GenoGAM 2.0: scalable and efficient implementation of genome-wide generalized additive models for gigabase-scale genomes

Background GenoGAM (Genome-wide generalized additive models) is a powerful statistical modeling tool for the analysis of ChIP-Seq data with flexible factorial design experiments. However large runtime and memory requirements of its current implementation prohibit its application to gigabase-scale ge...

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Bibliographic Details
Published in:	BMC bioinformatics Vol. 19; no. 1; pp. 247 - 9
Main Authors:	Stricker, Georg, Galinier, Mathilde, Gagneur, Julien
Format:	Journal Article
Language:	English
Published:	London BioMed Central 27.06.2018 BioMed Central Ltd Springer Nature B.V BMC
Subjects:	Algorithms Applied mathematics Binomial distribution Bioinformatics Biomedical and Life Sciences ChIP-Seq Cholesky factorization Chromatin Computational Biology/Bioinformatics Computer Appl. in Life Sciences Computer memory Data processing Factorial design Generalized additive models Genome-wide analysis Genomes Genomic libraries Genomics Genomics - methods Humans Life Sciences Mathematical models Microarrays Parameters Proteins Results and data Scale (ratio) Software Sparse inverse subset algorithm Sparsity Statistical analysis Statistical models Transcription factors Yeast Transcription factors ChIP-Seq Sparse inverse subset algorithm Generalized additive models Genome-wide analysis
ISSN:	1471-2105, 1471-2105
Online Access:	Get full text
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