the identification of molecular signatures of preeclampsia and intrauterine growth restriction (IUGR), and the identification of affected microRNAs METHODS Eighty-six human placentas from normal (40), growth-restricted (27), and preeclamptic (19) term pregnancies were profiled using Illumina Human-6 Beadarrays. MMC was utilized to generate modules based on similarities in placental transcriptome. Gene Set Enrichment Analysis (GSEA) was used to predict affected microRNAs. Expression levels of these candidate microRNAs were investigated in seventy-one human term placentas as follows: control (29); IUGR (26); and preeclampsia (16). RESULTS MMC identified two modules, one representing IUGR placentas and one representing preeclamptic placentas. 326 differentially expressed genes in the module representing IUGR and 889 differentially expressed genes in a module representing preeclampsia were identified. Functional analysis of molecular signatures associated with IUGR identified P13K/AKT, mTOR, p70S6K, apoptosis and IGF-1 signaling as being affected. Analysis of variance of GSEA-predicted microRNAs indicated that miR-194 was significantly down-regulated both in preeclampsia (p = 0.0001) and IUGR (p = 0.0304), and miR-149 was significantly down-regulated in preeclampsia (p = 0.0168). DISCUSSION Implementation of MMC, allowed identification of genes disregulated in IUGR and preeclampsia. The reliability of MMC was validated by comparing to previous linear modeling analysis of preeclamptic placentas. CONCLUSION MMC allowed the elucidation of a molecular signature associated with preeclampsia and a subset of IUGR samples. This allowed the identification of genes, pathways, and microRNAs affected in these diseases.
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Affiliation(s)
- L Guo
- Department of Molecular Biomedical Sciences, College of Veterinary Medicine, North Carolina State University, Raleigh, NC, USA
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Rappoport N, Linial M. Functional inference by ProtoNet family tree: the uncharacterized proteome of Daphnia pulex. BMC Bioinformatics 2013; 14 Suppl 3:S11. [PMID: 23514195 PMCID: PMC3584848 DOI: 10.1186/1471-2105-14-s3-s11] [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: 11/20/2022] Open
Abstract
Background Daphnia pulex (Water flea) is the first fully sequenced crustacean genome. The crustaceans and insects have diverged from a common ancestor. It is a model organism for studying the molecular makeup for coping with the environmental challenges. In the complete proteome, there are 30,550 putative proteins. However, about 10,000 of them have no known homologues. Currently, the UniProtoKB reports on 95% of the Daphnia's proteins as putative and uncharacterized proteins. Results We have applied ProtoNet, an unsupervised hierarchical protein clustering method that covers about 10 million sequences, for automatic annotation of the Daphnia's proteome. 98.7% (26,625) of the Daphnia full-length proteins were successfully mapped to 13,880 ProtoNet stable clusters, and only 1.3% remained unmapped. We compared the properties of the Daphnia's protein families with those of the mouse and the fruitfly proteomes. Functional annotations were successfully assigned for 86% of the proteins. Most proteins (61%) were mapped to only 2953 clusters that contain Daphnia's duplicated genes. We focused on the functionality of maximally amplified paralogs. Cuticle structure components and a variety of ion channels protein families were associated with a maximal level of gene amplification. We focused on gene amplification as a leading strategy of the Daphnia in coping with environmental toxicity. Conclusions Automatic inference is achieved through mapping of sequences to the protein family tree of ProtoNet 6.0. Applying a careful inference protocol resulted in functional assignments for over 86% of the complete proteome. We conclude that the scaffold of ProtoNet can be used as an alignment-free protocol for large-scale annotation task of uncharacterized proteomes.
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Affiliation(s)
- Nadav Rappoport
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, 91904, Israel
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Kelly S, Maini PK. DendroBLAST: approximate phylogenetic trees in the absence of multiple sequence alignments. PLoS One 2013; 8:e58537. [PMID: 23554899 PMCID: PMC3598851 DOI: 10.1371/journal.pone.0058537] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2012] [Accepted: 02/07/2013] [Indexed: 01/03/2023] Open
Abstract
The rapidly growing availability of genome information has created considerable demand for both fast and accurate phylogenetic inference algorithms. We present a novel method called DendroBLAST for reconstructing phylogenetic dendrograms/trees from protein sequences using BLAST. This method differs from other methods by incorporating a simple model of sequence evolution to test the effect of introducing sequence changes on the reliability of the bipartitions in the inferred tree. Using realistic simulated sequence data we demonstrate that this method produces phylogenetic trees that are more accurate than other commonly-used distance based methods though not as accurate as maximum likelihood methods from good quality multiple sequence alignments. In addition to tests on simulated data, we use DendroBLAST to generate input trees for a supertree reconstruction of the phylogeny of the Archaea. This independent analysis produces an approximate phylogeny of the Archaea that has both high precision and recall when compared to previously published analysis of the same dataset using conventional methods. Taken together these results demonstrate that approximate phylogenetic trees can be produced in the absence of multiple sequence alignments, and we propose that these trees will provide a platform for improving and informing downstream bioinformatic analysis. A web implementation of the DendroBLAST method is freely available for use at http://www.dendroblast.com/.
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Affiliation(s)
- Steven Kelly
- Department of Plant Sciences, University of Oxford, Oxford, United Kingdom.
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