@article {736, title = {COVID19 Disease Map, a computational knowledge repository of virus-host interaction mechanisms.}, journal = {Mol Syst Biol}, volume = {17}, year = {2021}, month = {2021 10}, pages = {e10387}, abstract = {

We need to effectively combine the knowledge from surging literature with complex datasets to propose mechanistic models of SARS-CoV-2 infection, improving data interpretation and predicting key targets of intervention. Here, we describe a large-scale community effort to build an open access, interoperable and computable repository of COVID-19 molecular mechanisms. The COVID-19 Disease Map (C19DMap) is a graphical, interactive representation of disease-relevant molecular mechanisms linking many knowledge sources. Notably, it is a computational resource for graph-based analyses and disease modelling. To this end, we established a framework of tools, platforms and guidelines necessary for a multifaceted community of biocurators, domain experts, bioinformaticians and computational biologists. The diagrams of the C19DMap, curated from the literature, are integrated with relevant interaction and text mining databases. We demonstrate the application of network analysis and modelling approaches by concrete examples to highlight new testable hypotheses. This framework helps to find signatures of SARS-CoV-2 predisposition, treatment response or prioritisation of drug candidates. Such an approach may help deal with new waves of COVID-19 or similar pandemics in the long-term perspective.

}, keywords = {Antiviral Agents, Computational Biology, Computer Graphics, COVID-19, Cytokines, Data Mining, Databases, Factual, Gene Expression Regulation, Host Microbial Interactions, Humans, Immunity, Cellular, Immunity, Humoral, Immunity, Innate, Lymphocytes, Metabolic Networks and Pathways, Myeloid Cells, Protein Interaction Mapping, SARS-CoV-2, Signal Transduction, Software, Transcription Factors, Viral Proteins}, issn = {1744-4292}, doi = {10.15252/msb.202110387}, author = {Ostaszewski, Marek and Niarakis, Anna and Mazein, Alexander and Kuperstein, Inna and Phair, Robert and Orta-Resendiz, Aurelio and Singh, Vidisha and Aghamiri, Sara Sadat and Acencio, Marcio Luis and Glaab, Enrico and Ruepp, Andreas and Fobo, Gisela and Montrone, Corinna and Brauner, Barbara and Frishman, Goar and Monraz G{\'o}mez, Luis Crist{\'o}bal and Somers, Julia and Hoch, Matti and Kumar Gupta, Shailendra and Scheel, Julia and Borlinghaus, Hanna and Czauderna, Tobias and Schreiber, Falk and Montagud, Arnau and Ponce de Leon, Miguel and Funahashi, Akira and Hiki, Yusuke and Hiroi, Noriko and Yamada, Takahiro G and Dr{\"a}ger, Andreas and Renz, Alina and Naveez, Muhammad and Bocskei, Zsolt and Messina, Francesco and B{\"o}rnigen, Daniela and Fergusson, Liam and Conti, Marta and Rameil, Marius and Nakonecnij, Vanessa and Vanhoefer, Jakob and Schmiester, Leonard and Wang, Muying and Ackerman, Emily E and Shoemaker, Jason E and Zucker, Jeremy and Oxford, Kristie and Teuton, Jeremy and Kocakaya, Ebru and Summak, G{\"o}k{\c c}e Ya{\u g}mur and Hanspers, Kristina and Kutmon, Martina and Coort, Susan and Eijssen, Lars and Ehrhart, Friederike and Rex, Devasahayam Arokia Balaya and Slenter, Denise and Martens, Marvin and Pham, Nhung and Haw, Robin and Jassal, Bijay and Matthews, Lisa and Orlic-Milacic, Marija and Senff Ribeiro, Andrea and Rothfels, Karen and Shamovsky, Veronica and Stephan, Ralf and Sevilla, Cristoffer and Varusai, Thawfeek and Ravel, Jean-Marie and Fraser, Rupsha and Ortseifen, Vera and Marchesi, Silvia and Gawron, Piotr and Smula, Ewa and Heirendt, Laurent and Satagopam, Venkata and Wu, Guanming and Riutta, Anders and Golebiewski, Martin and Owen, Stuart and Goble, Carole and Hu, Xiaoming and Overall, Rupert W and Maier, Dieter and Bauch, Angela and Gyori, Benjamin M and Bachman, John A and Vega, Carlos and Grou{\`e}s, Valentin and Vazquez, Miguel and Porras, Pablo and Licata, Luana and Iannuccelli, Marta and Sacco, Francesca and Nesterova, Anastasia and Yuryev, Anton and de Waard, Anita and Turei, Denes and Luna, Augustin and Babur, Ozgun and Soliman, Sylvain and Valdeolivas, Alberto and Esteban-Medina, Marina and Pe{\~n}a-Chilet, Maria and Rian, Kinza and Helikar, Tom{\'a}{\v s} and Puniya, Bhanwar Lal and Modos, Dezso and Treveil, Agatha and Olbei, Marton and De Meulder, Bertrand and Ballereau, Stephane and Dugourd, Aur{\'e}lien and Naldi, Aur{\'e}lien and No{\"e}l, Vincent and Calzone, Laurence and Sander, Chris and Demir, Emek and Korcsmaros, Tamas and Freeman, Tom C and Aug{\'e}, Franck and Beckmann, Jacques S and Hasenauer, Jan and Wolkenhauer, Olaf and Wilighagen, Egon L and Pico, Alexander R and Evelo, Chris T and Gillespie, Marc E and Stein, Lincoln D and Hermjakob, Henning and D{\textquoteright}Eustachio, Peter and Saez-Rodriguez, Julio and Dopazo, Joaquin and Valencia, Alfonso and Kitano, Hiroaki and Barillot, Emmanuel and Auffray, Charles and Balling, Rudi and Schneider, Reinhard} } @article {701, title = {CSVS, a crowdsourcing database of the Spanish population genetic variability.}, journal = {Nucleic Acids Res}, volume = {49}, year = {2021}, month = {2021 01 08}, pages = {D1130-D1137}, abstract = {

