@article {818, title = {A genomic strategy for precision medicine in rare diseases: integrating customized algorithms into clinical practice.}, journal = {J Transl Med}, volume = {23}, year = {2025}, month = {2025 Jan 20}, pages = {86}, abstract = {

BACKGROUND: Despite the use of Next-Generation Sequencing (NGS) as the gold standard for the diagnosis of rare diseases, its clinical implementation has been challenging, limiting the cost-effectiveness of NGS and the understanding, control and safety essential for decision-making in clinical applications. Here, we describe a personalized NGS-based strategy integrating precision medicine into a public healthcare system and its implementation in the routine diagnosis process during a five-year pilot program.

METHODS: Our approach involved customized probe designs, the generation of virtual panels and the development of a personalized medicine module (PMM) for variant prioritization. This strategy was applied to 6500 individuals including 6267 index patients and 233 NGS-based carrier screenings.

RESULTS: Causative variants were identified in 2061 index patients (average 32.9\%, ranging from 12 to 62\% by condition). Also, 131 autosomal-recessive cases could be partially genetically diagnosed. These results led to over 5000 additional studies including carrier, prenatal and preimplantational tests or pharmacological and gene therapy treatments.

CONCLUSION: This strategy has shown promising improvements in the diagnostic rate, facilitating timely diagnosis and gradually expanding our services portfolio for rare diseases. The steps taken towards the integration of clinical and genomic data are opening new possibilities for conducting both retrospective and prospective healthcare studies. Overall, this study represents a major milestone in the ongoing efforts to improve our understanding and clinical management of rare diseases, a crucial area of medical research and care.

}, keywords = {Algorithms, Female, Genomics, High-Throughput Nucleotide Sequencing, Humans, Precision Medicine, rare diseases}, issn = {1479-5876}, doi = {10.1186/s12967-025-06069-2}, author = {M{\'e}ndez-Vidal, Cristina and Bravo-Gil, Nereida and Perez-Florido, Javier and Marcos-Luque, Irene and Fern{\'a}ndez, Raquel M and Fernandez-Rueda, Jose Luis and Gonz{\'a}lez-del Pozo, Mar{\'\i}a and Mart{\'\i}n-S{\'a}nchez, Marta and Fern{\'a}ndez-Su{\'a}rez, Elena and Mena, Marcela and Carmona, Rosario and Dopazo, Joaquin and Borrego, Salud and Anti{\v n}olo, Guillermo} } @article {813, title = {The integrated genomic surveillance system of Andalusia (SIEGA) provides a One Health regional resource connected with the clinic.}, journal = {Sci Rep}, volume = {14}, year = {2024}, month = {2024 Aug 19}, pages = {19200}, abstract = {

The One Health approach, recognizing the interconnectedness of human, animal, and environmental health, has gained significance amid emerging zoonotic diseases and antibiotic resistance concerns. This paper aims to demonstrate the utility of a collaborative tool, the SIEGA, for monitoring infectious diseases across domains, fostering a comprehensive understanding of disease dynamics and risk factors, highlighting the pivotal role of One Health surveillance systems. Raw whole-genome sequencing is processed through different species-specific open software that additionally reports the presence of genes associated to anti-microbial resistances and virulence. The SIEGA application is a Laboratory Information Management System, that allows customizing reports, detect transmission chains, and promptly alert on alarming genetic similarities. The SIEGA initiative has successfully accumulated a comprehensive collection of more than 1900 bacterial genomes, including Salmonella enterica, Listeria monocytogenes, Campylobacter jejuni, Escherichia coli, Yersinia enterocolitica and Legionella pneumophila, showcasing its potential in monitoring pathogen transmission, resistance patterns, and virulence factors. SIEGA enables customizable reports and prompt detection of transmission chains, highlighting its contribution to enhancing vigilance and response capabilities. Here we show the potential of genomics in One Health surveillance when supported by an appropriate bioinformatic tool. By facilitating precise disease control strategies and antimicrobial resistance management, SIEGA enhances global health security and reduces the burden of infectious diseases. The integration of health data from humans, animals, and the environment, coupled with advanced genomics, underscores the importance of a holistic One Health approach in mitigating health threats.

