Models for transcript quantification from RNA-Seq

RNA-Seq is rapidly becoming the standard technology for transcriptome analysis. Fundamental to many of the applications of RNA-Seq is the quantification problem, which is the accurate measurement of relative transcript abundances from the sequenced reads. We focus on this problem, and review many recently published models that are used to estimate the relative abundances. In addition to describing the models and the different approaches to inference, we also explain how methods are related to each other. A key result is that we show how inference with many of the models results in identical estimates of relative abundances, even though model formulations can be very different. In fact, we are able to show how a single general model captures many of the elements of previously published methods. We also review the applications of RNA-Seq models to differential analysis, and explain why accurate relative transcript abundance estimates are crucial for downstream analyses

Statistical approaches to harness high throughput sequencing data in diverse biological systems

The development of novel statistical approaches to questions specific to biological systems of interest is becoming more valuable as we tackle increasingly complex problems. This thesis explores three distinct biological systems in which high throughput sequencing data is utilised, varying in research area, organism, number of sequencing platforms and datasets integrated, and structure such as matched samples; showcasing the variety of study designs and thus the need for tailored statistical approaches. First, we characterise allelic imbalance from RNA-Seq data including stringent filtering criteria and a count based likelihood ratio test. This work identified genes of particular importance in livestock genomics such as those related to energy use. Second, we outline a novel methodology to identify highly expressed genes and cells for single cell RNA-Seq data. We derive a gamma-normal mixture model to identify lowly and highly expressed components, and use this to identify novel markers for olfactory sensory neuron (OSN) maturity across publicly available mouse neuron datasets. In addition we estimate single cell networks and find that mature OSN single cell networks are more centralised than immature OSN single cell networks. Third, we develop two novel frameworks for relating information from Whole Exome DNA-Seq and RNA-Seq data when i) samples are matched and when ii) samples are not necessary matched between platforms. In the latter case, we relate functional somatic mutation driver gene scores to transcriptional network correlation disturbance using a permutation testing framework, identifying potential candidate genes for targeted therapies. In the former case, we estimate directed mutation-expression networks for each cancer using linear models, providing a useful exploratory tool for identifying novel relationships among genes. This thesis demonstrates the importance of tailored statistical approaches to further understanding across many biological systems

Doctor of Philosophy

dissertationSuccessful molecular diagnosis using an exome sequence hinges on accurate association of damaging variants to the patient's phenotype. Unfortunately, many clinical scenarios (e.g., single affected or small nuclear families) have little power to confidently identify damaging alleles using sequence data alone. Today's diagnostic tools are simply underpowered for accurate diagnosis in these situations, limiting successful diagnoses. In response, clinical genetics relies on candidate-gene and variant lists to limit the search space. Despite their practical utility, these lists suffer from inherent and significant limitations. The impact of false negatives on diagnostic accuracy is considerable because candidate-genes and variants lists are assembled ad hoc, choosing alleles based upon expert knowledge. Alleles not in the list are not considered-ending hope for novel discoveries. Rational alternatives to ad hoc assemblages of candidate lists are thus badly needed. In response, I created Phevor, the Phenotype Driven Variant Ontological Re-ranking tool. Phevor works by combining knowledge resident in biomedical ontologies, like the human phenotype and gene ontologies, with the outputs of variant-interpretation tools such as SIFT, GERP+, Annovar and VAAST. Phevor can then accurately to prioritize candidates identified by third-party variant-interpretation tools in light of knowledge found in the ontologies, effectively bypassing the need for candidate-gene and variant lists. Phevor differs from tools such as Phenomizer and Exomiser, as it does not postulate a set of fixed associations between genes and phenotypes. Rather, Phevor dynamically integrates knowledge resident in multiple bio-ontologies into the prioritization process. This enables Phevor to improve diagnostic accuracy for established diseases and previously undescribed or atypical phenotypes. Inserting known disease-alleles into otherwise healthy exomes benchmarked Phevor. Using the phenotype of the known disease, and the variant interpretation tool VAAST (Variant Annotation, Analysis and Search Tool), Phevor can rank 100% of the known alleles in the top 10 and 80% as the top candidate. Phevor is currently part of the pipeline used to diagnose cases as part the Utah Genome Project. Successful diagnoses of several phenotypes have proven Phevor to be a reliable diagnostic tool that can improve the analysis of any disease-gene search

