Bioinformatics for personal genome interpretation

none4An international consortium released the first draft sequence of the human genome 10 years ago. Although the analysis of this data has suggested the genetic underpinnings of many diseases, we have not yet been able to fully quantify the relationship between genotype and phenotype. Thus, a major current effort of the scientific community focuses on evaluating individual predispositions to specific phenotypic traits given their genetic backgrounds. Many resources aim to identify and annotate the specific genes responsible for the observed phenotypes. Some of these use intra-species genetic variability as a means for better understanding this relationship. In addition, several online resources are now dedicated to collecting single nucleotide variants and other types of variants, and annotating their functional effects and associations with phenotypic traits. This information has enabled researchers to develop bioinformatics tools to analyze the rapidly increasing amount of newly extracted variation data and to predict the effect of uncharacterized variants. In this work, we review the most important developments in the field-the databases and bioinformatics tools that will be of utmost importance in our concerted effort to interpret the human variome. © The Author 2012. Published by Oxford University Press.openCapriotti, Emidio; Nehrt, Nathan L.; Kann, Maricel G.; Bromberg, YanaCapriotti, Emidio; Nehrt, Nathan L.; Kann, Maricel G.; Bromberg, Yan

HUMA: A platform for the analysis of genetic variation in humans

The completion of the human genome project at the beginning of the 21st century, along with the rapid advancement of sequencing technologies thereafter, has resulted in exponential growth of biological data. In genetics, this has given rise to numerous variation databases, created to store and annotate the ever-expanding dataset of known mutations. Usually, these databases focus on variation at the sequence level. Few databases focus on the analysis of variation at the 3D level, that is, mapping, visualizing, and determining the effects of variation in protein structures. Additionally, these Web servers seldom incorporate tools to help analyze these data. Here, we present the Human Mutation Analysis (HUMA) Web server and database. HUMA integrates sequence, structure, variation, and disease data into a single, connected database. A user-friendly interface provides click-based data access and visualization, whereas a RESTfulWebAPI provides programmatic access to the data. Tools have been integrated into HUMA to allow initial analyses to be carried out on the server. Furthermore, users can upload their private variation datasets, which are automatically mapped to public data and can be analyzed using the integrated tools. HUMA is freely accessible at https://huma.rubi.ru.ac.za