ArticleBMC bioinformatics2021
Fast and exact quantification of motif occurrences in biological sequences.
Article in BMC bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Explainable AI in Genomics: Transcription Factor Binding Site Prediction with Mixture of Experts.ArXiv · 2025Article
- Optimizing resource utilization for large scale problems through architecture aware scheduling.Scientific reports · 2024Article
- An average-case efficient two-stage algorithm for enumerating all longest common substrings of minimum lengthProceedings. IEEE International Conference on Healthcare Informatics · 2024Article
- OCTOPUS: Disk-based, Multiplatform, Mobile-friendly Metagenomics Classifier.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
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3 authors.
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Abstract
backgroundIdentification of motifs and quantification of their occurrences are important for the study of genetic diseases, gene evolution, transcription sites, and other biological mechanisms. Exact formulae for estimating count distributions of motifs under Markovian assumptions have high computational complexity and are impractical to be used on large motif sets. Approximated formulae, e.g. based on compound Poisson, are faster, but reliable p value calculation remains challenging. Here, we introduce 'motif_prob', a fast implementation of an exact formula for motif count distribution through progressive approximation with arbitrary precision. Our implementation speeds up the exact calculation, usually impractical, making it feasible and posit to substitute currently employed heuristics.
resultsWe implement motif_prob in both Perl and C+ + languages, using an efficient error-bound iterative process for the exact formula, providing comparison with state-of-the-art tools (e.g. MoSDi) in terms of precision, run time benchmarks, along with a real-world use case on bacterial motif characterization. Our software is able to process a million of motifs (13-31 bases) over genome lengths of 5 million bases within the minute on a regular laptop, and the run times for both the Perl and C+ + code are several orders of magnitude smaller (50-1000× faster) than MoSDi, even when using their fast compound Poisson approximation (60-120× faster). In the real-world use cases, we first show the consistency of motif_prob with MoSDi, and then how the p-value quantification is crucial for enrichment quantification when bacteria have different GC content, using motifs found in antimicrobial resistance genes. The software and the code sources are available under the MIT license at https://github.com/DataIntellSystLab/motif_prob .
conclusionsThe motif_prob software is a multi-platform and efficient open source solution for calculating exact frequency distributions of motifs. It can be integrated with motif discovery/characterization tools for quantifying enrichment and deviation from expected frequency ranges with exact p values, without loss in data processing efficiency.
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