PSSMCOOL: a comprehensive R package for generating evolutionary-based descriptors of protein sequences from PSSM profiles

Author:

Mohammadi Alireza1,Zahiri Javad23ORCID,Mohammadi Saber1ORCID,Khodarahmi Mohsen456,Arab Seyed Shahriar7

Affiliation:

1. Bioinformatics and Computational Omics Lab (BioCOOL), Department of Biophysics, Faculty of Biological Sciences, Tarbiat Modares University, Tehran 14115111, Iran

2. Department of Neuroscience, University of California San Diego, San Diego, CA 92093, USA

3. Department of Pediatrics, University of California San Diego, La Jolla, CA 92093, USA

4. Department of Radiology, Shahid Madani Hospital, Karaj 44693, Iran

5. Bahar Medical Imaging Center, Karaj 3144615931, Iran

6. Dr. Khodarahmi Medical Imaging Center, Karaj 3144615931, Iran

7. Department of Biophysics, Faculty of Biological Sciences, Tarbiat Modares University, Tehran 14115111, Iran

Abstract

Abstract Position-specific scoring matrix (PSSM), also called profile, is broadly used for representing the evolutionary history of a given protein sequence. Several investigations reported that the PSSM-based feature descriptors can improve the prediction of various protein attributes such as interaction, function, subcellular localization, secondary structure, disorder regions, and accessible surface area. While plenty of algorithms have been suggested for extracting evolutionary features from PSSM in recent years, there is not any integrated standalone tool for providing these descriptors. Here, we introduce PSSMCOOL, a flexible comprehensive R package that generates 38 PSSM-based feature vectors. To our best knowledge, PSSMCOOL is the first PSSM-based feature extraction tool implemented in R. With the growing demand for exploiting machine-learning algorithms in computational biology, this package would be a practical tool for machine-learning predictions.

Publisher

Oxford University Press (OUP)

Subject

General Agricultural and Biological Sciences,General Biochemistry, Genetics and Molecular Biology

Reference52 articles.

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