Academic literature on the topic 'Next-generation sequencing RNA-Seq'

Abstract BACKGROUND It was recently discovered that abundant and stable extracellular RNA (exRNA) species exist in bodily fluids. Saliva is an emerging biofluid for biomarker development for noninvasive detection and screening of local and systemic diseases. Use of RNA-Sequencing (RNA-Seq) to profile exRNA is rapidly growing; however, no single preparation and analysis protocol can be used for all biofluids. Specifically, RNA-Seq of saliva is particularly challenging owing to high abundance of bacterial contents and low abundance of salivary exRNA. Given the laborious procedures needed for RNA-Seq library construction, sequencing, data storage, and data analysis, saliva-specific and optimized protocols are essential. METHODS We compared different RNA isolation methods and library construction kits for long and small RNA sequencing. The role of ribosomal RNA (rRNA) depletion also was evaluated. RESULTS The miRNeasy Micro Kit (Qiagen) showed the highest total RNA yield (70.8 ng/mL cell-free saliva) and best small RNA recovery, and the NEBNext library preparation kits resulted in the highest number of detected human genes [5649–6813 at 1 reads per kilobase RNA per million mapped (RPKM)] and small RNAs [482–696 microRNAs (miRNAs) and 190–214 other small RNAs]. The proportion of human RNA-Seq reads was much higher in rRNA-depleted saliva samples (41%) than in samples without rRNA depletion (14%). In addition, the transfer RNA (tRNA)-derived RNA fragments (tRFs), a novel class of small RNAs, were highly abundant in human saliva, specifically tRF-4 (4%) and tRF-5 (15.25%). CONCLUSIONS Our results may help in selection of the best adapted methods of RNA isolation and small and long RNA library constructions for salivary exRNA studies.

3065 Background: The development of new biomarkers often requires fresh frozen (FF) samples. Recently we showed that microarray gene expression data generated from FFPE material are comparable to data extracted from the FF counterpart, including known signatures such as the 70-gene prognosis signature (Mittempergher L et al., 2011). As described by Luo et al (2010) RNA profiling using next generation sequencing (RNA-Seq) is now applicable to archival FFPE specimens. Methods: Technical performance and the comparison between the RNA-Seq 70-gene read-out and the MammaPrint test (Glas et al., 2006) is evaluated in a series of 15 patients (11/15 with matched FFPE/FF material). RNA-Seq was carried out using minor adjustments of the Illumina TruSeq RNA preparation method. RNA sequencing libraries were prepared starting from 100ng of total RNA. Next, the DSN (Duplex-Specific Nuclease) normalization process was used to remove ribosomal RNA and other abundant transcripts (Luo et al, 2010). The libraries were paired-end sequenced on the Illumina HiSeq 2000 instrument with multiplexing of 4 libraries per lane. The resulting sequences were mapped to the human reference genome (build 37) using TopHat 1.3.1(Trapnell et al., 2009). The HTSeq-count tool was used to generate the total number of uniquely mapped reads for each gene. Results: Between 14% and 45% of the total number of reads were assigned to protein-coding genes. The minimum coverage per 1000bp of CDS was 38 reads. The 70 MammaPrint genes were successfully mapped to the RNA-Seq transcripts. We calculated the Pearson correlation coefficient between the centroids of the original good prognosis template (van’t Veer et al., 2002) and the 70-gene read count determined by RNA-Seq of each sample. Predictions based on the 70-gene RNA-Seq data showed a high agreement with the actual MammaPrint test predictions (>90%), irrespective of whether the RNA-seq was performed on FF or FFPE tissue. Conclusions: New generation RNA-sequencing is a feasible technology to assess diagnostic signatures.

