Polymorphism identification and improved genome annotation of Brassica rapa through Deep RNA sequencing.

The mapping and functional analysis of quantitative traits in Brassica rapa can be greatly improved with the availability of physically positioned, gene-based genetic markers and accurate genome annotation. In this study, deep transcriptome RNA sequencing (RNA-Seq) of Brassica rapa was undertaken with two objectives: SNP detection and improved transcriptome annotation. We performed SNP detection on two varieties that are parents of a mapping population to aid in development of a marker system for this population and subsequent development of high-resolution genetic map. An improved Brassica rapa transcriptome was constructed to detect novel transcripts and to improve the current genome annotation. This is useful for accurate mRNA abundance and detection of expression QTL (eQTLs) in mapping populations. Deep RNA-Seq of two Brassica rapa genotypes-R500 (var. trilocularis, Yellow Sarson) and IMB211 (a rapid cycling variety)-using eight different tissues (root, internode, leaf, petiole, apical meristem, floral meristem, silique, and seedling) grown across three different environments (growth chamber, greenhouse and field) and under two different treatments (simulated sun and simulated shade) generated 2.3 billion high-quality Illumina reads. A total of 330,995 SNPs were identified in transcribed regions between the two genotypes with an average frequency of one SNP in every 200 bases. The deep RNA-Seq reassembled Brassica rapa transcriptome identified 44,239 protein-coding genes. Compared with current gene models of B. rapa, we detected 3537 novel transcripts, 23,754 gene models had structural modifications, and 3655 annotated proteins changed. Gaps in the current genome assembly of B. rapa are highlighted by our identification of 780 unmapped transcripts. All the SNPs, annotations, and predicted transcripts can be viewed at http://phytonetworks.ucdavis.edu/

A high quality Arabidopsis transcriptome for accurate transcript-level analysis of alternative splicing

Alternative splicing generates multiple transcript and protein isoforms from the same gene and thus is important in gene expression regulation. To date, RNA-sequencing (RNA-seq) is the standard method for quantifying changes in alternative splicing on a genome-wide scale. Understanding the current limitations of RNA-seq is crucial for reliable analysis and the lack of high quality, comprehensive transcriptomes for most species, including model organisms such as Arabidopsis, is a major constraint in accurate quantification of transcript isoforms. To address this, we designed a novel pipeline with stringent filters and assembled a comprehensive Reference Transcript Dataset for Arabidopsis (AtRTD2) containing 82,190 non-redundant transcripts from 34 212 genes. Extensive experimental validation showed that AtRTD2 and its modified version, AtRTD2-QUASI, for use in Quantification of Alternatively Spliced Isoforms, outperform other available transcriptomes in RNA-seq analysis. This strategy can be implemented in other species to build a pipeline for transcript-level expression and alternative splicing analyses

Assembly, quantification, and downstream analysis for high trhoughput sequencing data

Next Generation Sequencing is a set of relatively recent but already well-established technologies with a wide range of applications in life sciences. Despite the fact that they are constantly being improved, multiple challenging problems still exist in the analysis of high throughput sequencing data. In particular, genome assembly still suffers from inability of technologies to overcome issues related to such structural properties of genomes as single nucleotide polymorphisms and repeats, not even mentioning the drawbacks of technologies themselves like sequencing errors which also hinder the reconstruction of the true reference genomes. Other types of issues arise in transcriptome quantification and differential gene expression analysis. Processing millions of reads requires sophisticated algorithms which are able to compute gene expression with high precision and in reasonable amount of time. Following downstream analysis, the utmost computational task is to infer the activity of biological pathways (e.g., metabolic). With many overlapping pathways challenge is to infer the role of each gene in activity of a given pathway. Assignment products of a gene to a wrong pathway may result in misleading differential activity analysis, and thus, wrong scientific conclusions. In this dissertation I present several algorithmic solutions to some of the enumerated problems above. In particular, I designed scaffolding algorithm for genome assembly and created new tools for differential gene and biological pathways expression analysis