}, keywords = {Animals, Drug Resistance, Bacterial, Genome, Bacterial, Genomics, Humans, One Health, Virulence Factors, Whole Genome Sequencing}, issn = {2045-2322}, doi = {10.1038/s41598-024-70107-0}, author = {Casimiro-Soriguer, Carlos S and Perez-Florido, Javier and Robles, Enrique A and Lara, Mar{\'\i}a and Aguado, Andrea and Rodr{\'\i}guez Iglesias, Manuel A and Lepe, Jose A and Garc{\'\i}a, Federico and P{\'e}rez-Alegre, M{\'o}nica and And{\'u}jar, Elo{\'\i}sa and Jim{\'e}nez, Victoria E and Camino, Lola P and Loruso, Nicola and Ameyugo, Ulises and Vazquez, Isabel Mar{\'\i}a and Lozano, Carlota M and Chaves, J Alberto and Dopazo, Joaquin} } @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 {696, title = {Community Assessment of the Predictability of Cancer Protein and Phosphoprotein Levels from Genomics and Transcriptomics.}, journal = {Cell Syst}, volume = {11}, year = {2020}, month = {2020 08 26}, pages = {186-195.e9}, abstract = {

Cancer is driven by genomic alterations, but the processes causing this disease are largely performed by proteins. However, proteins are harder and more expensive to measure than genes and transcripts. To catalyze developments of methods to infer protein levels from other omics measurements, we leveraged crowdsourcing via the NCI-CPTAC DREAM proteogenomic challenge. We asked for methods to predict protein and phosphorylation levels from genomic and transcriptomic data in cancer patients. The best performance was achieved by an ensemble of models, including as predictors transcript level of the corresponding genes, interaction between genes, conservation across tumor types, and phosphosite proximity for phosphorylation prediction. Proteins from metabolic pathways and complexes were the best and worst predicted, respectively. The performance of even the best-performing model was modest, suggesting that many proteins are strongly regulated through translational control and degradation. Our results set a reference for the limitations of computational inference in proteogenomics. A record of this paper{\textquoteright}s transparent peer review process is included in the Supplemental Information.

}, keywords = {Crowdsourcing, Female, Genomics, Humans, Machine Learning, Male, Neoplasms, Phosphoproteins, Proteins, Proteomics, Transcriptome}, issn = {2405-4720}, doi = {10.1016/j.cels.2020.06.013}, author = {Yang, Mi and Petralia, Francesca and Li, Zhi and Li, Hongyang and Ma, Weiping and Song, Xiaoyu and Kim, Sunkyu and Lee, Heewon and Yu, Han and Lee, Bora and Bae, Seohui and Heo, Eunji and Kaczmarczyk, Jan and St{\k e}pniak, Piotr and Warcho{\l}, Micha{\l} and Yu, Thomas and Calinawan, Anna P and Boutros, Paul C and Payne, Samuel H and Reva, Boris and Boja, Emily and Rodriguez, Henry and Stolovitzky, Gustavo and Guan, Yuanfang and Kang, Jaewoo and Wang, Pei and Feny{\"o}, David and Saez-Rodriguez, Julio} } @article {612, title = {Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen.}, journal = {Nat Commun}, volume = {10}, year = {2019}, month = {2019 06 17}, pages = {2674}, abstract = {

The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca{\textquoteright}s large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60\% of combinations. However, 20\% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells.