The knowledge of the genetic variability of the local population is of utmost importance in personalized medicine and has been revealed as a critical factor for the discovery of new disease variants. Here, we present the Collaborative Spanish Variability Server (CSVS), which currently contains more than 2000 genomes and exomes of unrelated Spanish individuals. This database has been generated in a collaborative crowdsourcing effort collecting sequencing data produced by local genomic projects and for other purposes. Sequences have been grouped by ICD10 upper categories. A web interface allows querying the database removing one or more ICD10 categories. In this way, aggregated counts of allele frequencies of the pseudo-control Spanish population can be obtained for diseases belonging to the category removed. Interestingly, in addition to pseudo-control studies, some population studies can be made, as, for example, prevalence of pharmacogenomic variants, etc. In addition, this genomic data has been used to define the first Spanish Genome Reference Panel (SGRP1.0) for imputation. This is the first local repository of variability entirely produced by a crowdsourcing effort and constitutes an example for future initiatives to characterize local variability worldwide. CSVS is also part of the GA4GH Beacon network. CSVS can be accessed at: http://csvs.babelomics.org/.

}, keywords = {Alleles, Chromosome Mapping, Crowdsourcing, Databases, Genetic, Exome, Gene Frequency, Genetic Variation, Genetics, Population, Genome, Human, Genomics, Humans, Internet, Precision Medicine, Software, Spain}, issn = {1362-4962}, doi = {10.1093/nar/gkaa794}, author = {Pe{\~n}a-Chilet, Maria and Rold{\'a}n, Gema and Perez-Florido, Javier and Ortuno, Francisco M and Carmona, Rosario and Aquino, Virginia and L{\'o}pez-L{\'o}pez, Daniel and Loucera, Carlos and Fernandez-Rueda, Jose L and Gallego, Asunci{\'o}n and Garcia-Garcia, Francisco and Gonz{\'a}lez-Neira, Anna and Pita, Guillermo and N{\'u}{\~n}ez-Torres, Roc{\'\i}o and Santoyo-L{\'o}pez, Javier and Ayuso, Carmen and Minguez, Pablo and Avila-Fernandez, Almudena and Corton, Marta and Moreno-Pelayo, Miguel {\'A}ngel and Morin, Mat{\'\i}as and Gallego-Martinez, Alvaro and Lopez-Escamez, Jose A and Borrego, Salud and Anti{\v n}olo, Guillermo and Amigo, Jorge and Salgado-Garrido, Josefa and Pasalodos-Sanchez, Sara and Morte, Beatriz and Carracedo, {\'A}ngel and Alonso, {\'A}ngel and Dopazo, Joaquin} } @article {712, title = {A versatile workflow to integrate RNA-seq genomic and transcriptomic data into mechanistic models of signaling pathways.}, journal = {PLoS Comput Biol}, volume = {17}, year = {2021}, month = {2021 02}, pages = {e1008748}, abstract = {

MIGNON is a workflow for the analysis of RNA-Seq experiments, which not only efficiently manages the estimation of gene expression levels from raw sequencing reads, but also calls genomic variants present in the transcripts analyzed. Moreover, this is the first workflow that provides a framework for the integration of transcriptomic and genomic data based on a mechanistic model of signaling pathway activities that allows a detailed biological interpretation of the results, including a comprehensive functional profiling of cell activity. MIGNON covers the whole process, from reads to signaling circuit activity estimations, using state-of-the-art tools, it is easy to use and it is deployable in different computational environments, allowing an optimized use of the resources available.