Abstract It is widely accepted that diverse viral infections can at least contribute to the development of tumor cells in various ways. The viruses can directly alter the eukaryotic genome by DNA integration, alter the gene regulation, or may cause a chronic inflammation. Viral infections can either be lytic causing the production of new viruses or latent with no viral replication. However, in both cases various viral genes are constantly transcribed in the eukaryotic cells. Next generation sequencing (NGS) offers the possibility to capture the whole transcriptome of the cells via RNA-seq including host and viral mRNA. Over the years, we have detected human pathogenic viruses as well as other viruses potentially infecting human cell lines from our cell lines collection applying PCR or RT-PCR. The viruses comprise Epstein-Barr-virus (EBV; human herpesvirus type 4), hepatitis B virus (HBV), hepatitis C virus (HCV), human herpesvirus type 8 (HHV-8), human immunodeficiency virus type 1 and 2 (HIV-1/-2), human papilloma virus (HPV), human T-lymphotropic virus type 1 and 2 (HTLV-1/-2), squirrel monkey retrovirus (SMRV), xenotropic murine leukemia virus (XMLV; including xenotropic murine leukemia virus related virus, XMRV). These data were now compared with the results obtained from the evaluation of RNA-seq and whole exome sequencing (WES) data of the Cancer Cell Lines Encyclopedia (CCLE). We screened 133 RNA seq and 62 WES datasets of the CCLE sequence database for the presence of the previously mentioned viral sequences. NCBI reference complete genome sequences of the respective viruses and the human hg38 genome were used to for the alignment. In these two datasets 118 and 58 cell lines were leukemia/lymphoma cell lines, respectively, comprising the different hematopoietic lineages. Eleven B-cell derived cell lines were concordantly EBV positive in PCR analysis and in RNA-seq. The DOHH-2 cell line exhibited a relatively low number of alignments. This is concordant with our finding applying fluorescent in situ hybridization that this cell line consists of two clones: one infected with EBV and one EBV-free clone. Both clones could be separated by single cell cloning. Comparing RNA-seq and WES, RNA-seq revealed more virus specific reads relative to the total reads (max. 0.5425% vs. max. 0.0026%). Thus, RNA-seq appears to be more sensitive than WES. HHV-8 was concordantly clearly detected by PCR and RNA-seq as well as SMRV in the NAMALWA cell line. To further evaluate the robustness of the virus detection method, we included some viruses not specific for hematopoietic cells, but shown to be positive in distinct cell lines applying PCR: HBV and XMLV. We found complete concordance between PCR and RNA-seq in two liver cell lines (HEP-3B and SNU-886) and - except for the melanoma cell line SK-MEL-1 - XMLV was also detected in PCR positive cell lines by RNA-seq (three hematopoietic cell lines and six non-hematopoietic cell lines). Concerning the SK-MEL-1 cell line, different subcultures of the cell line might have been tested with the two methods, one subculture after heterotransplantation into rodents with PCR assay and the RNA-seq negative one originating from a subclone without previous heterotransplantation. Taken together, only one out of 21 virus positive cell lines were discordant applying RNA-seq. On the other hand, all PCR-negative cell lines were also negative by RNA-seq. Significant background alignments in the range of 0 to 850 reads could be detected only with the retroviruses XMLV, SMRV and HTLV-1 regarding RNA-seq, whereas the positive samples were all above 1x105. The background alignments might be attributed to some homologue sequences to endogenous retroviral elements in the human genome. In summary, RNA-seq can be used as reliable and single-step method to analyze simultaneously a panel of potential virus infections in cell cultures and thereby delivering additional viral information beside host gene expression. Future studies might demonstrate whether non-human mappable reads in RNA-seq data could be used to detect new viruses infecting human cells and being potentially implicated in tumor formation. Disclosures No relevant conflicts of interest to declare.

Schizophrenia (SCZ) is a serious psychiatric disorder that affects 1% of general population and places a heavy burden worldwide. The underlying genetic mechanism of SCZ remains unknown, but studies indicate that the disease is associated with a global gene expression disturbance across many genes. Next-generation sequencing, particularly of RNA sequencing (RNA-Seq), provides a powerful genome-scale technology to investigate the pathological processes of SCZ. RNA-Seq has been used to analyze the gene expressions and identify the novel splice isoforms and rare transcripts associated with SCZ. This paper provides an overview on the genetics of SCZ, the advantages of RNA-Seq for transcriptome analysis, the accomplishments of RNA-Seq in SCZ cohorts, and the applications of induced pluripotent stem cells and RNA-Seq in SCZ research.

Mutations identified in acute myeloid leukemia patients are useful for prognosis and for selecting targeted therapies. Detection of such mutations using next-generation sequencing data requires a computationally intensive read mapping step followed by several variant calling methods. Targeted mutation identification drastically shifts the usual tradeoff between accuracy and performance by concentrating all computations over a small portion of sequence space. Here, we present km, an efficient approach leveraging k-mer decomposition of reads to identify targeted mutations. Our approach is versatile, as it can detect single-base mutations, several types of insertions and deletions, as well as fusions. We used two independent cohorts (The Cancer Genome Atlas and Leucegene) to show that mutation detection by km is fast, accurate, and mainly limited by sequencing depth. Therefore, km allows the establishment of fast diagnostics from next-generation sequencing data and could be suitable for clinical applications.