}, keywords = {ADAM17 Protein, Antineoplastic Combined Chemotherapy Protocols, Benchmarking, Biomarkers, Tumor, Cell Line, Tumor, Computational Biology, Datasets as Topic, Drug Antagonism, Drug Resistance, Neoplasm, Drug Synergism, Genomics, Humans, Molecular Targeted Therapy, mutation, Neoplasms, pharmacogenetics, Phosphatidylinositol 3-Kinases, Phosphoinositide-3 Kinase Inhibitors, Treatment Outcome}, issn = {2041-1723}, doi = {10.1038/s41467-019-09799-2}, author = {Menden, Michael P and Wang, Dennis and Mason, Mike J and Szalai, Bence and Bulusu, Krishna C and Guan, Yuanfang and Yu, Thomas and Kang, Jaewoo and Jeon, Minji and Wolfinger, Russ and Nguyen, Tin and Zaslavskiy, Mikhail and Jang, In Sock and Ghazoui, Zara and Ahsen, Mehmet Eren and Vogel, Robert and Neto, Elias Chaibub and Norman, Thea and Tang, Eric K Y and Garnett, Mathew J and Veroli, Giovanni Y Di and Fawell, Stephen and Stolovitzky, Gustavo and Guinney, Justin and Dry, Jonathan R and Saez-Rodriguez, Julio} } @article {610, title = {Exploring the druggable space around the Fanconi anemia pathway using machine learning and mechanistic models.}, journal = {BMC Bioinformatics}, volume = {20}, year = {2019}, month = {2019 Jul 02}, pages = {370}, abstract = {

BACKGROUND: In spite of the abundance of genomic data, predictive models that describe phenotypes as a function of gene expression or mutations are difficult to obtain because they are affected by the curse of dimensionality, given the disbalance between samples and candidate genes. And this is especially dramatic in scenarios in which the availability of samples is difficult, such as the case of rare diseases.

RESULTS: The application of multi-output regression machine learning methodologies to predict the potential effect of external proteins over the signaling circuits that trigger Fanconi anemia related cell functionalities, inferred with a mechanistic model, allowed us to detect over 20 potential therapeutic targets.

CONCLUSIONS: The use of artificial intelligence methods for the prediction of potentially causal relationships between proteins of interest and cell activities related with disease-related phenotypes opens promising avenues for the systematic search of new targets in rare diseases.

}, keywords = {Databases, Factual, Fanconi Anemia, Genomics, Humans, Machine Learning, Phenotype, Proteins, Signal Transduction}, issn = {1471-2105}, doi = {10.1186/s12859-019-2969-0}, author = {Esteban-Medina, Marina and Pe{\~n}a-Chilet, Maria and Loucera, Carlos and Dopazo, Joaquin} } @article {406, title = {The first complete genomic structure of Butyrivibrio fibrisolvens and its chromid.}, journal = {Microb Genom}, volume = {4}, year = {2018}, month = {2018 Oct}, abstract = {

Butyrivibrio fibrisolvens forms part of the gastrointestinal microbiome of ruminants and other mammals, including humans. Indeed, it is one of the most common bacteria found in the rumen and plays an important role in ruminal fermentation of polysaccharides, yet, to date, there is no closed reference genome published for this species in any ruminant animal. We successfully assembled the nearly complete genome sequence of B. fibrisolvens strain INBov1 isolated from cow rumen using Illumina paired-end reads, 454 Roche single-end and mate pair sequencing technology. Additionally, we constructed an optical restriction map of this strain to aid in scaffold ordering and positioning, and completed the first genomic structure of this species. Moreover, we identified and assembled the first chromid of this species (pINBov266). The INBov1 genome encodes a large set of genes involved in the cellulolytic process but lacks key genes. This seems to indicate that B. fibrisolvens plays an important role in ruminal cellulolytic processes, but does not have autonomous cellulolytic capacity. When searching for genes involved in the biohydrogenation of unsaturated fatty acids, no linoleate isomerase gene was found in this strain. INBov1 does encode oleate hydratase genes known to participate in the hydrogenation of oleic acids. Furthermore, INBov1 contains an enolase gene, which has been recently determined to participate in the synthesis of conjugated linoleic acids. This work confirms the presence of a novel chromid in B. fibrisolvens and provides a new potential reference genome sequence for this species, providing new insight into its role in biohydrogenation and carbohydrate degradation.

