Abstract
The twenty–first-century precision medicine aims at using a systems-oriented approach to find the root cause of disease specific to an individual by including molecular pathology tests. The challenges of genomic data analysis for precision medicine are multifold, they are a combination of big data, high dimensionality, and with often multimodal distributions. Advanced investigations use techniques such as Next Generation Sequencing (NGS) which rely on complex statistical methods for gaining useful insights. Analysis of the exome and transcriptome data allow for in-depth study of the 22 thousand genes in the human body, many of which relate to phenotype and disease state. Not all genes are expressed in all tissues. In disease state, some genes are even deleted in the genome. Therefore, as part of knowledge discovery, exome and transcriptome big data needs to be analyzed to determine whether a gene is actually absent (deleted/not expressed) or present. In this paper, we present a statistical technique to identify the genes that are present or absent in exome or transcriptome data (big data) to improve the accuracy for precision medicine.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Eisenstein, Michael. “Big data: the power of petabytes.” Nature 527.7576 (2015): S2–S4.
Bock, Hans-Hermann, and Edwin Diday, eds. Analysis of symbolic data: exploratory methods for extracting statistical information from complex data. Springer Science & Business Media, 2012.
Morley, Michael, et al. “Genetic analysis of genome-wide variation in human gene expression.” Nature 430.7001 (2004): 743–747.
Ried, Thomas, et al. “Genomic changes defining the genesis, progression, and malignancy potential in solid human tumors: a phenotype/genotype correlation.” Genes, Chromosomes and Cancer 25.3 (1999): 195–204.
Kitano, Hiroaki. “Computational systems biology.” Nature 420.6912 (2002): 206–210.
Maniatis, Tom, Stephen Goodbourn, and Janice A. Fischer. “Regulation of inducible and tissue-specific gene expression.” Science 236 (1987): 1237–1246.
Komura, Daisuke, et al. “Noise reduction from genotyping microarrays using probe level information.” In silico biology 6.1, 2 (2006): 79–92.
Schwartz, Schraga, Ram Oren, and Gil Ast. “Detection and removal of biases in the analysis of next-generation sequencing reads.” PloS one 6.1 (2011): e16685.
Trapnell, Cole, et al. “Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks.” Nature protocols 7.3 (2012): 562–578.
iOMICS-Research Version 4.0.
Reynolds, Douglas. “Gaussian mixture models.” Encyclopedia of biometrics (2015): 827–832.
Moon, Todd K. “The expectation-maximization algorithm.” IEEE Signal processing magazine 13.6 (1996): 47–60.
Lappalainen, Tuuli, et al. “Transcriptome and genome sequencing uncovers functional variation in humans.” Nature 501.7468 (2013): 506–511.
Petryszak, Robert, et al. “Expression Atlas update—an integrated database of gene and protein expression in humans, animals and plants.” Nucleic acids research (2015): gkv1045.
Pleasance, Erin D., et al. “A comprehensive catalogue of somatic mutations from a human cancer genome.” Nature 463.7278 (2010): 191–196.
Talukder, Asoke K., et al. “Tracking Cancer Genetic Evolution using OncoTrack.” Scientific Reports 6 (2016).
Gracia-Aznarez, Francisco Javier, et al. “Whole exome sequencing suggests much of non-BRCA1/BRCA2 familial breast cancer is due to moderate and low penetrance susceptibility alleles.” PloS one 8.2 (2013): e55681.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Adhil, M., Agarwal, M., Ghosh, K., Sule, M., Talukder, A.K. (2018). Gene Presence and Absence in Genomic Big Data for Precision Medicine. In: Bhalla, S., Bhateja, V., Chandavale, A., Hiwale, A., Satapathy, S. (eds) Intelligent Computing and Information and Communication. Advances in Intelligent Systems and Computing, vol 673. Springer, Singapore. https://doi.org/10.1007/978-981-10-7245-1_22
Download citation
DOI: https://doi.org/10.1007/978-981-10-7245-1_22
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-7244-4
Online ISBN: 978-981-10-7245-1
eBook Packages: EngineeringEngineering (R0)