}, keywords = {Algorithms, Cell Line, Tumor, Computational Biology, Databases, Factual, Gene Expression Profiling, Genomics, High-Throughput Nucleotide Sequencing, Humans, Models, Theoretical, mutation, RNA-seq, Signal Transduction, Software, Transcriptome, whole exome sequencing, Workflow}, issn = {1553-7358}, doi = {10.1371/journal.pcbi.1008748}, author = {Garrido-Rodriguez, Mart{\'\i}n and L{\'o}pez-L{\'o}pez, Daniel and Ortuno, Francisco M and Pe{\~n}a-Chilet, Maria and Mu{\~n}oz, Eduardo and Calzado, Marco A and Dopazo, Joaquin} } @article {705, title = {SMN1 copy-number and sequence variant analysis from next-generation sequencing data.}, journal = {Hum Mutat}, volume = {41}, year = {2020}, month = {2020 12}, pages = {2073-2077}, abstract = {

Spinal muscular atrophy (SMA) is a severe neuromuscular autosomal recessive disorder affecting 1/10,000 live births. Most SMA patients present homozygous deletion of SMN1, while the vast majority of SMA carriers present only a single SMN1 copy. The sequence similarity between SMN1 and SMN2, and the complexity of the SMN locus makes the estimation of the SMN1 copy-number by next-generation sequencing (NGS) very difficult. Here, we present SMAca, the first python tool to detect SMA carriers and estimate the absolute SMN1 copy-number using NGS data. Moreover, SMAca takes advantage of the knowledge of certain variants specific to SMN1 duplication to also identify silent carriers. This tool has been validated with a cohort of 326 samples from the Navarra 1000 Genomes Project (NAGEN1000). SMAca was developed with a focus on execution speed and easy installation. This combination makes it especially suitable to be integrated into production NGS pipelines. Source code and documentation are available at https://www.github.com/babelomics/SMAca.

}, keywords = {Base Sequence, DNA Copy Number Variations, High-Throughput Nucleotide Sequencing, Humans, Reproducibility of Results, Software, Survival of Motor Neuron 1 Protein}, issn = {1098-1004}, doi = {10.1002/humu.24120}, author = {L{\'o}pez-L{\'o}pez, Daniel and Loucera, Carlos and Carmona, Rosario and Aquino, Virginia and Salgado, Josefa and Pasalodos, Sara and Miranda, Mar{\'\i}a and Alonso, {\'A}ngel and Dopazo, Joaquin} } @article {718, title = {Using AnABlast for intergenic sORF prediction in the Caenorhabditis elegans genome.}, journal = {Bioinformatics}, volume = {36}, year = {2020}, month = {2020 12 08}, pages = {4827-4832}, abstract = {

MOTIVATION: Short bioactive peptides encoded by small open reading frames (sORFs) play important roles in eukaryotes. Bioinformatics prediction of ORFs is an early step in a genome sequence analysis, but sORFs encoding short peptides, often using non-AUG initiation codons, are not easily discriminated from false ORFs occurring by chance.

RESULTS: AnABlast is a computational tool designed to highlight putative protein-coding regions in genomic DNA sequences. This protein-coding finder is independent of ORF length and reading frame shifts, thus making of AnABlast a potentially useful tool to predict sORFs. Using this algorithm, here, we report the identification of 82 putative new intergenic sORFs in the Caenorhabditis elegans genome. Sequence similarity, motif presence, expression data and RNA interference experiments support that the underlined sORFs likely encode functional peptides, encouraging the use of AnABlast as a new approach for the accurate prediction of intergenic sORFs in annotated eukaryotic genomes.

AVAILABILITY AND IMPLEMENTATION: AnABlast is freely available at http://www.bioinfocabd.upo.es/ab/. The C.elegans genome browser with AnABlast results, annotated genes and all data used in this study is available at http://www.bioinfocabd.upo.es/celegans.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

}, keywords = {Animals, Caenorhabditis elegans, Computational Biology, Genome, Open Reading Frames, Software}, issn = {1367-4811}, doi = {10.1093/bioinformatics/btaa608}, author = {Casimiro-Soriguer, C S and Rigual, M M and Brokate-Llanos, A M and Mu{\~n}oz, M J and Garz{\'o}n, A and P{\'e}rez-Pulido, A J and Jimenez, J} } @article {422, title = {Differential metabolic activity and discovery of therapeutic targets using summarized metabolic pathway models.}, journal = {NPJ Syst Biol Appl}, volume = {5}, year = {2019}, month = {2019}, pages = {7}, abstract = {

In spite of the increasing availability of genomic and transcriptomic data, there is still a gap between the detection of perturbations in gene expression and the understanding of their contribution to the molecular mechanisms that ultimately account for the phenotype studied. Alterations in the metabolism are behind the initiation and progression of many diseases, including cancer. The wealth of available knowledge on metabolic processes can therefore be used to derive mechanistic models that link gene expression perturbations to changes in metabolic activity that provide relevant clues on molecular mechanisms of disease and drug modes of action (MoA). In particular, pathway modules, which recapitulate the main aspects of metabolism, are especially suitable for this type of modeling. We present Metabolizer, a web-based application that offers an intuitive, easy-to-use interactive interface to analyze differences in pathway metabolic module activities that can also be used for class prediction and in silico prediction of knock-out (KO) effects. Moreover, Metabolizer can automatically predict the optimal KO intervention for restoring a diseased phenotype. We provide different types of validations of some of the predictions made by Metabolizer. Metabolizer is a web tool that allows understanding molecular mechanisms of disease or the MoA of drugs within the context of the metabolism by using gene expression measurements. In addition, this tool automatically suggests potential therapeutic targets for individualized therapeutic interventions.

