title: A community-based resource for automatic exome variant-calling and annotation in Mendelian disorders.
abstract: BACKGROUND
  Mendelian disorders are mostly caused by single mutations in the DNA sequence of a gene, leading to a phenotype with pathologic consequences. Whole Exome Sequencing of patients can be a cost-effective alternative to standard genetic screenings to find causative mutations of genetic diseases, especially when the number of cases is limited. Analyzing exome sequencing data requires specific expertise, high computational resources and a reference variant database to identify pathogenic variants.
  
  
  RESULTS
  We developed a database of variations collected from patients with Mendelian disorders, which is automatically populated thanks to an associated exome-sequencing pipeline. The pipeline is able to automatically identify, annotate and store insertions, deletions and mutations in the database. The resource is freely available online http://exome.tigem.it. The exome sequencing pipeline automates the analysis workflow (quality control and read trimming, mapping on reference genome, post-alignment processing, variation calling and annotation) using state-of-the-art software tools. The exome-sequencing pipeline has been designed to run on a computing cluster in order to analyse several samples simultaneously. The detected variants are annotated by the pipeline not only with the standard variant annotations (e.g. allele frequency in the general population, the predicted effect on gene product activity, etc.) but, more importantly, with allele frequencies across samples progressively collected in the database itself, stratified by Mendelian disorder.
  
  
  CONCLUSIONS
  We aim at providing a resource for the genetic disease community to automatically analyse whole exome-sequencing samples with a standard and uniform analysis pipeline, thus collecting variant allele frequencies by disorder. This resource may become a valuable tool to help dissecting the genotype underlying the disease phenotype through an improved selection of putative patient-specific causative or phenotype-associated variations.
authors: Margherita
authors: M
authors: Mutarelli
authors: Veer
authors: V
authors: Marwah
authors: Rossella
authors: R
authors: Rispoli
authors: Diego
authors: D
authors: Carrella
authors: Gopuraja
authors: G
authors: Dharmalingam
authors: Gennaro
authors: G
authors: Oliva
authors: Diego
authors: D
authors: di Bernardo
mesh_terms: D019295:Computational Biology / Q000379:methods; D003628:Database Management Systems; D030541:Databases, Genetic; D059472:Exome; D030342:Genetic Diseases, Inborn / Q000175:diagnosis / Q000235:genetics*; D014644:Genetic Variation; D059014:High-Throughput Nucleotide Sequencing; D006801:Humans; D054643:INDEL Mutation; D058977:Molecular Sequence Annotation; D020641:Polymorphism, Single Nucleotide; D012984:Software; D064878:Web Browser; D057188:Workflow