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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Summary: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 genomes such as mammalian genomes. Results Here we present GenoGAM 2.0, a scalable and efficient implementation that is 2 to 3 orders of magnitude faster than the previous version. This is achieved by exploiting the sparsity of the model using the SuperLU direct solver for parameter fitting, and sparse Cholesky factorization together with the sparse inverse subset algorithm for computing standard errors. Furthermore the HDF5 library is employed to store data efficiently on hard drive, reducing memory footprint while keeping I/O low. Whole-genome fits for human ChIP-seq datasets (ca. 300 million parameters) could be obtained in less than 9 hours on a standard 60-core server. GenoGAM 2.0 is implemented as an open source R package and currently available on GitHub. A Bioconductor release of the new version is in preparation. Conclusions We have vastly improved the performance of the GenoGAM framework, opening up its application to all types of organisms. Moreover, our algorithmic improvements for fitting large GAMs could be of interest to the statistical community beyond the genomics field.
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ISSN:1471-2105
1471-2105
DOI:10.1186/s12859-018-2238-7