BABELOMICS: a systems biology perspective in the functional annotation of genome-scale experiments

By A. Chande. Nucleic Acids Res., 2006

317 citations recorded by the source corpus.

We present a new version of Babelomics, a complete suite of web tools for functional analysis of genome-scale experiments, with new and improved tools. New functionally relevant terms have been included such as CisRed motifs or bioentities obtained by text-mining procedures. An improved indexing has considerably speeded up several of the modules. An improved version of the FatiScan method for studying the coordinate behaviour of groups of functionally related genes is presented, along with a similar tool, the Gene Set Enrichment Analysis. Babelomics is now more oriented to test systems biology inspired hypotheses. Babelomics can be found at .

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/b5959ed4a1c5ad9e07fb086e4114f4574ebadc2a

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Citations in the corpus

The human phylome (2007, methodology): “We performed this analysis automatically with the aid of the program Fatigo+, from the Babelomics suite [56].”
Gene Set Knowledge Discovery with Enrichr (2021, unknown): “…Kuehn, Gould, Tamayo, & Mesirov, 2007), WebGestalt (Zhang, Kirov, & Snoddy, 2005), Metascape (Zhou et al., 2019), Gorilla (Eden, Navon, Steinfeld, Lip- son, & Yakhini, 2009), ConsensusPathDB (Kamburov, Stelzl, Lehrach, & Herwig, 2013), Babelomics (Al-Shahrour et al., 2006), and Enrichr.”
Significance Analysis of Spectral Count Data in Label-free Shotgun Proteomics*S (2008, methodology): “Interpretation of data was assisted by two annotation tools, FATIGO (cid:1) (30) and DAVID (31).”
Gene-set approach for expression pattern analysis (2008, unknown): “org [45] Catmap H Perl script http://bioinfo.”
High-coverage whole-genome sequencing of the expanded 1000 Genomes Project cohort including 602 trios (2021, unknown): “The 1kGP resources have been collectively cited over 16,000 times to date and have been utilized for foundational applications such as genotype imputation, eQTL mapping, variant pathogenicity prioritization, population history, and evolutionary genetics studies (Almeida et al., 2014; Hara et al., 20”
Graph Neural Networks and Their Current Applications in Bioinformatics (2021, unknown): “, 2021) and disease prediction (Parisot et al., 2017; Kazi et al., 2018, 2019a,b,c; Anirudh and Thiagarajan, 2019; Yang et al., 2019; Stankevičiūtė et al., 2020; Zhang and Pierre, 2020; Zhang et al., 2021), etc.”
Lineage-specific polycomb targets and de novo DNA methylation define restriction and potential of neuronal progenitors. (2008, unknown): “individual tools use (Al-Shahrour et al., 2006; Beissbarth and Speed, 2004; Boyle et al., 2004).”