Biomedical Informatics Colloquium, BIO 4050, Course Outline

A seminar-based course that exposes students to current research topics in the fields of Bioinformatics and Medical Informatics. Weekly presentations by invited speakers and/or faculty introduce students to the broad diversity of research areas in both fields, and engages them in critical thinking and writing. Online lectures and reading activities will be given periodically

Bioinformatics II, BIO 3352, Course Outline

This course is a continuation of Bioinformatics I. Topics include gene expression, microarrays, next- generation sequencing methods, RNA-seq, large genomic projects, protein structure and stability, protein folding, and computational structure prediction of proteins; proteomics; and protein-nucleic acid interactions. The lab component includes R-based statistical data analysis on large datasets, introduction to big data analysis tools, protein visualization software, internet-based tools and high-level programming languages

Visualizing Meta-Features in Proteomic Maps

<p>Abstract</p> <p>Background</p> <p>The steps of a high-throughput proteomics experiment include the separation, differential expression and mass spectrometry-based identification of proteins. However, the last and more challenging step is inferring the biological role of the identified proteins through their association with interaction networks, biological pathways, analysis of the effect of post-translational modifications, and other protein-related information.</p> <p>Results</p> <p>In this paper, we present an integrative visualization methodology that allows combining experimentally produced proteomic features with protein meta-features, typically coming from meta-analysis tools and databases, in synthetic Proteomic Feature Maps. Using three proteomics analysis scenarios, we show that the proposed visualization approach is effective in filtering, navigating and interacting with the proteomics data in order to address visually challenging biological questions. The novelty of our approach lies in the ease of integration of any user-defined proteomic features in easy-to-comprehend visual representations that resemble the familiar 2D-gel images, and can be adapted to the user's needs. The main capabilities of the developed VIP software, which implements the presented visualization methodology, are also highlighted and discussed.</p> <p>Conclusions</p> <p>By using this visualization and the associated VIP software, researchers can explore a complex heterogeneous proteomics dataset from different perspectives in order to address visually important biological queries and formulate new hypotheses for further investigation. VIP is freely available at <url>http://pelopas.uop.gr/~egian/VIP/index.html</url>.</p

Epigenetic profiles signify cell fate plasticity in unipotent spermatogonial stem and progenitor cells

Spermatogonial stem and progenitor cells (SSCs) generate adult male gametes. During in vitro expansion, these unipotent murine cells spontaneously convert to multipotent adult spermatogonial-derived stem cells (MASCs). Here we investigate this conversion process through integrative transcriptomic and epigenomic analyses. We find in SSCs that promoters essential to maintenance and differentiation of embryonic stem cells (ESCs) are enriched with histone H3-lysine4 and -lysine 27 trimethylations. These bivalent modifications are maintained at most somatic promoters after conversion, bestowing MASCs an ESC-like promoter chromatin. At enhancers, the core pluripotency circuitry is activated partially in SSCs and completely in MASCs, concomitant with loss of germ cell-specific gene expression and initiation of embryonic-like programs. Furthermore, SSCs in vitro maintain the epigenomic characteristics of germ cells in vivo. Our observations suggest that SSCs encode innate plasticity through the epigenome and that both conversion of promoter chromatin states and activation of cell type-specific enhancers are prominent features of reprogramming

Epigenomic Alterations in Localized and Advanced Prostate Cancer

AbstractAlthough prostate cancer (PCa) is the second leading cause of cancer death among men worldwide, not all men diagnosed with PCa will die from the disease. A critical challenge, therefore, is to distinguish indolent PCa from more advanced forms to guide appropriate treatment decisions. We used Enhanced Reduced Representation Bisulfite Sequencing, a genome-wide high-coverage single-base resolution DNA methylation method to profile seven localized PCa samples, seven matched benign prostate tissues, and six aggressive castration-resistant prostate cancer (CRPC) samples. We integrated these data with RNA-seq and whole-genome DNA-seq data to comprehensively characterize the PCa methylome, detect changes associated with disease progression, and identify novel candidate prognostic biomarkers. Our analyses revealed the correlation of cytosine guanine dinucleotide island (CGI)-specific hypermethylation with disease severity and association of certain breakpoints (deletion, tandem duplications, and interchromosomal translocations) with DNA methylation. Furthermore, integrative analysis of methylation and single-nucleotide polymorphisms (SNPs) uncovered widespread allele-specific methylation (ASM) for the first time in PCa. We found that most DNA methylation changes occurred in the context of ASM, suggesting that variations in tumor epigenetic landscape of individuals are partly mediated by genetic differences, which may affect PCa disease progression. We further selected a panel of 13 CGIs demonstrating increased DNA methylation with disease progression and validated this panel in an independent cohort of 20 benign prostate tissues, 16 PCa, and 8 aggressive CRPCs. These results warrant clinical evaluation in larger cohorts to help distinguish indolent PCa from advanced disease

Bone Protection by Inhibition of MicroRNA-182

Targeting microRNAs recently shows significant therapeutic promise; however, such progress is underdeveloped in treatment of skeletal diseases with osteolysis, such as osteoporosis and rheumatoid arthritis (RA). Here, we identified miR-182 as a key osteoclastogenic regulator in bone homeostasis and diseases. Myeloid-specific deletion of miR-182 protects mice against excessive osteoclastogenesis and bone resorption in disease models of ovariectomy-induced osteoporosis and inflammatory arthritis. Pharmacological treatment of these diseases with miR-182 inhibitors completely suppresses pathologic bone erosion. Mechanistically, we identify protein kinase double-stranded RNA-dependent (PKR) as a new and essential miR-182 target that is a novel inhibitor of osteoclastogenesis via regulation of the endogenous interferon (IFN)-β-mediated autocrine feedback loop. The expression levels of miR-182, PKR, and IFN-β are altered in RA and are significantly correlated with the osteoclastogenic capacity of RA monocytes. Our findings reveal a previously unrecognized regulatory network mediated by miR-182-PKR-IFN-β axis in osteoclastogenesis, and highlight the therapeutic implications of miR-182 inhibition in osteoprotection

An integrated ChIP-seq analysis platform with customizable workflows

<p>Abstract</p> <p>Background</p> <p>Chromatin immunoprecipitation followed by next generation sequencing (ChIP-seq), enables unbiased and genome-wide mapping of protein-DNA interactions and epigenetic marks. The first step in ChIP-seq data analysis involves the identification of peaks (i.e., genomic locations with high density of mapped sequence reads). The next step consists of interpreting the biological meaning of the peaks through their association with known genes, pathways, regulatory elements, and integration with other experiments. Although several programs have been published for the analysis of ChIP-seq data, they often focus on the peak detection step and are usually not well suited for thorough, integrative analysis of the detected peaks.</p> <p>Results</p> <p>To address the peak interpretation challenge, we have developed ChIPseeqer, an integrative, comprehensive, fast and user-friendly computational framework for in-depth analysis of ChIP-seq datasets. The novelty of our approach is the capability to combine several computational tools in order to create easily customized workflows that can be adapted to the user's needs and objectives. In this paper, we describe the main components of the ChIPseeqer framework, and also demonstrate the utility and diversity of the analyses offered, by analyzing a published ChIP-seq dataset.</p> <p>Conclusions</p> <p>ChIPseeqer facilitates ChIP-seq data analysis by offering a flexible and powerful set of computational tools that can be used in combination with one another. The framework is freely available as a user-friendly GUI application, but all programs are also executable from the command line, thus providing flexibility and automatability for advanced users.</p