A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases <s> Differential Expression <s> Gliomas are the most common of the primary intracranial tumors with astrocytomas constituting about 40%. Using clinically and histologically assessed astrocytomas, we have studied their protein profiles using a two-dimensional gel electrophoresis-mass spectrometry approach and identified differentially expressed proteins which may be useful molecular indicators to understand these tumors. Examination of the protein profiles of 27 astrocytoma samples of different grades revealed 72 distinct, differentially expressed proteins belonging to various functional groups such as cytoskeleton and intermediate filament proteins, heat shock proteins (HSPs), enzymes and regulatory proteins. Based on the consistency of their differential expression, 29 distinct proteins could be short-listed and may have a role in the pathology of astrocytomas. Some were found to be differentially expressed in both Grade III and IV astrocytomas while others were associated with a particular grade. A notable observation was underexpression of Prohibitin, a potential tumor suppressor protein, Rho-GDP dissociation inhibitor, Rho-GDI, a regulator of Rho GTPases and HSPs as well as destabilization of glial fibrillary acidic protein, GFAP, major protein of the glial filaments, in Grade III malignant tumors. We attempt to explain glioma malignancy and progression in terms of their combined role. <s> BIB001 </s> A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases <s> Differential Expression <s> An attractive path forward in proteomics is to experimentally annotate the human protein complement of the genome in a genecentric manner. Using antibodies, it might be possible to design protein-specific probes for a representative protein from every protein-coding gene and to subsequently use the antibodies for systematical analysis of cellular distribution and subcellular localization of proteins in normal and disease tissues. A new version (4.0) of the Human Protein Atlas has been developed in a genecentric manner with the inclusion of all human genes and splice variants predicted from genome efforts together with a visualization of each protein with characteristics such as predicted membrane regions, signal peptide, and protein domains and new plots showing the uniqueness (sequence similarity) of every fraction of each protein toward all other human proteins. The new version is based on tissue profiles generated from 6120 antibodies with more than five million immunohistochemistry-based images covering 5067 human genes, corresponding to approximately 25% of the human genome. Version 4.0 includes a putative list of members in various protein classes, both functional classes, such as kinases, transcription factors, G-protein-coupled receptors, etc., and project-related classes, such as candidate genes for cancer or cardiovascular diseases. The exact antigen sequence for the internally generated antibodies has also been released together with a visualization of the application-specific validation performed for each antibody, including a protein array assay, Western blot analysis, immunohistochemistry, and, for a large fraction, immunofluorescence-based confocal microscopy. New search functionalities have been added to allow complex queries regarding protein expression profiles, protein classes, and chromosome location. The new version of the protein atlas thus is a resource for many areas of biomedical research, including protein science and biomarker discovery. <s> BIB002 </s> A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases <s> Differential Expression <s> Variation in gene expression is an important mechanism underlying susceptibility to complex disease. The simultaneous genome-wide assay of gene expression and genetic variation allows the mapping of the genetic factors that underpin individual differences in quantitative levels of expression (expression QTLs; eQTLs). The availability of systematically generated eQTL information could provide immediate insight into a biological basis for disease associations identified through genome-wide association (GWA) studies, and can help to identify networks of genes involved in disease pathogenesis. Although there are limitations to current eQTL maps, understanding of disease will be enhanced with novel technologies and international efforts that extend to a wide range of new samples and tissues. <s> BIB003 </s> A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases <s> Differential Expression <s> Epigenetics is one of the most rapidly expanding fields in biology. The recent characterization of a human DNA methylome at single nucleotide resolution, the discovery of the CpG island shores, the finding of new histone variants and modifications, and the unveiling of genome-wide nucleosome positioning maps highlight the accelerating speed of discovery over the past two years. Increasing interest in epigenetics has been accompanied by technological breakthroughs that now make it possible to undertake large-scale epigenomic studies. These allow the mapping of epigenetic marks, such as DNA methylation, histone modifications and nucleosome positioning, which are critical for regulating gene and noncoding RNA expression. In turn, we are learning how aberrant placement of these epigenetic marks and mutations in the epigenetic machinery is involved in disease. Thus, a comprehensive understanding of epigenetic mechanisms, their interactions and alterations in health and disease, has become a priority in biomedical research. <s> BIB004 </s> A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases <s> Differential Expression <s> Since antiquity, humans have used body fluids like saliva, urine and sweat for the diagnosis of diseases. The amount, color and smell of body fluids are still used in many traditional medical practices to evaluate an illness and make a diagnosis. The development and application of analytical methods for the detailed analysis of body fluids has led to the discovery of numerous disease biomarkers. Recently, mass spectrometry (MS), nuclear magnetic resonance spectroscopy (NMR), and multivariate statistical techniques have been incorporated into a multidisciplinary approach to profile changes in small molecules associated with the onset and progression of human diseases. The goal of these efforts is to identify metabolites that are uniquely correlated with a specific human disease in order to accurately diagnose and treat the malady. In this review we will discuss recent developments in sample preparation, experimental techniques, the identification and quantification of metabolites, and