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Liao X, Zhu W, Zhou J, Li H, Xu X, Zhang B, Gao X. Repetitive DNA sequence detection and its role in the human genome. Commun Biol 2023; 6:954. [PMID: 37726397 PMCID: PMC10509279 DOI: 10.1038/s42003-023-05322-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Accepted: 09/04/2023] [Indexed: 09/21/2023] Open
Abstract
Repetitive DNA sequences playing critical roles in driving evolution, inducing variation, and regulating gene expression. In this review, we summarized the definition, arrangement, and structural characteristics of repeats. Besides, we introduced diverse biological functions of repeats and reviewed existing methods for automatic repeat detection, classification, and masking. Finally, we analyzed the type, structure, and regulation of repeats in the human genome and their role in the induction of complex diseases. We believe that this review will facilitate a comprehensive understanding of repeats and provide guidance for repeat annotation and in-depth exploration of its association with human diseases.
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Affiliation(s)
- Xingyu Liao
- Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955, Saudi Arabia
| | - Wufei Zhu
- Department of Endocrinology, Yichang Central People's Hospital, The First College of Clinical Medical Science, China Three Gorges University, 443000, Yichang, P.R. China
| | - Juexiao Zhou
- Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955, Saudi Arabia
| | - Haoyang Li
- Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955, Saudi Arabia
| | - Xiaopeng Xu
- Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955, Saudi Arabia
| | - Bin Zhang
- Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955, Saudi Arabia
| | - Xin Gao
- Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955, Saudi Arabia.
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2
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Wei J, Liu P, Liu F, Jiang A, Qiao J, Pu Z, Wang B, Zhang J, Jia D, Li Y, Wang S, Dong B. EDomics: a comprehensive and comparative multi-omics database for animal evo-devo. Nucleic Acids Res 2022; 51:D913-D923. [PMID: 36318263 PMCID: PMC9825439 DOI: 10.1093/nar/gkac944] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 09/26/2022] [Accepted: 10/11/2022] [Indexed: 01/09/2023] Open
Abstract
Evolutionary developmental biology (evo-devo) has been among the most fascinating interdisciplinary fields for decades, which aims to elucidate the origin and evolution of diverse developmental processes. The rapid accumulation of omics data provides unprecedented opportunities to answer many interesting but unresolved evo-devo questions. However, the access and utilization of these resources are hindered by challenges particularly in non-model animals. Here, we establish a comparative multi-omics database for animal evo-devo (EDomics, http://edomics.qnlm.ac) containing comprehensive genomes, bulk transcriptomes, and single-cell data across 40 representative species, many of which are generally used as model organisms for animal evo-devo study. EDomics provides a systematic view of genomic/transcriptomic information from various aspects, including genome assembly statistics, gene features and families, transcription factors, transposable elements, and gene expressional profiles/networks. It also exhibits spatiotemporal gene expression profiles at a single-cell level, such as cell atlas, cell markers, and spatial-map information. Moreover, EDomics provides highly valuable, customized datasets/resources for evo-devo research, including gene family expansion/contraction, inferred core gene repertoires, macrosynteny analysis for karyotype evolution, and cell type evolution analysis. EDomics presents a comprehensive and comparative multi-omics platform for animal evo-devo community to decipher the whole history of developmental evolution across the tree of life.