RNA-Seq, named as an abbreviation of "RNA sequencing" and sometimes spelled RNA-seq, RNAseq, or RNASeq, uses next-generation sequencing (NGS) to reveal the presence and quantity of ribonucleic acid (RNA) in a biological sample at a given moment.[1][2] RNA-Seq is used to analyze the continuously changing cellular transcriptome (Figure 1). Specifically, RNA-Seq facilitates the ability to look at alternative gene spliced transcripts, post-transcriptional modifications, gene fusion, mutations/single nucleotide polymorphisms (SNPs) and changes in gene expression over time, or differences in gene expression in different groups or treatments.[3] In addition to messenger RNA (mRNA) transcripts, RNA-Seq can look at different populations of RNA to include total RNA, small RNA, such as microRNA (miRNA), transfer RNA (tRNA), and ribosomal profiling.[4] RNA-Seq can also be used to determine exon/intron boundaries and verify or amend previously annotated 5' and 3' gene boundaries. Recent advances in RNA-Seq include single cell sequencing, in situ sequencing of fixed tissue, and native RNA molecule sequencing with single-molecule real-time sequencing.[5] Prior to RNA-Seq, gene expression studies were done with hybridization-based microarrays. Issues with microarrays include cross-hybridization artifacts, poor quantification of lowly and highly expressed genes, and needing to know the sequence a priori.[6] Because of these technical issues, transcriptomics transitioned to sequencing-based methods. These progressed from Sanger sequencing of Expressed Sequence Tag libraries, to chemical tag-based methods (e.g., serial analysis of gene expression), and finally to the current technology, next-gen sequencing of complementary DNA ( cDNA), notably RNA-Seq.

Latest breakthrough in high-throughput DNA sequencing have been launched different arenas for transcriptome analyses, jointly named RNA-seq (RNA-sequencing). It exposes the existence and amount of RNA in a biotic sample at a specific time by utilizing next generation sequencing (NGS). In this review, we aimed to explore the several methods which are applied in analyzing RNA-seq data. We also discussed its importance over microarray data. As establishment of several methods have already taken place to analyze RNA-seq data, therefore, further analysis is very essential to select the best one to avoid false positive outcomes.

Purpose Patients with cancer who graciously consent for autopsy represent an invaluable resource for the study of cancer biology. To advance the study of tumor evolution, metastases, and resistance to treatment, we developed a next-generation rapid autopsy program integrated within a broader precision medicine clinical trial that interrogates pre- and postmortem tissue samples for patients of all ages and cancer types. Materials and Methods One hundred twenty-three (22%) of 554 patients who consented to the clinical trial also consented for rapid autopsy. This report comprises the first 15 autopsies, including patients with metastatic carcinoma (n = 10), melanoma (n = 1), and glioma (n = 4). Whole-exome sequencing (WES) was performed on frozen autopsy tumor samples from multiple anatomic sites and on non-neoplastic tissue. RNA sequencing (RNA-Seq) was performed on a subset of frozen samples. Tissue was also used for the development of preclinical models, including tumor organoids and patient-derived xenografts. Results Three hundred forty-six frozen samples were procured in total. WES was performed on 113 samples and RNA-Seq on 72 samples. Successful cell strain, tumor organoid, and/or patient-derived xenograft development was achieved in four samples, including an inoperable pediatric glioma. WES data were used to assess clonal evolution and molecular heterogeneity of tumors in individual patients. Mutational profiles of primary tumors and metastases yielded candidate mediators of metastatic spread and organotropism including CUL9 and PIGM in metastatic ependymoma and ANKRD52 in metastatic melanoma to the lung. RNA-Seq data identified novel gene fusion candidates. Conclusion A next-generation sequencing–based autopsy program in conjunction with a premortem precision medicine pipeline for diverse tumors affords a valuable window into clonal evolution, metastasis, and alterations underlying treatment. Moreover, such an autopsy program yields robust preclinical models of disease.

Next-generation sequencing technologies have revolutionarily advanced sequence-based research with the advantages of high-throughput, high-sensitivity, and high-speed. RNA-seq is now being used widely for uncovering multiple facets of transcriptome to facilitate the biological applications. However, the large-scale data analyses associated with RNA-seq harbors challenges. In this study, we present a detailed overview of the applications of this technology and the challenges that need to be addressed, including data preprocessing, differential gene expression analysis, alternative splicing analysis, variants detection and allele-specific expression, pathway analysis, co-expression network analysis, and applications combining various experimental procedures beyond the achievements that have been made. Specifically, we discuss essential principles of computational methods that are required to meet the key challenges of the RNA-seq data analyses, development of various bioinformatics tools, challenges associated with the RNA-seq applications, and examples that represent the advances made so far in the characterization of the transcriptome.

