Understanding human genome regulation through entropic graph-based regularization and submodular optimization

Thesis (Ph.D.)--University of Washington, 2016-06

Flexible strategies for association analysis with genomic phenotypes

Thesis (Ph.D.)--University of Washington, 2016-08

RNA-seq analysis of the Quercus suber root response to drought

Dissertação de mestrado em Plant Molecular Biology, Biotechnology and Bioentrepreneurship

Design and analysis of ChIP-Seq experiments for nuclear factor I DNA-binding proteins

SUMMARY : Eukaryotic DNA interacts with the nuclear proteins using non-covalent ionic interactions. Proteins can recognize specific nucleotide sequences based on the sterical interactions with the DNA and these specific protein-DNA interactions are the basis for many nuclear processes, e.g. gene transcription, chromosomal replication, and recombination. New technology termed ChIP-Seq has been recently developed for the analysis of protein-DNA interactions on a whole genome scale and it is based on immunoprecipitation of chromatin and high-throughput DNA sequencing procedure. ChIP-Seq is a novel technique with a great potential to replace older techniques for mapping of protein-DNA interactions. In this thesis, we bring some new insights into the ChIP-Seq data analysis. First, we point out to some common and so far unknown artifacts of the method. Sequence tag distribution in the genome does not follow uniform distribution and we have found extreme hot-spots of tag accumulation over specific loci in the human and mouse genomes. These artifactual sequence tags accumulations will create false peaks in every ChIP-Seq dataset and we propose different filtering methods to reduce the number of false positives. Next, we propose random sampling as a powerful analytical tool in the ChIP-Seq data analysis that could be used to infer biological knowledge from the massive ChIP-Seq datasets. We created unbiased random sampling algorithm and we used this methodology to reveal some of the important biological properties of Nuclear Factor I DNA binding proteins. Finally, by analyzing the ChIP-Seq data in detail, we revealed that Nuclear Factor I transcription factors mainly act as activators of transcription, and that they are associated with specific chromatin modifications that are markers of open chromatin. We speculate that NFI factors only interact with the DNA wrapped around the nucleosome. We also found multiple loci that indicate possible chromatin barrier activity of NFI proteins, which could suggest the use of NFI binding sequences as chromatin insulators in biotechnology applications. RESUME : L'ADN des eucaryotes interagit avec les protéines nucléaires par des interactions noncovalentes ioniques. Les protéines peuvent reconnaître les séquences nucléotidiques spécifiques basées sur l'interaction stérique avec l'ADN, et des interactions spécifiques contrôlent de nombreux processus nucléaire, p.ex. transcription du gène, la réplication chromosomique, et la recombinaison. Une nouvelle technologie appelée ChIP-Seq a été récemment développée pour l'analyse des interactions protéine-ADN à l'échelle du génome entier et cette approche est basée sur l'immuno-précipitation de la chromatine et sur la procédure de séquençage de l'ADN à haut débit. La nouvelle approche ChIP-Seq a donc un fort potentiel pour remplacer les anciennes techniques de cartographie des interactions protéine-ADN. Dans cette thèse, nous apportons de nouvelles perspectives dans l'analyse des données ChIP-Seq. Tout d'abord, nous avons identifié des artefacts très communs associés à cette méthode qui étaient jusqu'à présent insoupçonnés. La distribution des séquences dans le génome ne suit pas une distribution uniforme et nous avons constaté des positions extrêmes d'accumulation de séquence à des régions spécifiques, des génomes humains et de la souris. Ces accumulations des séquences artéfactuelles créera de faux pics dans toutes les données ChIP-Seq, et nous proposons différentes méthodes de filtrage pour réduire le nombre de faux positifs. Ensuite, nous proposons un nouvel échantillonnage aléatoire comme un outil puissant d'analyse des données ChIP-Seq, ce qui pourraient augmenter l'acquisition de connaissances biologiques à partir des données ChIP-Seq. Nous avons créé un algorithme d'échantillonnage aléatoire et nous avons utilisé cette méthode pour révéler certaines des propriétés biologiques importantes de protéines liant à l'ADN nommés Facteur Nucléaire I (NFI). Enfin, en analysant en détail les données de ChIP-Seq pour la famille de facteurs de transcription nommés Facteur Nucléaire I, nous avons révélé que ces protéines agissent principalement comme des activateurs de transcription, et qu'elles sont associées à des modifications de la chromatine spécifiques qui sont des marqueurs de la chromatine ouverte. Nous pensons que lés facteurs NFI interagir uniquement avec l'ADN enroulé autour du nucléosome. Nous avons également constaté plusieurs régions génomiques qui indiquent une éventuelle activité de barrière chromatinienne des protéines NFI, ce qui pourrait suggérer l'utilisation de séquences de liaison NFI comme séquences isolatrices dans des applications de la biotechnologie.