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Affiliation(s)
| | | | | | - An Jiang
- Sars-Fang Centre, MOE Key Laboratory of Marine Genetics and Breeding, College of Marine Life Sciences, Ocean University of China, Qingdao 266003, China
| | - Jinghan Qiao
- Sars-Fang Centre, MOE Key Laboratory of Marine Genetics and Breeding, College of Marine Life Sciences, Ocean University of China, Qingdao 266003, China
| | - Zhongqi Pu
- Sars-Fang Centre, MOE Key Laboratory of Marine Genetics and Breeding, College of Marine Life Sciences, Ocean University of China, Qingdao 266003, China
| | - Bingrou Wang
- Sars-Fang Centre, MOE Key Laboratory of Marine Genetics and Breeding, College of Marine Life Sciences, Ocean University of China, Qingdao 266003, China
| | - Jin Zhang
- Sars-Fang Centre, MOE Key Laboratory of Marine Genetics and Breeding, College of Marine Life Sciences, Ocean University of China, Qingdao 266003, China
| | - Dongning Jia
- Laboratory for Marine Biology and Biotechnology, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266237, China
| | - Yuli Li
- Correspondence may also be addressed to Yuli Li. Tel: +86 532 85906587; Fax: +86 532 85906587;
| | - Shi Wang
- Correspondence may also be addressed to Shi Wang. Tel: +86 532 85906589; Fax: +86 532 85906589;
| | - Bo Dong
- To whom correspondence should be addressed. Tel: +86 532 85906578; Fax: +86 532 85906578;
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3
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Liu F, Li Y, Yu H, Zhang L, Hu J, Bao Z, Wang S. MolluscDB: an integrated functional and evolutionary genomics database for the hyper-diverse animal phylum Mollusca. Nucleic Acids Res 2021; 49:D988-D997. [PMID: 33219670 PMCID: PMC7779068 DOI: 10.1093/nar/gkaa918] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2020] [Revised: 09/16/2020] [Accepted: 10/03/2020] [Indexed: 12/11/2022] Open
Abstract
Mollusca represents the second largest animal phylum but remains poorly explored from a genomic perspective. While the recent increase in genomic resources holds great promise for a deep understanding of molluscan biology and evolution, access and utilization of these resources still pose a challenge. Here, we present the first comprehensive molluscan genomics database, MolluscDB (http://mgbase.qnlm.ac), which compiles and integrates current molluscan genomic/transcriptomic resources and provides convenient tools for multi-level integrative and comparative genomic analyses. MolluscDB enables a systematic view of genomic information from various aspects, such as genome assembly statistics, genome phylogenies, fossil records, gene information, expression profiles, gene families, transcription factors, transposable elements and mitogenome organization information. Moreover, MolluscDB offers valuable customized datasets or resources, such as gene coexpression networks across various developmental stages and adult tissues/organs, core gene repertoires inferred for major molluscan lineages, and macrosynteny analysis for chromosomal evolution. MolluscDB presents an integrative and comprehensive genomics platform that will allow the molluscan community to cope with ever-growing genomic resources and will expedite new scientific discoveries for understanding molluscan biology and evolution.
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Affiliation(s)
- Fuyun Liu
- MOE Key Laboratory of Marine Genetics and Breeding and Sars-Fang Centre, Ocean University of China, Qingdao 266003, China
| | - Yuli Li
- MOE Key Laboratory of Marine Genetics and Breeding and Sars-Fang Centre, Ocean University of China, Qingdao 266003, China.,Laboratory for Marine Biology and Biotechnology, Pilot Qingdao National Laboratory for Marine Science and Technology, Qingdao 266237, China
| | - Hongwei Yu
- MOE Key Laboratory of Marine Genetics and Breeding and Sars-Fang Centre, Ocean University of China, Qingdao 266003, China
| | - Lingling Zhang
- MOE Key Laboratory of Marine Genetics and Breeding and Sars-Fang Centre, Ocean University of China, Qingdao 266003, China.,Laboratory for Marine Biology and Biotechnology, Pilot Qingdao National Laboratory for Marine Science and Technology, Qingdao 266237, China
| | - Jingjie Hu
- MOE Key Laboratory of Marine Genetics and Breeding and Sars-Fang Centre, Ocean University of China, Qingdao 266003, China.,Laboratory of Tropical Marine Germplasm Resources and Breeding Engineering, Sanya Oceanographic Institution, Ocean University of China, Sanya 572000, China
