Representation learning applications in biological sequence analysis

Hitoshi Iuchi; Taro Matsutani; Keisuke Yamada; Natsuki Iwano; Shunsuke Sumi; Shion Hosoda; Shitao Zhao; Tsukasa Fukunaga; Michiaki Hamada

doi:10.1016/j.csbj.2021.05.039

Representation learning applications in biological sequence analysis

Comput Struct Biotechnol J. 2021 May 23:19:3198-3208. doi: 10.1016/j.csbj.2021.05.039. eCollection 2021.

Authors

Hitoshi Iuchi^{1

2}, Taro Matsutani^{2

3}, Keisuke Yamada⁴, Natsuki Iwano³, Shunsuke Sumi^{3

5}, Shion Hosoda^{2

3}, Shitao Zhao¹, Tsukasa Fukunaga^{6

7}, Michiaki Hamada^{2

3

4

8}

Affiliations

¹ Waseda Research Institute for Science and Engineering, Waseda University, Tokyo 169-8555, Japan.
² Computational Bio Big-Data Open Innovation Laboratory (CBBD-OIL), National Institute of Advanced Industrial Science and Technology (AIST), Tokyo 169-8555, Japan.
³ Graduate School of Advanced Science and Engineering, Waseda University, Tokyo 169-8555, Japan.
⁴ School of Advanced Science and Engineering, Waseda University, Tokyo 169-8555, Japan.
⁵ Department of Life Science Frontiers, Center for iPS Cell Research and Application, Kyoto University, Kyoto 606-8507, Japan.
⁶ Waseda Institute for Advanced Study, Waseda University, Tokyo 169-0051, Japan.
⁷ Department of Computer Science, Graduate School of Information Science and Technology, The University of Tokyo, Tokyo 113-0032, Japan.
⁸ Graduate School of Medicine, Nippon Medical School, Tokyo 113-8602, Japan.

Abstract

Although remarkable advances have been reported in high-throughput sequencing, the ability to aptly analyze a substantial amount of rapidly generated biological (DNA/RNA/protein) sequencing data remains a critical hurdle. To tackle this issue, the application of natural language processing (NLP) to biological sequence analysis has received increased attention. In this method, biological sequences are regarded as sentences while the single nucleic acids/amino acids or k-mers in these sequences represent the words. Embedding is an essential step in NLP, which performs the conversion of these words into vectors. Specifically, representation learning is an approach used for this transformation process, which can be applied to biological sequences. Vectorized biological sequences can then be applied for function and structure estimation, or as input for other probabilistic models. Considering the importance and growing trend for the application of representation learning to biological research, in the present study, we have reviewed the existing knowledge in representation learning for biological sequence analysis.

Keywords: BERT; Natural language processing; Representation learning; Sequence analysis; Word2vec.

Publication types

Review