Long-read transcriptome sequencing analysis with IsoTools

Long-read transcriptome sequencing (LRTS) holds the promise to boost our understanding of alternative splicing. Recent advances in accuracy and throughput have diminished the major limitations and enabled the direct quantification of isoforms. Considering the complexity of the data and the broad range of potential applications, it is clear that highly flexible, accurate analysis tools are crucial. Here, we present IsoTools, a comprehensive Python-based analysis package, for the improvement of alternative and differential splicing analysis. Iso-Tools provides a comprehensive data structure that integrates genomic information from LRTS transcripts together with the reference annotation, and enables broad functionality to quality control, visualize and analyze the data. Additionally, we implemented a graph-based method for the identification of alternative splicing events and a statistical approach based on the beta binomial distribution for the detection of differential events. To demonstrate our methods, we generated PacBio Iso-Seq data of human hepatocytes treated with the HDAC inhibitor valproic acid, a compound known to induce widespread transcriptional changes. Contrasted with short read RNA-Seq of the same samples, this analysis shows that LRTS provides valuable additional insights for a better understanding of alternative splicing, in particular with respect to complex novel and differential splicing events. IsoTools is made available for the community along with extensive documentation at https://github.com/MatthiasLienhard/isotools

Linking gene expression and orthology in mammals

The overall aim of biomedical research is to understand disease mechanisms and to provide a drug to eventually cure the disease. This challenging endeavour requires an early research phase that deals with identifying target genes or proteins playing an important role in the disease. At this stage one uses animal models mimicking human disease to determine differences between healthy and diseased animals. Once potential drug targets have been found, compounds are screened and promising compounds go into the preclinical phase where their efficacy and, most importantly, safety are assessed. Those having proven to be efficacious and safe proceed to toxicology where the maximum tolerable dosage is assessed in, mainly, non-rodent species. According to the Bundesministerium für Ernährung und Landwirtschaft, more than 2 million animals were used for animal testing in German laboratories in 2017. The majority of these animals were mice and rats but also dogs, cats and monkeys are model organisms used for testing. While it is commonly accepted that other mammalian species resemble human biology to a great extent, one has to bear in mind that there are species-specific differences. One of the aims of this thesis was to investigate how similar widely used model species are to human and to each other on a molecular level. For this purpose we assessed the relationship between protein sequence identity and gene expression correlation with an emphasis on mouse and rat. We found that the majority of genes are highly similar, both on sequence and gene expression level. There were, however, cases with low sequence identity but high expression correlation. These cases were investigated in greater detail and the hypothesis that sequences annotated in widely used databases like Ensembl, UniProt, or RefSeq, may contain errors or are incomplete, was confirmed. Therefore, we investigated whether sequence information from related species can be used to derive a target’s sequence in a species with poor annotation. The a&o-tool was developed to exploit sequence similarity between related species and short-read RNA-Seq data to refine or validate target sequences. Since longread RNA-Seq data would greatly improve the results as entire transcripts are sequenced as a whole, we conducted a pilot study for comparing short- and long-read sequencing data. Even though PacBio’s SMRT sequencing technology still shows some issues with respect to data quality, it is a very promising approach that is going to prove valuable for sequence refinement. Another important goal of this thesis was to develop a score to assess a human target’s conservation across several model species. Publicly available data on the homology relationships between genes and RNA-Seq data build the basis for this score. Using a set of presumably highly conserved genes in human and mouse, we found that the proposed score yields reasonable results. An enrichment of Gene Ontology terms further strengthened our confidence in the conservation score

SSP: An interval integer linear programming for de novo transcriptome assembly and isoform discovery of RNA-seq reads

AbstractRecent advances in the sequencing technologies have provided a handful of RNA-seq datasets for transcriptome analysis. However, reconstruction of full-length isoforms and estimation of the expression level of transcripts with a low cost are challenging tasks. We propose a novel de novo method named SSP that incorporates interval integer linear programming to resolve alternatively spliced isoforms and reconstruct the whole transcriptome from short reads. Experimental results show that SSP is fast and precise in determining different alternatively spliced isoforms along with the estimation of reconstructed transcript abundances. The SSP software package is available at http://www.bioinf.cs.ipm.ir/software/ssp