| | - Zhenmin Bao
- MOE Key Laboratory of Marine Genetics and Breeding and Sars-Fang Centre, Ocean University of China, Qingdao 266003, China.,Laboratory of Tropical Marine Germplasm Resources and Breeding Engineering, Sanya Oceanographic Institution, Ocean University of China, Sanya 572000, China.,Laboratory for Marine Fisheries Science and Food Production Processes, Pilot Qingdao National Laboratory for Marine Science and Technology, Qingdao 266237, China
| | - Shi Wang
- MOE Key Laboratory of Marine Genetics and Breeding and Sars-Fang Centre, Ocean University of China, Qingdao 266003, China.,Laboratory for Marine Biology and Biotechnology, Pilot Qingdao National Laboratory for Marine Science and Technology, Qingdao 266237, China.,Laboratory of Tropical Marine Germplasm Resources and Breeding Engineering, Sanya Oceanographic Institution, Ocean University of China, Sanya 572000, China
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4
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Michaelis M, Sobczak A, Koczan D, Langhammer M, Reinsch N, Schoen J, Weitzel JM. Selection for female traits of high fertility affects male reproductive performance and alters the testicular transcriptional profile. BMC Genomics 2017; 18:889. [PMID: 29157197 PMCID: PMC5697431 DOI: 10.1186/s12864-017-4288-z] [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: 10/17/2016] [Accepted: 11/08/2017] [Indexed: 02/08/2023] Open
Abstract
Background Many genes important for reproductive performance are shared by both sexes. However, fecundity indices are primarily based on female parameters such as litter size. We examined a fertility mouse line (FL2), which has a considerably increased number of offspring and a total litter weight of 180% compared to a randomly bred control line (Ctrl) after more than 170 generations of breeding. In the present study, we investigated whether there might be a parallel evolution in males after more than 40 years of breeding in this outbred mouse model. Results Males of the fertility mouse line FL2 showed reduced sperm motility performance in a 5 h thermal stress experiment and reduced birth rate in the outbred mouse line. Transcriptional analysis of the FL2 testis showed the differential expression of genes associated with steroid metabolic processes (Cyp1b1, Cyp19a1, Hsd3b6, and Cyp21a1) and female fecundity (Gdf9), accompanied by 150% elevated serum progesterone levels in the FL2 males. Cluster analysis revealed the downregulation of genes of the kallikrein-related peptidases (KLK) cluster located on chromosome 7 in addition to alterations in gene expression with serine peptidase activity, e.g., angiotensinogen (Agt), of the renin-angiotensin system essential for ovulation. Although a majority of functional annotations map to female reproduction and ovulation, these genes are differentially expressed in FL2 testis. Conclusions These data indicate that selection for primary female traits of increased litter size not only affects sperm characteristics but also manifests as transcriptional alterations of the male side likely with direct long-term consequences for the reproductive performance of the mouse line. Electronic supplementary material The online version of this article (10.1186/s12864-017-4288-z) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Marten Michaelis
- Institute of Reproductive Biology, University of Rostock, Rostock, Germany. .,Leibniz Institute for Farm Animal Biology (FBN), Institute of Reproductive Biology, FBN Dummerstorf, Wilhelm-Stahl-Allee 2, 18196, Dummerstorf, Germany.
| | - Alexander Sobczak
- Institute of Reproductive Biology, University of Rostock, Rostock, Germany
| | - Dirk Koczan
- Institute of Immunology, University of Rostock, Rostock, Germany
| | - Martina Langhammer
- Institute of Genetics and Biometry, Leibniz Institute for Farm Animal Biology (FBN), Dummerstorf, Germany
| | - Norbert Reinsch
- Institute of Genetics and Biometry, Leibniz Institute for Farm Animal Biology (FBN), Dummerstorf, Germany
| | - Jennifer Schoen
- Institute of Reproductive Biology, University of Rostock, Rostock, Germany
| | - Joachim M Weitzel
- Institute of Reproductive Biology, University of Rostock, Rostock, Germany. .,Leibniz Institute for Farm Animal Biology (FBN), Institute of Reproductive Biology, FBN Dummerstorf, Wilhelm-Stahl-Allee 2, 18196, Dummerstorf, Germany.
