ArticleBioinformatics advances2026
AmalgaMo: flexible DNA motif merging.
Article in Bioinformatics advances, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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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
1 citing paper in PubMed.
- Refining sequence-to-expression modelling with chromatin accessibility.Bioinformatics (Oxford, England) · 2026Article
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.