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dc.contributor.authorSrivastava, Anuj
dc.date.accessioned2014-03-04T20:25:16Z
dc.date.available2014-03-04T20:25:16Z
dc.date.issued2011-12
dc.identifier.othersrivastava_anuj_201112_phd
dc.identifier.urihttp://purl.galileo.usg.edu/uga_etd/srivastava_anuj_201112_phd
dc.identifier.urihttp://hdl.handle.net/10724/27823
dc.description.abstractOver the last decade, with the advent of high-throughput technologies, a massive flood of biological data has occurred and is continuing to occur. These technologies generate diverse biological datasets including whole-genome sequences, transcriptome sequencing (RNA-Seq, EST sequence), epigenetics (ChIP-chip, ChIP-Seq), and other -omics. These datasets offer unprecedented opportunities to increase our understanding of the functions and dynamics of the genome and the cell. This dissertation entitled “Evolution & Detection of ncRNA and Transcriptome Analyses of Two Non-Model Systems” combines an evolutionary approach to study non-coding RNAs (ncRNA), and their identification in genomic data using patterns of chromatin modifications, and the analysis of transcriptomes of non-model species chosen for their evolutionary and ecological interest. The evolutionary study of non-coding RNA involves analyzing the patterns of mutations which causes the variability’s in the secondary structure of RNA. From the analysis, I found that secondary structures evolve both by whole stem insertion/deletion, and by mutations that create or disrupt stem base pairing. I analyzed the evolution of stem lengths and constructed substitution matrices describing the changes responsible for the variation in the RNA stem length. I believe that data generated from the study will provide new insights into the evolution of RNA secondary structures and will facilitate design of improved mutational models for RNA structure evolution. I also developed a novel machine learning based approach, based upon using patterns of chromatin-modification to discriminate/detect different genomic features such as protein coding gene, RNA gene, pseudogene and transposon element gene. I implemented this approach on the model plant species Arabidopsis and detected 33 novel genes. I believe this approach will help in improving the annotation of newly sequenced species. From the transcriptome analysis of two non-model systems (Pitcher plants and Songbird), I was able to identify the polymorphic loci which are fixed and shared between sub-species. I also performed functional annotation of all the genes and identified the fast evolving genes by substitution rate determination. I believe that genomic resources developed during these studies will contribute greatly to future research on these genera and their distinctive ecological adaptations.
dc.languageeng
dc.publisheruga
dc.rightspublic
dc.subjecttmRNA
dc.subjectRNAseP
dc.subjecttelomerase
dc.subjectRNA
dc.subjectEvolution
dc.subjecthistone
dc.subjectSVM
dc.subjectSarracenia
dc.subjectDuplication
dc.subjectSongbird
dc.titleEvolution & detection of non-coding RNA, and transcriptome analyses of two non-model systems
dc.typeDissertation
dc.description.degreePhD
dc.description.departmentBioinformatics
dc.description.majorBioinformatics
dc.description.advisorRussell Malmberg
dc.description.committeeRussell Malmberg
dc.description.committeeYing Xu
dc.description.committeechung.J Tsai
dc.description.committeeJan Mrazek
dc.description.committeeLiming Cai


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