Thesis advisor: Gabor Marth
While a PhD student in Dr. Gabor Marth's laboratory, I have had primary responsibility for two projects focused on using RNA-Seq to measure differential gene expression. In the first project we used RNA-Seq to identify differentially expressed genes in four yeast species and I analyzed the findings in terms of the evolution of gene expression. In this experiment, gene expression was measured using two biological replicates of each species of yeast. While we had several interesting biological findings, during the analysis we dealt with several statistical issues that were caused by the experiment's low number of replicates. The cost of sequencing has decreased rapidly since this experiment's design and many of these statistical issues can now practically be avoided by sequencing a greater number of samples. However, there is little guidance in the literature as to how to intelligently design an RNA-Seq experiment in terms of the number of replicates that are required and how deeply each replicate must be sequenced. My second project, therefore, was to develop Scotty, a web-based program that allows users to perform power analysis for RNA-Seq experiments. The yeast project resulted in a highly accessed first author publication in BMC Genomics in 2011. I have structured my dissertation as follows: The first chapter, entitled General Issues in RNA-Seq, is intended to synthesize the themes and issues of RNA-Seq statistical analysis that were common to both papers. In this section, I have discussed the main findings from the two papers as they relate to analyzing RNA-Seq data. Like the Scotty application, this section is designed to be "used" by wet-lab biologists who have a limited background in statistics. While some background in statistics would be required to fully understand the following chapters, the essence of this background can be gained by reading this first chapter. The second and third chapters contain the two papers that resulted from the two RNA-Seq projects. Each chapter contains both the original manuscript and original supplementary methods and data section. Finally, I include brief summaries of my contributions to the two papers on which I was a middle author. The first was a functional analysis of the genomic regions affected by mobile element insertions as a part of Chip Stewart's paper with the 1000 Genome Consortium. This paper was published in Plos Genetics. The second was a cluster analysis of microarray gene expression in Toxoplasma gondii, which was included as part of Alexander Lorestani et al.'s paper, Targeted proteomic dissection of Toxoplasma cytoskeleton sub-compartments using MORN1. This paper is currently under review. The yeast project was a collaborative effort between Jesse Gray, Michael Springer, and Allen Costa at Harvard Medical School, Jeffery Chuang here at Boston College, and members of the Marth lab. Jesse Gray conceived of the project. While I wrote the draft for the manuscript, many people, particularly Gabor Marth, provided substantial guidance on the actual text. I conceived of and implemented Scotty and wrote its manuscript with only editorial assistance from my co-authors. I produced all figures for the two manuscripts. Chip Stewart provided extensive guidance and mentorship to me on all aspects of my statistical analyses for both projects
Thesis (PhD) — Boston College, 2012
Submitted to: Boston College. Graduate School of Arts and Sciences
Discipline: Biology

Next Generation Sequencing (NGS) is a new technology that has revolutionized the way we study living organisms. Where previously only a few genes could be studied at a time through targeted direct probing, NGS offers the possibility to perform measurements for a whole genome at once. The drawback is that the amount of data generated in the process is large and extracting useful information from it requires new methods to process and analyze it. The main contribution of this thesis is the development of a novel experimental method coined tagRNA-seq, combining 5’tagRACE, a previously developed technique, with RNA-sequencing technology. Briefly, tagRNA-seq makes it possible to identify the 5’ ends of RNAs in bacteria and directly probe for their type, primary or processed, by ligating short RNA sequences, the tags, to the beginnings of RNA molecules. We used the method to directly probe for transcription start and processing sites in two bacterial species, Escherichiacoli and Enterococcus faecalis. It was also used to study polyadenylation in E. coli, where the ability to identify processed RNA molecules proved to be useful to separate direct and indirect regulatory effects of this mechanism. We also demonstrate how data from tagRNA-seq experiments can be used to increase confidence on the discovery of anti-sense transcripts in bacteria. Analyses of RNA-seq data obtained in the context of these experiments revealed subtle artifacts in the coverage signal towards gene ends, that we were able to explain and quantify based Kolmogorov’s broken stick model. We also discovered evidences for circularization of a few RNA transcripts, both in our own data sets and publicly available data. Designing the tags used in tagRNA-seq led us to the problem of words absent from a text. We focus on a particular subset of these, the minimal absent words (MAWs), and develop a theory providing a complete description of their size distribution in random text. We also show that MAWs in genomes from viruses and living organisms almost always exhibit a behavior different from random texts in the tail of the distribution, and that MAWs from this tail are closely related to sequences present in the genome that preferentially appear in regions with important regulatory functions. Finally, and independently from tagRNA-seq, we propose a new approach to the problem of bacterial community reconstruction in metagenomic, based on techniques from compressed sensing. We provide a novel algorithm competing with state-of-the-art techniques in the field.