Protein-DNA associations in a gender-specific gene of Schistosoma mansoni: characterization by UV cross-linking, DNase I footprinting and band shift assays

Protein extracts obtained from male and female shistosomes were incubated with a gender-specific gene, F-10, transcribed only in adult females and encoding a major egg-shell protein. The protein/DNA interaction was measured using the band shift, DNase-I-footprinting and UV cross-linking techniques. The results showed a clear band shift when a 302 bp restriction fragment containing the 3'end of the gene was incubated with either female or male proteins. This fragment also contained a putative steroid hormone regulatory element (HRE). In contrast, only the male proteins produced a shift with the 495 bp fragment corresponding to the middle region of the gene. DNase I footprinting showed that proteins from males and females interacted with the F-10 gene by binding to multiple adjacent sites along the DNA, thus generatingrelatively long protected fragments of approximately 100 bp. This result suggested that the adjacent binding of several moles of proteins occured at the 5'end of the gene. UV cross-linking between schistosome proteins and a 21 bp synthetic oligonucleotide the F-10 HRE, evidence proteins having MWS of 30,45 and 65 kDNA. These proteins are presumably involved in the regulation of transcription of the F-10 gene.

Annotating the human genome

2 Abstract2.1 En françaisLe séquençage du génome humain est un pré-requis fondamental à la compréhension de la biologie de l'être humain. Ce projet achevé, les scientifiques ont dû faire face à une tâche aussi importante, comprendre cette suite de 3 milliards de lettres qui compose notre génome. Le consortium ENCODE (ENCyclopedia Of Dna Elements) fût formé comme une suite logique au projet du génome humain. Son rôle est d'identifier tous les éléments fonctionnels de notre génome incluant les régions transcrites, les sites d'attachement des facteurs de transcription, les sites hypersensibles à la DNAse I ainsi que les marqueurs de modification des histones. Dans le cadre de ma thèse doctorale, j'ai participé à 2 sous-projets d'ENCODE. En premier lieu, j'ai eu la tâche de développer et d'optimiser une technique de validation expérimentale à haut rendement de modèles de gènes qui m'a permis d'estimer la qualité de la plus récente annotation manuelle. Ce nouveau processus de validation est bien plus efficace que la technique RNAseq qui est actuellement en train de devenir la norme. Cette technique basée sur la RT-PCR, m'a notamment permis de découvrir de nouveaux exons dans 10% des régions interrogées. En second lieu j'ai participé à une étude ayant pour but d'identifier les extrémités de tous les gènes des chromosomes humains 21 et 22. Cette étude à permis l'identification à large échelle de transcrits chimères comportant des séquences provenant de deux gènes distincts pouvant être à une grande distance l'un de autre.2.2 In EnglishThe completion of the human genome sequence js the prerequisite to fully understand the biology of human beings. This project achieved, scientists had to face another challenging task, understanding the meaning of the 3 billion letters composing this genome. As a logical continuation of the human genome project, the ENCODE (ENCyclopedia Of DNA Elements) consortium was formed with the aim of annotating all its functional elements. These elements include transcribed regions, transcription binding sites, DNAse I hypersensitive sites and histone modification marks. In the frame of my PhD thesis, I was involved in two sub-projects of ENCODE. Firstly I developed and optimized an high throughput method to validate gene models, which allowed me to assess the quality of the most recent manually-curated annotation. This novel experimental validation pipeline is extremely effective, far more so than transcriptome profiling through RNA sequencing, which is becoming the norm. This RT-PCR-seq targeted-approach is likewise particularly efficient in identifying novel exons, as we discovered about 10% of loci with unannotated exons. Secondly, I participated to a study aiming to identify the gene boundaries of all genes in the human chromosome 21 and 22. This study led to the identification of chimeric transcripts that are composed of sequences coming form two distinct genes that can be map far away from each other.

Mouse interleukin-2 receptor alpha gene expression. Delimitation of cis-acting regulatory elements in transgenic mice and by mapping of DNase-I hypersensitive sites.