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5
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Haque MM, Nilsson EE, Holder LB, Skinner MK. Genomic Clustering of differential DNA methylated regions (epimutations) associated with the epigenetic transgenerational inheritance of disease and phenotypic variation. BMC Genomics 2016; 17:418. [PMID: 27245821 PMCID: PMC4888261 DOI: 10.1186/s12864-016-2748-5] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2015] [Accepted: 05/17/2016] [Indexed: 11/24/2022] Open
Abstract
Background A variety of environmental factors have been shown to promote the epigenetic transgenerational inheritance of disease and phenotypic variation in numerous species. Exposure to environmental factors such as toxicants can promote epigenetic changes (epimutations) involving alterations in DNA methylation to produce specific differential DNA methylation regions (DMRs). The germline (e.g. sperm) transmission of epimutations is associated with epigenetic transgenerational inheritance phenomena. The current study was designed to determine the genomic locations of environmentally induced transgenerational DMRs and assess their potential clustering. Results The exposure specific DMRs (epimutations) from a number of different studies were used. The clustering approach identified areas of the genome that have statistically significant over represented numbers of epimutations. The location of DMR clusters was compared to the gene clusters of differentially expressed genes found in tissues and cells associated with the transgenerational inheritance of disease. Such gene clusters, termed epigenetic control regions (ECRs), have been previously suggested to regulate gene expression in regions spanning up to 2-5 million bases. DMR clusters were often found to associate with inherent gene clusters within the genome. Conclusion The current study used a number of epigenetic datasets from previous studies to identify novel DMR clusters across the genome. Observations suggest these clustered DMR within an ECR may be susceptible to epigenetic reprogramming and dramatically influence genome activity. Electronic supplementary material The online version of this article (doi:10.1186/s12864-016-2748-5) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- M Muksitul Haque
- Center for Reproductive Biology, School of Biological Sciences, Washington State University, Pullman, WA, 99164-4236, USA.,School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA, 99164, USA
| | - Eric E Nilsson
- Center for Reproductive Biology, School of Biological Sciences, Washington State University, Pullman, WA, 99164-4236, USA
| | - Lawrence B Holder
- School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA, 99164, USA
| | - Michael K Skinner
- Center for Reproductive Biology, School of Biological Sciences, Washington State University, Pullman, WA, 99164-4236, USA.
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A roadmap for natural product discovery based on large-scale genomics and metabolomics. Nat Chem Biol 2014; 10:963-8. [PMID: 25262415 PMCID: PMC4201863 DOI: 10.1038/nchembio.1659] [Citation(s) in RCA: 343] [Impact Index Per Article: 34.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2014] [Accepted: 09/04/2014] [Indexed: 11/25/2022]
Abstract
Actinobacteria encode a wealth of natural product biosynthetic gene clusters (NPGCs), whose systematic study is complicated by numerous repetitive motifs. By combining several metrics we developed a method for global classification of these gene clusters into families (GCFs) and analyzed the biosynthetic capacity of Actinobacteria in 830 genome sequences, including 344 obtained for this project. The GCF network, comprised of 11,422 gene clusters grouped into 4,122 GCFs, was validated in hundreds of strains by correlating confident mass spectrometric detection of known small molecules with the presence/absence of their established biosynthetic gene clusters. The method also linked previously unassigned GCFs to known natural products, an approach that will enable de novo, bioassay-free discovery of novel natural products using large data sets. Extrapolation from the 830-genome dataset reveals that Actinobacteria encode hundreds of thousands of future drug leads, while the strong correlation between phylogeny and GCFs frames a roadmap to efficiently access them.