}, keywords = {Computational Biology, Computer Simulation, Drug discovery, Gene Regulatory Networks, Humans, Internet, Metabolic Networks and Pathways, Models, Biological, Neoplasms, Phenotype, Software, Transcriptome}, issn = {2056-7189}, doi = {10.1038/s41540-019-0087-2}, author = {Cubuk, Cankut and Hidalgo, Marta R and Amadoz, Alicia and Rian, Kinza and Salavert, Francisco and Pujana, Miguel A and Mateo, Francesca and Herranz, Carmen and Carbonell-Caballero, Jos{\'e} and Dopazo, Joaquin} } @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 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 {388, title = {Reference genome assessment from a population scale perspective: an accurate profile of variability and noise.}, journal = {Bioinformatics}, volume = {33}, year = {2017}, month = {2017 Nov 15}, pages = {3511-3517}, abstract = {

Motivation: Current plant and animal genomic studies are often based on newly assembled genomes that have not been properly consolidated. In this scenario, misassembled regions can easily lead to false-positive findings. Despite quality control scores are included within genotyping protocols, they are usually employed to evaluate individual sample quality rather than reference sequence reliability. We propose a statistical model that combines quality control scores across samples in order to detect incongruent patterns at every genomic region. Our model is inherently robust since common artifact signals are expected to be shared between independent samples over misassembled regions of the genome.

Results: The reliability of our protocol has been extensively tested through different experiments and organisms with accurate results, improving state-of-the-art methods. Our analysis demonstrates synergistic relations between quality control scores and allelic variability estimators, that improve the detection of misassembled regions, and is able to find strong artifact signals even within the human reference assembly. Furthermore, we demonstrated how our model can be trained to properly rank the confidence of a set of candidate variants obtained from new independent samples.

Availability and implementation: This tool is freely available at http://gitlab.com/carbonell/ces.

Contact: jcarbonell.cipf@gmail.com or joaquin.dopazo@juntadeandalucia.es.

Supplementary information: Supplementary data are available at Bioinformatics online.

}, keywords = {Animals, Genetic Variation, Genome, Genomics, Genotype, Humans, Models, Statistical, Quality Control, Reproducibility of Results, Software}, issn = {1367-4811}, doi = {10.1093/bioinformatics/btx482}, url = {https://academic.oup.com/bioinformatics/article-lookup/doi/10.1093/bioinformatics/btx482}, author = {Carbonell-Caballero, Jos{\'e} and Amadoz, Alicia and Alonso, Roberto and Hidalgo, Marta R and Cubuk, Cankut and Conesa, David and L{\'o}pez-Qu{\'\i}lez, Antonio and Dopazo, Joaquin} } @article {438, title = {Web-based network analysis and visualization using CellMaps.}, journal = {Bioinformatics}, volume = {32}, year = {2016}, month = {2016 10 01}, pages = {3041-3}, abstract = {

UNLABELLED: : CellMaps is an HTML5 open-source web tool that allows displaying, editing, exploring and analyzing biological networks as well as integrating metadata into them. Computations and analyses are remotely executed in high-end servers, and all the functionalities are available through RESTful web services. CellMaps can easily be integrated in any web page by using an available JavaScript API.

AVAILABILITY AND IMPLEMENTATION: The application is available at: http://cellmaps.babelomics.org/ and the code can be found in: https://github.com/opencb/cell-maps The client is implemented in JavaScript and the server in C and Java.

CONTACT: jdopazo@cipf.es

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

}, keywords = {Biochemical Phenomena, Internet, Software}, issn = {1367-4811}, doi = {10.1093/bioinformatics/btw332}, author = {Salavert, Francisco and Garc{\'\i}a-Alonso, Luz and S{\'a}nchez, Rub{\'e}n and Alonso, Roberto and Bleda, Marta and Medina, Ignacio and Dopazo, Joaquin} } @article {472, title = {Fast inexact mapping using advanced tree exploration on backward search methods.}, journal = {BMC Bioinformatics}, volume = {16}, year = {2015}, month = {2015 Jan 28}, pages = {18}, abstract = {

BACKGROUND: Short sequence mapping methods for Next Generation Sequencing consist on a combination of seeding techniques followed by local alignment based on dynamic programming approaches. Most seeding algorithms are based on backward search alignment, using the Burrows Wheeler Transform, the Ferragina and Manzini Index or Suffix Arrays. All these backward search algorithms have excellent performance, but their computational cost highly increases when allowing errors. In this paper, we discuss an inexact mapping algorithm based on pruning strategies for search tree exploration over genomic data.

