A graphical model approach visualizes regulatory relationships between genome-wide transcription factor binding profiles.

Integrated analysis of multiple genome-wide transcription factor (TF)-binding profiles will be vital to advance our understanding of the global impact of TF binding. However, existing methods for measuring similarity in large numbers of chromatin immunoprecipitation assays with sequencing (ChIP-seq), such as correlation, mutual information or enrichment analysis, are limited in their ability to display functionally relevant TF relationships. In this study, we propose the use of graphical models to determine conditional independence between TFs and showed that network visualization provides a promising alternative to distinguish 'direct' versus 'indirect' TF interactions. We applied four algorithms to measure 'direct' dependence to a compendium of 367 mouse haematopoietic TF ChIP-seq samples and obtained a consensus network known as a 'TF association network' where edges in the network corresponded to likely causal pairwise relationships between TFs. The 'TF association network' illustrates the role of TFs in developmental pathways, is reminiscent of combinatorial TF regulation, corresponds to known protein-protein interactions and indicates substantial TF-binding reorganization in leukemic cell types. With the rapid increase in TF ChIP-Seq data sets, the approach presented here will be a powerful tool to study transcriptional programmes across a wide range of biological systems.Bloodwise, the Biotechnology and Biological Sciences Research Council, the Leukaemia and Lymphoma Society, Cancer Research UK, the National Institute for Health Research Cambridge Biomedical Research Centre, and the Wellcome Trust and MRC Cambridge Institute for Medical Research and Wellcome Trust—Medical Research Council Cambridge Stem Cell Institute; Yousef Jameel scholarship awarded by the Cambridge Commonwealth, European and International Trust (to F.S.L.N.).This is the final version of the article. It first appeared from Oxford University Press via https://doi.org/10.1093/bib/bbw10

eQTL Viewer: visualizing how sequence variation affects genome-wide transcription

BACKGROUND: Expression Quantitative Trait Locus (eQTL) mapping methods have been used to identify the genetic basis of gene expression variations. To map eQTL, thousands of expression profiles are related with sequence polymorphisms across the genome through their correlated variations. These eQTL distribute in many chromosomal regions, each of which can include many genes. The large number of mapping results produced makes it difficult to consider simultaneously the relationships between multiple genomic regions and multiple expressional profiles. There is a need for informative bioinformatics tools to assist the visualization and interpretation of these mapping results. RESULTS: We have developed a web-based tool, called eQTL Viewer, to visualize the relationships between the expression trait genes and the candidate genes in the eQTL regions using Scalable Vector Graphics. The plot generated by eQTL Viewer has the capacity to display mapping results with high resolutions at a variety of scales, and superimpose biological annotations onto the mapping results dynamically. CONCLUSION: Our tool provides an efficient and intuitive way for biologists to explore transcriptional regulation patterns, and to generate hypotheses on the genetic basis of transcriptional regulations

CoryneRegNet: An ontology-based data warehouse of corynebacterial transcription factors and regulatory networks

Baumbach J, Brinkrolf K, Czaja LF, Rahmann S, Tauch A. CoryneRegNet: An ontology-based data warehouse of corynebacterial transcription factors and regulatory networks. BMC Genomics. 2006;7(1): 24

Visualization and analysis of cancer genome sequencing studies

Large-scale genomics projects such as the Cancer Genome Atlas (TCGA), and the Encyclopedia of DNA Elements (ENCODE) involve generation of data at an unprecedented scale, requiring new computational techniques for analysis and interpretation. In the three studies I present in this thesis, I utilize these data sources to derive biological insights or created visualization tools that enable others to obtain insights more easily. First, I examine the distribution of the lengths for copy number variations (CNVs) in the cancer genome. This analysis shows that a small number of genes are altered at a greater frequency than expected from a power law distribution, suggesting that a large number of genomes must be sequenced for a given tumor type to a comprehensive discovery of somatic mutations. Second, I investigate germline CNVs in thousands of TCGA samples using single nucleotide polymorphism (SNP) array data to find variants that may confer increased susceptibility to cancer. This CNV-based genome-wide association study resulted in many germline CNVs that potentially increase risk in brain, breast, colorectal, renal, or ovarian cancers. Finally, I apply several visualization techniques to create tools for the TCGA and ENCODE projects in order to help investigators better process and synthesize meaning from large volume of data. Seqeyes combines linear and circular genomic views to explore predicted structural variations to help guide experimental validation. The modEncode browser visualizes chromatin organization by integrating data from a multitude of histone marks and chromosomal proteins. These results present visualization as a useful strategy for rapid identification of salient genomic features from large, heterogeneous genomic datasets

