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GO: :TermFinder---open source software for accessing Gene Ontology information and finding significantly enriched Gene Ontology terms associated with a list of genes

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TLDR
GO::TermFinder comprises a set of object-oriented Perl modules for accessing Gene Ontology information and evaluating and visualizing the collective annotation of a list of genes to GO terms, which can be used to draw conclusions from microarray and other biological data.
Abstract
Summary: GO::TermFinder comprises a set of object-oriented Perl modules for accessing Gene Ontology (GO) information and evaluating and visualizing the collective annotation of a list of genes to GO terms. It can be used to draw conclusions from microarray and other biological data, calculating the statistical significance of each annotation. GO::TermFinder can be used on any system on which Perl can be run, either as a command line application, in single or batch mode, or as a web-based CGI script. Availability: The full source code and documentation for GO::TermFinder are freely available from http://search.cpan.org/dist/GO-TermFinder/

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clusterProfiler: an R Package for Comparing Biological Themes Among Gene Clusters

TL;DR: An R package, clusterProfiler that automates the process of biological-term classification and the enrichment analysis of gene clusters and can be easily extended to other species and ontologies is presented.
Journal ArticleDOI

Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists

TL;DR: The survey will help tool designers/developers and experienced end users understand the underlying algorithms and pertinent details of particular tool categories/tools, enabling them to make the best choices for their particular research interests.
Journal ArticleDOI

Enrichr: a comprehensive gene set enrichment analysis web server 2016 update

TL;DR: A significant update to one of the tools in this domain called Enrichr, a comprehensive resource for curated gene sets and a search engine that accumulates biological knowledge for further biological discoveries is presented.
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Uncovering the overlapping community structure of complex networks in nature and society

TL;DR: After defining a set of new characteristic quantities for the statistics of communities, this work applies an efficient technique for exploring overlapping communities on a large scale and finds that overlaps are significant, and the distributions introduced reveal universal features of networks.
Journal ArticleDOI

ClueGO: a Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks

TL;DR: ClueGO is an easy to use Cytoscape plug-in that strongly improves biological interpretation of large lists of genes and creates a functionally organized GO/pathway term network.
References
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Journal ArticleDOI

Controlling the false discovery rate: a practical and powerful approach to multiple testing

TL;DR: In this paper, a different approach to problems of multiple significance testing is presented, which calls for controlling the expected proportion of falsely rejected hypotheses -the false discovery rate, which is equivalent to the FWER when all hypotheses are true but is smaller otherwise.
Journal ArticleDOI

Gene Ontology: tool for the unification of biology

TL;DR: The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing.

Biometery: The principles and practice of statistics in biological research

TL;DR: In this paper, the authors present a model for the analysis of variance in a single-classification and two-way and multiway analysis of Variance with the assumption of correlation.
Book

Biometry: The Principles and Practice of Statistics in Biological Research

TL;DR: In this paper, the authors present a model for the analysis of variance in a single-classification and two-way and multiway analysis of Variance with the assumption of correlation.
Journal ArticleDOI

Comprehensive Identification of Cell Cycle–regulated Genes of the Yeast Saccharomyces cerevisiae by Microarray Hybridization

TL;DR: A comprehensive catalog of yeast genes whose transcript levels vary periodically within the cell cycle is created, and it is found that the mRNA levels of more than half of these 800 genes respond to one or both of these cyclins.
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