RESULTS: The proposed algorithm achieves a 13x speed-up over similar algorithms when allowing 6 base errors, including insertions, deletions and mismatches. This algorithm can deal with 400 bps reads with up to 9 errors in a high quality Illumina dataset. In this example, the algorithm works as a preprocessor that reduces by 55\% the number of reads to be aligned. Depending on the aligner the overall execution time is reduced between 20-40\%.

CONCLUSIONS: Although not intended as a complete sequence mapping tool, the proposed algorithm could be used as a preprocessing step to modern sequence mappers. This step significantly reduces the number reads to be aligned, accelerating overall alignment time. Furthermore, this algorithm could be used for accelerating the seeding step of already available sequence mappers. In addition, an out-of-core index has been implemented for working with large genomes on systems without expensive memory configurations.

}, keywords = {Algorithms, Genome, Human, Genomics, High-Throughput Nucleotide Sequencing, Humans, Sequence Alignment, Sequence Analysis, DNA, Software}, issn = {1471-2105}, doi = {10.1186/s12859-014-0438-3}, author = {Salavert, Jos{\'e} and Tom{\'a}s, Andr{\'e}s and T{\'a}rraga, Joaqu{\'\i}n and Medina, Ignacio and Dopazo, Joaquin and Blanquer, 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} } @article {500, title = {Inferring the functional effect of gene expression changes in signaling pathways.}, journal = {Nucleic Acids Res}, volume = {41}, year = {2013}, month = {2013 Jul}, pages = {W213-7}, abstract = {

Signaling pathways constitute a valuable source of information that allows interpreting the way in which alterations in gene activities affect to particular cell functionalities. There are web tools available that allow viewing and editing pathways, as well as representing experimental data on them. However, few methods aimed to identify the signaling circuits, within a pathway, associated to the biological problem studied exist and none of them provide a convenient graphical web interface. We present PATHiWAYS, a web-based signaling pathway visualization system that infers changes in signaling that affect cell functionality from the measurements of gene expression values in typical expression microarray case-control experiments. A simple probabilistic model of the pathway is used to estimate the probabilities for signal transmission from any receptor to any final effector molecule (taking into account the pathway topology) using for this the individual probabilities of gene product presence/absence inferred from gene expression values. Significant changes in these probabilities allow linking different cell functionalities triggered by the pathway to the biological problem studied. PATHiWAYS is available at: http://pathiways.babelomics.org/.

}, keywords = {Animals, Humans, Internet, Mice, Models, Statistical, Receptors, Cell Surface, Signal Transduction, Software, Transcriptome}, issn = {1362-4962}, doi = {10.1093/nar/gkt451}, author = {Sebasti{\'a}n-Leon, Patricia and Carbonell, Jos{\'e} and Salavert, Francisco and S{\'a}nchez, Rub{\'e}n and Medina, Ignacio and Dopazo, Joaquin} } @article {517, title = {Inferring the regulatory network behind a gene expression experiment.}, journal = {Nucleic Acids Res}, volume = {40}, year = {2012}, month = {2012 Jul}, pages = {W168-72}, abstract = {

Transcription factors (TFs) and miRNAs are the most important dynamic regulators in the control of gene expression in multicellular organisms. These regulatory elements play crucial roles in development, cell cycling and cell signaling, and they have also been associated with many diseases. The Regulatory Network Analysis Tool (RENATO) web server makes the exploration of regulatory networks easy, enabling a better understanding of functional modularity and network integrity under specific perturbations. RENATO is suitable for the analysis of the result of expression profiling experiments. The program analyses lists of genes and search for the regulators compatible with its activation or deactivation. Tests of single enrichment or gene set enrichment allow the selection of the subset of TFs or miRNAs significantly involved in the regulation of the query genes. RENATO also offers an interactive advanced graphical interface that allows exploring the regulatory network found.RENATO is available at: http://renato.bioinfo.cipf.es/.

}, keywords = {Binding Sites, Databases, Genetic, Fanconi Anemia, Gene Regulatory Networks, Internet, MicroRNAs, Software, Transcription Factors, Transcriptome}, issn = {1362-4962}, doi = {10.1093/nar/gks573}, author = {Bleda, Marta and Medina, Ignacio and Alonso, Roberto and De Maria, Alejandro and Salavert, Francisco 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 {539, title = {Phylemon 2.0: a suite of web-tools for molecular evolution, phylogenetics, phylogenomics and hypotheses testing.}, journal = {Nucleic Acids Res}, volume = {39}, year = {2011}, month = {2011 Jul}, pages = {W470-4}, abstract = {

Phylemon 2.0 is a new release of the suite of web tools for molecular evolution, phylogenetics, phylogenomics and hypotheses testing. It has been designed as a response to the increasing demand of molecular sequence analyses for experts and non-expert users. Phylemon 2.0 has several unique features that differentiates it from other similar web resources: (i) it offers an integrated environment that enables evolutionary analyses, format conversion, file storage and edition of results; (ii) it suggests further analyses, thereby guiding the users through the web server; and (iii) it allows users to design and save phylogenetic pipelines to be used over multiple genes (phylogenomics). Altogether, Phylemon 2.0 integrates a suite of 30 tools covering sequence alignment reconstruction and trimming; tree reconstruction, visualization and manipulation; and evolutionary hypotheses testing.

