The DNA-binding domain of the Chd1 chromatin-remodelling enzyme contains SANT and SLIDE domains

The ATP-dependent chromatin-remodelling enzyme Chd1 is a 168-kDa protein consisting of a double chromodomain, Snf2-related ATPase domain, and a C-terminal DNA-binding domain. Here, we show the DNA-binding domain is required for Saccharomyces cerevisiae Chd1 to bind and remodel nucleosomes. The crystal structure of this domain reveals the presence of structural homology to SANT and SLIDE domains previously identified in ISWI remodelling enzymes. The presence of these domains in ISWI and Chd1 chromatin-remodelling enzymes may provide a means of efficiently harnessing the action of the Snf2-related ATPase domain for the purpose of nucleosome spacing and provide an explanation for partial redundancy between these proteins. Site directed mutagenesis was used to identify residues important for DNA binding and generate a model describing the interaction of this domain with DNA. Through inclusion of Chd1 sequences in homology searches SLIDE domains were identified in CHD6–9 proteins. Point mutations to conserved amino acids within the human CHD7 SLIDE domain have been identified in patients with CHARGE syndrome

Predicting gene expression in the human malaria parasite Plasmodium falciparum using histone modification, nucleosome positioning, and 3D localization features.

Empirical evidence suggests that the malaria parasite Plasmodium falciparum employs a broad range of mechanisms to regulate gene transcription throughout the organism's complex life cycle. To better understand this regulatory machinery, we assembled a rich collection of genomic and epigenomic data sets, including information about transcription factor (TF) binding motifs, patterns of covalent histone modifications, nucleosome occupancy, GC content, and global 3D genome architecture. We used these data to train machine learning models to discriminate between high-expression and low-expression genes, focusing on three distinct stages of the red blood cell phase of the Plasmodium life cycle. Our results highlight the importance of histone modifications and 3D chromatin architecture in Plasmodium transcriptional regulation and suggest that AP2 transcription factors may play a limited regulatory role, perhaps operating in conjunction with epigenetic factors

Prediction and analysis of nucleosome exclusion regions in the human genome

<p>Abstract</p> <p>Background</p> <p>Nucleosomes are the basic structural units of eukaryotic chromatin, and they play a significant role in regulating gene expression. Specific DNA sequence patterns are known, from empirical and theoretical studies, to influence DNA bending and flexibility, and have been shown to exclude nucleosomes. A whole genome localization of these patterns, and their analysis, can add important insights on the gene regulation mechanisms that depend upon the structure of chromatin in and around a gene.</p> <p>Results</p> <p>A whole genome annotation for nucleosome exclusion regions (NXRegions) was carried out on the human genome. Nucleosome exclusion scores (NXScores) were calculated individually for each nucleotide, giving a measure of how likely a specific nucleotide and its immediate neighborhood would impair DNA bending and, consequently, exclude nucleosomes. The resulting annotations were correlated with 19055 gene expression profiles. We developed a new method based on Grubbs' outliers test for ranking genes based on their tissue specificity, and correlated this ranking with NXScores. The results show a strong correlation between tissue specificity of a gene and the propensity of its promoter to exclude nucleosomes (the promoter region was taken as -1500 to +500 bp from the RefSeq-annotated transcription start site). In addition, NXScores correlated well with gene density, gene expression levels, and DNaseI hypersensitive sites.</p> <p>Conclusion</p> <p>We present, for the first time, a whole genome prediction of nucleosome exclusion regions for the human genome (the data are available for download from Additional Materials). Nucleosome exclusion patterns are correlated with various factors that regulate gene expression, which emphasizes the need to include chromatin structural parameters in experimental analysis of gene expression.</p

Accurate Prediction Methods on Biomolecular Data

With the recent advancements in sequencing technologies, molecular biologists are producing ever-increasing amounts of biomolecular data. Extracting useful information from these massive data sets requires efficient and effective data mining and machine learning methods. In this dissertation, we explore the use of supervised machine learning (ML) to solve some challenging classification problems in molecular biology.First, we devise an ML model for classifying cancer types from very sparse somatic point mutation data. Accumulation of mutation and epigenetic modifications in somatic cells results in various cancer. For this purpose, we propose a method called mClass for efficient feature (gene) ranking that uses clustering, normalized mutual information and logistic regression. We show that somatic mutation data has sufficient discriminative power for cancer type classification.Next, we address the problem of gene essentiality prediction in microbes. Essential genes are significant to identify since their function is vital for the survival of the organism. Our proposed deep learning architecture called DeeplyEssential exclusively uses features extracted from the primary sequence of genes and their corresponding proteins, to maximize the utility and practicality of the tool. DeeplyEssential achieved state-of-the-art performance over previously proposed methods as well as expose and study a hidden performance bias affected previous models.Finally, we consider the problem of predicting the enhancer regions in the human genome from chromatin data. Enhancers contribute to the transcription of target genes. We propose a convolutional neural network framework named Epi2En that takes advantage of epigenetic ChIP-seq data. Epi2En's classification performance is not only very strong on cross-validation experiments, but also when testing across different cell-lines

Multi Layer Analysis

This thesis presents a new methodology to analyze one-dimensional signals trough a new approach called Multi Layer Analysis, for short MLA. It also provides some new insights on the relationship between one-dimensional signals processed by MLA and tree kernels, test of randomness and signal processing techniques. The MLA approach has a wide range of application to the fields of pattern discovery and matching, computational biology and many other areas of computer science and signal processing. This thesis includes also some applications of this approach to real problems in biology and seismology