The integrated stress response remodels the microtubule-organizing center to clear unfolded proteins following proteotoxic stress

Cells encountering stressful situations activate the integrated stress response (ISR) pathway to limit protein synthesis and redirect translation to better cope. The ISR has also been implicated in cancers, but redundancies in the stress-sensing kinases that trigger the ISR have posed hurdles to dissecting physiological relevance. To overcome this challenge, we targeted the regulatory node of these kinases, namely, the S51 phosphorylation site of eukaryotic translation initiation factor eIF2α and genetically replaced eIF2α with eIF2α-S51A in mouse squamous cell carcinoma (SCC) stem cells of skin. While inconsequential under normal growth conditions, the vulnerability of this ISR-null state was unveiled when SCC stem cells experienced proteotoxic stress. Seeking mechanistic insights into the protective roles of the ISR, we combined ribosome profiling and functional approaches to identify and probe the functional importance of translational differences between ISR-competent and ISR-null SCC stem cells when exposed to proteotoxic stress. In doing so, we learned that the ISR redirects translation to centrosomal proteins that orchestrate the microtubule dynamics needed to efficiently concentrate unfolded proteins at the microtubule-organizing center so that they can be cleared by the perinuclear degradation machinery. Thus, rather than merely maintaining survival during proteotoxic stress, the ISR also functions in promoting cellular recovery once the stress has subsided. Remarkably, this molecular program is unique to transformed skin stem cells, hence exposing a vulnerability in cancer that could be exploited therapeutically

Comprehensive identification and characterisation of germline structural variation within the Iberian population

