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From RNA-Seq to gene annotation using the splicegrapher method

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dc.contributor.authorRogers, Mark F., author
dc.contributor.authorBen-Hur, Asa, advisor
dc.contributor.authorBoucher, Christina, committee member
dc.contributor.authorAnderson, Charles, committee member
dc.contributor.authorReddy, Anireddy S. N., committee member
dc.date.accessioned2007-01-03T06:09:12Z
dc.date.available2007-01-03T06:09:12Z
dc.date.issued2013
dc.description.abstractMessenger RNA (mRNA) plays a central role in carrying out the instructions encoded in a gene. A gene's components may be combined in various ways to generate a diverse range of mRNA molecules, or transcripts, through a process called alternative splicing (AS). This allows each gene to produce different products under different conditions, such as different stages of development or in different tissues. Researchers can study the diverse set of transcripts a gene produces by sequencing its mRNA. The latest sequencing technology produces millions of short sequence reads (RNA-Seq) from mRNA transcripts, providing researchers with unprecedented opportunities to assess how genetic instructions change under different conditions. It is relatively inexpensive and easy to obtain these reads, but one limitation has been the lack of versatile methods to analyze the data. Most methods attempt to predict complete mRNA transcripts from patterns of RNA-Seq reads ascribed to a particular gene, but the short length of these reads makes transcript prediction problematic. We present a method, called SpliceGrapherXT, that takes a different approach by predicting splice graphs that capture in a single structure all the ways in which a gene's components may be assembled. Whereas other methods make predictions primarily from RNA-Seq evidence, SpliceGrapherXT uses gene annotations describing known transcripts to guide its predictions. We show that this approach allows SpliceGrapherXT to make predictions that encapsulate gene architectures more accurately than other state-of-the-art methods. This accuracy is crucial not only for updating gene annotations, but our splice graph predictions can contribute to more accurate transcript predictions as well. Finally we demonstrate that by using SpliceGrapherXT to assess AS on a genome-wide scale, we can gain new insights into the ways that specific genes and environmental conditions may impact an organism's transcriptome. SpliceGrapherXT is available for download at http://splicegrapher.sourceforge.net.
dc.format.mediumborn digital
dc.format.mediumdoctoral dissertations
dc.identifierRogers_colostate_0053A_11884.pdf
dc.identifierETDF2013500328COMS
dc.identifier.urihttp://hdl.handle.net/10217/80971
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartof2000-2019
dc.rightsCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.
dc.subjectRNA-seq
dc.subjectmachine learning
dc.subjectsupport vector machine
dc.subjectcomputational biology
dc.subjectinference
dc.titleFrom RNA-Seq to gene annotation using the splicegrapher method
dc.typeText
dcterms.rights.dplaThis Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
thesis.degree.disciplineComputer Science
thesis.degree.grantorColorado State University
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy (Ph.D.)

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