Cancer risk prediction with next generation sequencing data using machine learning

The use of computational biology for next generation sequencing (NGS) analysis is rapidly increasing in genomics research. However, the effectiveness of NGS data to predict disease abundance is yet unclear. This research investigates the problem in the whole exome NGS data of the chronic lymphocytic leukemia (CLL) available at dbGaP. Initially, raw reads from samples are aligned to the human reference genome using burrows wheeler aligner. From the samples, structural variants, namely, Single Nucleotide Polymorphism (SNP) and Insertion Deletion (INDEL) are identified and are filtered using SAMtools as well as with Genome Analyzer Tool Kit (GATK). Subsequently, the variants are encoded and feature selection is performed with the Pearson correlation coefficient (PCC) and the chi-square 2-df statistical test. Finally, 90:10 cross validation is performed by applying the support vector machine algorithm on sets of top selected features. It is found that the variants detected with SAMtools and GATK achieve similar prediction accuracies. It is also noted that the features that are ranked with the PCC yield better accuracy than the chi-square test. In all of the analyses, the SNPs are identified to have superior accuracy as compared to the INDELs or the full dataset. Later, an exome capture kit is introduced for analysis. The SNPs, ranked with the PCC, along with the exome capture kit yield prediction accuracy of 85.1 % and area under curve of 0.94. Overall, this study shows the effective application of the machine learning methods and the strength of the NGS data for the CLL risk prediction.

Name thesis:degree_name

Master of Science in Bioinformatics - (M.S.)

Discipline thesis:degree_discipline

Computer Science

Year

2015

Author dc:creator

• Patel, Nihir

Contributors dc:contributor

• Usman W. Roshan
• Jason T. L. Wang
• Zhi Wei

• Cancer risk prediction
• Next generation sequencing
• Machine learning
• Bioinformatics
• Computer Sciences

Repository record dc:identifier

https://digitalcommons.njit.edu/theses/220

OAI identifier oai:identifier

oai:digitalcommons.njit.edu:theses-1219