Efficient and Accurate Detection of Topologically Associating Domains from Contact Maps

Continuous improvements to high-throughput conformation capture (Hi-C) are revealing richerinformation about the spatial organization of the chromatin and its role in cellular functions.Several studies have confirmed the existence of structural features of the genome 3D organiza-tion that are stable across cell types and conserved across species, calledtopological associatingdomains(TADs). The detection of TADs has become a critical step in the analysis of Hi-C data,e.g., to identify enhancer-promoter associations. Here we presentEast, a novel TAD identifi-cation algorithm based on fast 2D convolution of Haar-like features, that is as accurate as thestate-of-the-art method based on the directionality index, but 75-80x faster.Eastis availablein the public domain at https://github.com/ucrbioinfo/EAST

An Extended Local Binary Pattern for Gender Classification

The face is one of the most important biometric features of humans, conveying race, identity, age, gender and facial expression information, among which gender plays a significant role in social interactions. An automatic gender recognition system has many applications in computer-human interaction, psychology, security, demographic and business issues. In this work, we designed and implemented an efficient gender recognition system with high classification accuracy. In this regard, we proposed a novel local binary descriptor capable of extracting more informative and discriminative local features for the purpose of gender classification. We have evaluated our approach on the standard FERET and CAS-PEAL databases and our experiments show that the proposed approach offers superior results compared to techniques using state-of-the-art descriptors such as LBP, LDP and HoG. Our results demonstrate the effectiveness and robustness of the proposed system with 98.33% classification accuracy

Efficient Algorithms for the Analysis of Hi-C Contact Maps

This dissertation deals with the analysis of high-throughput chromatin conformation capture (Hi-C) data. Hi-C experiments provide genome-wide maps of chromatin interactions and has enabled Life Scientists to investigate the role of the three-dimensional structure of genomes in gene regulation and other essential cellular functions. Several studies have confirmed the existence of fundamental 3D structural features of different scales that are stable across cell types and conserved across species, e.g., topological associating domains (TADs) and chromatin loops.The research presented here is articulated around three main topics on the analysis of contact maps, namely (1) the detection of TADs, (2) how to compare two maps, and (3) how to detect chromatin loops. The detection of TADs has become a critical step in the analysis of Hi-C data, e.g., to identify enhancer-promoter associations. First, we present \textsc{East}, a novel TAD identification algorithm based on fast 2D convolution of Haar-like features, that is as accurate as the state-of-the-art method based on the directionality index, but 75-80$\times$ faster.Another fundamental problem in the analysis of Hi-C data is to compare two contact maps derived from Hi-C experiments to identify the functional differences. Detecting similarities and differences between contact maps is critical in evaluating the reproducibility of replicate experiments and identifying differential genomic regions with biological significance. Due to the complexity of chromatin conformations and the presence of technology-driven and sequence-specific biases, the comparative analysis of Hi-C data is analytically and computationally challenging. Second, we present a novel approach called Selfish for the comparative analysis of Hi-C data that takes advantage of the structural self-similarity in contact maps. We define a self-similarity measure to design algorithms for (i) measuring reproducibility for Hi-C replicate experiments and (ii) finding differential chromatin interactions between two contact maps. Extensive experimental results on simulated and real data show that Selfish is more accurate and robust than state-of-the-art methods.Regulatory elements at large genomic distances can engage in gene regulation by making direct physical contacts to their target genes or loci bringing distant loci in close spatial proximity of each other forming chromatin loops. These long-range interactions form complex regulatory networks that need to be carefully studied. Analyzing chromatin interactions between regulatory elements and genes at high resolution using high-throughput chromosome conformation capture method Hi-C, can provide fundamental insights into the spatial organization of chromosomes and its effect on gene regulation. Third, we present a new method Mustache to detect significant chromatin interactions genome-wide. Mustache robustly finds chromatin pairs of loci that interacts significantly compared with the expected interaction. We show that detected interactions are biologically supported by running a wide range of experiments. The experiments indicate that these interactions are associated with contacts between promoters and enhancers, promoters to promoters, mediated by different proteins and are stable between cell types

