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

Overview of attention for article published in BMC Bioinformatics, June 2018
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Title
GenoGAM 2.0: scalable and efficient implementation of genome-wide generalized additive models for gigabase-scale genomes
Published in
BMC Bioinformatics, June 2018
DOI 10.1186/s12859-018-2238-7
Pubmed ID
Authors

Georg Stricker, Mathilde Galinier, Julien Gagneur

Abstract

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. 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. 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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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 22 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 22 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 5 23%
Other 4 18%
Student > Ph. D. Student 2 9%
Professor > Associate Professor 2 9%
Student > Master 2 9%
Other 3 14%
Unknown 4 18%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 7 32%
Agricultural and Biological Sciences 7 32%
Medicine and Dentistry 2 9%
Computer Science 1 5%
Mathematics 1 5%
Other 0 0%
Unknown 4 18%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 09 October 2019.
All research outputs
#18,345,702
of 23,577,761 outputs
Outputs from BMC Bioinformatics
#6,094
of 7,418 outputs
Outputs of similar age
#239,696
of 330,265 outputs
Outputs of similar age from BMC Bioinformatics
#74
of 98 outputs
Altmetric has tracked 23,577,761 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,418 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 12th percentile – i.e., 12% of its peers scored the same or lower than it.
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