Genomic data mining through python language

Paper Details

Research Paper 01/09/2017
Views (1127)
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

Optimizing soybean (Glycine max L. Merr.) performance through rhizobial inoculation and planting density in Kétou, Benin

Mahougnon Charlotte Carmelle Zoundji*, Ibouraïman Balogoun, Pascal Gbenou, Tobi Moriaque Akplo, Carlosse Djeho, Félix Kouélo Alladassi, Int. J. Biosci. 28(6), 99-107, June 2026.

Genetic admixture and the philosophy of diplomacy in central Asia: Evidence from intercultural dialogue, governance and genomic data

Shafee Ur Rehman, Waqar Ahmed Khan, Iqra Jamil, Muhammad Abdullah, Int. J. Biosci. 28(6), 89-98, June 2026.

Synthesizing and integrating environmental awareness and bio-intensive gardening under the Gulayan sa Paaralan (SIBUG) extension project

Violeta F. Collado*, Analyn V. Sagun, Angelina T. Gonzales, Marilyn D. Respicio, Int. J. Biosci. 28(6), 82-88, June 2026.

Diversity of insects related to maize (Zea mays) production in Ferkéssédougou region, Côte d’Ivoire

Fondio Drissa, Dao Hassane, Soro Lacina*, Sib Ollo, Kouadio Roger Hosphade Kouassi, Soro Senan, Yeboue N’guessan Lucie, Int. J. Biosci. 28(6), 75-81, June 2026.

Diuretic activity assessment of an aqueous extract of Zanthoxylum gilletii (Rutaceae) stem bark in rats

Akoua Jeanne Kanga*, Essoi Kouametchi Hermann, Françoise Assamala Fossou, Kacou Jules Marius Djetouan, Kouao Augustin Amonkan, Int. J. Biosci. 28(6), 68-74, June 2026.

Phytochemical investigation and in vitro evaluation of cholinesterase inhibitory and antioxidant properties of Aglaonema hookerianum stems

K. M. Monirul Islam, Simin Shabnam Lopa, Joya Rani, Md. Aslam Sheikh, Md. Golam Sadik*, Int. J. Biosci. 28(6), 60-67, June 2026.

Comparative responses of rice (Oryza sativa L.) to iron toxicity, drought and salinity stress: Morphological, physiological, biochemical and molecular regulation mechanisms

Yaya Touré*, Brahima André Soumahoro, Arthur Martin Affery, Tchoa Koné, Mongomaké Koné, Int. J. Biosci. 28(6), 37-50, June 2026.