A Computational Method for Sequence Analyzing
DOI:
https://doi.org/10.53555/cse.v2i7.177Keywords:
Clustering, RNA sequences,, , RNA-Seq, Data Mining, Bioinformatics,, Graph MiningAbstract
To study how normal cellular activities are altered in different disease states, the biological data must be combined to form a comprehensive picture of these activities. Therefore, the field of bioinformatics has evolved such that the most pressing task now involves the analysis and interpretation of various types of data. This includes nucleotide and amino acid sequences, protein domains, and protein structures. Common uses of bioinformatics include the identification of candidate genes and nucleotides (SNPs). Often, such identification is made with the aim of better understanding the genetic basis of disease, unique adaptations, desirable properties (esp. in agricultural species), or differences between populations. In a less formal way, ioinformatics also tries to understand the organisational principles within nucleic acid and protein sequences.
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