Phylogenetic Profiling for Probing the Modular Architecture of the Human Genome

Dey, G.K.; Meyer, Tobias

doi:10.1016/j.cels.2015.08.006

Cited by 22 publications

(20 citation statements)

References 96 publications

(132 reference statements)

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“…The variability between clades and the complexity of evolution make it reasonable to assume that co-evolution of genes might not be reflected by the PP signals across the full tree of life. These signals may instead be hidden ‘locally’ in specific clades ( 14 , 26 , 27 ). ‘Clade-wise’ PP analysis aims to detect local co-evolution signals.…”

Section: Introductionmentioning

confidence: 99%

CladeOScope: functional interactions through the prism of clade-wise co-evolution

Tsaban

Stupp

Sherill-Rofe

et al. 2021

NAR Genomics and Bioinformatics

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Mapping co-evolved genes via phylogenetic profiling (PP) is a powerful approach to uncover functional interactions between genes and to associate them with pathways. Despite many successful endeavors, the understanding of co-evolutionary signals in eukaryotes remains partial. Our hypothesis is that ‘Clades’, branches of the tree of life (e.g. primates and mammals), encompass signals that cannot be detected by PP using all eukaryotes. As such, integrating information from different clades should reveal local co-evolution signals and improve function prediction. Accordingly, we analyzed 1028 genomes in 66 clades and demonstrated that the co-evolutionary signal was scattered across clades. We showed that functionally related genes are frequently co-evolved in only parts of the eukaryotic tree and that clades are complementary in detecting functional interactions within pathways. We examined the non-homologous end joining pathway and the UFM1 ubiquitin-like protein pathway and showed that both demonstrated distinguished co-evolution patterns in specific clades. Our research offers a different way to look at co-evolution across eukaryotes and points to the importance of modular co-evolution analysis. We developed the ‘CladeOScope’ PP method to integrate information from 16 clades across over 1000 eukaryotic genomes and is accessible via an easy to use web server at http://cladeoscope.cs.huji.ac.il.

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Section: Introductionmentioning

confidence: 99%

CladeOScope: functional interactions through the prism of clade-wise co-evolution

Tsaban

Stupp

Sherill-Rofe

et al. 2021

NAR Genomics and Bioinformatics

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“…The similarity analysis of protein sequences is one of the major topics in bioinformatics. It has many applications in the study of protein evolution and functions, as well as gene annotation, gene function prediction, identification and construction of gene families, and gene discovery [ 1 ].…”

Section: Introductionmentioning

confidence: 99%

FEGS: a novel feature extraction model for protein sequences and its applications

Zhang

et al. 2021

BMC Bioinformatics

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Background Feature extraction of protein sequences is widely used in various research areas related to protein analysis, such as protein similarity analysis and prediction of protein functions or interactions. Results In this study, we introduce FEGS (Feature Extraction based on Graphical and Statistical features), a novel feature extraction model of protein sequences, by developing a new technique for graphical representation of protein sequences based on the physicochemical properties of amino acids and effectively employing the statistical features of protein sequences. By fusing the graphical and statistical features, FEGS transforms a protein sequence into a 578-dimensional numerical vector. When FEGS is applied to phylogenetic analysis on five protein sequence data sets, its performance is notably better than all of the other compared methods. Conclusion The FEGS method is carefully designed, which is practically powerful for extracting features of protein sequences. The current version of FEGS is developed to be user-friendly and is expected to play a crucial role in the related studies of protein sequence analyses.

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“…In order to find the hierarchical modular structure in the network, several methods to obtain modules from a given network have been developed (Girvan and Newman, 2002 ; Clauset et al, 2004 ; Adamcsek et al, 2006 ; Alves, 2007 ; Fortunato and Barthelemy, 2007 ; Arenas et al, 2008 ; Amini et al, 2013 for an in-depth revision, see Fortunato, 2010 ). Additionally, several of these algorithms have been used to find modules into gene regulatory networks (Dey and Meyer, 2015 ; Binder et al, 2016 ; Bonsang-Kitzis et al, 2016 ; Feng et al, 2016 ; Miecznikowski et al, 2016 ). For this work, we decide to use the generalization of the map equation , developed by Rosvall and Bergstrom (Rosvall and Bergstrom, 2011 ), as this is one of the best performing network partition algorithms as revealed by several stringent benchmark tests (Lancichinetti et al, 2009 ).…”

Section: Methodsmentioning

confidence: 99%

The Hierarchical Modular Structure of HER2+ Breast Cancer Network

et al. 2018

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HER2-enriched breast cancer is a complex disease characterized by the overexpression of the ERBB2 amplicon. While the effects of this genomic aberration on the pathology have been studied, genome-wide deregulation patterns in this subtype of cancer are also observed. A novel approach to the study of this malignant neoplasy is the use of transcriptional networks. These networks generally exhibit modular structures, which in turn may be associated to biological processes. This modular regulation of biological functions may also exhibit a hierarchical structure, with deeper levels of modular organization accounting for more specific functional regulation. In this work, we identified the most probable (maximum likelihood) model of the hierarchical modular structure of the HER2-enriched transcriptional network as reconstructed from gene expression data, and analyzed the statistical associations of modules and submodules to biological functions. We found modular structures, independent from direct ERBB2 amplicon regulation, involved in different biological functions such as signaling, immunity, and cellular morphology. Higher resolution submodules were identified in more specific functions, such as micro-RNA regulation and the activation of viral-like immune response. We propose the approach presented here as one that may help to unveil mechanisms involved in the development of the pathology.

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Phylogenetic Profiling for Probing the Modular Architecture of the Human Genome

Cited by 22 publications

References 96 publications

CladeOScope: functional interactions through the prism of clade-wise co-evolution

CladeOScope: functional interactions through the prism of clade-wise co-evolution

FEGS: a novel feature extraction model for protein sequences and its applications

The Hierarchical Modular Structure of HER2+ Breast Cancer Network

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