[eng] One of the central aims of biology and biomedicine has been the characterisation and understanding of genetic variation across humans, to answer important evolutionary questions and to explain phenotypic variability concerning the diseases. Understanding genetic variability, is key to study this relationship (through imputation and GWASs) and to translate the results into improved clinical protocols. Different initiatives have emerged around the world to systematically characterise the genetic variability of specific human populations from whole-genome sequences, usually by selecting geographical regions. Examples such as 1000 Genomes (1000G)1, GoNL2, HRC, UK10K3 or Estonian population4, have already identified and characterised millions of genetic variants across different populations. In combination with imputation analysis, these sequenced-based projects allow increasing the statistical power and resolution of Genome-Wide Association Studies (GWAS), identifying and discovering new disease-associated variants5. Additionally, genetic variability among population groups is associated with geographic ancestry and can affect the disease risk or treatment efficacy differently6,7. For this reason, population- specific reference panels are necessary to characterise their genetic diversity and to assess its effect on human phenotypes, improving GWAS studies, as one of the cornerstones of precision medicine7. Existing genetic variability panels include Single Nucleotide Variants (SNVs) and indels (<50bp) but are limited in large Structural Variants (SV) (≥50bp). Technical and methodological limitations hindered the discovery of SVs using Next-generation Sequencing (NGS) technologies, as it produced False-Discovery Rates between 9-89% and recall 10-70%, depending on the SV type and size8. On average, the genomic variation between two human genomes is around 0.1%, but this difference increases to 1.5% with SVs8. The SVs also affect 3-10 times more nucleotides than SNVs9 (4M SNVs per genome10), showing their potential effect on human phenotypes. For this reason, including a complete catalogue of SVs in reference panels will increase the power in GWAS studies and provide opportunities to find new disease-associated variants. To overcome these limitations, in this thesis, we have generated the first genome-wide Iberian haplotype reference panel, mainly focused on Structural Variants, using 785 samples whole-genome sequenced (WGS) at high coverage (30X) from the GCAT-Genomics for life project. We designed a complete strategy, including an extensive benchmarking of multiple variant calling programs and by building specific Logistic Regression Models (LRM) for SV types, as well as phasing strategies to come up with a high quality and comprehensive genetic variability panel. This strategy was benchmarked using different controlled sets of variants, showing high precision and recall values across all variant types and sizes. The application of this strategy to our GCAT whole-genome samples resulted in the identification of 35,431,441 genetic variants, classified as 30,325,064 SNPs, 5,017,19 small indels (< 50bp), and 89,178 larger SV (≥ 50bp). The latter group was further subclassified into 33,244 deletions, 6,269 duplications, 12,782 insertions, 10,115 inversions, 18,779 transposons and 7,989 translocations, covering all ranges of frequencies and sizes. Besides, 60% of the discovered SVs were not catalogued in any repository, thus increasing the insights of SV in humans. Additionally, 52.44% of common and 71.63% of low-frequency SVs were not included in any haplotype reference panel. Thus, new SVs could be used in GWAS, adding more value to the Iberian-GCAT catalogue. The prediction of the functional impact of the SVs shows that these variants might have a central role in several diseases. Of all SVs included in the Iberian-GCAT catalogue, 46% overlapped in genes (both protein-coding genes and non-protein-coding genes), highlighting their potential impact on human traits. Besides, 92.7% of protein-coding genes were located outside low-complexity (repeated) genomic regions, expecting short-reads from NGS to capture the most interpretable SVs in humans11. Moreover, 32.93% of SVs affected protein-coding genes with a predicted loss of function intolerance (pLI) effect, further supporting the potential implication of these variants on complex diseases and therefore enabling a better explanation of missing heritability. Importantly, taking advantage of high coverage (30X), we accurately determine the genotypes of SVs, enabling to phase together with SNVs and indels, and increasing the SV phasing accuracy, in contrast to 1000G and GoNL. Besides, high coverage allowed to use Phasing Informative Reads (PIRs), increasing the phasing performance. The overall strategy enables the community to expand and improve the imputation possibilities within GWAS. The Iberian-GCAT haplotype reference panel created in this thesis, imputes accurately common SVs, with near ~100% of agreement with sequencing results. Although the Iberian- GCAT haplotype reference panel can be used in all populations from different continental groups, due to closer ancestries, the imputation performance is high in European and Latin American populations, reflected in the amount of low-frequency (1% ≤ MAF MAF) variants imputed at high info scores. These results demonstrated the versatility of our resource, increasing their performance in closer ancestries. In general, we observed that when the allele frequency decreases, the imputation accuracy drops too, highlighting the necessity to include more samples in reference panels, to impute low-frequency and rare variants efficiently, which normally are expected to have more functional impact on diseases. Finally, we compared the imputation possibilities of the 1000G and GoNL reference panels, with our Iberian-GCAT reference panel. We observed that the Iberian-GCAT reference panel outperformed the imputation of high-quality SVs by 2.7 and 1.6-fold compared to 1000G and GoNL, respectively. Also, the overall imputation quality is higher, showing the value of this new resource in GWAS as it includes more SVs than previous reference panels. The combination of different reference panels will improve the resolution and statistical power of GWAS, thus increasing the chances to find more risk variants in complex diseases, and ultimately, translated this insight to precision medicine

SpliceNet: recovering splicing isoform-specific differential gene networks from RNA-Seq data of normal and diseased samples

Conventionally, overall gene expressions from microarrays are used to infer gene networks, but it is challenging to account splicing isoforms. High-throughput RNA Sequencing has made splice variant profiling practical. However, its true merit in quantifying splicing isoforms and isoform-specific exon expressions is not well explored in inferring gene networks. This study demonstrates SpliceNet, a method to infer isoform-specific co-expression networks from exon-level RNA-Seq data, using large dimensional trace. It goes beyond differentially expressed genes and infers splicing isoform network changes between normal and diseased samples. It eases the sample size bottleneck; evaluations on simulated data and lung cancer-specific ERBB2 and MAPK signaling pathways, with varying number of samples, evince the merit in handling high exon to sample size ratio datasets. Inferred network rewiring of well established Bcl-x and EGFR centered networks from lung adenocarcinoma expression data is in good agreement with literature. Gene level evaluations demonstrate a substantial performance of SpliceNet over canonical correlation analysis, a method that is currently applied to exon level RNA-Seq data. SpliceNet can also be applied to exon array data. SpliceNet is distributed as an R package available at http://www.jjwanglab.org/SpliceNet.published_or_final_versio