The alpha chain of the interleukin-2 receptor (IL-2R alpha) is a key regulator of lymphocyte proliferation. To analyze the mechanisms controlling its expression in normal cells, we used the 5'-flanking region (base pairs -2539/+93) of the mouse gene to drive chloramphenicol acetyltransferase expression in four transgenic mouse lines. Constitutive transgene activity was restricted to lymphoid organs. In mature T lymphocytes, transgene and endogenous IL-2R alpha gene expression was stimulated by concanavalin A and up-regulated by IL-2 with very similar kinetics. In thymic T cell precursors, IL-1 and IL-2 cooperatively induced transgene and IL-2R alpha gene expression. These results show that regulation of the endogenous IL-2R alpha gene occurs mainly at the transcriptional level. They demonstrate that cis-acting elements in the 5'-flanking region present in the transgene confer correct tissue specificity and inducible expression in mature T cells and their precursors in response to antigen, IL-1, and IL-2. In a complementary approach, we screened the 5' end of the endogenous IL-2R alpha gene for DNase-I hypersensitive sites. We found three lymphocyte specific DNase-I hypersensitive sites. Two, at -0.05 and -5.3 kilobase pairs, are present in resting T cells. A third site appears at -1.35 kilobase pairs in activated T cells. It co-localizes with IL-2-responsive elements identified by transient transfection experiments.

Interaction of Leukocyte Elastase Inhibitor/L-DNase II with BCL-2 and BAX

Leukocyte Elastase Inhibitor (LEI, also called serpin B1) is a protein involved in apoptosis among other physiological processes. We have previously shown that upon cleavage by its cognate protease, LEI is transformed into L-DNase II, a protein with a pro-apoptotic activity. The caspase independent apoptotic pathway, in which L-DNase II is the final effector, interacts with other pro-apoptotic molecules like Poly-ADP-Ribose polymerase (PARP) or Apoptosis Inducing Factor (AIF). The screening of LEI/L-DNase II interactions showed a possible interaction with several members of the BCL-2 family of proteins which are known to have a central role in the regulation of caspase dependent cell death. In this study, we investigated the regulation of LEI/L-DNase II pathway by two members of this family of proteins: BAX and BCL-2, which have opposite effects on cell survival. We show that, in both BHK and HeLa cells, LEI/L-DNase II can interact with BCL-2 and BAX in apoptotic and non-apoptotic conditions. These proteins which are usually thought to be anti-apoptotic and pro-apoptotic respectively, both inhibit the L-DNase II pro-apoptotic activity. These results give further insight in the regulation of caspase-independent pathways and highlight the involvement of the intracellular environment of a given protein in the determinism of its function. They also add a link between caspase-dependent and independent pathways of apoptosis.

Quantifying ChIP-seq data: a spiking method providing an internal reference for sample-to-sample normalization.

Chromatin immunoprecipitation followed by deep sequencing (ChIP-seq) experiments are widely used to determine, within entire genomes, the occupancy sites of any protein of interest, including, for example, transcription factors, RNA polymerases, or histones with or without various modifications. In addition to allowing the determination of occupancy sites within one cell type and under one condition, this method allows, in principle, the establishment and comparison of occupancy maps in various cell types, tissues, and conditions. Such comparisons require, however, that samples be normalized. Widely used normalization methods that include a quantile normalization step perform well when factor occupancy varies at a subset of sites, but may miss uniform genome-wide increases or decreases in site occupancy. We describe a spike adjustment procedure (SAP) that, unlike commonly used normalization methods intervening at the analysis stage, entails an experimental step prior to immunoprecipitation. A constant, low amount from a single batch of chromatin of a foreign genome is added to the experimental chromatin. This "spike" chromatin then serves as an internal control to which the experimental signals can be adjusted. We show that the method improves similarity between replicates and reveals biological differences including global and largely uniform changes.

Assessment of transcript reconstruction methods for RNA-seq.

We evaluated 25 protocol variants of 14 independent computational methods for exon identification, transcript reconstruction and expression-level quantification from RNA-seq data. Our results show that most algorithms are able to identify discrete transcript components with high success rates but that assembly of complete isoform structures poses a major challenge even when all constituent elements are identified. Expression-level estimates also varied widely across methods, even when based on similar transcript models. Consequently, the complexity of higher eukaryotic genomes imposes severe limitations on transcript recall and splice product discrimination that are likely to remain limiting factors for the analysis of current-generation RNA-seq data.

Identification and removal of low-complexity sites in allele-specific analysis of ChIP-seq data.

MOTIVATION: High-throughput sequencing technologies enable the genome-wide analysis of the impact of genetic variation on molecular phenotypes at unprecedented resolution. However, although powerful, these technologies can also introduce unexpected artifacts. Results: We investigated the impact of library amplification bias on the identification of allele-specific (AS) molecular events from high-throughput sequencing data derived from chromatin immunoprecipitation assays (ChIP-seq). Putative AS DNA binding activity for RNA polymerase II was determined using ChIP-seq data derived from lymphoblastoid cell lines of two parent-daughter trios. We found that, at high-sequencing depth, many significant AS binding sites suffered from an amplification bias, as evidenced by a larger number of clonal reads representing one of the two alleles. To alleviate this bias, we devised an amplification bias detection strategy, which filters out sites with low read complexity and sites featuring a significant excess of clonal reads. This method will be useful for AS analyses involving ChIP-seq and other functional sequencing assays.

