Statistical analysis of genomic binding sites using high-throughput ChIP-seq data

This thesis focuses on the statistical analysis of Chromatin immunoprecipitation sequencing (ChIP-Seq) data produced by Next Generation Sequencing (NGS). ChIP-Seq is a method to investigate interactions between protein and DNA. Specifically, the method aims to identify the binding sites of a particular protein of interest, such as a transcription factor, in the genome. In the context of cancer research, this information is important to check whether, for example, a particular transcription factor can be considered as a therapeutic target. The sequence data produced by ChIP-Seq experiment are in the form of mapped short sequences, which are called reads. The reads are counted at each single genomic position, and the read counts are the data to be analysed. There are many problems related to the analysis of ChIP-Seq data, and in this research we focus on three of them. First, in the analysis of ChIP-Seq data, the genome is not analysed in its entirety; instead the intensity of read counts is estimated locally. Estimating the intensity of read counts usually involves dividing the genome into small regions (windows). If the window size is small, the noise level (low read counts) would dominate and many empty windows would be observed. If the window size is large, the windows would have many small read counts, which would smooth out some important features. The need exists for an approach that enables researchers to choose an appropriate window size. To address this problem, an approach was developed to optimise the window size. The approach optimises the window size based on histogram construction. Note, the developed methodology is published in [46]. Second, different studies of ChIP-Seq can target different transcription factors and then give different conclusions, which is expected. However, they are all ChIP-Seq datasets and many of them are performed on the same genome, for example the human genome. So is there a pattern for the distribution of the counts? If the answer is yes, is the pattern common in all ChIP-Seq data? Answering this question can help in better understanding the biology behind this experiment. We try to answer this question by investigating RUNX1/ETO ChIP-Seq data. We try to develop a statistical model that is able to describe the data. We employ some observed features in ChIP-Seq data to improve the performance of the model. Although we obtained a model that is able to describe the RUNX1/ETO data, the model does not provide a good statistical fit to the data. Third, it is biologically important to know what changes (if any) occur at the binding sites under some biological conditions, for example in knock-out experiments. Changes in the binding sites can be either in the location of the sites or in the characteristics of the sites (for example, the density of the read counts), or sometimes both. Current approaches for differential binding sites analysis suffer from major drawbacks. First, unclear underlying models as a result of dependencies between methods used, for example peak finding and testing methods. Second, lack of accurate control of type-I error. Hence there is a need for approach(es) to address these drawbacks. To address this problem, we developed three statistical tests that are able to detect significantly differential regions between two ChIPSeq datasets. The tests are evaluated and compared to some current methodologies by using simulated and real ChIP-Seq datasets. The proposed tests exhibit more power as well as accuracy compared to current methodologies

