De novo profile generation based on sequence context specificity with the long short-term memory network release_axndrte7gjactpjd6u4rc32wvm

by Kazunori D Yamada, Kengo Kinoshita

Released as a post by Cold Spring Harbor Laboratory.

2017  

Abstract

Long short-term memory (LSTM) is one of the most attractive deep learning methods to learn time series or contexts of input data. Increasing studies, including biological sequence analyses in bioinformatics, utilize this architecture. Amino acid sequence profiles are widely used for bioinformatics studies, such as sequence similarity searches, multiple alignments, and evolutionary analyses. Currently, many biological sequences are becoming available, and the rapidly increasing amount of sequence data emphasizes the importance of scalable generators of amino acid sequence profiles. We employed the LSTM network and developed a novel profile generator to construct profiles without any assumptions, except for input sequence context. Our method could generate better profiles than existing de novo profile generators, including CSBuild and RPS-BLAST, on the basis of profile-sequence similarity search performance with linear calculation costs against input sequence size. In addition, we analyzed the effects of the memory power of LSTM and found that LSTM had high potential power to detect long-range interactions between amino acids, as in the case of beta-strand formation, which has been a difficult problem in protein bioinformatics using sequence information. We demonstrated the importance of sequence context and the feasibility of LSTM on biological sequence analyses. Our results demonstrated the effectiveness of memories in LSTM and showed that our de novo profile generator, SPBuild, achieved higher performance than that of existing methods for profile prediction of beta-strands, where long-range interactions of amino acids are important and are known to be difficult for the existing window-based prediction methods. Our findings will be useful for the development of other prediction methods related to biological sequences by machine learning methods.
In application/xml+jats format

Archived Files and Locations

application/pdf  781.1 kB
file_fcgmrvjtknhj7o4sxjym3ewhra
www.biorxiv.org (repository)
web.archive.org (webarchive)
application/pdf  744.2 kB
file_4255gwovsfbxleyrsaimbka32i
www.biorxiv.org (repository)
web.archive.org (webarchive)
application/pdf  744.1 kB
file_umvcqz6m4jcgvhljffpt7ngyfq
web.archive.org (webarchive)
www.biorxiv.org (web)
application/pdf  781.9 kB
file_qv5n2zuaszdnbkzlybauyra4km
web.archive.org (webarchive)
www.biorxiv.org (web)
Read Archived PDF
Preserved and Accessible
Type  post
Stage   unknown
Date   2017-12-28
Work Entity
access all versions, variants, and formats of this works (eg, pre-prints)
Catalog Record
Revision: 13aa7957-169f-4848-952c-15666a4d53e5
API URL: JSON