RCAS: an RNA centric annotation system for transcriptome-wide regions of interest

In the field of RNA, the technologies for studying the transcriptome have created a tremendous potential for deciphering the puzzles of the RNA biology. Along with the excitement, the unprecedented volume of RNA related omics data is creating great challenges in bioinformatics analyses. Here, we present the RNA Centric Annotation System (RCAS), an R package, which is designed to ease the process of creating gene-centric annotations and analysis for the genomic regions of interest obtained from various RNA-based omics technologies. The design of RCAS is modular, which enables flexible usage and convenient integration with other bioinformatics workflows. RCAS is an R/Bioconductor package but we also created graphical user interfaces including a Galaxy wrapper and a stand-alone web service. The application of RCAS on published datasets shows that RCAS is not only able to reproduce published findings but also helps generate novel knowledge and hypotheses. The meta-gene profiles, gene-centric annotation, motif analysis and gene-set analysis provided by RCAS provide contextual knowledge which is necessary for understanding the functional aspects of different biological events that involve RNAs. In addition, the array of different interfaces and deployment options adds the convenience of use for different levels of users. RCAS is available at http://bioconductor.org/packages/release/bioc/html/RCAS.html and http://rcas.mdc-berlin.de

The ancient mammalian KRAB zinc finger gene cluster on human chromosome 8q24.3 illustrates principles of C2H2 zinc finger evolution associated with unique expression profiles in human tissues

<p>Abstract</p> <p>Background</p> <p>Expansion of multi-C2H2 domain zinc finger (ZNF) genes, including the Krüppel-associated box (KRAB) subfamily, paralleled the evolution of tetrapodes, particularly in mammalian lineages. Advances in their cataloging and characterization suggest that the functions of the KRAB-ZNF gene family contributed to mammalian speciation.</p> <p>Results</p> <p>Here, we characterized the human 8q24.3 ZNF cluster on the genomic, the phylogenetic, the structural and the transcriptome level. Six (ZNF7, ZNF34, ZNF250, ZNF251, ZNF252, ZNF517) of the seven locus members contain exons encoding KRAB domains, one (ZNF16) does not. They form a paralog group in which the encoded KRAB and ZNF protein domains generally share more similarities with each other than with other members of the human ZNF superfamily. The closest relatives with respect to their DNA-binding domain were ZNF7 and ZNF251. The analysis of orthologs in therian mammalian species revealed strong conservation and purifying selection of the KRAB-A and zinc finger domains. These findings underscore structural/functional constraints during evolution. Gene losses in the murine lineage (ZNF16, ZNF34, ZNF252, ZNF517) and potential protein truncations in primates (ZNF252) illustrate ongoing speciation processes. Tissue expression profiling by quantitative real-time PCR showed similar but distinct patterns for all tested ZNF genes with the most prominent expression in fetal brain. Based on accompanying expression signatures in twenty-six other human tissues ZNF34 and ZNF250 revealed the closest expression profiles. Together, the 8q24.3 ZNF genes can be assigned to a cerebellum, a testis or a prostate/thyroid subgroup. These results are consistent with potential functions of the ZNF genes in morphogenesis and differentiation. Promoter regions of the seven 8q24.3 ZNF genes display common characteristics like missing TATA-box, CpG island-association and transcription factor binding site (TFBS) modules. Common TFBS modules partly explain the observed expression pattern similarities.</p> <p>Conclusions</p> <p>The ZNF genes at human 8q24.3 form a relatively old mammalian paralog group conserved in eutherian mammals for at least 130 million years. The members persisted after initial duplications by undergoing subfunctionalizations in their expression patterns and target site recognition. KRAB-ZNF mediated repression of transcription might have shaped organogenesis in mammalian ontogeny.</p

Genome-wide identification and characterisation of exapted transposable elements in the large genome of sunflower (Helianthus annuus L.)

