title: Next generation sequencing analysis of lung cancer datasets: A functional genomics perspective.
abstract: Cigarette smoking leads to serious epidemics in humans, creating torsion of infection in epithelial cells lining the respiratory tracts. Several researchers in the recent past have theorized that the next generation sequencing (NGS), especially transcriptome sequencing has enhanced understanding lung cancers and other epithelial epidemics. Conversely, pathogenesis specific to lung cancer with respect to molecular fraction of genomic ribonucleic acid has some mutant effect in various populations like smokers with lung cancer, healthy never smokers and vice versa. We review the impending impact of NGS data while providing insights into the biology of lung cancer affecting various populations, which we believe would be an add-on service for predictive biology approaches. Furthermore, we conclude what would be the outcome of such analysis for Indian population. Bioinformatics analysis was performed using various tools. We identified five genes namely epidermal growth factor receptor, Kirsten rat sarcoma, adenomatosis polyposis down regulated-1, N-ethylmaleimide-sensitive factor attachment protein, gamma and Piezo type mechanosensitive ion channel component 2 whose role was implicated in lung cancer and further analysis has to be performed to check whether or not the genes are indeed completely involved in causing lung cancer.
authors.forename: P
authors.initials: P
authors.lastname: Pappu
authors.forename: D
authors.initials: D
authors.lastname: Madduru
authors.forename: M
authors.initials: M
authors.lastname: Chandrasekharan
authors.forename: V
authors.initials: V
authors.lastname: Modhukur
authors.forename: S
authors.initials: S
authors.lastname: Nallapeta
authors.affiliation: Bioinformatics and Systems Biology, Bioclues Organization, IKP Knowledge Park, Secunderabad, Andhra Pradesh, India.
authors.forename: P
authors.initials: P
authors.lastname: Suravajhala
mesh_terms: D000818:Animals; D030541:Databases, Genetic; D066264:Datasets as Topic; D020022:Genetic Predisposition to Disease; D023281:Genomics / Q000379:methods*; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods*; D006801:Humans; D008175:Lung Neoplasms / Q000235:genetics* / Q000473:pathology