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7
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Lucas JMEX, Muffato M, Roest Crollius H. PhylDiag: identifying complex synteny blocks that include tandem duplications using phylogenetic gene trees. BMC Bioinformatics 2014; 15:268. [PMID: 25103980 PMCID: PMC4155083 DOI: 10.1186/1471-2105-15-268] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2014] [Accepted: 07/17/2014] [Indexed: 11/21/2022] Open
Abstract
Background Extant genomes share regions where genes have the same order and orientation, which are thought to arise from the conservation of an ancestral order of genes during evolution. Such regions of so-called conserved synteny, or synteny blocks, must be precisely identified and quantified, as a prerequisite to better understand the evolutionary history of genomes. Results Here we describe PhylDiag, a software that identifies statistically significant synteny blocks in pairwise comparisons of eukaryote genomes. Compared to previous methods, PhylDiag uses gene trees to define gene homologies, thus allowing gene deletions to be considered as events that may break the synteny. PhylDiag also accounts for gene orientations, blocks of tandem duplicates and lineage specific de novo gene births. Starting from two genomes and the corresponding gene trees, PhylDiag returns synteny blocks with gaps less than or equal to the maximum gap parameter gapmax. This parameter is theoretically estimated, and together with a utility to graphically display results, contributes to making PhylDiag a user friendly method. In addition, putative synteny blocks are subject to a statistical validation to verify that they are unlikely to be due to a random combination of genes. Conclusions We benchmark several known metrics to measure 2D-distances in a matrix of homologies and we compare PhylDiag to i-ADHoRe 3.0 on real and simulated data. We show that PhylDiag correctly identifies small synteny blocks even with insertions, deletions, incorrect annotations or micro-inversions. Finally, PhylDiag allowed us to identify the most relevant distance metric for 2D-distance calculation between homologies. Electronic supplementary material The online version of this article (doi:10.1186/1471-2105-15-268) contains supplementary material, which is available to authorized users.
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Affiliation(s)
| | | | - Hugues Roest Crollius
- Ecole Normale Supérieure, Institut de Biologie de l'ENS, IBENS, 46 rue d'Ulm, 75005 Paris, France.
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8
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Tiirikka T, Siermala M, Vihinen M. Clustering of gene ontology terms in genomes. Gene 2014; 550:155-64. [PMID: 24995610 DOI: 10.1016/j.gene.2014.06.060] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2012] [Revised: 06/26/2014] [Accepted: 06/27/2014] [Indexed: 01/08/2023]
Abstract
Although protein coding genes occupy only a small fraction of genomes in higher species, they are not randomly distributed within or between chromosomes. Clustering of genes with related function(s) and/or characteristics has been evident at several different levels. To study how common the clustering of functionally related genes is and what kind of functions the end products of these genes are involved, we collected gene ontology (GO) terms for complete genomes and developed a method to detect previously undefined gene clustering. Exhaustive analysis was performed for seven widely studied species ranging from human to Escherichia coli. To overcome problems related to varying gene lengths and densities, a novel method was developed and a fixed number of genes were analyzed irrespective of the genome span covered. Statistically very significant GO term clustering was apparent in all the investigated genomes. The analysis window, which ranged from 5 to 50 consecutive genes, revealed extensive GO term clusters for genes with widely varying functions. Here, the most interesting and significant results are discussed and the complete dataset for each analyzed species is available at the GOme database at http://bioinf.uta.fi/GOme. The results indicated that clusters of genes with related functions are very common, not only in bacteria, in which operons are frequent, but also in all the studied species irrespective of how complex they are. There are some differences between species but in all of them GO term clusters are common and of widely differing sizes. The presented method can be applied to analyze any genome or part of a genome for which descriptive features are available, and thus is not restricted to ontology terms. This method can also be applied to investigate gene and protein expression patterns. The results pave a way for further studies of mechanisms that shape genome structure and evolutionary forces related to them.
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Affiliation(s)
- Timo Tiirikka
- Institute of Biomedical Technology, University of Tampere, Finland and BioMediTech, FI-33014 Tampere, Finland; Medical Research Center Oulu, Oulu University Hospital, University of Oulu, Finland.
| | - Markku Siermala
- Institute of Biomedical Technology, University of Tampere, Finland and BioMediTech, FI-33014 Tampere, Finland.
| | - Mauno Vihinen
- Institute of Biomedical Technology, University of Tampere, Finland and BioMediTech, FI-33014 Tampere, Finland; Department of Experimental Medical Science, Lund University, SE-22 184 Lund, Sweden.
