Genomic data mining through python language

Paper Details

Research Paper 01/09/2017
Views (1020)
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

Anti-proliferative potential of seed derived proteins from Vitis vinifera and Mangifera indica

Hareeshthulasi, V. Vinotha, R. Rajakumar*, Int. J. Biosci. 28(4), 129-137, April 2026.

Valorisation of table waste and fruit waste by black soldiers (Ullicens hermetica)

Ayaba Adéline Hounnou, Vanessa Chabi, Jomini Marc Sène Alitonou, Franck Sokenou, Mickael Vitus Martin Kpessou Saïzonou, Fidèle Paul Tchobo, Guy Alain Alitonou*, Int. J. Biosci. 28(4), 123-128, April 2026.

Murraya koenigii (Linn.) Spreng.: An opulent source of fatty acid

Shahin Aziz*, Int. J. Biosci. 28(4), 116-122, April 2026.

Design and architecture of an IoT-enabled bamboo resource management system: Data-driven approach for sustainable agriculture

Charlot L. Maramag*, Dorothy M. Ayuyang, Richard R. Ayuyang, Int. J. Biosci. 28(4), 107-115, April 2026.

Physicochemical and microbiological characterization of flours from the local variety of purple corn (Zea mays L.) produced and marketed in Katiola (Côte d’Ivoire)

Moumouny Traore*, N´Zebo Desiré Kouame, Pepiesin Marie Ange Melem Soro, Zamblé Bi Irié Abel Boli, Int. J. Biosci. 28(4), 98-106, April 2026.

In the shadows of governance: Exploring youth participation in local peacebuilding initiatives

Juramie R. Rubia, Benny R. Rubia, Nancy E. Aranjuez*, Int. J. Biosci. 28(4), 85-97, April 2026.

Evaluation of the agronomic performance, beta-carotene content and dry matter content of 228 sweet potatoes (Ipomoea batatas (L.) Lam) genotypes in Burkina Faso

Nattan Gamsore*, Koussao Some, Djakaridja Tiama, Pauline Bationo_Kando, Int. J. Biosci. 28(4), 73-84, April 2026.

Geomatics tools for agricultural and farm disaster risk management and reduction: A survey of farmers in South-South coastal communities, Nigeria

G. O. Nwodo, O. J. Ugwu, E. U. Onah, A. Ugwuoti, E. Elijah Ebinne*, O. P. Nogheghase, S. I. Ogbaa, U. E. Ahuchaogu, T. O. Ogbuji, C. P. Owuamalam-Chidi, C. O. Osuagwu, M. O. Igwenagu, O. E. Mbakaogu, J. U. Chikaire, Int. J. Biosci. 28(4), 59-72, April 2026.