Efficient Algorithms for the Analysis of Hi-C Contact Maps

This dissertation deals with the analysis of high-throughput chromatin conformation capture (Hi-C) data. Hi-C experiments provide genome-wide maps of chromatin interactions and has enabled Life Scientists to investigate the role of the three-dimensional structure of genomes in gene regulation and other essential cellular functions. Several studies have confirmed the existence of fundamental 3D structural features of different scales that are stable across cell types and conserved across species, e.g., topological associating domains (TADs) and chromatin loops.The research presented here is articulated around three main topics on the analysis of contact maps, namely (1) the detection of TADs, (2) how to compare two maps, and (3) how to detect chromatin loops. The detection of TADs has become a critical step in the analysis of Hi-C data, e.g., to identify enhancer-promoter associations. First, we present \textsc{East}, a novel TAD identification algorithm based on fast 2D convolution of Haar-like features, that is as accurate as the state-of-the-art method based on the directionality index, but 75-80$\times$ faster.Another fundamental problem in the analysis of Hi-C data is to compare two contact maps derived from Hi-C experiments to identify the functional differences. Detecting similarities and differences between contact maps is critical in evaluating the reproducibility of replicate experiments and identifying differential genomic regions with biological significance. Due to the complexity of chromatin conformations and the presence of technology-driven and sequence-specific biases, the comparative analysis of Hi-C data is analytically and computationally challenging. Second, we present a novel approach called Selfish for the comparative analysis of Hi-C data that takes advantage of the structural self-similarity in contact maps. We define a self-similarity measure to design algorithms for (i) measuring reproducibility for Hi-C replicate experiments and (ii) finding differential chromatin interactions between two contact maps. Extensive experimental results on simulated and real data show that Selfish is more accurate and robust than state-of-the-art methods.Regulatory elements at large genomic distances can engage in gene regulation by making direct physical contacts to their target genes or loci bringing distant loci in close spatial proximity of each other forming chromatin loops. These long-range interactions form complex regulatory networks that need to be carefully studied. Analyzing chromatin interactions between regulatory elements and genes at high resolution using high-throughput chromosome conformation capture method Hi-C, can provide fundamental insights into the spatial organization of chromosomes and its effect on gene regulation. Third, we present a new method Mustache to detect significant chromatin interactions genome-wide. Mustache robustly finds chromatin pairs of loci that interacts significantly compared with the expected interaction. We show that detected interactions are biologically supported by running a wide range of experiments. The experiments indicate that these interactions are associated with contacts between promoters and enhancers, promoters to promoters, mediated by different proteins and are stable between cell types

Mustache: multi-scale detection of chromatin loops from Hi-C and Micro-C maps using scale-space representation.

We present MUSTACHE, a new method for multi-scale detection of chromatin loops from Hi-C and Micro-C contact maps. MUSTACHE employs scale-space theory, a technical advance in computer vision, to detect blob-shaped objects in contact maps. MUSTACHE is scalable to kilobase-resolution maps and reports loops that are highly consistent between replicates and between Hi-C and Micro-C datasets. Compared to other loop callers, such as HiCCUPS and SIP, MUSTACHE recovers a higher number of published ChIA-PET and HiChIP loops as well as loops linking promoters to regulatory elements. Overall, MUSTACHE enables an efficient and comprehensive analysis of chromatin loops. Available at: https://github.com/ay-lab/mustache

Mustache: multi-scale detection of chromatin loops from Hi-C and Micro-C maps using scale-space representation

We present MUSTACHE, a new method for multi-scale detection of chromatin loops from Hi-C and Micro-C contact maps. MUSTACHE employs scale-space theory, a technical advance in computer vision, to detect blob-shaped objects in contact maps. MUSTACHE is scalable to kilobase-resolution maps and reports loops that are highly consistent between replicates and between Hi-C and Micro-C datasets. Compared to other loop callers, such as HiCCUPS and SIP, MUSTACHE recovers a higher number of published ChIA-PET and HiChIP loops as well as loops linking promoters to regulatory elements. Overall, MUSTACHE enables an efficient and comprehensive analysis of chromatin loops. Available at: https://github.com/ay-lab/mustache