QC 20150930

Mestrado em Biomedicina Molecular
The non-standard decoding of the CUG codon in Candida cylindracea raises a number of questions about the evolutionary process of this organism and other species Candida clade for which the codon is ambiguous. In order to find some answers we studied the transcriptome of C. cylindracea, comparing its behavior with that of Saccharomyces cerevisiae (standard decoder) and Candida albicans (ambiguous decoder). The transcriptome characterization was performed using RNA-seq. This approach has several advantages over microarrays and its application is booming. TopHat and Cufflinks were the software used to build the protocol that allowed for gene quantification. About 95% of the reads were mapped on the genome. 3693 genes were analyzed, of which 1338 had a non-standard start codon (TTG/CTG) and the percentage of expressed genes was 99.4%. Most genes have intermediate levels of expression, some have little or no expression and a minority is highly expressed. The distribution profile of the CUG between the three species is different, but it can be significantly associated to gene expression levels: genes with fewer CUGs are the most highly expressed. However, CUG content is not related to the conservation level: more and less conserved genes have, on average, an equal number of CUGs. The most conserved genes are the most expressed. The lipase genes corroborate the results obtained for most genes of C. cylindracea since they are very rich in CUGs and nothing conserved. The reduced amount of CUG codons that was observed in highly expressed genes may be due, possibly, to an insufficient number of tRNA genes to cope with more CUGs without compromising translational efficiency. From the enrichment analysis, it was confirmed that the most conserved genes are associated with basic functions such as translation, pathogenesis and metabolism. From this set, genes with more or less CUGs seem to have different functions. The key issues on the evolutionary phenomenon remain unclear. However, the results are consistent with previous observations and shows a variety of conclusions that in future analyzes should be taken into consideration, since it was the first time that such a study was conducted.
A descodificação não-standard do codão CUG na Candida cylindracea levanta uma série de questões sobre o processo evolutivo deste organismo e de outras espécies do subtipo Candida para as quais o codão é ambíguo. No sentido de encontrar algumas respostas procedeu-se ao estudo do transcriptoma de C. cylindracea, comparando o seu comportamento com o de Saccharomyces cerevisiae (descodificador standard) e de Candida albicans (descodificador ambíguo). A caracterização do transcriptoma foi realizada a partir de RNA-seq. Esta metodologia apresenta várias vantagens em relação aos microarrays e a sua aplicação encontra-se em franca expansão. TopHat e Cufflinks foram os softwares utilizados na construção do protocolo que permitiu efectuar a quantificação génica. Cerca de 95% das reads alinharam contra o genoma. Foram analisados 3693 genes, 1338 dos quais com codão start não-standard (TTG/CTG) e a percentagem de genoma expresso foi de 99,4%. Maioritarimente, os genes têm níveis de expressão intermédios, alguns apresentam pouca ou nenhuma expressão e uma minoria é altamente expressa. O perfil de distribuição do codão CUG entre as três espécies é muito diferente, mas pode associar-se significativamente aos níveis de expressão: os genes com menos CUGs são os mais altamente expressos. Porém, o conteúdo em CUG não se relaciona com o nível de conservação: genes mais e menos conservados têm, em média, igual número de CUGs. Os genes mais conservados são os mais expressos. Os genes de lipases corroboram os resultados obtidos para os genes de C. cylindracea em geral, sendo muito ricos em CUGs e nada conservados. A quantidade reduzida de codões CUG que se observa em genes altamente expressos pode dever-se, eventualmente, a um número insuficiente de genes de tRNA para fazer face a mais CUGs sem comprometer a eficiência da tradução. A partir da análise de enriquecimento foi possível confirmar que os genes mais conservados estão associados a funções básicas como tradução, patogénese e metabolismo. Dentro destes, os genes com mais e menos CUGs parecem ter funções diferentes. As questões-chave sobre o fenómeno evolutivo permanecem por esclarecer. No entanto, os resultados são compatíveis com as observações anteriores e são apresentadas várias conclusões que em futuras análises devem ser tidas em consideração, já que foi a primeira vez que um estudo deste tipo foi realizado.