Transposable elements (TEs) are an important source of genome variability, playing many roles in the evolution of eukaryotic species. Besides well-known phenomena, TEs may undergo the exaptation process and generate the so-called exapted transposable element genes (ETEs). Here we present a genome-wide survey of ETEs in the large genome of sunflower (Helianthus annuus L.), in which the massive amount of TEs, provides a significant source for exaptation. A library of sunflower TEs was used to build TE-specific Hidden Markov Model profiles, to search for all available sunflower gene products. In doing so, 20,016 putative ETEs were identified and further investigated for the characteristics that distinguish TEs from genes, leading to the validation of 3,530 ETEs. The analysis of ETEs transcription patterns under different stress conditions showed a differential regulation triggered by treatments mimicking biotic and abiotic stress; furthermore, the distribution of functional domains of differentially regulated ETEs revealed a relevant presence of domains involved in many aspects of cellular functions. A comparative genomic investigation was performed including species representative of Asterids and appropriate outgroups: the bulk of ETEs resulted specific to the sunflower, while few ETEs presented orthologues in the genome of all analysed species, making the hypothesis of a conserved function. This study highlights the crucial role played by exaptation, actively contributing to species evolution

Automatic identification of informative regions with epigenomic changes associated to hematopoiesis

Hematopoiesis is one of the best characterized biological systems but the connection between chromatin changes and lineage differentiation is not yet well understood. We have developed a bioinformatic workflow to generate a chromatin space that allows to classify 42 human healthy blood epigenomes from the BLUEPRINT, NIH ROADMAP and ENCODE consortia by their cell type. This approach let us to distinguish different cells types based on their epigenomic profiles, thus recapitulating important aspects of human hematopoiesis. The analysis of the orthogonal dimension of the chromatin space identify 32,662 chromatin determinant regions (CDRs), genomic regions with different epigenetic characteristics between the cell types. Functional analysis revealed that these regions are linked with cell identities. The inclusion of leukemia epigenomes in the healthy hematological chromatin sample space gives us insights on the healthy cell types that are more epigenetically similar to the disease samples. Further analysis of tumoral epigenetic alterations in hematopoietic CDRs points to sets of genes that are tightly regulated in leukemic transformations and commonly mutated in other tumors. Our method provides an analytical approach to study the relationship between epigenomic changes and cell lineage differentiation. Method availability: https://github.com/david-juan/ChromDet.European Union’s Seventh Framework Programme [FP7/2007–2013, 282510 (BLUEPRINT)]; Spanish Ministry of Economy, Industry and Competitiveness and European Regional Development Fund [Project Retos BFU2015–71241-R]. Funding for open access charge: Project Retos BFU2015–71241-R (to A.V.).Peer ReviewedPostprint (published version

Characterizing the Huntington's disease, Parkinson's disease, and pan-neurodegenerative gene expression signature with RNA sequencing

Huntington's disease (HD) and Parkinson's disease (PD) are devastating neurodegenerative disorders that are characterized pathologically by degeneration of neurons in the brain and clinically by loss of motor function and cognitive decline in mid to late life. The cause of neuronal degeneration in these diseases is unclear, but both are histologically marked by aggregation of specific proteins in specific brain regions. In HD, fragments of a mutant Huntingtin protein aggregate and cause medium spiny interneurons of the striatum to degenerate. In contrast, PD brains exhibit aggregation of toxic fragments of the alpha synuclein protein throughout the central nervous system and trigger degeneration of dopaminergic neurons in the substantia nigra. Considering the commonalities and differences between these diseases, identifying common biological patterns across HD and PD as well as signatures unique to each may provide significant insight into the molecular mechanisms underlying neurodegeneration as a general process. State-of-the-art high-throughput sequencing technology allows for unbiased, whole genome quantification of RNA molecules within a biological sample that can be used to assess the level of activity, or expression, of thousands of genes simultaneously. In this thesis, I present three studies characterizing the RNA expression profiles of post-mortem HD and PD subjects using high-throughput mRNA sequencing data sets. The first study describes an analysis of differential expression between HD individuals and neurologically normal controls that indicates a widespread increase in immune, neuroinflammatory, and developmental gene expression. The second study expands upon the first study by making methodological improvements and extends the differential expression analysis to include PD subjects, with the goal of comparing and contrasting HD and PD gene expression profiles. This study was designed to identify common mechanisms underlying the neurodegenerative phenotype, transcending those of each unique disease, and has revealed specific biological processes, in particular those related to NFkB inflammation, common to HD and PD. The last study describes a novel methodology for combining mRNA and miRNA expression that seeks to identify associations between mRNA-miRNA modules and continuous clinical variables of interest, including CAG repeat length and clinical age of onset in HD