ArticleBioinformatics (Oxford, England)2018
YAMDA: thousandfold speedup of EM-based motif discovery using deep learning libraries and GPU.
Article in Bioinformatics (Oxford, England), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- ExplaiNN: interpretable and transparent neural networks for genomics.Genome biology · 2023Article
- GPrimer: a fast GPU-based pipeline for primer design for qPCR experiments.BMC bioinformatics · 2021Article
- Recent Advances of Deep Learning in Bioinformatics and Computational Biology.Frontiers in genetics · 2019Review
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Authors and funding
3 authors.
Funding
Abstract
Motivation: Motif discovery in large biopolymer sequence datasets can be computationally demanding, presenting significant challenges for discovery in omics research. MEME, arguably one of the most popular motif discovery software, takes quadratic time with respect to dataset size, leading to excessively long runtimes for large datasets. Therefore, there is a demand for fast programs that can generate results of the same quality as MEME. Results: Here we describe YAMDA, a highly scalable motif discovery software package. It is built on Pytorch, a tensor computation deep learning library with strong GPU acceleration that is highly optimized for tensor operations that are also useful for motifs. YAMDA takes linear time to find motifs as accurately as MEME, completing in seconds or minutes, which translates to speedups over a thousandfold. Availability and implementation: YAMDA is freely available on Github (https://github.com/daquang/YAMDA). Supplementary information: Supplementary data are available at Bioinformatics online.
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Registered trials
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