Taipale, Jussi

Toivonen, Jarkko

Ukkonen, Esko

Helsingin yliopiston digitaalinen arkisto

Seed-driven Learning of Position Probability Matrices from Large Sequence Sets

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.f

Dagstuhl Research Online Publication Server

https://helda.helsinki.fi/bitstream/10138/232540/1/LIPIcs_WABI_2017_25.pdf

Seed-driven Learning of Position Probability Matrices from Large Sequence Sets

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Helsingin yliopiston digitaalinen arkisto

Dagstuhl Research Online Publication Server