the chemometric tools used to search for biomarkers of human diseases using NMR. <s> BIB005 </s> A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases <s> Differential Expression <s> The ArrayExpress Archive of Functional Genomics Data (http://www.ebi.ac.uk/arrayexpress) is an international functional genomics database at the European Bioinformatics Institute (EMBL-EBI) recommended by most journals as a repository for data supporting peer-reviewed publications. It contains data from over 7000 public sequencing and 42 000 array-based studies comprising over 1.5 million assays in total. The proportion of sequencing-based submissions has grown significantly over the last few years and has doubled in the last 18 months, whilst the rate of microarray submissions is growing slightly. All data in ArrayExpress are available in the MAGE-TAB format, which allows robust linking to data analysis and visualization tools and standardized analysis. The main development over the last two years has been the release of a new data submission tool Annotare, which has reduced the average submission time almost 3-fold. In the near future, Annotare will become the only submission route into ArrayExpress, alongside MAGE-TAB format-based pipelines. ArrayExpress is a stable and highly accessed resource. Our future tasks include automation of data flows and further integration with other EMBL-EBI resources for the representation of multi-omics data. <s> BIB006 </s> A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases <s> Differential Expression <s> Understanding the functional consequences of genetic variation, and how it affects complex human disease and quantitative traits, remains a critical challenge for biomedicine. We present an analysis of RNA sequencing data from 1641 samples across 43 tissues from 175 individuals, generated as part of the pilot phase of the Genotype-Tissue Expression (GTEx) project. We describe the landscape of gene expression across tissues, catalog thousands of tissue-specific and shared regulatory expression quantitative trait loci (eQTL) variants, describe complex network relationships, and identify signals from genome-wide association studies explained by eQTLs. These findings provide a systematic understanding of the cellular and biological consequences of human genetic variation and of the heterogeneity of such effects among a diverse set of human tissues. <s> BIB007 </s> A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases <s> Differential Expression <s> Resolving the molecular details of proteome variation in the different tissues and organs of the human body will greatly increase our knowledge of human biology and disease. Here, we present a map of the human tissue proteome based on an integrated omics approach that involves quantitative transcriptomics at the tissue and organ level, combined with tissue microarray-based immunohistochemistry, to achieve spatial localization of proteins down to the single-cell level. Our tissue-based analysis detected more than 90% of the putative protein-coding genes. We used this approach to explore the human secretome, the membrane proteome, the druggable proteome, the cancer proteome, and the metabolic functions in 32 different tissues and organs. All the data are integrated in an interactive Web-based database that allows exploration of individual proteins, as well as navigation of global expression patterns, in all major tissues and organs in the human body. <s> BIB008 </s> A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases <s> Differential Expression <s> Motivation: In attempts to determine the genetic causes of human disease, researchers are often faced with a large number of candidate genes. Linkage studies can point to a genomic region containing hundreds of genes, while the high-throughput sequencing approach will often identify a great number of non-synonymous genetic variants. Since systematic experimental verification of each such candidate gene is not feasible, a method is needed to decide which genes are worth investigating further. Computational gene prioritization presents itself as a solution to this problem, systematically analyzing and sorting each gene from the most to least likely to be the disease-causing gene, in a fraction of the time it would take a researcher to perform such queries manually. ::: ::: Results: Here, we present Gene TIssue Expression Ranker (GeneTIER), a new web-based application for candidate gene prioritization. GeneTIER replaces knowledge-based inference traditionally used in candidate disease gene prioritization applications with experimental data from tissue-specific gene expression datasets and thus largely overcomes the bias toward the better characterized genes/diseases that commonly afflict other methods. We show that our approach is capable of accurate candidate gene prioritization and illustrate its strengths and weaknesses using case study examples. ::: ::: Availability and Implementation: Freely available on the web at http://dna.leeds.ac.uk/GeneTIER/. ::: ::: Contact: ku.ca.sdeel@naamu ::: ::: Supplementary information: Supplementary data are available at Bioinformatics online. <s> BIB009
Besides DNA sequence variations, many other kinds of biological evidence may be used for the inference of gene-disease associations. Since disease manifestation usually accompanied by various molecular changes, case-control omics studies allow identifying coding and non-coding transcripts BIB003 , proteins BIB001 , and other entities such as metabolites BIB005 and epigenetic marks BIB004 altered in disease samples compared to controls and thus potentially involved into the pathogenesis. Genes and proteins differentially expressed under various biological conditions, including diseases and developmental stages in human and other organisms can be obtained from Expression Atlas BIB006 , a curated database of expression profiles in human and many other species derived from selected RNA-seq and microarray datasets. Expression Atlas uniformly processes and analyse expression data obtained from multiple sources, from individual Gene Expression Omnibus  datasets to large expression studies, such as GTEx BIB007 and Human Protein Atlas BIB002 , BIB008 . Also, Expression Atlas provides baseline expression levels in tissues and cell types and allows retrieving tissue-and cell type-specific genes. This information can also give a hint about gene function. For example, geneTIER BIB009 assumes that plausible candidate genes are highly expressed in tissues, affected by the disease. However, this assumption is only applicable when affected tissues or cell types are well known, which is not always the case.