02565nas a2200253   4500008004100000022001400041245011000055210006900165260001600234300000800250490000700258520169900265653002601964653002301990653001302013653002802026100002202054700002402076700002002100700002402120700002002144700002002164856012702184       2019                        eng d  a1471-210500aPyCellBase, an efficient python package for easy retrieval of biological data from heterogeneous sources.0 aPyCellBase an efficient python package for easy retrieval of bio  c2019 Mar 28  a1590 v203 a<p><b>BACKGROUND: </b>Biological databases and repositories are incrementing in diversity and complexity over the years. This rapid expansion of current and new sources of biological knowledge raises serious problems of data accessibility and integration. To handle the growing necessity of unification, CellBase was created as an integrative solution. CellBase provides a centralized NoSQL database containing biological information from different and heterogeneous sources. Access to this information is done through a RESTful web service API, which provides an efficient interface to the data.</p><p><b>RESULTS: </b>In this work we present PyCellBase, a Python package that provides programmatic access to the rich RESTful web service API offered by CellBase. This package offers a fast and user-friendly access to biological information without the need of installing any local database. In addition, a series of command-line tools are provided to perform common bioinformatic tasks, such as variant annotation. CellBase data is always available by a high-availability cluster and queries have been tuned to ensure a real-time performance.</p><p><b>CONCLUSION: </b>PyCellBase is an open-source Python package that provides an efficient access to heterogeneous biological information. It allows to perform tasks that require a comprehensive set of knowledge resources, as for example variant annotation. Queries can be easily fine-tuned to retrieve the desired information of particular biological features. PyCellBase offers the convenience of an object-oriented scripting language and provides the ability to integrate the obtained results into other Python applications and pipelines.</p>10aComputational Biology10aDatabases, Factual10aSoftware10aUser-Computer Interface1 aPerez-Gil, Daniel1 aLopez, Francisco, J1 aDopazo, Joaquin1 aMarin-Garcia, Pablo1 aRendon, Augusto1 aMedina, Ignacio  uhttp://clinbioinfosspa.es/content/pycellbase-efficient-python-package-easy-retrieval-biological-data-heterogeneous-sources02299nas a2200361   4500008004100000022001400041245004600055210004400101260001600145300001400161490000700175520132500182653002201507653001801529653001101547653001301558653001301571653002801584100001801612700001701630700002101647700002301668700002301691700002201714700001601736700001701752700001901769700001801788700002001806700002001826700002001846856007101866       2017                        eng d  a1362-496200aHGVA: the Human Genome Variation Archive.0 aHGVA the Human Genome Variation Archive  c2017 Jul 03  aW189-W1940 v453 a<p>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'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.</p>10aGenetic Variation10aGenome, Human10aHumans10aInternet10aSoftware10aUser-Computer Interface1 aLopez, Javier1 aColl, Jacobo1 aHaimel, Matthias1 aKandasamy, Swaathi1 aTárraga, Joaquín1 aFurio-Tari, Pedro1 aBari, Wasim1 aBleda, Marta1 aRueda, Antonio1 aGräf, Stefan1 aRendon, Augusto1 aDopazo, Joaquin1 aMedina, Ignacio  uhttps://academic.oup.com/nar/article-lookup/doi/10.1093/nar/gkx445