Bmc Genomics
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Soybean Knowledge Base (SoyKB) is a comprehensive all-inclusive web resource for soybean translational genomics. SoyKB is designed to handle the management and integration of soybean genomics, transcriptomics, proteomics and metabolomics data along with annotation of gene function and biological pathway. It contains information on four entities, namely genes, microRNAs, metabolites and single nucleotide polymorphisms (SNPs). ⋯ SoyKB addresses the increasing need of the soybean research community to have a one-stop-shop functional and translational omics web resource for information retrieval and analysis in a user-friendly way. SoyKB can be publicly accessed at http://soykb.org/.
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Various computational methods are presently available to classify whether a protein variation is disease-associated or not. However data derived from recent technological advancements make it feasible to extend the annotation of disease-associated variations in order to include specific phenotypes. Here we tackle the problem of distinguishing between genetic variations associated to cancer and variations associated to other genetic diseases. ⋯ Here we prove feasible that a large set of cancer associated germline protein variations can be successfully discriminated from those associated to other genetic disorders. This is a step further in the process of protein variant annotation. Scoring largely improves when protein function as encoded by Gene Ontology terms is considered, corroborating the role of protein function as a key feature for a correct annotation of its variations.
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Protein-coding regions in human genes harbor 85% of the mutations that are associated with disease-related traits. Compared with whole-genome sequencing of complex samples, exome sequencing serves as an alternative option because of its dramatically reduced cost. In fact, exome sequencing has been successfully applied to identify the cause of several Mendelian disorders, such as Miller and Schinzel-Giedio syndrome. However, there remain great challenges in handling the huge data generated by exome sequencing and in identifying potential disease-related genetic variations. ⋯ In summary, by exploring exome sequencing data, Exome-assistant can provide researchers with detailed biological insights into genetic variation events and permits the identification of potential genetic causes of human diseases and related traits.