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Class Discovery, Comparison, and Prediction Methods for RNA-Seq Data

Class Discovery, Comparison, and Prediction Methods for RNA-Seq Data

Ahu Cephe, Necla Koçhan, Gözde Ertürk Zararsız, Vahap Eldem, Gökmen Zararsız
Copyright: © 2024 |Pages: 25
ISBN13: 9798369330265|EISBN13: 9798369330272
DOI: 10.4018/979-8-3693-3026-5.ch021
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MLA

Cephe, Ahu, et al. "Class Discovery, Comparison, and Prediction Methods for RNA-Seq Data." Research Anthology on Bioinformatics, Genomics, and Computational Biology, edited by Information Resources Management Association, IGI Global, 2024, pp. 457-481. https://doi.org/10.4018/979-8-3693-3026-5.ch021

APA

Cephe, A., Koçhan, N., Ertürk Zararsız, G., Eldem, V., & Zararsız, G. (2024). Class Discovery, Comparison, and Prediction Methods for RNA-Seq Data. In I. Management Association (Ed.), Research Anthology on Bioinformatics, Genomics, and Computational Biology (pp. 457-481). IGI Global. https://doi.org/10.4018/979-8-3693-3026-5.ch021

Chicago

Cephe, Ahu, et al. "Class Discovery, Comparison, and Prediction Methods for RNA-Seq Data." In Research Anthology on Bioinformatics, Genomics, and Computational Biology, edited by Information Resources Management Association, 457-481. Hershey, PA: IGI Global, 2024. https://doi.org/10.4018/979-8-3693-3026-5.ch021

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Abstract

Gene-expression studies have been studied using microarray data for many years, and numerous methods have been developed for these data. However, microarray technology is old technology and has some limitations. RNA-sequencing (RNA-seq) is a new transcriptomics technique capable of coping with these limitations, using the capabilities of new generation sequencing technologies, and performing operations quickly and cheaply based on the principle of high-throughput sequencing technology. Compared to microarrays, RNA-seq offers several advantages: (1) having less noisy data, (2) being able to detect new transcripts and coding regions, (3) not requiring pre-determination of the transcriptomes of interest. However, RNA-seq data has several features that pose statistical challenges. Thus, one cannot directly use methods developed for microarray analyses, which has a discrete and overdispersed nature of data, quite different from the continuous data structure of microarrays. This article aims to provide an overview and practical guidance to researchers working with RNA-seq data for different purposes.

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