BioSeq-Analysis2.0: an updated platform for analyzing DNA, RNA, and protein sequences at sequence level and residue level based on machine learning approaches

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Descriptions of BioSeq-Analysis2.0

Established in 2017, the platform BioSeq-Analysis (1) is for the first time proposed to analyze various biological sequences at sequence-level based on machine learning approaches, which can do the following main steps automatically: feature extraction, predictor construction, and performance evaluation. BioSeq-Analysis has been increasingly and extensively applied in many areas of computational biology.

Moreover, many new and powerful predictors in the field of computational biology were developed by using the BioSeq-Analysis, such as iLearn (2), QSPred-FL (3), etc.

As shown in Fig. 1, there are two main important tasks in the field of biological sequence analysis, including residue-level analysis and sequence-level analysis.The aim of the residue-level analysis task is to study the properties of the residues, for instance protein-protein interaction site prediction (4), protein disordered region prediction (5), N6-Methyladenosine site prediction (6), etc, while the aim of the sequence-level analysis task is to investigate the structure and function characteristics of the entire sequences, such as enhancer identification (7, 8), protein remote homology detection and fold recognition (9-12), recombination spot identification (13, 14), DNA or RNA binding protein identification (15, 16), etc. All these biological sequence analysis tasks share three main processes, including feature extraction, predictor construction, and performance evaluation. The BioSeq-Analysis mainly focuses on analysing biological sequences at the sequence-level, meaning that the BioSeq-Analysis can be only applied to the sequence-level analysis tasks. Can we construct an intelligent tool to generate predictors for both residue-level and sequence-level analysis by automatically implementing all the three processes listed in Fig. 1? To answer this question, we have decided to publish an important updated platform called BioSeq-Analysis2.0. Compared with BioSeq-Analysis and other existing tools, BioSeq-Analysis 2.0 has the following new and novel functions and features:

I. 26 new feature extraction methods at the residue-level were added, of which 7 for DNA residues (17-21), 6 for RNA residues (17-19, 22), and 13 for amino acid residues (11, 17, 18, 23-32). To the best of our knowledge, BioSeq-Analysis2.0 is the first web server proposed to generate various residue-level feature extraction methods. As a result,BioSeq-Analysis2.0 covers a total of 26 modes at the residue-level and 56 modes at the sequence-level.

II. For the residue-level analysis tasks, a sliding window approach was applied to extract the information of the sequential neighboring residues, and a sequence labelling model Conditional Random Field (CRF) was added in BioSeq-Analysis2.0 so as to capture the global sequence order information of residues.


Fig. 1 The flowchart of BioSeq-Analysis2.0.


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