A telescope for the RNA universe : novel bioinformatic approaches to analyze RNA sequencing data

In this thesis I focus on the application of bioinformatics to analyze RNA. The type of experimental data of interest is sequencing data generated with various Next Generation Sequencing technique: nuclear RNA, cytoplasmic RNA, captured polyadenylated RNA fragments, etc. I highlight the necessity in developing new tools (e.g., to analyze nuclear RNA) and give a showcase example of implementing such tool and showing its usability on a real biological experiment. The thesis also covers existing tools to perform various types of RNA analysis and shows how these tools can be twigged and expanded to answer certain biological questions (e.g., studying changes in RNA specific to human aging). I also show how current bioinformatic approaches can be used in a particularly complex study such as investigating cancer (in this thesis, breast cancer) pathogenesis.UBL - phd migration 201

Determining the quality and complexity of next-generation sequencing data without a reference genome

Genomics, epigenetics, population genetics and bioinformatic

Transcriptome and genome sequencing uncovers functional variation in humans

Summary Genome sequencing projects are discovering millions of genetic variants in humans, and interpretation of their functional effects is essential for understanding the genetic basis of variation in human traits. Here we report sequencing and deep analysis of mRNA and miRNA from lymphoblastoid cell lines of 462 individuals from the 1000 Genomes Project – the first uniformly processed RNA-seq data from multiple human populations with high-quality genome sequences. We discovered extremely widespread genetic variation affecting regulation of the majority of genes, with transcript structure and expression level variation being equally common but genetically largely independent. Our characterization of causal regulatory variation sheds light on cellular mechanisms of regulatory and loss-of-function variation, and allowed us to infer putative causal variants for dozens of disease-associated loci. Altogether, this study provides a deep understanding of the cellular mechanisms of transcriptome variation and of the landscape of functional variants in the human genome

A telescope for the RNA universe : novel bioinformatic approaches to analyze RNA sequencing data

In this thesis I focus on the application of bioinformatics to analyze RNA. The type of experimental data of interest is sequencing data generated with various Next Generation Sequencing technique: nuclear RNA, cytoplasmic RNA, captured polyadenylated RNA fragments, etc. I highlight the necessity in developing new tools (e.g., to analyze nuclear RNA) and give a showcase example of implementing such tool and showing its usability on a real biological experiment. The thesis also covers existing tools to perform various types of RNA analysis and shows how these tools can be twigged and expanded to answer certain biological questions (e.g., studying changes in RNA specific to human aging). I also show how current bioinformatic approaches can be used in a particularly complex study such as investigating cancer (in this thesis, breast cancer) pathogenesis

A telescope for the RNA universe : novel bioinformatic approaches to analyze RNA sequencing data

In this thesis I focus on the application of bioinformatics to analyze RNA. The type of experimental data of interest is sequencing data generated with various Next Generation Sequencing technique: nuclear RNA, cytoplasmic RNA, captured polyadenylated RNA fragments, etc. I highlight the necessity in developing new tools (e.g., to analyze nuclear RNA) and give a showcase example of implementing such tool and showing its usability on a real biological experiment. The thesis also covers existing tools to perform various types of RNA analysis and shows how these tools can be twigged and expanded to answer certain biological questions (e.g., studying changes in RNA specific to human aging). I also show how current bioinformatic approaches can be used in a particularly complex study such as investigating cancer (in this thesis, breast cancer) pathogenesis

Non-sequential and multi-step splicing of the dystrophin transcript

Molecular Technology and Informatics for Personalised Medicine and HealthFunctional Genomics of Muscle, Nerve and Brain Disorder

Non-sequential and multi-step splicing of the dystrophin transcript

<p>The dystrophin protein encoding DMD gene is the longest human gene. The 2.2 Mb long human dystrophin transcript takes 16 hours to be transcribed and is co-transcriptionally spliced. It contains long introns (24 over 10kb long, 5 over 100kb long) and the heterogeneity in intron size makes it an ideal transcript to study different aspects of the human splicing process. Splicing is a complex process and much is unknown regarding the splicing of long introns in human genes.</p> <p>Here, we used ultra-deep transcript sequencing to characterize splicing of the dystrophin transcripts in 3 different human skeletal muscle cell lines, and explored the order of intron removal and multi-step splicing. Coverage and read pair analyses showed that around 40% of the introns were not always removed sequentially. Additionally, for the first time, we report that non-consecutive intron removal resulted in 3 or more joined exons which are flanked by unspliced introns and we defined these joined exons as an exon block. Lastly, computational and experimental data revealed that, for the majority of dystrophin introns, multistep splicing events are used to splice out a single intron.</p> <p>Overall, our data show for the first time in a human transcript, that multi-step intron removal is a general feature of mRNA splicing.</p

Tumor cell migration screen identifies SRPK1 as breast cancer metastasis determinant

Tumor cell migration is a key process for cancer cell dissemination and metastasis that is controlled by signal-mediated cytoskeletal and cell matrix adhesion remodeling. Using a phagokinetic track assay with migratory H1299 cells, we performed an siRNA screen of almost 1,500 genes encoding kinases/phosphatases and adhesome- and migration-related proteins to identify genes that affect tumor cell migration speed and persistence. Thirty candidate genes that altered cell migration were validated in live tumor cell migration assays. Eight were associated with metastasis-free survival in breast cancer patients, with integrin β(3)–binding protein (ITGB3BP), MAP3K8, NIMA-related kinase (NEK2), and SHC-transforming protein 1 (SHC1) being the most predictive. Examination of genes that modulate migration indicated that SRPK1, encoding the splicing factor kinase SRSF protein kinase 1, is relevant to breast cancer outcomes, as it was highly expressed in basal breast cancer. Furthermore, high SRPK1 expression correlated with poor breast cancer disease outcome and preferential metastasis to the lungs and brain. In 2 independent murine models of breast tumor metastasis, stable shRNA-based SRPK1 knockdown suppressed metastasis to distant organs, including lung, liver, and spleen, and inhibited focal adhesion reorganization. Our study provides comprehensive information on the molecular determinants of tumor cell migration and suggests that SRPK1 has potential as a drug target for limiting breast cancer metastasis

Reproducibility of high-throughput mRNA and small RNA sequencing across laboratories

RNA sequencing is an increasingly popular technology for genome-wide analysis of transcript sequence and abundance. However, understanding of the sources of technical and interlaboratory variation is still limited. To address this, the GEUVADIS consortium sequenced mRNAs and small RNAs of lymphoblastoid cell lines of 465 individuals in seven sequencing centers, with a large number of replicates. The variation between laboratories appeared to be considerably smaller than the already limited biological variation. Laboratory effects were mainly seen in differences in insert size and GC content and could be adequately corrected for. In small-RNA sequencing, the microRNA (miRNA) content differed widely between samples owing to competitive sequencing of rRNA fragments. This did not affect relative quantification of miRNAs. We conclude that distributing RNA sequencing among different laboratories is feasible, given proper standardization and randomization procedures. We provide a set of quality measures and guidelines for assessing technical biases in RNA-seq data