May 2018 Vol.6 No.4

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Original Research Article

Computational Proteomic Data Mining of Heat Shock Protein-β1 using Mathematica Software

Rashid Saif^{1, 2*}, Saeeda Zia^{2, 3}, Kinza Qazi⁴, Tania Mahmood¹, Fatima Asif¹, Aniqa Ejaz¹ and Talha Tamseel⁴

¹Institute of Biotechnology, Gulab Devi Educational Complex, Ferozepur Road, Lahore, Pakistan
²Decode Genomics, 264-Q, Johar Town, Lahore, Pakistan
³Department of Mathematics, National University of Computer and Emerging Sciences, Lahore, Pakistan
⁴Department of Bioinformatics and Computational Biology, Virtual University of Pakistan, Lahore, Pakistan

*Corresponding Author’s E-mail: rashid.saif37@gmail.com

Accepted April 02, 2018

Abstract

Omics data mining approach helps us to discover the hidden patterns of today’s molecular life. Current study is conducted to compare and analyze 75 homologous Hspb1 protein sequences from distinct 62 species. Multiple sequence alignment and phylogenetic analysis are performed which revealed that, the fungi, bacteria, plants, human and other mammals appeared in different clades on the basis of orthology and paralogy being used through Mathematica software. Sequence based clustering analysis and physiochemical properties are observed which might serve as an alternative technique to characterize the protein further. Structure and motif analysis are also examined to have insight of conserved domains of this protein, similarly, orthology of human Hspb1 presented optimal alignment scores which demonstrate important characteristics of this protein. Hspb1 interacting network and pathway analysis are also analyzed with other proteins through the built-in Mathematica algorithms through gene expression enrichment scores. This tool may be further use to analyze the bigger genomics, transcriptomics and metabolomics dataset to check the reliability of clustering algorithm of this software with other contemporary packages in the field of bioinformatics and computational biology.

Keywords: Mathematica, Cluster analysis, Proteomic data mining, Orthology, Network and Pathway analysis, Gene expression enrichment scores