}, keywords = {Animals, Butyrivibrio fibrisolvens, Cattle, Genome, Bacterial, Genomics, Humans, Milk, Rumen, Sequence Analysis, DNA}, issn = {2057-5858}, doi = {10.1099/mgen.0.000216}, author = {Rodr{\'\i}guez Hern{\'a}ez, Javier and Cer{\'o}n Cucchi, Maria Esperanza and Cravero, Silvio and Martinez, Maria Carolina and Gonzalez, Sergio and Puebla, Andrea and Dopazo, Joaquin and Farber, Marisa and Paniego, Norma and Rivarola, M{\'a}ximo} } @article {398, title = {Genomics of the origin and evolution of Citrus.}, journal = {Nature}, volume = {554}, year = {2018}, month = {2018 Feb 15}, pages = {311-316}, abstract = {

The genus Citrus, comprising some of the most widely cultivated fruit crops worldwide, includes an uncertain number of species. Here we describe ten natural citrus species, using genomic, phylogenetic and biogeographic analyses of 60 accessions representing diverse citrus germ plasms, and propose that citrus diversified during the late Miocene epoch through a rapid southeast Asian radiation that correlates with a marked weakening of the monsoons. A second radiation enabled by migration across the Wallace line gave rise to the Australian limes in the early Pliocene epoch. Further identification and analyses of hybrids and admixed genomes provides insights into the genealogy of major commercial cultivars of citrus. Among mandarins and sweet orange, we find an extensive network of relatedness that illuminates the domestication of these groups. Widespread pummelo admixture among these mandarins and its correlation with fruit size and acidity suggests a plausible role of pummelo introgression in the selection of palatable mandarins. This work provides a new evolutionary framework for the genus Citrus.

}, keywords = {Asia, Southeastern, Biodiversity, citrus, Crop Production, Evolution, Molecular, Genetic Speciation, Genome, Plant, Genomics, Haplotypes, Heterozygote, History, Ancient, Human Migration, Hybridization, Genetic, Phylogeny}, issn = {1476-4687}, doi = {10.1038/nature25447}, author = {Wu, Guohong Albert and Terol, Javier and Iba{\~n}ez, Victoria and L{\'o}pez-Garc{\'\i}a, Antonio and P{\'e}rez-Rom{\'a}n, Estela and Borred{\'a}, Carles and Domingo, Concha and Tadeo, Francisco R and Carbonell-Caballero, Jos{\'e} and Alonso, Roberto and Curk, Franck and Du, Dongliang and Ollitrault, Patrick and Roose, Mikeal L and Dopazo, Joaquin and Gmitter, Frederick G and Rokhsar, Daniel S and Talon, Manuel} } @article {384, title = {Genomic expression differences between cutaneous cells from red hair color individuals and black hair color individuals based on bioinformatic analysis.}, journal = {Oncotarget}, volume = {8}, year = {2017}, month = {2017 Feb 14}, pages = {11589-11599}, abstract = {

The MC1R gene plays a crucial role in pigmentation synthesis. Loss-of-function MC1R variants, which impair protein function, are associated with red hair color (RHC) phenotype and increased skin cancer risk. Cultured cutaneous cells bearing loss-of-function MC1R variants show a distinct gene expression profile compared to wild-type MC1R cultured cutaneous cells. We analysed the gene signature associated with RHC co-cultured melanocytes and keratinocytes by Protein-Protein interaction (PPI) network analysis to identify genes related with non-functional MC1R variants. From two detected networks, we selected 23 nodes as hub genes based on topological parameters. Differential expression of hub genes was then evaluated in healthy skin biopsies from RHC and black hair color (BHC) individuals. We also compared gene expression in melanoma tumors from individuals with RHC versus BHC. Gene expression in normal skin from RHC cutaneous cells showed dysregulation in 8 out of 23 hub genes (CLN3, ATG10, WIPI2, SNX2, GABARAPL2, YWHA, PCNA and GBAS). Hub genes did not differ between melanoma tumors in RHC versus BHC individuals. The study suggests that healthy skin cells from RHC individuals present a constitutive genomic deregulation associated with the red hair phenotype and identify novel genes involved in melanocyte biology.