SUFFIX TREE, MINWISE HASHING AND STREAMING ALGORITHMS FOR BIG DATA ANALYSIS IN BIOINFORMATICS

In this dissertation, we worked on several algorithmic problems in bioinformatics using mainly three approaches: (a) a streaming model, (b) sux-tree based indexing, and (c) minwise-hashing (minhash) and locality-sensitive hashing (LSH). The streaming models are useful for large data problems where a good approximation needs to be achieved with limited space usage. We developed an approximation algorithm (Kmer-Estimate) using the streaming approach to obtain a better estimation of the frequency of k-mer counts. A k-mer, a subsequence of length k, plays an important role in many bioinformatics analyses such as genome distance estimation. We also developed new methods that use sux tree, a trie data structure, for alignment-free, non-pairwise algorithms for a conserved non-coding sequence (CNS) identification problem. We provided two different algorithms: STAG-CNS to identify exact-matched CNSs and DiCE to identify CNSs with mismatches. Using our algorithms, CNSs among various grass species were identified. A different approach was employed for identification of longer CNSs ( 100 bp, mostly found in animals). In our new method (MinCNE), the minhash approach was used to estimate the Jaccard similarity. Using also LSH, k-mers extracted from genomic sequences were clustered and CNSs were identified. Another new algorithm (MinIsoClust) that also uses minhash and LSH techniques was developed for an isoform clustering problem. Isoforms are generated from the same gene but by alternative splicing. As the isoform sequences share some exons but in different combinations, regular sequencing clustering methods do not work well. Our algorithm generates clusters for isoform sequences based on their shared minhash signatures. Finally, we discuss de novo transcriptome assembly algorithms and how to improve the assembly accuracy using ensemble approaches. First, we did a comprehensive performance analysis on different transcriptome assemblers using simulated benchmark datasets. Then, we developed a new ensemble approach (Minsemble) for the de novo transcriptome assembly problem that integrates isoform-clustering using minhash technique to identify potentially correct transcripts from various de novo transcriptome assemblers. Minsemble identified more correctly assembled transcripts as well as genes compared to other de novo and ensemble methods. Adviser: Jitender S. Deogu

Development and Application of Next-Generation Sequencing Methods to Profile Cellular Translational Dynamics

The transmission of genetic information from the transcription of DNA to RNA and the subsequent translation of RNA into protein is often abstracted into a linear process. However, as methods and technologies to measure the genomic, transcriptomic, and proteomic content of cells have advanced, so too has our understanding that the transmission of genetic information does not always flow in a lossless manner. For instance, changes observed in messenger RNA (mRNA) abundance are not always retained at the proteomic level. Indeed, a diverse array of mechanisms have been identified that exert regulatory control over this transmission of information. Next-generation short read sequencing has driven many of these insights and provided increasingly nuanced understanding of these regulatory mechanisms. However, the continued development and application of sequencing methodologies and analytics are required to properly contextualize many of these insights on a more global scale. Ribosome profiling is one such recent advancement which enriches for ribosome-protected fragments of mRNA; sequencing and analysis of these ribosome-protected mRNA fragments enables profiling of the translational content of a sample. The aim of this dissertation is to address the need for the development and application of statistical and analytical algorithms to profile the regulatory factors that contribute to the translational dynamics in cells. In the first chapter, I survey the development and application of next-generation sequencing methods for the profiling and computational analysis of translation and translational dynamics. In the second chapter of this thesis, I present SPECtre, a software package that identifies regions of active translation through measurement of the translational engagement of ribosomes over a transcript. SPECtre achieves high sensitivity and specificity in its classification of regions undergoing translation by leveraging the codon-dependent elongation of peptides; this tri-nucleotide periodicity is evident in the alignment of ribosome profiling sequence reads to a reference transcriptome. SPECtre classifies actively translated transcripts according to their coherence in read coverage over a region to an optimal tri-nucleotide signal. In the third chapter, I describe the application of SPECtre to identify the translation of upstream-initiated open-reading frames that may regulate differentiation in a neuron-like cell model. uORFs are transcripts that result from the initiation of translation from AUG, and under certain biological constraints, from non-AUG sequences localized in the 5’ untranslated regions of annotated protein-coding genes. Subsets of these uORFs have been implicated in the regulation of their downstream protein-coding genes in yeast, mice and humans. In this chapter, I provide further evidence for this regulation as well as the spatial context for the functional consequences of uORF translation on downstream protein-coding genes in a neuron-like cell line model of differentiation. Finally, in the fourth chapter, I outline a strategy using our coherence-based translational scoring algorithm to profile ribosomal engagement over chimeric gene fusion breakpoints in prostate cancer. Here, known breakpoints from current annotation databases are integrated with novel junctions nominated by existing whole genome and transcriptomic gene fusion detection algorithms, and the translational profile over these chimeric junctions using SPECtre is measured. This provides an additional layer of translational evidence to known and novel gene fusion breakpoints in prostate cancer. Ongoing development of a database and visualization platform based on these results will enable integrative insights into the transcriptional and translational topology of these breakpoints.PHDBioinformaticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/144106/1/stonyc_1.pd