}, keywords = {Evolution, Molecular, Genomics, Internet, Phylogeny, Sequence Alignment, Software}, issn = {1362-4962}, doi = {10.1093/nar/gkr408}, author = {S{\'a}nchez, Rub{\'e}n and Serra, Fran{\c c}ois and T{\'a}rraga, Joaqu{\'\i}n and Medina, Ignacio and Carbonell, Jos{\'e} and Pulido, Luis and De Maria, Alejandro and Capella-Gut{\'\i}errez, Salvador and Huerta-Cepas, Jaime and Gabald{\'o}n, Toni and Dopazo, Joaquin and Dopazo, Hern{\'a}n} } @article {548, title = {ETE: a python Environment for Tree Exploration.}, journal = {BMC Bioinformatics}, volume = {11}, year = {2010}, month = {2010 Jan 13}, pages = {24}, abstract = {

BACKGROUND: Many bioinformatics analyses, ranging from gene clustering to phylogenetics, produce hierarchical trees as their main result. These are used to represent the relationships among different biological entities, thus facilitating their analysis and interpretation. A number of standalone programs are available that focus on tree visualization or that perform specific analyses on them. However, such applications are rarely suitable for large-scale surveys, in which a higher level of automation is required. Currently, many genome-wide analyses rely on tree-like data representation and hence there is a growing need for scalable tools to handle tree structures at large scale.

RESULTS: Here we present the Environment for Tree Exploration (ETE), a python programming toolkit that assists in the automated manipulation, analysis and visualization of hierarchical trees. ETE libraries provide a broad set of tree handling options as well as specific methods to analyze phylogenetic and clustering trees. Among other features, ETE allows for the independent analysis of tree partitions, has support for the extended newick format, provides an integrated node annotation system and permits to link trees to external data such as multiple sequence alignments or numerical arrays. In addition, ETE implements a number of built-in analytical tools, including phylogeny-based orthology prediction and cluster validation techniques. Finally, ETE{\textquoteright}s programmable tree drawing engine can be used to automate the graphical rendering of trees with customized node-specific visualizations.

CONCLUSIONS: ETE provides a complete set of methods to manipulate tree data structures that extends current functionality in other bioinformatic toolkits of a more general purpose. ETE is free software and can be downloaded from http://ete.cgenomics.org.

}, keywords = {Computational Biology, Databases, Genetic, Phylogeny, Software}, issn = {1471-2105}, doi = {10.1186/1471-2105-11-24}, author = {Huerta-Cepas, Jaime and Dopazo, Joaquin and Gabald{\'o}n, Toni} } @article {576, title = {Serial Expression Analysis: a web tool for the analysis of serial gene expression data.}, journal = {Nucleic Acids Res}, volume = {38}, year = {2010}, month = {2010 Jul}, pages = {W239-45}, abstract = {

Serial transcriptomics experiments investigate the dynamics of gene expression changes associated with a quantitative variable such as time or dosage. The statistical analysis of these data implies the study of global and gene-specific expression trends, the identification of significant serial changes, the comparison of expression profiles and the assessment of transcriptional changes in terms of cellular processes. We have created the SEA (Serial Expression Analysis) suite to provide a complete web-based resource for the analysis of serial transcriptomics data. SEA offers five different algorithms based on univariate, multivariate and functional profiling strategies framed within a user-friendly interface and a project-oriented architecture to facilitate the analysis of serial gene expression data sets from different perspectives. SEA is available at sea.bioinfo.cipf.es.

}, keywords = {Algorithms, Gene Expression Profiling, Internet, Kinetics, Linear Models, Oligonucleotide Array Sequence Analysis, Software}, issn = {1362-4962}, doi = {10.1093/nar/gkq488}, author = {Nueda, Maria Jos{\'e} and Carbonell, Jos{\'e} and Medina, Ignacio and Dopazo, Joaquin and Conesa, Ana} } @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 {586, title = {SNOW, a web-based tool for the statistical analysis of protein-protein interaction networks.}, journal = {Nucleic Acids Res}, volume = {37}, year = {2009}, month = {2009 Jul}, pages = {W109-14}, abstract = {

Understanding the structure and the dynamics of the complex intercellular network of interactions that contributes to the structure and function of a living cell is one of the main challenges of today{\textquoteright}s biology. SNOW inputs a collection of protein (or gene) identifiers and, by using the interactome as scaffold, draws the connections among them, calculates several relevant network parameters and, as a novelty among the rest of tools of its class, it estimates their statistical significance. The parameters calculated for each node are: connectivity, betweenness and clustering coefficient. It also calculates the number of components, number of bicomponents and articulation points. An interactive network viewer is also available to explore the resulting network. SNOW is available at http://snow.bioinfo.cipf.es.