}, keywords = {Adult, Coculture Techniques, Computational Biology, gene expression, Genetic Predisposition to Disease, Genomics, Hair Color, Humans, Keratinocytes, Melanocytes, Middle Aged, Phenotype, Receptor, Melanocortin, Type 1}, issn = {1949-2553}, doi = {10.18632/oncotarget.14140}, url = {http://www.impactjournals.com/oncotarget/index.php?journal=oncotarget\&page=article\&op=view\&path\%5B\%5D=14140\&path\%5B\%5D=45094}, author = {Puig-Butille, Joan Anton and Gimenez-Xavier, Pol and Visconti, Alessia and Nsengimana, J{\'e}r{\'e}mie and Garcia-Garcia, Francisco and Tell-Marti, Gemma and Escamez, Maria Jos{\'e} and Newton-Bishop, Julia and Bataille, Veronique and Del Rio, Marcela and Dopazo, Joaquin and Falchi, Mario and Puig, Susana} } @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 {441, title = {Highly sensitive and ultrafast read mapping for RNA-seq analysis.}, journal = {DNA Res}, volume = {23}, year = {2016}, month = {2016 Apr}, pages = {93-100}, abstract = {

As sequencing technologies progress, the amount of data produced grows exponentially, shifting the bottleneck of discovery towards the data analysis phase. In particular, currently available mapping solutions for RNA-seq leave room for improvement in terms of sensitivity and performance, hindering an efficient analysis of transcriptomes by massive sequencing. Here, we present an innovative approach that combines re-engineering, optimization and parallelization. This solution results in a significant increase of mapping sensitivity over a wide range of read lengths and substantial shorter runtimes when compared with current RNA-seq mapping methods available.

}, keywords = {Genomics, High-Throughput Nucleotide Sequencing, Humans, Sensitivity and Specificity, Sequence Analysis, RNA, Transcriptome}, issn = {1756-1663}, doi = {10.1093/dnares/dsv039}, author = {Medina, I and T{\'a}rraga, J and Mart{\'\i}nez, H and Barrachina, S and Castillo, M I and Paschall, J and Salavert-Torres, J and Blanquer-Espert, I and Hern{\'a}ndez-Garc{\'\i}a, V and Quintana-Ort{\'\i}, E S and Dopazo, J} } @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 {494, title = {The role of the interactome in the maintenance of deleterious variability in human populations.}, journal = {Mol Syst Biol}, volume = {10}, year = {2014}, month = {2014 Sep 26}, pages = {752}, abstract = {

Recent genomic projects have revealed the existence of an unexpectedly large amount of deleterious variability in the human genome. Several hypotheses have been proposed to explain such an apparently high mutational load. However, the mechanisms by which deleterious mutations in some genes cause a pathological effect but are apparently innocuous in other genes remain largely unknown. This study searched for deleterious variants in the 1,000 genomes populations, as well as in a newly sequenced population of 252 healthy Spanish individuals. In addition, variants causative of monogenic diseases and somatic variants from 41 chronic lymphocytic leukaemia patients were analysed. The deleterious variants found were analysed in the context of the interactome to understand the role of network topology in the maintenance of the observed mutational load. Our results suggest that one of the mechanisms whereby the effect of these deleterious variants on the phenotype is suppressed could be related to the configuration of the protein interaction network. Most of the deleterious variants observed in healthy individuals are concentrated in peripheral regions of the interactome, in combinations that preserve their connectivity, and have a marginal effect on interactome integrity. On the contrary, likely pathogenic cancer somatic deleterious variants tend to occur in internal regions of the interactome, often with associated structural consequences. Finally, variants causative of monogenic diseases seem to occupy an intermediate position. Our observations suggest that the real pathological potential of a variant might be more a systems property rather than an intrinsic property of individual proteins.

}, keywords = {Alleles, Exome, Gene Library, Genetic Variation, Genetics, Population, Genome, Human, Genomics, Humans, Models, Genetic, mutation, Phenotype, Protein Conformation, Protein Interaction Maps, Sequence Analysis, DNA, Whites}, issn = {1744-4292}, doi = {10.15252/msb.20145222}, author = {Garc{\'\i}a-Alonso, Luz and Jim{\'e}nez-Almaz{\'a}n, Jorge and Carbonell-Caballero, Jos{\'e} and Vela-Boza, Alicia and Santoyo-L{\'o}pez, Javier and Anti{\v n}olo, Guillermo and Dopazo, Joaquin} } @article {512, title = {Discovering the hidden sub-network component in a ranked list of genes or proteins derived from genomic experiments.}, journal = {Nucleic Acids Res}, volume = {40}, year = {2012}, month = {2012 Nov 01}, pages = {e158}, abstract = {

