RT Journal Article
SR Electronic
T1 An Experiment in Learning the Language of Sequence Motifs: Sequence Logos vs. Finite-State Machines
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 143024
DO 10.1101/143024
A1 Alexandre P Francisco
A1 Travis Gagie
A1 Dominik Kempa
A1 Leena Salmela
A1 Sophie Sverdlov
A1 Jarkko Toivonen
A1 Esko Ukkonen
YR 2017
UL http://biorxiv.org/content/early/2017/05/27/143024.abstract
AB Position weight matrices (PWMs) are the standard way to model binding site affinities in bioinformatics. However, they assume that symbol occurrences are position independent and, hence, they do not take into account symbols co-occurrence at different sequence positions. To address this problem, we propose to construct finite-state machines (FSMs) instead. A modified version of the Evidence-Driven State Merging (EDSM) heuristic is used to reduce the number of states as FSMs grow too quickly as a function of the number of sequences to reveal any useful structure. We tested our approach on sequence data for the transcription factor HNF4 and found out that the constructed FSMs provide small representations and an intuitive visualization. Furthermore, the FSM was better than PWMs at discriminating the positive and negative sequences in our data set.