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9
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Abstract
Background Genes occurring co-localized in multiple genomes can be strong indicators for either functional constraints on the genome organization or remnant ancestral gene order. The computational detection of these patterns, which are usually referred to as gene clusters, has become increasingly sensitive over the past decade. The most powerful approaches allow for various types of imperfect cluster conservation: Cluster locations may be internally rearranged. The individual cluster locations may contain only a subset of the cluster genes and may be disrupted by uninvolved genes. Moreover cluster locations may not at all occur in some or even most of the studied genomes. The detection of such low quality clusters increases the risk of mistaking faint patterns that occur merely by chance for genuine findings. Therefore, it is crucial to estimate the significance of computational gene cluster predictions and discriminate between true conservation and coincidental clustering. Results In this paper, we present an efficient and accurate approach to estimate the significance of gene cluster predictions under the approximate common intervals model. Given a single gene cluster prediction, we calculate the probability to observe it with the same or a higher degree of conservation under the null hypothesis of random gene order, and add a correction factor to account for multiple testing. Our approach considers all parameters that define the quality of gene cluster conservation: the number of genomes in which the cluster occurs, the number of involved genes, the degree of conservation in the different genomes, as well as the frequency of the clustered genes within each genome. We apply our approach to evaluate gene cluster predictions in a large set of well annotated genomes.
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10
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An L, Ling H, Obradovic Z, Smith DJ, Megalooikonomou V. Learning pair-wise gene functional similarity by multiplex gene expression maps. BMC Bioinformatics 2012; 13 Suppl 3:S1. [PMID: 22536893 PMCID: PMC3375633 DOI: 10.1186/1471-2105-13-s3-s1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Background The relationships between the gene functional similarity and gene expression profile, and between gene function annotation and gene sequence have been studied extensively. However, not much work has considered the connection between gene functions and location of a gene's expression in the mammalian tissues. On the other hand, although unsupervised learning methods have been commonly used in functional genomics, supervised learning cannot be directly applied to a set of normal genes without having a target (class) attribute. Results Here, we propose a supervised learning methodology to predict pair-wise gene functional similarity from multiplex gene expression maps that provide information about the location of gene expression. The features are extracted from expression maps and the labels denote the functional similarities of pairs of genes. We make use of wavelet features, original expression values, difference and average values of neighboring voxels and other features to perform boosting analysis. The experimental results show that with increasing similarities of gene expression maps, the functional similarities are increased too. The model predicts the functional similarities between genes to a certain degree. The weights of the features in the model indicate the features that are more significant for this prediction. Conclusions By considering pairs of genes, we propose a supervised learning methodology to predict pair-wise gene functional similarity from multiplex gene expression maps. We also explore the relationship between similarities of gene maps and gene functions. By using AdaBoost coupled with our proposed weak classifier we analyze a large-scale gene expression dataset and predict gene functional similarities. We also detect the most significant single voxels and pairs of neighboring voxels and visualize them in the expression map image of a mouse brain. This work is very important for predicting functions of unknown genes. It also has broader applicability since the methodology can be applied to analyze any large-scale dataset without a target attribute and is not restricted to gene expressions.
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Affiliation(s)
- Li An
- Data Engineering Laboratory, Department of Computer and Information Sciences, Temple University, PA, USA.
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11
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Yi G, Jung J. Algorithm for large-scale clustering across multiple genomes. Bioinformation 2011; 7:251-6. [PMID: 22125394 PMCID: PMC3218420 DOI: 10.6026/97320630007251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2011] [Accepted: 10/24/2011] [Indexed: 11/23/2022] Open
Abstract
Identifying genomic regions that descended from a common ancestor helps us study the gene function and genome evolution. In distantly related genomes, clusters of homologous gene pairs are evidently used in function prediction, operon detection, etc. Currently, there are many kinds of computational methods that have been proposed defining gene clusters to identify gene families and operons. However, most of those algorithms are only available on a data set of small size. We developed an efficient gene clustering algorithm that can be applied on hundreds of genomes at the same time. This approach allows for large-scale study of evolutionary relationships of gene clusters and study of operon formation and destruction. An analysis of proposed algorithms shows that more biological insight can be obtained by analyzing gene clusters across hundreds of genomes, which can help us understand operon occurrences, gene orientations and gene rearrangements.
