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Browsing by Subject "RNA-Seq"

Now showing 1 - 4 of 4
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    Computational Analysis of Transcript Interactions and Variants in Cancer
    (2015-11) Zhang, Wei
    New sequencing and array technologies for transcriptome-wide profiling of RNAs have greatly promoted the interest in gene and isoform-based functional characterizations of a cellular system. Many statistical and machine learning methods have been developed to quantify the isoform/gene expression and identify the transcript variants for cancer outcome prediction. Since building reliable learning models for cancer transcriptome analysis relies on accurate modeling of prior knowledge and interactions between the cellular components, it is still a computational challenge. This thesis proposes several robust and reliable learning models to integrate both large-scale array and sequencing data with biological prior knowledge for cancer transcriptome analysis. First, we explore two signed network propagation algorithms and general optimization frameworks for detecting differential gene expressions and DNA copy number variations (CNV). Second, we present a network-based Cox regression model called Net-Cox and applied Net-Cox for a large-scale survival analysis across multiple ovarian cancer datasets to identify highly consistent signature genes and improve the accuracy of survival prediction. Third, we introduce a Network-based method for RNA-Seq-based Transcript Quantification (Net-RSTQ) to integrate protein domain-domain interaction network with short read alignments for transcript abundance estimation. Finally, we perform computational analysis of mRNA 3'-UTR shortening on mouse embryonic fibroblast (MEF) cell lines to understand changes of molecular features on dysregulated activation of mammalian target of rapamycin (mTOR). We evaluate our models and findings with simulations and real genomic datasets. The results suggest that our models explore the global topological information in the networks, improve the transcript quantification for better sample classification, identified consistent biomarkers to improve cancer prognosis and survival prediction. The analysis of 3'-UTR with RNA-Seq data find an unexpected link between mTOR and ubiquitin-mediated proteolysis pathway through 3'-UTR shortening.
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    A novel bioinformatics approach to characterize and integrate messenger RNAs, circular RNAs and micro RNAs
    (2018-04) Nair, Asha
    High-throughput Next Generation RNA sequencing (RNA-Seq) technology is affluent with information about the transcriptome, which includes both protein-coding and multiple non-coding regions. In a diseased state, complex interactions between these regions can go awry. Identification of such interactions is critical to translate the underlying biology of the transcriptome, especially for lethal diseases such as cancer. The field of bioinformatics is currently deficient in workflows that can analyze both coding and non-coding regions together efficiently, to identify disease-specific interactions. In this dissertation, I developed three coherent bioinformatics solutions that aim to address these shortcomings in RNA-Seq. First, a comprehensive workflow called MAPR-Seq was developed to analyze and report various features of protein-coding messenger RNAs. Second, a workflow for non-coding circular RNAs, called Circ-Seq, was developed to identify, quantify and annotate expressed circular RNAs. Third, an integration workflow called ReMIx was developed to identify microRNA response elements (MREs) and integrate them with the different types of RNAs (messenger RNAs, circular RNAs, and microRNAs). Collectively, the three workflows were applied to the largest cohort of breast cancer samples (n=885) from The Cancer Genome Atlas (TCGA). Based on the results obtained from these workflows, I present several key findings that are pertinent to breast cancer. I show that circular RNAs may be a marker for tumor proliferation in estrogen response positive (ER+) breast cancer subtype. I also show how triple negative (TN) breast cancer subtype-specific MRE signatures of messenger RNA – microRNA interactions can be obtained using RNA-Seq data, which has not been explored to date and thus, is a novel undertaking. In the end, my results highlight candidate messenger RNAs, circular RNAs and microRNAs that are found to be associated with MAPK and PI3K/AKT signaling cascades in TN breast cancer subtype. In general, the developed bioinformatics solutions can also be applied to RNA-Seq data of other cancer subtypes and diseases to identify unique messenger RNA – microRNA – circular RNA candidates that could be promising diagnostic targets towards improving treatment options for complex diseases.
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    Transcriptional Changes in the Breast Muscle of Thermally Challenged Turkey Poults
    (2018-05) Barnes, Natalie
    Thermal stress in poultry causes reduction in growth, impaired meat quality, and increased mortality. Growth selected and very young birds are especially susceptible. To investigate the transcriptional pathways involved in thermal stress we looked at the breast muscle (an economically important muscle group) of 1-day old turkey poults from a fast growth selected line and a related slow growing line. These young birds were brooded at one of 3 temperatures for 3 days: control (35°C), hot (39°C), or cold (31°C). RNA was isolated from the breast muscle after treatment and euthanasia. 28 libraries were sequenced for analysis (average 18 million reads per library). The reads were mapped to the current turkey genome assembly an analyzed for differential expression. As expected, the fast growing line responded differently to thermal stress than the slow growing line. It had a greater number of differentially expressed genes belonging to pathways that included: transcriptional control and ubiquitination. The slow growing line’s affected pathways were almost exclusively lipid metabolism There are no shared differentially expressed genes or pathways between the two lines. This divergence in response is highlighted by comparing the two lines at each temperature as well as there are more differentially expressed genes between the lines at the treatment temperatures than at the control temperature.
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    Transcriptome Meta Data Compilation for Chinese hamster tissues and CHO cell lines
    (2016-06-06) Vishwanathan, Nandita; Yongky, Andrew; Johnson, Kathryn C; Fu, Hsu-Yuan; Jacob, Nithya M; Le, Huong; Bandyopadhyay, Arpan; wshu@umn.edu; Hu, Wei-Shou; University of Minnesota Department of Chemical Engineering and Material Sciences, Hu Group
    Transcriptomics is increasingly being used on Chinese hamster ovary (CHO) cells to unveil physiological insights related to their performance during production processes. The rich transcriptome data can be exploited to provide impetus for systems investigation such as modeling the central carbon metabolism or glycosylation pathways, or even building genome-scale models. To harness the power of transcriptome assays, we assembled and annotated a set of RNA-Seq data from multiple CHO cell lines and Chinese hamster tissues, and constructed a DNA microarray. These tools were used to measure the transcript expression of tissues (liver, brain, ovary), 3 parental cell lines (DG44, DXB11, CHO-K1) and 16 recombinant cell lines. Transcript expression levels for tissues and cell lines have been compiled as an excel spreadsheet to allow for a rapid survey of transcript levels of different genes.