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Genomic data mining through python language

By: Rashid Saif, Kinza Qazi, Talha Tamseel, Saeeda Zia

Key Words: Python, Sequence analysis, Python syntax, Programing data structure

Int. J. Biosci. 11(3), 116-125, September 2017.

DOI: http://dx.doi.org/10.12692/ijb/11.3.116-125

Certification: ijb 2017 0080 [Generate Certificate]

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.

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Genomic data mining through python language

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Rashid Saif, Kinza Qazi, Talha Tamseel, Saeeda Zia.
Genomic data mining through python language.
Int. J. Biosci. 11(3), 116-125, September 2017.
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