Genome-wide association studies (GWASs) led to impressive advances in the elucidation of genetic factors underlying complex phenotypes and diseases. However, the ability of GWAS to identify new susceptibility loci in a hypothesis-free approach requires tools to quickly retrieve comprehensive information about a genomic region and analyze the potential effects of coding and non-coding SNPs in a candidate gene region. Furthermore, once a candidate region is chosen for resequencing and fine-mapping studies, the identification of several rare mutations is likely and requires strong bioinformatic support to properly evaluate and prioritize the found mutations for further analysis. Due to the variety of regulatory layers that can be affected by a mutation, a comprehensive in-silico evaluation of candidate SNPs can be a demanding and very time-consuming task. Although many bioinformatic tools that significantly simplify this task were made available in the last years, their utility is often still unknown to researches not intensively involved in bioinformatics. We present a comprehensive guide of 64 tools and databases to bioinformatically analyze gene regions of interest to predict SNP effects. In addition, we discuss tools to perform data mining of large genetic regions, predict the presence of regulatory elements, make in-silico evaluations of SNPs effects and address issues ranging from interactome analysis to graphically annotated proteins sequences. Finally, we exemplify the use of these tools by applying them to hits of a recently performed GWAS. Taken together a combination of the discussed tools are summarized and constantly updated in the web-based "GenEpi Toolbox" (http://genepi_toolbox.i-med.ac.at) and can help to get a glimpse at the potential functional relevance of both large genetic regions and single nucleotide mutations which might help to prioritize the next steps.
"The functional consequences of polymorphisms in miRNA genes and/or their binding sites, the downstream targets of miRNAs and the mechanisms by which miRNAs regulate circuitries and processes modulating nociception and endogenous analgesia are entirely unaddressed. SNPs in miRNAs or their target sites are not only bioinformatically predicted to be associated with the pathogenesis of diseases but are also experimentally validated (Wu et al., 2008; Coassin et al., 2010). It is known that SNPs are less common in miRNAs or their target sites than in other parts of the genome which points to the importance of miRNAs for cellular processes. "
[Show abstract][Hide abstract] ABSTRACT: Neuro-immune alterations in the peripheral and central nervous system play a role in the pathophysiology of chronic pain, and non-coding RNAs - and microRNAs (miRNAs) in particular - regulate both immune and neuronal processes. Specifically, miRNAs control macromolecular complexes in neurons, glia and immune cells and regulate signals used for neuro-immune communication in the pain pathway. Therefore, miRNAs may be hypothesized as critically important master switches modulating chronic pain. In particular, understanding the concerted function of miRNA in the regulation of nociception and endogenous analgesia and defining the importance of miRNAs in the circuitries and cognitive, emotional and behavioral components involved in pain is expected to shed new light on the enigmatic pathophysiology of neuropathic pain, migraine and complex regional pain syndrome. Specific miRNAs may evolve as new druggable molecular targets for pain prevention and relief. Furthermore, predisposing miRNA expression patterns and inter-individual variations and polymorphisms in miRNAs and/or their binding sites may serve as biomarkers for pain and help to predict individual risks for certain types of pain and responsiveness to analgesic drugs. miRNA-based diagnostics are expected to develop into hands-on tools that allow better patient stratification, improved mechanism-based treatment, and targeted prevention strategies for high risk individuals.
"Given the extensive number of variants identified by whole genome or exome re-sequencing studies, it is infeasible to interrogate the functional consequences of all variant alleles at all gene loci experimentally. A number of bioinformatics solutions for the annotation, scoring and classification of variants have been developed to address this challenge [1–27] and several comprehensive overviews of the available tools and methods have been carried out    . Such tools are providing a supportive role in the experimental validation of disease-related alleles, by prioritizing candidate variants with predicted functional consequences as causes of specific inherited diseases and cancers. "
[Show abstract][Hide abstract] ABSTRACT: The study of DNA sequence variation has been transformed by recent advances in DNA sequencing technologies. Determination of the functional consequences of sequence variant alleles offers potential insight as to how genotype may influence phenotype. Even within protein coding regions of the genome, establishing the consequences of variation on gene and protein function is challenging and often requires substantial laboratory investigation. However, a series of bioinformatics tools have been developed to predict whether non-synonymous variants are neutral or disease-causing. In this study we evaluate the performance of nine such methods (SIFT, PolyPhen2, SNPs&GO, PhD-SNP, PANTHER, Mutation Assessor, MutPred, Condel and CAROL) and developed CoVEC (Consensus Variant Effect Classification), a tool that integrates the prediction results from four of these methods. We demonstrate that the CoVEC approach outperforms most individual methods and highlights the benefit of combining results from multiple tools.
"These, apart from providing other tools as discussed later, annotate protein structures and facilitate checking the protein for functional sites such as catalytic sites, DNA and protein binding sites and also those harbouring post translational modifications ( and references therein). Users should note that knowing the methodology used by the software is imperative to assess the accuracy and its relevance to the case being investigated . "
[Show abstract][Hide abstract] ABSTRACT: The candidate gene approach has been a pioneer in the field of genetic epidemiology, identifying risk alleles and their association with clinical traits. With the advent of rapidly changing technology, there has been an explosion of in silico tools available to researchers, giving them fast, efficient resources and reliable strategies important to find casual gene variants for candidate or genome wide association studies (GWAS). In this review, following a description of candidate gene prioritisation, we summarise the approaches to single nucleotide polymorphism (SNP) prioritisation and discuss the tools available to assess functional relevance of the risk variant with consideration to its genomic location. The strategy and the tools discussed are applicable to any study investigating genetic risk factors associated with a particular disease. Some of the tools are also applicable for the functional validation of variants relevant to the era of GWAS and next generation sequencing (NGS).
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