@article {555, title = {PyCellBase, an efficient python package for easy retrieval of biological data from heterogeneous sources.}, journal = {BMC Bioinformatics}, volume = {20}, year = {2019}, month = {2019 Mar 28}, pages = {159}, abstract = {

BACKGROUND: 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.

RESULTS: 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.

CONCLUSION: 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.

}, keywords = {Computational Biology, Databases, Factual, Software, User-Computer Interface}, issn = {1471-2105}, doi = {10.1186/s12859-019-2726-4}, author = {Perez-Gil, Daniel and Lopez, Francisco J and Dopazo, Joaquin and Marin-Garcia, Pablo and Rendon, Augusto and Medina, Ignacio} } @article {387, title = {HGVA: the Human Genome Variation Archive.}, journal = {Nucleic Acids Res}, volume = {45}, year = {2017}, month = {2017 Jul 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 {382, title = {VISMapper: ultra-fast exhaustive cartography of viral insertion sites for gene therapy.}, journal = {BMC Bioinformatics}, volume = {18}, year = {2017}, month = {2017 Sep 20}, pages = {421}, abstract = {

BACKGROUND: The possibility of integrating viral vectors to become a persistent part of the host genome makes them a crucial element of clinical gene therapy. However, viral integration has associated risks, such as the unintentional activation of oncogenes that can result in cancer. Therefore, the analysis of integration sites of retroviral vectors is a crucial step in developing safer vectors for therapeutic use.

RESULTS: Here we present VISMapper, a vector integration site analysis web server, to analyze next-generation sequencing data for retroviral vector integration sites. VISMapper can be found at: http://vismapper.babelomics.org .

CONCLUSIONS: Because it uses novel mapping algorithms VISMapper is remarkably faster than previous available programs. It also provides a useful graphical interface to analyze the integration sites found in the genomic context.

}, keywords = {Base Sequence, Genetic Therapy, Genetic Vectors, High-Throughput Nucleotide Sequencing, Humans, Internet, User-Computer Interface, Virus Integration}, issn = {1471-2105}, doi = {10.1186/s12859-017-1837-z}, author = {Juanes, Jos{\'e} M and Gallego, Asunci{\'o}n and T{\'a}rraga, Joaqu{\'\i}n and Chaves, Felipe J and Marin-Garcia, Pablo and Medina, Ignacio and Arnau, Vicente and Dopazo, Joaquin} } @article {523, title = {VARIANT: Command Line, Web service and Web interface for fast and accurate functional characterization of variants found by Next-Generation Sequencing.}, journal = {Nucleic Acids Res}, volume = {40}, year = {2012}, month = {2012 Jul}, pages = {W54-8}, abstract = {

The massive use of Next-Generation Sequencing (NGS) technologies is uncovering an unexpected amount of variability. The functional characterization of such variability, particularly in the most common form of variation found, the Single Nucleotide Variants (SNVs), has become a priority that needs to be addressed in a systematic way. VARIANT (VARIant ANalyis Tool) reports information on the variants found that include consequence type and annotations taken from different databases and repositories (SNPs and variants from dbSNP and 1000 genomes, and disease-related variants from the Genome-Wide Association Study (GWAS) catalog, Online Mendelian Inheritance in Man (OMIM), Catalog of Somatic Mutations in Cancer (COSMIC) mutations, etc). VARIANT also produces a rich variety of annotations that include information on the regulatory (transcription factor or miRNA-binding sites, etc.) or structural roles, or on the selective pressures on the sites affected by the variation. This information allows extending the conventional reports beyond the coding regions and expands the knowledge on the contribution of non-coding or synonymous variants to the phenotype studied. Contrarily to other tools, VARIANT uses a remote database and operates through efficient RESTful Web Services that optimize search and transaction operations. In this way, local problems of installation, update or disk size limitations are overcome without the need of sacrifice speed (thousands of variants are processed per minute). VARIANT is available at: http://variant.bioinfo.cipf.es.

}, keywords = {Databases, Nucleic Acid, Genetic Variation, High-Throughput Nucleotide Sequencing, Internet, Molecular Sequence Annotation, mutation, Polymorphism, Single Nucleotide, Software, User-Computer Interface}, issn = {1362-4962}, doi = {10.1093/nar/gks572}, author = {Medina, Ignacio and De Maria, Alejandro and Bleda, Marta and Salavert, Francisco and Alonso, Roberto and Gonzalez, Cristina Y and Dopazo, Joaquin} } @article {583, title = {Gene set-based analysis of polymorphisms: finding pathways or biological processes associated to traits in genome-wide association studies.}, journal = {Nucleic Acids Res}, volume = {37}, year = {2009}, month = {2009 Jul}, pages = {W340-4}, abstract = {

Genome-wide association studies have become a popular strategy to find associations of genes to traits of interest. Despite the high-resolution available today to carry out genotyping studies, the success of its application in real studies has been limited by the testing strategy used. As an alternative to brute force solutions involving the use of very large cohorts, we propose the use of the Gene Set Analysis (GSA), a different analysis strategy based on testing the association of modules of functionally related genes. We show here how the Gene Set-based Analysis of Polymorphisms (GeSBAP), which is a simple implementation of the GSA strategy for the analysis of genome-wide association studies, provides a significant increase in the power testing for this type of studies. GeSBAP is freely available at http://bioinfo.cipf.es/gesbap/.

}, keywords = {Biological Phenomena, Breast Neoplasms, Female, Genes, Genetic Variation, Genome-Wide Association Study, Humans, Polymorphism, Single Nucleotide, Software, User-Computer Interface}, issn = {1362-4962}, doi = {10.1093/nar/gkp481}, author = {Medina, Ignacio and Montaner, David and Bonifaci, N{\'u}ria and Pujana, Miguel Angel and Carbonell, Jos{\'e} and T{\'a}rraga, Joaqu{\'\i}n and Al-Shahrour, F{\'a}tima and Dopazo, Joaquin} } @article {608, title = {ISACGH: a web-based environment for the analysis of Array CGH and gene expression which includes functional profiling.}, journal = {Nucleic Acids Res}, volume = {35}, year = {2007}, month = {2007 Jul}, pages = {W81-5}, abstract = {

We present the ISACGH, a web-based system that allows for the combination of genomic data with gene expression values and provides different options for functional profiling of the regions found. Several visualization options offer a convenient representation of the results. Different efficient methods for accurate estimation of genomic copy number from array-CGH hybridization data have been included in the program. Moreover, the connection to the gene expression analysis package GEPAS allows the use of different facilities for data pre-processing and analysis. A DAS server allows exporting the results to the Ensembl viewer where contextual genomic information can be obtained. The program is freely available at: http://isacgh.bioinfo.cipf.es or within http://www.gepas.org.

}, keywords = {Animals, Cluster Analysis, Computational Biology, Computer Graphics, Gene Expression Profiling, Humans, Internet, Models, Genetic, Nucleic Acid Hybridization, Oligonucleotide Array Sequence Analysis, Programming Languages, Software, Systems Integration, User-Computer Interface}, issn = {1362-4962}, doi = {10.1093/nar/gkm257}, author = {Conde, Lucia and Montaner, David and Burguet-Castell, Jordi and T{\'a}rraga, Joaqu{\'\i}n and Medina, Ignacio and Al-Shahrour, F{\'a}tima and Dopazo, Joaquin} }