@article {387, title = {HGVA: the Human Genome Variation Archive.}, journal = {Nucleic Acids Res}, volume = {45}, year = {2017}, month = {2017 07 03}, pages = {W189-W194}, abstract = {

High-profile genomic variation projects like the 1000 Genomes project or the Exome Aggregation Consortium, are generating a wealth of human genomic variation knowledge which can be used as an essential reference for identifying disease-causing genotypes. However, accessing these data, contrasting the various studies and integrating those data in downstream analyses remains cumbersome. The Human Genome Variation Archive (HGVA) tackles these challenges and facilitates access to genomic data for key reference projects in a clean, fast and integrated fashion. HGVA provides an efficient and intuitive web-interface for easy data mining, a comprehensive RESTful API and client libraries in Python, Java and JavaScript for fast programmatic access to its knowledge base. HGVA calculates population frequencies for these projects and enriches their data with variant annotation provided by CellBase, a rich and fast annotation solution. HGVA serves as a proof-of-concept of the genome analysis developments being carried out by the University of Cambridge together with UK{\textquoteright}s 100 000 genomes project and the National Institute for Health Research BioResource Rare-Diseases, in particular, deploying open-source for Computational Biology (OpenCB) software platform for storing and analyzing massive genomic datasets.

}, keywords = {Genetic Variation, Genome, Human, Humans, Internet, Software, User-Computer Interface}, issn = {1362-4962}, doi = {10.1093/nar/gkx445}, url = {https://academic.oup.com/nar/article-lookup/doi/10.1093/nar/gkx445}, author = {Lopez, Javier and Coll, Jacobo and Haimel, Matthias and Kandasamy, Swaathi and T{\'a}rraga, Joaqu{\'\i}n and Furio-Tari, Pedro and Bari, Wasim and Bleda, Marta and Rueda, Antonio and Gr{\"a}f, Stefan and Rendon, Augusto and Dopazo, Joaquin and Medina, Ignacio} } @article {482, title = {ngsCAT: a tool to assess the efficiency of targeted enrichment sequencing.}, journal = {Bioinformatics}, volume = {30}, year = {2014}, month = {2014 Jun 15}, pages = {1767-8}, abstract = {

MOTIVATION: Targeted enrichment sequencing by next-generation sequencing is a common approach to interrogate specific loci or the whole exome in the human genome. The efficiency and the lack of bias in the enrichment process need to be assessed as a quality control step before performing downstream analysis of the sequence data. Tools that can report on the sensitivity, specificity, uniformity and other enrichment-specific features are needed.

RESULTS: We have implemented the next-generation sequencing data Capture Assessment Tool (ngsCAT), a tool that takes the information of the mapped reads and the coordinates of the targeted regions as input files, and generates a report with metrics and figures that allows the evaluation of the efficiency of the enrichment process. The tool can also take as input the information of two samples allowing the comparison of two different experiments.

AVAILABILITY AND IMPLEMENTATION: Documentation and downloads for ngsCAT can be found at http://www.bioinfomgp.org/ngscat.

}, keywords = {Exome, Genome, Human, High-Throughput Nucleotide Sequencing, Humans, Sequence Analysis, DNA, Software}, issn = {1367-4811}, doi = {10.1093/bioinformatics/btu108}, author = {L{\'o}pez-Domingo, Francisco J and Florido, Javier P and Rueda, Antonio and Dopazo, Joaquin and Santoyo-L{\'o}pez, Javier} }