Genomic experiments (e.g. differential gene expression, single-nucleotide polymorphism association) typically produce ranked list of genes. We present a simple but powerful approach which uses protein-protein interaction data to detect sub-networks within such ranked lists of genes or proteins. We performed an exhaustive study of network parameters that allowed us concluding that the average number of components and the average number of nodes per component are the parameters that best discriminate between real and random networks. A novel aspect that increases the efficiency of this strategy in finding sub-networks is that, in addition to direct connections, also connections mediated by intermediate nodes are considered to build up the sub-networks. The possibility of using of such intermediate nodes makes this approach more robust to noise. It also overcomes some limitations intrinsic to experimental designs based on differential expression, in which some nodes are invariant across conditions. The proposed approach can also be used for candidate disease-gene prioritization. Here, we demonstrate the usefulness of the approach by means of several case examples that include a differential expression analysis in Fanconi Anemia, a genome-wide association study of bipolar disorder and a genome-scale study of essentiality in cancer genes. An efficient and easy-to-use web interface (available at http://www.babelomics.org) based on HTML5 technologies is also provided to run the algorithm and represent the network.

}, keywords = {Bipolar Disorder, Fanconi Anemia, Gene Regulatory Networks, Genes, Neoplasm, Genome-Wide Association Study, Genomics, Humans, Protein Interaction Mapping}, issn = {1362-4962}, doi = {10.1093/nar/gks699}, author = {Garc{\'\i}a-Alonso, Luz and Alonso, Roberto and Vidal, Enrique and Amadoz, Alicia and De Maria, Alejandro and Minguez, Pablo and Medina, Ignacio and Dopazo, Joaquin} } @article {541, title = {myKaryoView: a light-weight client for visualization of genomic data.}, journal = {PLoS One}, volume = {6}, year = {2011}, month = {2011}, pages = {e26345}, abstract = {

The Distributed Annotation System (DAS) is a protocol for easy sharing and integration of biological annotations. In order to visualize feature annotations in a genomic context a client is required. Here we present myKaryoView, a simple light-weight DAS tool for visualization of genomic annotation. myKaryoView has been specifically configured to help analyse data derived from personal genomics, although it can also be used as a generic genome browser visualization. Several well-known data sources are provided to facilitate comparison of known genes and normal variation regions. The navigation experience is enhanced by simultaneous rendering of different levels of detail across chromosomes. A simple interface is provided to allow searches for any SNP, gene or chromosomal region. User-defined DAS data sources may also be added when querying the system. We demonstrate myKaryoView capabilities for adding user-defined sources with a set of genetic profiles of family-related individuals downloaded directly from 23andMe. myKaryoView is a web tool for visualization of genomic data specifically designed for direct-to-consumer genomic data that uses publicly available data distributed throughout the Internet. It does not require data to be held locally and it is capable of rendering any feature as long as it conforms to DAS specifications. Configuration and addition of sources to myKaryoView can be done through the interface. Here we show a proof of principle of myKaryoView{\textquoteright}s ability to display personal genomics data with 23andMe genome data sources. The tool is available at: http://mykaryoview.com.

}, keywords = {Computer Graphics, Databases, Genetic, Genomics, Internet, Molecular Sequence Annotation, User-Computer Interface}, issn = {1932-6203}, doi = {10.1371/journal.pone.0026345}, author = {Jimenez, Rafael C and Salazar, Gustavo A and Gel, Bernat and Dopazo, Joaquin and Mulder, Nicola and Corpas, Manuel} } @article {537, title = {Natural selection on functional modules, a genome-wide analysis.}, journal = {PLoS Comput Biol}, volume = {7}, year = {2011}, month = {2011 Mar}, pages = {e1001093}, abstract = {