A comparison of methods for differential expression analysis of RNA-seq data.

BACKGROUND: Finding genes that are differentially expressed between conditions is an integral part of understanding the molecular basis of phenotypic variation. In the past decades, DNA microarrays have been used extensively to quantify the abundance of mRNA corresponding to different genes, and more recently high-throughput sequencing of cDNA (RNA-seq) has emerged as a powerful competitor. As the cost of sequencing decreases, it is conceivable that the use of RNA-seq for differential expression analysis will increase rapidly. To exploit the possibilities and address the challenges posed by this relatively new type of data, a number of software packages have been developed especially for differential expression analysis of RNA-seq data. RESULTS: We conducted an extensive comparison of eleven methods for differential expression analysis of RNA-seq data. All methods are freely available within the R framework and take as input a matrix of counts, i.e. the number of reads mapping to each genomic feature of interest in each of a number of samples. We evaluate the methods based on both simulated data and real RNA-seq data. CONCLUSIONS: Very small sample sizes, which are still common in RNA-seq experiments, impose problems for all evaluated methods and any results obtained under such conditions should be interpreted with caution. For larger sample sizes, the methods combining a variance-stabilizing transformation with the 'limma' method for differential expression analysis perform well under many different conditions, as does the nonparametric SAMseq method.

Detecting long-range chromatin interactions using the chromosome conformation capture sequencing (4C-seq) method.

Eukaryotic transcription is tightly regulated by transcriptional regulatory elements, even though these elements may be located far away from their target genes. It is now widely recognized that these regulatory elements can be brought in close proximity through the formation of chromatin loops, and that these loops are crucial for transcriptional regulation of their target genes. The chromosome conformation capture (3C) technique presents a snapshot of long-range interactions, by fixing physically interacting elements with formaldehyde, digestion of the DNA, and ligation to obtain a library of unique ligation products. Recently, several large-scale modifications to the 3C technique have been presented. Here, we describe chromosome conformation capture sequencing (4C-seq), a high-throughput version of the 3C technique that combines the 3C-on-chip (4C) protocol with next-generation Illumina sequencing. The method is presented for use in mammalian cell lines, but can be adapted to use in mammalian tissues and any other eukaryotic genome.

Inference of chromosomal inversion dynamics from Pool-Seq data in natural and laboratory populations of Drosophila melanogaster.

Sequencing of pools of individuals (Pool-Seq) represents a reliable and cost-effective approach for estimating genome-wide SNP and transposable element insertion frequencies. However, Pool-Seq does not provide direct information on haplotypes so that, for example, obtaining inversion frequencies has not been possible until now. Here, we have developed a new set of diagnostic marker SNPs for seven cosmopolitan inversions in Drosophila melanogaster that can be used to infer inversion frequencies from Pool-Seq data. We applied our novel marker set to Pool-Seq data from an experimental evolution study and from North American and Australian latitudinal clines. In the experimental evolution data, we find evidence that positive selection has driven the frequencies of In(3R)C and In(3R)Mo to increase over time. In the clinal data, we confirm the existence of frequency clines for In(2L)t, In(3L)P and In(3R)Payne in both North America and Australia and detect a previously unknown latitudinal cline for In(3R)Mo in North America. The inversion markers developed here provide a versatile and robust tool for characterizing inversion frequencies and their dynamics in Pool-Seq data from diverse D. melanogaster populations.

GSVA: gene set variation analysis for microarray and RNA-seq data

Gene set enrichment (GSE) analysis is a popular framework for condensing information from gene expression profiles into a pathway or signature summary. The strengths of this approach over single gene analysis include noise and dimension reduction, as well as greater biological interpretability. As molecular profiling experiments move beyond simple case-control studies, robust and flexible GSE methodologies are needed that can model pathway activity within highly heterogeneous data sets. To address this challenge, we introduce Gene Set Variation Analysis (GSVA), a GSE method that estimates variation of pathway activity over a sample population in an unsupervised manner. We demonstrate the robustness of GSVA in a comparison with current state of the art sample-wise enrichment methods. Further, we provide examples of its utility in differential pathway activity and survival analysis. Lastly, we show how GSVA works analogously with data from both microarray and RNA-seq experiments. GSVA provides increased power to detect subtle pathway activity changes over a sample population in comparison to corresponding methods. While GSE methods are generally regarded as end points of a bioinformatic analysis, GSVA constitutes a starting point to build pathway-centric models of biology. Moreover, GSVA contributes to the current need of GSE methods for RNA-seq data. GSVA is an open source software package for R which forms part of the Bioconductor project and can be downloaded at http://www.bioconductor.org.