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Leveraging Omics Data to Expand the Value and Understanding of Alternative Splicing Public

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Utilizing ‘omics’ data of diverse types such as genomics, proteomics, transcriptomics, epigenomics, and others has largely been attributed as holding great promise for solving the complexity of many health and ecological problems such as complex genetic diseases and parasitic destruction of farming crops. By using bioinformatics, it is possible to take advantage of ‘omics’ data to gain a systems level molecular perspective to achieve insight into possible solutions. One possible solution is understanding and expanding the use of alternative splicing (A.S.) of mRNA precursors. Typically, genes are considered the focal point as the main players in the molecular world. However, due to recent ‘omics’ analysis across the past decade, A.S. has been demonstrated to be the main player in causing protein diversity. This is possible as A.S. rearranges the key components of a gene (exon, intron, and untranslated regions) to generate diverse functionally unique proteins and regulatory RNAs. A.S. is highly prevalent, where on average 10 AS transcripts occur for every gene in humans. Furthermore, multiple transcripts can be expressed at the same moment leading to different protein products that can interact within their molecular environment in unique ways. The prevalence on which transcripts are alternatively spliced has been demonstrated to be based on age, tissue, cell type, and disease state.\n\nThis work brings together different ‘omics’ data to expand our understanding and promote the value of A.S. Specifically, there are six projects described here which make use of transcriptomics, proteomics, genomics, and epigenomics, which often overlap, on the focus in a couple of complex genetic diseases as well as analyzing a parasite, which infects soybeans. The projects range from systemically profiling machine learning methods utilizing RNA-Seq based alternative splicing expression data to promote its use, development of a method to predict whether an alternative spliced protein affects its interaction, a systematic analysis across the transcriptome for comparing binding sites and domains with alternative splicing and expression patterns, assessment of single nucleotide variation on protein binding sites in cancer, assessment of epigenomics with transcriptomics within the context of acute lymphoblastic leukemia, and looking for patterns of alternative splicing on parasites infecting soybeans.\nThis work brings together different ‘omics' data to expand our understanding and promote the value of A.S. Specifically, there are six projects described here which make use of transcriptomics, proteomics, genomics, and epigenomics, which often overlap, on the focus in a couple of complex genetic diseases as well as analyzing a parasite, which infects soybeans. The projects range from systemically profiling machine learning methods utilizing RNA-Seq based alternative splicing expression data to promote its use, development of a method to predict whether an alternative spliced protein affects its interaction, a systematic analysis across the transcriptome for comparing binding sites and domains with alternative splicing and expression patterns, assessment of single nucleotide variation on protein binding sites in cancer, assessment of epigenomics with transcriptomics within the context of acute lymphoblastic leukemia, and looking for patterns of alternative splicing on parasites infecting soybeans.

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  • 01/05/2021
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  • etd-040819-145002
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  • 2019
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  • 2019-04-08
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