Genomic data mining through python language
Paper Details
Genomic data mining through python language
Abstract
Pythonis a rigorous programming language, which may be used for many purposes including genomic data mining. This language was designed to emphasize on code readability and syntax, which allows programmer to express code in lesser space with comprehensive and exhaustive manner. Different analysis through Python can be conducted during dry labs sessions, which infer concrete and generalizable results from the wet lab genomic experiments, such as gene expression analysis, phylogenetic, GC percentage and gene sequencing. In this article, built-in Python functions like variables, stings, operators and formatting styles are introduced, and short programs are structured, implemented and executed. Basic operators are used to perform calculations through this language, gene sequences are analyzed and small built-in functions e.g. “length, print, integers and types” of Python are also conversed in this communication. Case sensitive commands are elaborated to avoid errors during the process of computing. This endeavor also shed light on the topic that how different Python methods and functions may be used to compute data structures, dictionaries, sets, lists, tuples, loops and statements on the genomic sequences. Finally, different programs are constructed to count undefined bases in a given sequence with the help of statement, condition functions based on Boolean expressions, loops function are also used to analyze undefined amino acids present in protein sequences with the help of “for” and “while” loops.
Anders S, Pyl PT, Huber W. 2014. HTSeq–a Python framework to work with high-throughput sequencing data. Bioinformatics 32(2), 166-169. http://dx.doi.org/10.1093/bioinformatics/btu638
Cock PJ, Antao T, Chang JT, Chapman BA, Cox CJ, Dalke A, Friedberg I, Hamelryck T, Kauff F, Wilczynski B. 2009. Biopython: freely available Python tools for computational molecular biology and bioinformatics. Bioinformatics 25(11), 1422-1423. http://dx.doi.org/10.1093/bioinformatics/btp163
Goodstadt L. 2010. Ruffus: a lightweight Python library for computational pipelines. Bioinformatics 26(21), 2778-2779. www.10.1093/bioinformatics/btq524
Hamelryck T, Manderick B. 2003. PDB file parser and structure class implemented in Python. Bioinformatics 19(17), 2308-2310. http://dx.doi.org/10.1093/bioinformatics/btg299
Mann C. 2010. Python for bioinformatics. Kybernetes 39(8) http://dx.doi.org/10.1108/k.2010.06739hae.004
Lesk A. 2013. Introduction to bioinformatics. Oxford University Press.
List M, Ebert P, Albrecht F. 2017. Ten Simple Rules for Developing Usable Software in Computational Biology. PLOS Computational Biology 13(1), e1005265. http://dx.doi.org/10.1371/journal.pcbi.1005265
Oliphant TE. 2007. Python for scientific computing. Computing in Science & Engineering 9(3), 10-20. http://dx.doi.org/10.1109/mcse.2007.58
Pearson WR, Lipman DJ. 1988. Improved tools for biological sequence comparison. Proceedings of the National Academy of Sciences 85(8), 2444-2448. http://dx.doi.org/10.1073/pnas.85.8.2444
Perkins J. 2010. Python text processing with NLTK 2.0 cookbook. Packt Publ.
Przulj N. 2013. Introduction to the special issue on biological networks. Internet Mathematics 7(4), 207-208. http://dx.doi.org/10.1080/15427951.2011.621769
Rashid Saif, Kinza Qazi, Talha Tamseel, Saeeda Zia (2017), Genomic data mining through python language; IJB, V11, N3, September, P116-125
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