Background and Objective: The advent of Next Generation Sequencing (NGS) has created a high throughput platform, to identify disease traits and phenotypic characteristics using RNASeq Sequencing analysis in humans. Non-small cell lung cancer (NSCLC), a lethal disease accounts for 85 percent of most lung cancers with very small window ofsurvival rate. The decision of tumour image bio marker impression can be improved by gene profile. Hence there is a need to characterise the variants in the disease manifestation. Methods: To understand the SNP’s in the major genes responsible for NSCLC, RNASeq data of patients aged above 50 years, were downloaded from SRA database. The quality matrix analysis is mapped to Genome reference consortium human build 38 (GRCh38) to call the variants and identify SNP’s with the tuxedo protocol. Results: The SNP’s and the patterns of variants were analysed to see the comparison between healthy individual and NSCLC patients, and in between patients of different age. Oncogenes commonly associated with the NSCLC like KRAS, EGFR, ALK, BRAF and HER2 were mainly analysed to see the SNP’s and their characterisations with respect to the functional change was done. Conclusion: The SNP’s with the greater quality scores belonging to the above said genes were identified which gives us a baseline to understand the NSCLC at the Genomic level. Further fold change of these genes to the frequency of variant can be mapped to understand the NSCLC at a greater depth.

Lung cancer is the most common and fatal type of cancer. NSCLC refers to any kind of epithelial lung cancer that isn't small cell lung cancer (SCLC), which results for 85 percent of lung cancer cases. Differential gene expression is a type of gene analysis in which the RNA sequence data from next-generation sequencing is shown for any quantitative changes in the experimental data set's levels. Transcriptome analysis focuses on obtaining transcript statistics from a gene transcript file with a fold change of genes on a normalised scale in order to find quantitative differences in gene expression levels between the reference genome and NSCLC samples. The data has a significant clinical influence in terms of identifying and characterising candidate genes in order to validate them. The resultant data set and the plot display depicts the significant candidate genes in the respective location which are significant in expressing their changes in samples of NSCLC. The samples are differentiated with prominent gene labels of NSCLC disease samples. The significant values of this quantized analysis on read count data of expression, data tables prompt the candidate genes data set of NSCLC samples also the results explain the differential expression of particular samples across samples from genders namely male and female. The current research experiment focuses on the computational difficulty of read, search, match, and data enrichment of unstructured data with the goal of classifying biomarkers based on differential expression results and pathways found by classification algorithms.

Human genome data analysis is one of the molecular level information in health informatics, which enables genetic epidemiological analysis of complex data sets. The recent studies of the genomic sequence, a part of genome-wide association studies (GWAS) have led to understand the genetic architecture to identify the area of focus i.e. interactions with single-nucleotide polymorphism (SNP) is linked to causing complex diseases. The study and identification of these interactions and splicing of nucleic acids involves complexity in processing and computation. This article reviews current methods and trends in various machine learning and data mining approaches which are very complex and challenging to model and evaluate the performances.

Human genome data analysis is one of the molecular level information in health informatics, which enables genetic epidemiological analysis of complex data sets. The recent studies of the genomic sequence, a part of genome-wide association studies (GWAS) have led to understand the genetic architecture to identify the area of focus i.e. interactions with single-nucleotide polymorphism (SNP) is linked to causing complex diseases. The study and identification of these interactions and splicing of nucleic acids involves complexity in processing and computation. This article reviews current methods and trends in various machine learning and data mining approaches which are very complex and challenging to model and evaluate the performances.

Differential gene expression is an analysis of gene data, in which the RNA sequence data after next-generation sequencing are to be visualized for any quantitative changes in the levels of the experimental data set. This work aims to derive the transcript statistics on a gene transcript file with a fold change of genes on a normalized scale, in order to identify quantitative changes in gene expression of the difference between the reference genome and Non-Small Cell Lung Cancer (NSCLC) samples. This insight makes a clinical impact in assessing and characterizing candidate genes. The pipeline comprises tuxedo protocol and programming language R with the standard ballgown package. The resultant data set and the plot displays depict the candidate genes in their respective location which are significant in expressing their changes in NSCLC samples. The samples are compared with prominent gene labels of NSCLC samples. The results explain the differential expression of particular samples across samples from both genders.