Classically, the functional consequences of natural selection over genomes have been analyzed as the compound effects of individual genes. The current paradigm for large-scale analysis of adaptation is based on the observed significant deviations of rates of individual genes from neutral evolutionary expectation. This approach, which assumed independence among genes, has not been able to identify biological functions significantly enriched in positively selected genes in individual species. Alternatively, pooling related species has enhanced the search for signatures of selection. However, grouping signatures does not allow testing for adaptive differences between species. Here we introduce the Gene-Set Selection Analysis (GSSA), a new genome-wide approach to test for evidences of natural selection on functional modules. GSSA is able to detect lineage specific evolutionary rate changes in a notable number of functional modules. For example, in nine mammal and Drosophilae genomes GSSA identifies hundreds of functional modules with significant associations to high and low rates of evolution. Many of the detected functional modules with high evolutionary rates have been previously identified as biological functions under positive selection. Notably, GSSA identifies conserved functional modules with many positively selected genes, which questions whether they are exclusively selected for fitting genomes to environmental changes. Our results agree with previous studies suggesting that adaptation requires positive selection, but not every mutation under positive selection contributes to the adaptive dynamical process of the evolution of species.

}, keywords = {Animals, Databases, Genetic, Drosophila, Genome, Insect, Genome-Wide Association Study, Genomics, Mammals, Phylogeny, Selection, Genetic, Sequence Analysis, DNA}, issn = {1553-7358}, doi = {10.1371/journal.pcbi.1001093}, author = {Serra, Fran{\c c}ois and Arbiza, Leonardo and Dopazo, Joaquin and Dopazo, Hern{\'a}n} } @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 {542, title = {Functional analysis of multiple genomic signatures demonstrates that classification algorithms choose phenotype-related genes.}, journal = {Pharmacogenomics J}, volume = {10}, year = {2010}, month = {2010 Aug}, pages = {310-23}, abstract = {

Gene expression signatures of toxicity and clinical response benefit both safety assessment and clinical practice; however, difficulties in connecting signature genes with the predicted end points have limited their application. The Microarray Quality Control Consortium II (MAQCII) project generated 262 signatures for ten clinical and three toxicological end points from six gene expression data sets, an unprecedented collection of diverse signatures that has permitted a wide-ranging analysis on the nature of such predictive models. A comprehensive analysis of the genes of these signatures and their nonredundant unions using ontology enrichment, biological network building and interactome connectivity analyses demonstrated the link between gene signatures and the biological basis of their predictive power. Different signatures for a given end point were more similar at the level of biological properties and transcriptional control than at the gene level. Signatures tended to be enriched in function and pathway in an end point and model-specific manner, and showed a topological bias for incoming interactions. Importantly, the level of biological similarity between different signatures for a given end point correlated positively with the accuracy of the signature predictions. These findings will aid the understanding, and application of predictive genomic signatures, and support their broader application in predictive medicine.

}, keywords = {Algorithms, Databases, Genetic, Endpoint Determination, Gene Expression Profiling, Genomics, Humans, Neural Networks, Computer, Oligonucleotide Array Sequence Analysis, Phenotype, Predictive Value of Tests, Proteins, Quality Control}, issn = {1473-1150}, doi = {10.1038/tpj.2010.35}, author = {Shi, W and Bessarabova, M and Dosymbekov, D and Dezso, Z and Nikolskaya, T and Dudoladova, M and Serebryiskaya, T and Bugrim, A and Guryanov, A and Brennan, R J and Shah, R and Dopazo, J and Chen, M and Deng, Y and Shi, T and Jurman, G and Furlanello, C and Thomas, R S and Corton, J C and Tong, W and Shi, L and Nikolsky, Y} } @article {552, title = {Functional genomics and networks: new approaches in the extraction of complex gene modules.}, journal = {Expert Rev Proteomics}, volume = {7}, year = {2010}, month = {2010 Feb}, pages = {55-63}, abstract = {

The engine that makes the cell work is made of an intricate network of molecular interactions. Nowadays, the elements and relationships of this complex network can be studied with several types of high-throughput techniques. The dream of having a global picture of the cell from different perspectives that can jointly explain cell behavior is, at least technically, feasible. However, this task can only be accomplished by filling the gap between data and information. The availability of methods capable of accurately managing, integrating and analyzing the results from these experiments is crucial for this purpose. Here, we review the new challenges raised by the availability of different genomic data, as well as the new proposals presented to cope with the increasing data complexity. Special emphasis is given to approaches that explore the transcriptome trying to describe the modules of genes that account for the traits studied.