Next-generation sequencing (NGS) technologies have revolutionised research into nature and diversity of genomes and transcriptomes. Since the initial description of these technology platforms over a decade ago, massively parallel RNA sequencing (RNA-seq) has driven many advances in the characterization and quantification of transcriptomes. RNA-seq is a powerful gene expression profiling technology enabling transcript discovery and provides a far more precise measure of the levels of transcripts and their isoforms than other methods e.g. microarray. However, the analysis of RNA-seq data remains a significant challenge for many biologists. The data generated is large and the tools for its assembly, analysis and visualisation are still under development. Assemblies of reads can be inspected using tools such as the Integrative Genomics Viewer (IGV) where visualisation of results involves ‘stacking’ the reads onto a reference genome. Whilst sufficient for many needs, when the underlying variance of the genome or transcript assemblies is complex, this visualisation method can be limiting; errors in assembly can be difficult to spot and visualisation of splicing events may be challenging. Data visualisation is increasingly recognised as an essential component of genomic and transcriptomic data analysis, enabling large and complex datasets to be better understood. An approach that has been gaining traction in biological research is based on the application of network visualisation and analysis methods. Networks consist of nodes connected by edges (lines), where nodes usually represent an entity and edge a relationship between them. These are now widely used for plotting experimentally or computationally derived relationships between genes and proteins. The overall aim of this PhD project was to explore the use of network-based visualisation in the analysis and interpretation of RNA-seq data. In chapter 2, I describe the development of a data pipeline that has been designed to go from ‘raw’ RNA-seq data to a file format which supports data visualisation as a ‘DNA assembly graph’. In DNA assembly graphs, nodes represent sequence reads and edges denote a homology between reads above a defined threshold. Following the mapping of reads to a reference sequence and defining which reads a map to a given loci, pairwise sequence alignments are performed between reads using MegaBLAST. This provides a weighted similarity score that is used to define edges between reads. Visualisation of the resulting networks is then carried out using BioLayout Express3D that can render large networks in 3-D, thereby allowing a better appreciation of the often-complex network structure. This pipeline has formed the basis for my subsequent work on the exploring and analysing alternative splicing in human RNA-seq data. In the second half of this chapter, I provide a series of tutorials aimed at different types of users allowing them to perform such analyses. The first tutorial is aimed at computational novices who might want to generate networks using a web-browser and pre-prepared data. Other tutorials are designed for use by more advanced users who can access the code for the pipeline through GitHub or via an Amazon Machine Image (AMI). In chapter 3, the utility of network-based visualisations of RNA-seq data is explored using data processed through the pipeline described in Chapter 2. The aim of the work described in this chapter was to better understand the basic principles and challenges associated with network visualisation of RNA-seq data, in particular how it could be used to visualise transcript structure and splice-variation. These analyses were performed on data generated from four samples of human fibroblasts taken at different time points during their entry into cell division. One of the first challenges encountered was the fact that the existing network layout algorithm (Fruchterman- Reingold) implemented within BioLayout Express3D did not result in an optimal layout of the unusual graph structures produced by these analyses. Following the implementation of the more advanced layout algorithm FMMM within the tool, network structure could be far better appreciated. Using this layout method, the majority of genes sequenced to an adequate depth assemble into networks with a linear ‘corkscrew’ appearance and when representing single isoform transcripts add little to existing views of these data. However, in a small number of cases (~5%), the networks generated from transcripts expressed in human fibroblasts possess more complex structures, with ‘loops’, ‘knots’ and multiple ends being observed. In a majority of cases examined, these loops were associated with alternative splicing events, a fact confirmed by RT-PCR analyses. Other DNA assembly networks representing the mRNAs for genes such as MKI67 showed knot-like structures, which was found to be due to the presence of repetitive sequence within an exon of the gene. In another case, CENPO the unusual structure observed was due to reads derived from an overlapping gene of ADCY3 gene present on the opposite strand with reads being wrongly mapped to CENPO. Finally, I explored the use of a network reduction strategy as an approach to visualising highly expressed genes such as GAPDH and TUBA1C. Having successfully demonstrated the utility of networks in analysing transcript isoforms in data derived from a single cell type I set out to explore its utility in analysing transcript variation in tissue data where multiple isoforms expressed by different cells within the tissue might be present in a given sample. In chapter 4, I explore the analysis of transcript variation in an RNA-seq dataset derived from human tissue. The first half of this chapter describes the quality control of these data again using a network-based approach but this time based the correlation in expression between genes and samples. Of the 95 samples derived from 27 human tissues, 77 passed the quality control. A network was constructed using a correlation threshold of r ≥ 0.9, which comprised 6,109 nodes (genes) and 1,091,477 edges (correlations) and clustered. Subsequently, the profile and gene content of each cluster was examined and enrichment of GO terms analysed. In the second half of this chapter, the aim was to detect and analyse alternative splicing events between different tissues using the rMATS tool. By using a false-discovery rate (FDR) cut-off of < 0.01, I found that in comparisons of brain vs. heart, brain vs. liver and heart vs. liver, the program reported 4,992, 4,804 and 3,990 splicing events, respectively. Of these events, only 78 splicing events (52 genes) with more than 50% of exon inclusion level and expression level more than FPKM 30. To further explore the sometimes-complex structure of transcripts diversity derived from tissue, RNAseq assembly networks for KLC1, SORBS2, GUK1, and TPM1 were explored. Each of these networks showed different types of alternative splicing events and it was sometimes difficult to determine the isoforms expressed between tissues using other approaches. For instance, there is an issue in visualising the read assembly of long genes such as KLC1 and SORBS2, using a Sashimi plots or even Vials, just because of the number of exons and the size of their genomic loci. In another case of GUK1, tissue-specific isoform expression was observed when a network of three tissues was combined. Arguably the most complex analysis is the network of TPM1 where the uniquification step was employed for this highly expressed gene. In chapter 5, I perform a usability testing for NGS Graph Generator web application and visualising RNA-seq assemblies as a network using BioLayout Express3D. This test was important to ensure that the application is well received and utilised by the user.
Almost all participants of this usability test agree that this application would encourage biologists to visualise and understand the alternative splicing together with existing tools. The participants agreed that Sashimi plots rather difficult to view and visualise and perhaps would lose something interesting features. However, there were also reviews of this application that need improvements such as the capability to analyse big network in a short time, side-by-side analysis of network with Sashimi plot and Ensembl. Additional information of the network would be necessary to improve the understanding of the alternative splicing. In conclusion, this work demonstrates the utility of network visualisation of RNAseq data, where the unusual structure of these networks can be used to identify issues in assembly, repetitive sequences within transcripts and splice variation. As such, this approach has the potential to significantly improve our understanding of transcript complexity. Overall, this thesis demonstrates that network-based visualisation provides a new and complementary approach to characterise alternative splicing from RNA-seq data and has the potential to be useful for the analysis and interpretation of other kinds of sequencing data.