}, keywords = {Computer Graphics, Data Interpretation, Statistical, Databases, Protein, Humans, Internet, Protein Interaction Mapping, Software}, issn = {1362-4962}, doi = {10.1093/nar/gkp402}, author = {Minguez, Pablo and G{\"o}tz, Stefan and Montaner, David and Al-Shahrour, F{\'a}tima and Dopazo, Joaquin} } @article {593, title = {GEPAS, a web-based tool for microarray data analysis and interpretation.}, journal = {Nucleic Acids Res}, volume = {36}, year = {2008}, month = {2008 Jul 01}, pages = {W308-14}, abstract = {

Gene Expression Profile Analysis Suite (GEPAS) is one of the most complete and extensively used web-based packages for microarray data analysis. During its more than 5 years of activity it has continuously been updated to keep pace with the state-of-the-art in the changing microarray data analysis arena. GEPAS offers diverse analysis options that include well established as well as novel algorithms for normalization, gene selection, class prediction, clustering and functional profiling of the experiment. New options for time-course (or dose-response) experiments, microarray-based class prediction, new clustering methods and new tests for differential expression have been included. The new pipeliner module allows automating the execution of sequential analysis steps by means of a simple but powerful graphic interface. An extensive re-engineering of GEPAS has been carried out which includes the use of web services and Web 2.0 technology features, a new user interface with persistent sessions and a new extended database of gene identifiers. GEPAS is nowadays the most quoted web tool in its field and it is extensively used by researchers of many countries and its records indicate an average usage rate of 500 experiments per day. GEPAS, is available at http://www.gepas.org.

}, keywords = {Computer Graphics, Dose-Response Relationship, Drug, Gene Expression Profiling, Internet, Kinetics, Oligonucleotide Array Sequence Analysis, Software}, issn = {1362-4962}, doi = {10.1093/nar/gkn303}, author = {T{\'a}rraga, Joaqu{\'\i}n and Medina, Ignacio and Carbonell, Jos{\'e} and Huerta-Cepas, Jaime and Minguez, Pablo and Alloza, Eva and Al-Shahrour, F{\'a}tima and Vegas-Azc{\'a}rate, Susana and Goetz, Stefan and Escobar, Pablo and Garcia-Garcia, Francisco and Conesa, Ana and Montaner, David and Dopazo, Joaquin} } @article {594, title = {High-throughput functional annotation and data mining with the Blast2GO suite.}, journal = {Nucleic Acids Res}, volume = {36}, year = {2008}, month = {2008 Jun}, pages = {3420-35}, abstract = {

Functional genomics technologies have been widely adopted in the biological research of both model and non-model species. An efficient functional annotation of DNA or protein sequences is a major requirement for the successful application of these approaches as functional information on gene products is often the key to the interpretation of experimental results. Therefore, there is an increasing need for bioinformatics resources which are able to cope with large amount of sequence data, produce valuable annotation results and are easily accessible to laboratories where functional genomics projects are being undertaken. We present the Blast2GO suite as an integrated and biologist-oriented solution for the high-throughput and automatic functional annotation of DNA or protein sequences based on the Gene Ontology vocabulary. The most outstanding Blast2GO features are: (i) the combination of various annotation strategies and tools controlling type and intensity of annotation, (ii) the numerous graphical features such as the interactive GO-graph visualization for gene-set function profiling or descriptive charts, (iii) the general sequence management features and (iv) high-throughput capabilities. We used the Blast2GO framework to carry out a detailed analysis of annotation behaviour through homology transfer and its impact in functional genomics research. Our aim is to offer biologists useful information to take into account when addressing the task of functionally characterizing their sequence data.

}, keywords = {Animals, Computational Biology, Computer Graphics, Databases, Genetic, Expressed Sequence Tags, Genes, Genomics, Sequence Analysis, DNA, Sequence Analysis, Protein, Software, Vocabulary, Controlled}, issn = {1362-4962}, doi = {10.1093/nar/gkn176}, author = {G{\"o}tz, Stefan and Garc{\'\i}a-G{\'o}mez, Juan Miguel and Terol, Javier and Williams, Tim D and Nagaraj, Shivashankar H and Nueda, Maria Jos{\'e} and Robles, Montserrat and Talon, Manuel and Dopazo, Joaquin and Conesa, Ana} } @article {603, title = {DBAli tools: mining the protein structure space.}, journal = {Nucleic Acids Res}, volume = {35}, year = {2007}, month = {2007 Jul}, pages = {W393-7}, abstract = {

The DBAli tools use a comprehensive set of structural alignments in the DBAli database to leverage the structural information deposited in the Protein Data Bank (PDB). These tools include (i) the DBAlit program that allows users to input the 3D coordinates of a protein structure for comparison by MAMMOTH against all chains in the PDB; (ii) the AnnoLite and AnnoLyze programs that annotate a target structure based on its stored relationships to other structures; (iii) the ModClus program that clusters structures by sequence and structure similarities; (iv) the ModDom program that identifies domains as recurrent structural fragments and (v) an implementation of the COMPARER method in the SALIGN command in MODELLER that creates a multiple structure alignment for a set of related protein structures. Thus, the DBAli tools, which are freely accessible via the World Wide Web at http://salilab.org/DBAli/, allow users to mine the protein structure space by establishing relationships between protein structures and their functions.

