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OrthoClust: an orthology-based network framework for clustering data across multiple species

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TLDR
OrthoClust is a computational framework that integrates the co-association networks of individual species by utilizing the orthology relationships of genes between species and outputs optimized modules that are fundamentally cross-species, which can either be conserved or species-specific.
Abstract
Increasingly, high-dimensional genomics data are becoming available for many organisms.Here, we develop OrthoClust for simultaneously clustering data across multiple species. OrthoClust is a computational framework that integrates the co-association networks of individual species by utilizing the orthology relationships of genes between species. It outputs optimized modules that are fundamentally cross-species, which can either be conserved or species-specific. We demonstrate the application of OrthoClust using the RNA-Seq expression profiles of Caenorhabditis elegans and Drosophila melanogaster from the modENCODE consortium. A potential application of cross-species modules is to infer putative analogous functions of uncharacterized elements like non-coding RNAs based on guilt-by-association.

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Comparative analysis of the transcriptome across distant species

Mark Gerstein, +107 more
- 28 Aug 2014 - 
TL;DR: It is found in all three organisms that the gene-expression levels, both coding and non-coding, can be quantitatively predicted from chromatin features at the promoter using a ‘universal model’ based on a single set of organism-independent parameters.
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The yeast coexpression network has a small-world, scale-free architecture and can be explained by a simple model

TL;DR: A new model is derived based on the observation that there is a positive correlation between the sequence similarity of paralogues and their probability of coexpression or sharing of transcription factor binding sites (TFBSs) that reproduces the scale‐free, small‐world architecture of the coregulation network and the homology relations between coregulated genes without the need for selection.
Journal ArticleDOI

Discerning molecular interactions: A comprehensive review on biomolecular interaction databases and network analysis tools.

TL;DR: The distinctive attribute mentioned in this review is not only to provide an overview of tools and web servers for gene and protein-protein interaction (PPI) network analysis but also to extract useful and meaningful information from the interaction networks.
Journal ArticleDOI

Mapping single-cell atlases throughout Metazoa unravels cell type evolution

TL;DR: In this paper, a self-assembling manifold (SAM) algorithm is used to robustly reconstruct manifolds from single-cell data and to identify homologous cell types with shared expression programs across distant species within phyla.
References
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Journal ArticleDOI

Cluster analysis and display of genome-wide expression patterns

TL;DR: A system of cluster analysis for genome-wide expression data from DNA microarray hybridization is described that uses standard statistical algorithms to arrange genes according to similarity in pattern of gene expression, finding in the budding yeast Saccharomyces cerevisiae that clustering gene expression data groups together efficiently genes of known similar function.
Journal ArticleDOI

WGCNA: an R package for weighted correlation network analysis.

TL;DR: The WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis that includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software.
Journal ArticleDOI

Fast unfolding of communities in large networks

TL;DR: This work proposes a heuristic method that is shown to outperform all other known community detection methods in terms of computation time and the quality of the communities detected is very good, as measured by the so-called modularity.
Journal ArticleDOI

Mapping and quantifying mammalian transcriptomes by RNA-Seq.

TL;DR: Although >90% of uniquely mapped reads fell within known exons, the remaining data suggest new and revised gene models, including changed or additional promoters, exons and 3′ untranscribed regions, as well as new candidate microRNA precursors.
Journal ArticleDOI

RNA-Seq: a revolutionary tool for transcriptomics

TL;DR: The RNA-Seq approach to transcriptome profiling that uses deep-sequencing technologies provides a far more precise measurement of levels of transcripts and their isoforms than other methods.
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