Two of the main bioinformatics fields that have been influenced by the introduction of the Next-Generation Sequencing (NGS) techniques are transcriptomics and metagenomics. The adoption of these new methods to sequence DNA/RNA molecules has drastically changed the kind and also the amount of produced data. The effect is that all the developed algorithms and tools working on traditional data cannot be applied on NGS data. For this reason, in this thesis we face two central problems in two fields: transcriptmics and metagenomics. The first one regards the characterization of the Alternative Splicing (AS) events starting from NGS sequences coming from transcripts (called RNA-Seq reads). To this aim we have modeled the structure of a gene, with respect to the AS variations occurring in it, by using a graph representation (called splicing graph). More specifically, we have identified the conditions for the correct reconstruction of the splicing graph, starting from RNA-Seq data, and we have realized an algorithm for its construction. Moreover, our method is able to correct reconstruct the splicing graph even when the input RNA-Seq reads do not respect the identified conditions. Finally, we have performed an experimental analysis of our procedure in order to validated the obtained results. The second problem we face in this thesis is the assignment of NGS read, coming from a metagenomic sample, to a reference taxonomic tree, in order to assess the composition of the sample and classify the unknown micro-organisms in it. This is done by aligning the reads to the taxonomic tree and then choosing (when there are more valid matches) the node that best represents the read. This choice is based on the calculation of a Penalty Score (PS) function for all the nodes descending from the lowest common ancestor of the valid matches in the tree. We have realized an optimal algorithm for the computation of the PS function, based on the so called skeleton tree, which improve the performances of the taxonomic assignment procedure. We have also implemented the method by using more efficient data structures, with respect to the one used in the previous version of the procedure. Finally, we have offered the possibility to switch among different taxonomies by developing a method to map trees and translate the input alignments.

The main theme of this dissertation is to develop a distributed computational pipeline for processing next-generation RNA sequencing (RNA-seq) data. RNA-seq experiments generate hundreds of millions of short reads for each DNA/RNA sample. There are many existing bioinformatics tools developed for the analysis and visualization of this data, but very large studies present computational and organizational challenges that are difficult to overcome manually. We designed a comprehensive pipeline for the analysis of RNA sequencing which leverages many existing tools and parallel computing technology to facilitate the analysis of extremely large studies. RNA CoMPASS provides a web-based graphical user interface and distributed computational pipeline including endogenous transcriptome quantification and additionally the investigation of exogenous sequences.

Characterization of the RNA transcriptome by next-generation sequencing can produce an unprecedented yield of information that provides novel biologic insights. I describe four approaches for sequencing different aspects of the transcriptome and provide computational tools to analyze the resulting data. Methods that query the dynamic range of gene expression, low expressing transcripts, micro RNA levels, and start-site usage of transcripts are described.

The main theme of this thesis research is concerned with developing a computational pipeline for processing Next-generation RNA sequencing (RNA-seq) data. RNA-seq experiments generate tens of millions of short reads for each DNA/RNA sample. The alignment of a large volume of short reads to a reference genome is a key step in NGS data analysis. Although storing alignment information in the Sequence Alignment/Map (SAM) or Binary SAM (BAM) format is now standard, biomedical researchers still have difficulty accessing useful information. In order to assist biomedical researchers to conveniently access essential information from NGS data files in SAM/BAM format, we have developed a Graphical User Interface (GUI) software tool named SAMMate to pipeline human transcriptome quantification. SAMMate allows researchers to easily process NGS data files in SAM/BAM format and is compatible with both single-end and paired-end sequencing technologies. It also allows researchers to accurately calculate gene expression abundance scores.

