2026-08-11T17:50:18Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:7647 2024-03-06T10:41:28Z ddc:004 open_access

Seed-driven Learning of Position Probability Matrices from Large Sequence Sets Toivonen, Jarkko Taipale, Jussi Ukkonen, Esko motif finding transcription factor binding site sequence analysis Hamming distance seed We formulate and analyze a novel seed-driven algorithm SeedHam for PPM learning. To learn a PPM of length l, the algorithm uses the most frequent l-mer of the training data as a seed, and then restricts the learning into a small Hamming neighbourhood of the seed. The SeedHam method is intended for PPM learning from large sequence sets (up to hundreds of Mbases) containing enriched motif instances. A robust variant of the method is introduced that decreases contamination from artefact instances of the motif and thereby allows using larger Hamming neighbourhoods. To solve the motif orientation problem in two-stranded DNA we introduce a novel seed finding rule, based on analysis of the palindromic structure of sequences. Test experiments are reported, that illustrate the relative strengths of different variants of our methods, and show that our algorithms are fast and give stable and accurate results. Availability and implementation: A C++ implementation of the method is available from https://github.com/jttoivon/seedham/ Contact: jarkko.toivonen@cs.helsinki.fi Schloss Dagstuhl – Leibniz-Zentrum für Informatik Jarkko Toivonen and Jussi Taipale and Esko Ukkonen 2017 Is Part Of LIPIcs, Volume 88, 17th International Workshop on Algorithms in Bioinformatics (WABI 2017) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/LIPIcs.WABI.2017.25 urn:nbn:de:0030-drops-76470 https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.WABI.2017.25 eng https://creativecommons.org/licenses/by/3.0/legalcode