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Affiliation(s)
- Gangman Yi
- Samsung Electronics Co., Ltd. 416, Maetan 3-dong, Yeongtong-gu, Suwon-si, Gyeonggi-do, 442-742 Korea
| | - Jaehee Jung
- Samsung Electronics Co., Ltd. 416, Maetan 3-dong, Yeongtong-gu, Suwon-si, Gyeonggi-do, 442-742 Korea
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Grusea S. On the distribution of the number of cycles in the breakpoint graph of a random signed permutation. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2011; 8:1411-1416. [PMID: 21116045 DOI: 10.1109/tcbb.2010.123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
We use the finite Markov chain embedding technique to obtain the distribution of the number of cycles in the breakpoint graph of a random uniform signed permutation. This further gives a very good approximation of the distribution of the reversal distance between two random genomes.
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Affiliation(s)
- Simona Grusea
- LATP - UMR CNRS 6632, Equipe EBM, Université de Provence, France.
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13
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Grusea S, Pardoux E, Chabrol O, Pontarotti P. Compound Poisson Approximation and Testing for Gene Clusters with Multigene Families. J Comput Biol 2011; 18:579-94. [DOI: 10.1089/cmb.2010.0043] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022] Open
Affiliation(s)
- S. Grusea
- LATP–UMR CNRS 6632, Équipe Évolution Biologique et Modélisation, Université de Provence, Marseille, France
- Institut de Mathématiques de Toulouse, INSA de Toulouse, Université de Toulouse, Toulouse, France
| | - E. Pardoux
- LATP–UMR CNRS 6632, Équipe Évolution Biologique et Modélisation, Université de Provence, Marseille, France
| | - O. Chabrol
- LATP–UMR CNRS 6632, Équipe Évolution Biologique et Modélisation, Université de Provence, Marseille, France
| | - P. Pontarotti
- LATP–UMR CNRS 6632, Équipe Évolution Biologique et Modélisation, Université de Provence, Marseille, France
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14
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A conserved gene cluster as a putative functional unit in insect innate immunity. FEBS Lett 2010; 584:4375-8. [PMID: 20951134 DOI: 10.1016/j.febslet.2010.10.014] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2010] [Revised: 10/07/2010] [Accepted: 10/08/2010] [Indexed: 11/23/2022]
Abstract
The Nimrod gene superfamily is an important component of the innate immune response. The majority of its member genes are located in close proximity within the Drosophila melanogaster genome and they lie in a larger conserved cluster ("Nimrod cluster"), made up of non-related groups (families, superfamilies) of genes. This cluster has been a part of the Arthropod genomes for about 300-350 million years. The available data suggest that the Nimrod cluster is a functional module of the insect innate immune response.
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Baumbach J. On the power and limits of evolutionary conservation--unraveling bacterial gene regulatory networks. Nucleic Acids Res 2010; 38:7877-84. [PMID: 20699275 PMCID: PMC3001071 DOI: 10.1093/nar/gkq699] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
The National Center for Biotechnology Information (NCBI) recently announced ‘1000 prokaryotic genomes are now completed and available in the Genome database’. The increasing trend will provide us with thousands of sequenced microbial organisms over the next years. However, this is only the first step in understanding how cells survive, reproduce and adapt their behavior while being exposed to changing environmental conditions. One major control mechanism is transcriptional gene regulation. Here, striking is the direct juxtaposition of the handful of bacterial model organisms to the 1000 prokaryotic genomes. Next-generation sequencing technologies will further widen this gap drastically. However, several computational approaches have proven to be helpful. The main idea is to use the known transcriptional regulatory network of reference organisms as template in order to unravel evolutionarily conserved gene regulations in newly sequenced species. This transfer essentially depends on the reliable identification of several types of conserved DNA sequences. We decompose this problem into three computational processes, review the state of the art and illustrate future perspectives.
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Affiliation(s)
- Jan Baumbach
- Algorithms Group, International Computer Science Institute, Berkeley, USA.
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