}, keywords = {Algorithms, Amino Acid Sequence, Computational Biology, Data Interpretation, Statistical, Databases, Protein, Internet, Molecular Sequence Data, Protein Conformation, Proteins, Pseudomonas aeruginosa, Sequence Alignment, Sequence Analysis, Protein, Sequence Homology, Amino Acid, Software, Structure-Activity Relationship}, issn = {1362-4962}, doi = {10.1093/nar/gkm236}, author = {Marti-Renom, Marc A and Pieper, Ursula and Madhusudhan, M S and Rossi, Andrea and Eswar, Narayanan and Davis, Fred P and Al-Shahrour, F{\'a}tima and Dopazo, Joaquin and Sali, Andrej} } @article {605, title = {FatiGO +: a functional profiling tool for genomic data. Integration of functional annotation, regulatory motifs and interaction data with microarray experiments.}, journal = {Nucleic Acids Res}, volume = {35}, year = {2007}, month = {2007 Jul}, pages = {W91-6}, abstract = {

The ultimate goal of any genome-scale experiment is to provide a functional interpretation of the data, relating the available information with the hypotheses that originated the experiment. Thus, functional profiling methods have become essential in diverse scenarios such as microarray experiments, proteomics, etc. We present the FatiGO+, a web-based tool for the functional profiling of genome-scale experiments, specially oriented to the interpretation of microarray experiments. In addition to different functional annotations (gene ontology, KEGG pathways, Interpro motifs, Swissprot keywords and text-mining based bioentities related to diseases and chemical compounds) FatiGO+ includes, as a novelty, regulatory and structural information. The regulatory information used includes predictions of targets for distinct regulatory elements (obtained from the Transfac and CisRed databases). Additionally FatiGO+ uses predictions of target motifs of miRNA to infer which of these can be activated or deactivated in the sample of genes studied. Finally, properties of gene products related to their relative location and connections in the interactome have also been used. Also, enrichment of any of these functional terms can be directly analysed on chromosomal coordinates. FatiGO+ can be found at: http://www.fatigoplus.org and within the Babelomics environment http://www.babelomics.org.

}, keywords = {Amino Acid Motifs, Animals, Binding Sites, Computational Biology, Gene Expression Profiling, Genes, Genomics, Humans, Internet, Oligonucleotide Array Sequence Analysis, Programming Languages, Software, Systems Integration, Transcription Factors}, issn = {1362-4962}, doi = {10.1093/nar/gkm260}, author = {Al-Shahrour, F{\'a}tima and Minguez, Pablo and T{\'a}rraga, Joaqu{\'\i}n and Medina, Ignacio and Alloza, Eva and Montaner, David and Dopazo, Joaquin} } @article {606, title = {From genes to functional classes in the study of biological systems.}, journal = {BMC Bioinformatics}, volume = {8}, year = {2007}, month = {2007 Apr 03}, pages = {114}, abstract = {

BACKGROUND: With the popularization of high-throughput techniques, the need for procedures that help in the biological interpretation of results has increased enormously. Recently, new procedures inspired in systems biology criteria have started to be developed.

RESULTS: Here we present FatiScan, a web-based program which implements a threshold-independent test for the functional interpretation of large-scale experiments that does not depend on the pre-selection of genes based on the multiple application of independent tests to each gene. The test implemented aims to directly test the behaviour of blocks of functionally related genes, instead of focusing on single genes. In addition, the test does not depend on the type of the data used for obtaining significance values, and consequently different types of biologically informative terms (gene ontology, pathways, functional motifs, transcription factor binding sites or regulatory sites from CisRed) can be applied to different classes of genome-scale studies. We exemplify its application in microarray gene expression, evolution and interactomics.

CONCLUSION: Methods for gene set enrichment which, in addition, are independent from the original data and experimental design constitute a promising alternative for the functional profiling of genome-scale experiments. A web server that performs the test described and other similar ones can be found at: http://www.babelomics.org.

}, keywords = {Algorithms, Chromosome Mapping, Computer Simulation, Gene Expression Profiling, Models, Biological, Multigene Family, Signal Transduction, Software, Systems biology, User-Computer Interface}, issn = {1471-2105}, doi = {10.1186/1471-2105-8-114}, author = {Al-Shahrour, F{\'a}tima and Arbiza, Leonardo and Dopazo, Hern{\'a}n and Huerta-Cepas, Jaime and Minguez, Pablo and Montaner, David 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} }