Master of Science
Department of Plant Pathology
John P. Fellers
Allan K. Fritz
Leaf rust is caused by Puccinia triticina and is one of the most widespread diseases of wheat worldwide. Breeding for resistance is one of the most effective methods of control. Lr16 is a leaf rust resistance gene that provides partial resistance at the seedling stage. One objective of this study was to use RNA-seq and in silico subtraction to develop new resistance gene analog (RGA) markers linked to Lr16. RNA was isolated from the susceptible wheat cultivar Thatcher (Tc) and the resistant Thatcher isolines TcLr10, TcLr16, and TcLr21. Using in silico subtraction, Tc isoline ESTs that did not align to the Tc reference were assembled into contigs and analyzed using BLAST. Primers were designed from 137 resistance gene analog sequences not found in Tc. A population of 260 F[subscript]2 lines derived from a cross between the rust-susceptible cultivar Chinese Spring (CS) and a Thatcher isoline containing Lr16 (TcLr16) was developed for mapping these markers. Two RGA markers XRGA266585 and XRGA22128 were identified that mapped 1.1 cM and 23.8 cM from Lr16, respectively. Three SSR markers Xwmc764, Xwmc661, and Xbarc35 mapped between these two RGA markers at distances of 4.1 cM, 10.7 cM, and 16.1 cM from Lr16, respectively. Another objective of this study was to use genotyping-by-sequencing (GBS) to develop single nucleotide polymorphism (SNP) markers closely linked to Lr16. DNA from 22 resistant and 22 susceptible F[subscript]2 plants from a cross between CS and TcLr16 was used for GBS analysis. A total of 39 Kompetitive Allele Specific PCR (KASP) markers were designed from SNPs identified using the UNEAK and Tassel pipelines. The KASP marker XSNP16_TP1456 mapped 0.7 cM proximal to Lr16 in a TcxTcLr16 population consisting of 129 F[subscript]2 plants. These results indicate that both techniques are viable methods to develop new molecular markers. RNA-seq and in silico subtraction were successfully used to develop two new RGA markers linked to Lr16, one of which was more closely linked than known SSR markers. GBS was also successfully used on an F[subscript]2 population to develop a KASP marker that is the most closely linked marker to Lr16 to date.

Abstract: The long-term goals of our research are to understand the regulation of sclerotial development and pathogenicity in S. sclerotior11111. The focus in this project was on the elucidation of the signaling events and environmental cues involved in the regulation of these processes, utilizing and continuously developing tools our research groups have established and/or adapted for analysis of S. sclerotiorum, Our stated objectives: To take advantage of the recent conceptual (ROS/PPs signaling) and technical (amenability of S. sclerotiorumto manipulations coupled with chemical genomics and next generation sequencing) developments to address and extend our fundamental and potentially applicable knowledge of the following questions concerning the involvement of REDOX signaling and protein dephosphorylation in the regulation of hyphal/sclerotial development and pathogenicity of S. sclerotiorum: (i) How do defects in genes involved in ROS signaling affect S. sclerotiorumdevelopment and pathogenicity? (ii) In what manner do phosphotyrosinephosphatases affect S. sclerotiorumdevelopment and pathogenicity and how are they linked with ROS and other signaling pathways? And (iii) What is the nature of activity of newly identified compounds that affect S. sclerotiori,111 growth? What are the fungal targets and do they interfere with ROS signaling? We have met a significant portion of the specific goals set in our research project. Much of our work has been published. Briefly. we can summarize that: (a) Silencing of SsNox1(NADPHoxidase) expression indicated a central role for this enzyme in both virulence and pathogenic development, while inactivation of the SsNox2 gene resulted in limited sclerotial development, but the organism remained fully pathogenic. (b) A catalase gene (Scatl), whose expression was highly induced during host infection is involved in hyphal growth, branching, sclerotia formation and infection. (c) Protein tyrosine phosphatase l (ptpl) is required for sclerotial development and is involved in fungal infection. (d) Deletion of a superoxidedismutase gene (Sssodl) significantly reduced in virulence on both tomato and tobacco plants yet pathogenicity was mostly restored following supplementation with oxalate. (e) We have participated in comparative genome sequence analysis of S. sclerotiorumand B. cinerea. (f) S. sclerotiorumexhibits a potential switch between biotrophic and necrotrophic lifestyles (g) During plant­ microbe interactions cell death can occur in both resistant and susceptible events. Non­ pathogenic fungal mutants S. sclerotior111n also cause a cell death but with opposing results. We investigated PCD in more detail and showed that, although PCD occurs in both circumstances they exhibit distinctly different features. The mutants trigger a restricted cell death phenotype in the host that unexpectedly exhibits markers associated with the plant hypersensitive (resistant) response. Using electron and fluorescence microscopy, chemical effectors and reverse genetics, we have established that this restricted cell death is autophagic. Inhibition of autophagy rescued the non-pathogenic mutant phenotype. These findings indicate that autophagy is a defense response in this interaction Thus the control of cell death, dictated by the plant (autophagy) סr the fungus (apoptosis), is decisive to the outcome of certain plant­ microbe interactions. In addition to the time and efforts invested towards reaching the specific goals mentioned, both Pls have initiated utilizing (as stated as an objective in our proposal) state of the art RNA-seq tools in order to harness this technology for the study of S. sclerotiorum. The Pls have met twice (in Israel and in the US), in order to discuss .נחd coordinate the research efforts. This included a working visit at the US Pls laboratory for performing RNA-seq experiments and data analysis as well as working on a joint publication (now published). The work we have performed expands our understanding of the fundamental biology (developmental and pathogenic) of S. sclerotioז111וז. Furthermore, based on our results we have now reached the conclusion that this fungus is not a bona fide necrotroph, but can also display a biotrophic lifestyle at the early phases of infection. The data obtained can eventually serve .נ basis of rational intervention with the disease cycle of this pathogen.