MER41 Repeat Sequences Contain Inducible STAT1 Binding Sites

Chromatin immunoprecipitation combined with massively parallel sequencing methods (ChIP-seq) is becoming the standard approach to study interactions of transcription factors (TF) with genomic sequences. At the example of public STAT1 ChIP-seq data sets, we present novel approaches for the interpretation of ChIP-seq data

Annotation of gene promoters by integrative data-mining of ChIP-seq Pol-II enrichment data

BACKGROUND: Use of alternative gene promoters that drive widespread cell-type, tissue-type or developmental gene regulation in mammalian genomes is a common phenomenon. Chromatin immunoprecipitation methods coupled with DNA microarray (ChIP-chip) or massive parallel sequencing (ChIP-seq) are enabling genome-wide identification of active promoters in different cellular conditions using antibodies against Pol-II. However, these methods produce enrichment not only near the gene promoters but also inside the genes and other genomic regions due to the non-specificity of the antibodies used in ChIP. Further, the use of these methods is limited by their high cost and strong dependence on cellular type and context. METHODS: We trained and tested different state-of-art ensemble and meta classification methods for identification of Pol-II enriched promoter and Pol-II enriched non-promoter sequences, each of length 500 bp. The classification models were trained and tested on a bench-mark dataset, using a set of 39 different feature variables that are based on chromatin modification signatures and various DNA sequence features. The best performing model was applied on seven published ChIP-seq Pol-II datasets to provide genome wide annotation of mouse gene promoters. RESULTS: We present a novel algorithm based on supervised learning methods to discriminate promoter associated Pol-II enrichment from enrichment elsewhere in the genome in ChIP-chip/seq profiles. We accumulated a dataset of 11,773 promoter and 46,167 non-promoter sequences, each of length 500 bp, generated from RNA Pol-II ChIP-seq data of five tissues (Brain, Kidney, Liver, Lung and Spleen). We evaluated the classification models in building the best predictor and found that Bagging and Random Forest based approaches give the best accuracy. We implemented the algorithm on seven different published ChIP-seq datasets to provide a comprehensive set of promoter annotations for both protein-coding and non-coding genes in the mouse genome. The resulting annotations contain 13,413 (4,747) protein-coding (non-coding) genes with single promoters and 9,929 (1,858) protein-coding (non-coding) genes with two or more alternative promoters, and a significant number of unassigned novel promoters. CONCLUSION: Our new algorithm can successfully predict the promoters from the genome wide profile of Pol-II bound regions. In addition, our algorithm performs significantly better than existing promoter prediction methods and can be applied for genome-wide predictions of Pol-II promoters

Epigenetic Regulation of Lymphocyte Development and Transformation

Cell identity and function rely on intricately controlled programs of gene regulation, alterations of which underlie many diseases, including cancer. Epigenetic analyses of normal and diseased cells have started to elucidate different facets of epigenetic mechanisms for gene regulation. These include changes in nucleosome density, histone modifications, factor binding and chromosomal architecture. All of these aspects contribute to the activities of regulatory elements conferring promoter, enhancer and insulator functions and the cis-regulatory circuits formed by these elements. Despite this progress, an urgent need remains to profile these features and to study how they cooperatively function in normal and pathogenic settings. Here, using the mouse T cell receptor beta locus as a model, we first quantified 13 distinct features, including transcription, chromatin environment, spatial proximity, and predicted qualities of recombination signal sequences (RSS), to assess their relative contributions in shaping recombination frequencies of Vβ gene segments. We found that the most predictive parameters are chromatin modifications associated with transcription, but recombination efficiencies are largely independent of spatial proximity. These findings enabled us to build a novel computational model predicting Vβ usage that uses a minimum set of five features. Expanding on these results, we applied chromatin profiling and computational algorithms to other mouse antigen receptor loci, to classify and identify novel regulatory elements. We defined 38 chromatin states that reflect distinct regulatory potentials. One of these states corresponded to known enhancers and also identified new enhancer candidates in immunoglobulin loci. Indeed, all four candidate elements exhibited enhancer activity in B cells when subjected to functional assays, validating that our chromatin profiling and computational analyses successfully identified enhancers in antigen receptor loci. Finally, we translated these approaches to human B cell lymphoma to predict pathogenic cis-regulatory circuits composed of dysregulated enhancers and target genes. We then selected and functionally dissected a pathogenic cis-regulatory circuit for the mitosis-associated kinase, NEK6, which is overexpressed in human B cell lymphoma. We found that only a subset of predicted enhancers is required to maintain elevated NEK6 expression in transformed B cells. Surprisingly, a B cell-specific super-enhancer is completely dispensable to maintain NEK6 expression and chromatin architecture within its chromosomal neighborhood. Moreover, we showed that a cluster of binding sites for the CTCF architectural factor serves as a chromatin boundary, blocking the functional impact of a NEK6 regulatory hub on neighboring genes. These results emphasize the necessity to test predicted cis-regulatory circuits, especially the roles of enhancers and super-enhancers, when prioritizing elements as targets for epigenetic-based therapies. Our findings collectively pave the way for future investigations into the roles of cis-regulatory and architectural elements in regulating gene expression programs during normal development or pathogenesis