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Published on: February 6, 2020
AmalgaMo: flexible DNA motif merging
Orsolya Lapohos1,2,3, Gregory J Fonseca4
1Meakins-Christie Laboratories, Research Institute of the McGill University Health Centre, Montreal, Quebec, H4A 3J1, Canada.
Motivation:
Inference of candidate upstream regulators via motif enrichment analysis is a common step in the interpretation of genomic data. However, redundancy in motif databases can negatively impact predictive value, especially when relying on regression-based motif enrichment analysis. Although various forms of motif clustering have been used to mitigate problems caused by redundancy, an algorithm optimized for downstream regression-based analysis is needed.
Results:
We introduce AmalgaMo, an efficient and flexible command-line tool for merging highly similar motifs. Using publicly available human datasets, we demonstrate that merging motifs with our optimized settings greatly benefits regression-based motif enrichment analysis and provide detailed documentation that can serve as a reference for researchers inferring upstream regulators from genomic data.
Availability And Implementation:
AmalgaMo is available on GitHub at https://github.com/lapohosorsolya/AmalgaMo.
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