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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Veröffentlicht in:	BMC bioinformatics Jg. 19; H. 1; S. 247 - 9
Hauptverfasser:	Stricker, Georg, Galinier, Mathilde, Gagneur, Julien
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London BioMed Central 27.06.2018 BioMed Central Ltd Springer Nature B.V BMC
Schlagworte:	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-Zugang:	Volltext
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