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

BMC Bioinformatics
Kazunori D Yamada, Kengo Kinoshita

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 us...Continue Reading

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Citations

Apr 8, 2021·Physica Medica : PM : an International Journal Devoted to the Applications of Physics to Medicine and Biology : Official Journal of the Italian Association of Biomedical Physics (AIFB)·Luigi MancoLidia Strigari

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Methods Mentioned

BETA
sequence-based prediction
Synthetic
sequence-based predictions

Software Mentioned

blastpgp search
BLAST DELTA
LSTM
DELTA
RPS
SPBuild
BLAST
Chainer
blastpgp
MIQS

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