Genomic data mining through python language

Paper Details

Research Paper 01/09/2017
Views (863)
current_issue_feature_image
publication_file

Genomic data mining through python language

Rashid Saif, Kinza Qazi, Talha Tamseel, Saeeda Zia
Int. J. Biosci. 11(3), 116-125, September 2017.
Copyright Statement: Copyright 2017; The Author(s).
License: CC BY-NC 4.0

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

Related Articles

Sensory acceptability of gnocchi pasta added with different levels of malunggay (Moringa oleifera) leaves and blue ternate (Clitoria ternatea) flowers

Ralph Justyne B. Bague, James Troyo, Proceso C. Valleser Jr.*, Int. J. Biosci. 28(1), 103-114, January 2026.

Spatio-temporal analysis of vegetation cover and socio-environmental implications in Korhogo (Northern Côte d’Ivoire) from 1990-2020

Adechina Olayossimi*, Konan Kouassi Urbain, Ouattara Amidou, Yao-Kouamé Albert, Int. J. Biosci. 28(1), 94-102, January 2026.

Predicting the habitat suitability of Vitellaria paradoxa under climate change scenarios

Franck Placide Junior Pagny*, Anthelme Gnagbo, Dofoungo Kone, Blaise Kabré, Marie-Solange Tiébré6,, Int. J. Biosci. 28(1), 73-83, January 2026.

Performance response dynamics of rabbits (Oryctolagus cuniculus) to locally sourced, on-farm feed ingredients during the growing phase: Implications for the institutional rabbit multiplier project

Roel T. Calagui*, Janelle G. Cadiguin, Maricel F. Campańano, Jhaysel G. Rumbaoa, Louis Voltaire A. Pagalilauan, Mary Ann M. Santos, Int. J. Biosci. 28(1), 65-72, January 2026.

Chronopharmacology: Integration of circadian biology in modern pharmacotherapy

Sangram D. Chikane*, Vishal S. Adak, Shrikant R. Borate, Rajkumar V. Shete, Deepak V. Fajage, Int. J. Biosci. 28(1), 56-64, January 2026.

Evaluation of the impact of floristic diversity on the productivity of cocoa-based agroforestry systems in the new cocoa production area: The case of the Biankouma department (Western Côte d’Ivoire)

N'gouran Kobenan Pierre, Zanh Golou Gizele*, Kouadio Kayeli Anaïs Laurence, Kouakou Akoua Tamia Madeleine, N'gou Kessi Abel, Barima Yao Sadaiou Sabas, Int. J. Biosci. 28(1), 44-55, January 2026.

Utilization of locally sourced feed ingredients and their influence on the growth performance of broiler chickens (Gallus gallus domesticus): A study in support of the school’s chicken multiplier project

Roel T. Calagui*, Maricel F. Campańano, Joe Hmer Kyle T. Acorda, Louis Voltaire A. Pagalilauan, Mary Ann M. Santos, Jojo D. Cauilan, John Michael U. Tabil, Int. J. Biosci. 28(1), 35-43, January 2026.