A multidimensional and multi-feature framework for cardiac interoception

Interoception (the sensing of inner-body signals) is a multi-faceted construct with major relevance for basic and clinical neuroscience research. However, the neurocognitive signatures of this domain (cutting across behavioral, electrophysiological, and fMRI connectivity levels) are rarely reported in convergent or systematic fashion. Additionally, various controversies in the field might reflect the caveats of standard interoceptive accuracy (IA) indexes, mainly based on heartbeat detection (HBD) tasks. Here we profit from a novel IA index (md) to provide a convergent multidimensional and multi-feature approach to cardiac interoception. We found that outcomes from our IA-md index are associated with –and predicted by– canonical markers of interoception, including the hd-EEG-derived heart-evoked potential (HEP), fMRI functional connectivity within interoceptive hubs (insular, somatosensory, and frontal networks), and socio-emotional skills. Importantly, these associations proved more robust than those involving current IA indexes. Furthermore, this pattern of results persisted when taking into consideration confounding variables (gender, age, years of education, and executive functioning). This work has relevant theoretical and clinical implications concerning the characterization of cardiac interoception and its assessment in heterogeneous samples, such as those composed of neuropsychiatric patients.Fil: Fittipaldi, María Sol. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Neurociencia Cognitiva y Traslacional. Fundación Ineco Rosario Sede del Incyt | Instituto de Neurología Cognitiva. Instituto de Neurociencia Cognitiva y Traslacional. Fundación Ineco Rosario Sede del Incyt | Fundación Favaloro. Instituto de Neurociencia Cognitiva y Traslacional. Fundación Ineco Rosario Sede del Incyt; ArgentinaFil: Abrevaya, Sofia. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Neurociencia Cognitiva y Traslacional. Fundación Ineco Rosario Sede del Incyt | Instituto de Neurología Cognitiva. Instituto de Neurociencia Cognitiva y Traslacional. Fundación Ineco Rosario Sede del Incyt | Fundación Favaloro. Instituto de Neurociencia Cognitiva y Traslacional. Fundación Ineco Rosario Sede del Incyt; ArgentinaFil: de la Fuente de la Torre, Laura Alethia. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; ArgentinaFil: Pascariello, Guido Orlando. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas. Universidad Nacional de Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas; ArgentinaFil: Hesse Rizzi, Eugenia Fátima. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Neurociencia Cognitiva y Traslacional. Fundación Ineco Rosario Sede del Incyt | Instituto de Neurología Cognitiva. Instituto de Neurociencia Cognitiva y Traslacional. Fundación Ineco Rosario Sede del Incyt | Fundación Favaloro. Instituto de Neurociencia Cognitiva y Traslacional. Fundación Ineco Rosario Sede del Incyt; ArgentinaFil: Birba, Agustina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Neurociencia Cognitiva y Traslacional. Fundación Ineco Rosario Sede del Incyt | Instituto de Neurología Cognitiva. Instituto de Neurociencia Cognitiva y Traslacional. Fundación Ineco Rosario Sede del Incyt | Fundación Favaloro. Instituto de Neurociencia Cognitiva y Traslacional. Fundación Ineco Rosario Sede del Incyt; ArgentinaFil: Salamone, Paula Celeste. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Neurociencia Cognitiva y Traslacional. Fundación Ineco Rosario Sede del Incyt | Instituto de Neurología Cognitiva. Instituto de Neurociencia Cognitiva y Traslacional. Fundación Ineco Rosario Sede del Incyt | Fundación Favaloro. Instituto de Neurociencia Cognitiva y Traslacional. Fundación Ineco Rosario Sede del Incyt; ArgentinaFil: Hildebrandt, Malin. Institute for Clinical Psychology and Psychotherapy; AlemaniaFil: Alarco Martí, Sofía. Universidad Favaloro; ArgentinaFil: Pautassi, Ricardo Marcos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigación Médica Mercedes y Martín Ferreyra. Universidad Nacional de Córdoba. Instituto de Investigación Médica Mercedes y Martín Ferreyra; ArgentinaFil: Huepe, David. Universidad Adolfo Ibañez; ChileFil: Martorell Martorell, Miquel. Universidad Favaloro; ArgentinaFil: Yoris, Adrián. Universidad Favaloro; ArgentinaFil: Roca, María. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Neurociencia Cognitiva y Traslacional. Fundación Ineco Rosario Sede del Incyt | Instituto de Neurología Cognitiva. Instituto de Neurociencia Cognitiva y Traslacional. Fundación Ineco Rosario Sede del Incyt | Fundación Favaloro. Instituto de Neurociencia Cognitiva y Traslacional. Fundación Ineco Rosario Sede del Incyt; ArgentinaFil: García, Adolfo Martín. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Neurociencia Cognitiva y Traslacional. Fundación Ineco Rosario Sede del Incyt | Instituto de Neurología Cognitiva. Instituto de Neurociencia Cognitiva y Traslacional. Fundación Ineco Rosario Sede del Incyt | Fundación Favaloro. Instituto de Neurociencia Cognitiva y Traslacional. Fundación Ineco Rosario Sede del Incyt; ArgentinaFil: Sedeño, Lucas. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Neurociencia Cognitiva y Traslacional. Fundación Ineco Rosario Sede del Incyt | Instituto de Neurología Cognitiva. Instituto de Neurociencia Cognitiva y Traslacional. Fundación Ineco Rosario Sede del Incyt | Fundación Favaloro. Instituto de Neurociencia Cognitiva y Traslacional. Fundación Ineco Rosario Sede del Incyt; ArgentinaFil: Ibañez, Agustin Mariano. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Neurociencia Cognitiva y Traslacional. Fundación Ineco Rosario Sede del Incyt | Instituto de Neurología Cognitiva. Instituto de Neurociencia Cognitiva y Traslacional. Fundación Ineco Rosario Sede del Incyt | Fundación Favaloro. Instituto de Neurociencia Cognitiva y Traslacional. Fundación Ineco Rosario Sede del Incyt; Argentina. Universidad Adolfo Ibañez; Chile. Universidad Autónoma del Caribe; Colombi

Peak Finder Metaserver - a novel application for finding peaks in ChIP-seq data

Background: Finding peaks in ChIP-seq is an important process in biological inference. In some cases, such as positioning nucleosomes with specific histone modifications or finding transcription factor binding specificities, the precision of the detected peak plays a significant role. There are several applications for finding peaks (called peak finders) based on different algorithms (e.g. MACS, Erange and HPeak). Benchmark studies have shown that the existing peak finders identify different peaks for the same dataset and it is not known which one is the most accurate. We present the first meta-server called Peak Finder MetaServer (PFMS) that collects results from several peak finders and produces consensus peaks. Our application accepts three standard ChIP-seq data formats: BED, BAM, and SAM. Results: Sensitivity and specificity of seven widely used peak finders were examined. For the experiments we used three previously studied Transcription Factors (TF) ChIP-seq datasets and identified three of the selected peak finders that returned results with high specificity and very good sensitivity compared to the remaining four. We also ran PFMS using the three selected peak finders on the same TF datasets and achieved higher specificity and sensitivity than the peak finders individually. Conclusions: We show that combining outputs from up to seven peak finders yields better results than individual peak finders. In addition, three of the seven peak finders outperform the remaining four, and running PFMS with these three returns even more accurate results. Another added value of PFMS is a separate report of the peaks returned by each of the included peak finders

Carbohydrate-Active Enzymes

Carbohydrate-active enzymes are responsible for both biosynthesis and the breakdown of carbohydrates and glycoconjugates. They are involved in many metabolic pathways; in the biosynthesis and degradation of various biomolecules, such as bacterial exopolysaccharides, starch, cellulose and lignin; and in the glycosylation of proteins and lipids. Carbohydrate-active enzymes are classified into glycoside hydrolases, glycosyltransferases, polysaccharide lyases, carbohydrate esterases, and enzymes with auxiliary activities (CAZy database, www.cazy.org). Glycosyltransferases synthesize a huge variety of complex carbohydrates with different degrees of polymerization, moieties and branching. On the other hand, complex carbohydrate breakdown is carried out by glycoside hydrolases, polysaccharide lyases and carbohydrate esterases. Their interesting reactions have attracted the attention of researchers across scientific fields, ranging from basic research to biotechnology. Interest in carbohydrate-active enzymes is due not only to their ability to build and degrade biopolymers—which is highly relevant in biotechnology—but also because they are involved in bacterial biofilm formation, and in glycosylation of proteins and lipids, with important health implications. This book gathers new research results and reviews to broaden our understanding of carbohydrate-active enzymes, their mutants and their reaction products at the molecular level