BioSeq-Analysis: a platform for DNA, RNA and protein sequence analysis based on machine learning approaches

• Published in: Briefings in bioinformatics Vol. 20; no. 4; pp. 1280 - 1294
• Main Author: Liu, Bin
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 19.07.2019; Oxford Publishing Limited (England)
• Subjects: Algorithms; Amino acid sequence; Benchmarks; Bioinformatics; Biological analysis; Computational Biology - methods; Databases, Nucleic Acid - statistics & numerical data; Databases, Protein - statistics & numerical data; Datasets; Feature extraction; Humans; Internet; Learning algorithms; Machine Learning; Nucleotide sequence; Operating systems; Performance evaluation; Sequence analysis; Sequence Analysis - methods; Sequence Analysis - statistics & numerical data; Sequence Analysis, DNA - methods; Sequence Analysis, Protein - methods; Sequence Analysis, RNA - methods; Software; predictor construction; performance evaluation; machine learning; feature extraction; biological sequence analysis
• ISSN: 1467-5463; 1477-4054; 1477-4054
• DOI: 10.1093/bib/bbx165

Abstract With the avalanche of biological sequences generated in the post-genomic age, one of the most challenging problems is how to computationally analyze their structures and functions. Machine learning techniques are playing key roles in this field. Typically, predictors based on machine learning techniques contain three main steps: feature extraction, predictor construction and performance evaluation. Although several Web servers and stand-alone tools have been developed to facilitate the biological sequence analysis, they only focus on individual step. In this regard, in this study a powerful Web server called BioSeq-Analysis (http://bioinformatics.hitsz.edu.cn/BioSeq-Analysis/) has been proposed to automatically complete the three main steps for constructing a predictor. The user only needs to upload the benchmark data set. BioSeq-Analysis can generate the optimized predictor based on the benchmark data set, and the performance measures can be reported as well. Furthermore, to maximize user’s convenience, its stand-alone program was also released, which can be downloaded from http://bioinformatics.hitsz.edu.cn/BioSeq-Analysis/download/, and can be directly run on Windows, Linux and UNIX. Applied to three sequence analysis tasks, experimental results showed that the predictors generated by BioSeq-Analysis even outperformed some state-of-the-art methods. It is anticipated that BioSeq-Analysis will become a useful tool for biological sequence analysis.