}, keywords = {Gene Expression Regulation, Gene Regulatory Networks, Genomics, Protein Binding, Proteins, Systems biology}, issn = {1744-8387}, doi = {10.1586/epr.09.103}, author = {Minguez, Pablo and Dopazo, Joaquin} } @article {553, title = {Functional genomics of 5- to 8-cell stage human embryos by blastomere single-cell cDNA analysis.}, journal = {PLoS One}, volume = {5}, year = {2010}, month = {2010 Oct 26}, pages = {e13615}, abstract = {

Blastomere fate and embryonic genome activation (EGA) during human embryonic development are unsolved areas of high scientific and clinical interest. Forty-nine blastomeres from 5- to 8-cell human embryos have been investigated following an efficient single-cell cDNA amplification protocol to provide a template for high-density microarray analysis. The previously described markers, characteristic of Inner Cell Mass (ICM) (n = 120), stemness (n = 190) and Trophectoderm (TE) (n = 45), were analyzed, and a housekeeping pattern of 46 genes was established. All the human blastomeres from the 5- to 8-cell stage embryo displayed a common gene expression pattern corresponding to ICM markers (e.g., DDX3, FOXD3, LEFTY1, MYC, NANOG, POU5F1), stemness (e.g., POU5F1, DNMT3B, GABRB3, SOX2, ZFP42, TERT), and TE markers (e.g., GATA6, EOMES, CDX2, LHCGR). The EGA profile was also investigated between the 5-6- and 8-cell stage embryos, and compared to the blastocyst stage. Known genes (n = 92) such as depleted maternal transcripts (e.g., CCNA1, CCNB1, DPPA2) and embryo-specific activation (e.g., POU5F1, CDH1, DPPA4), as well as novel genes, were confirmed. In summary, the global single-cell cDNA amplification microarray analysis of the 5- to 8-cell stage human embryos reveals that blastomere fate is not committed to ICM or TE. Finally, new EGA features in human embryogenesis are presented.

}, keywords = {Blastomeres, DNA, Complementary, Gene Expression Profiling, Genomics, Humans, Oligonucleotide Array Sequence Analysis}, issn = {1932-6203}, doi = {10.1371/journal.pone.0013615}, author = {Galan, Amparo and Montaner, David and P{\'o}o, M Eugenia and Valbuena, Diana and Ruiz, Veronica and Aguilar, Crist{\'o}bal and Dopazo, Joaquin and Simon, Carlos} } @article {574, title = {Multidimensional gene set analysis of genomic data.}, journal = {PLoS One}, volume = {5}, year = {2010}, month = {2010 Apr 27}, pages = {e10348}, abstract = {

Understanding the functional implications of changes in gene expression, mutations, etc., is the aim of most genomic experiments. To achieve this, several functional profiling methods have been proposed. Such methods study the behaviour of different gene modules (e.g. gene ontology terms) in response to one particular variable (e.g. differential gene expression). In spite to the wealth of information provided by functional profiling methods, a common limitation to all of them is their inherent unidimensional nature. In order to overcome this restriction we present a multidimensional logistic model that allows studying the relationship of gene modules with different genome-scale measurements (e.g. differential expression, genotyping association, methylation, copy number alterations, heterozygosity, etc.) simultaneously. Moreover, the relationship of such functional modules with the interactions among the variables can also be studied, which produces novel results impossible to be derived from the conventional unidimensional functional profiling methods. We report sound results of gene sets associations that remained undetected by the conventional one-dimensional gene set analysis in several examples. Our findings demonstrate the potential of the proposed approach for the discovery of new cell functionalities with complex dependences on more than one variable.

}, keywords = {Databases, Genetic, Gene Expression Profiling, Gene Regulatory Networks, Genome, Human, Genomics, Humans, Models, Statistical}, issn = {1932-6203}, doi = {10.1371/journal.pone.0010348}, author = {Montaner, David and Dopazo, Joaquin} } @article {579, title = {Formulating and testing hypotheses in functional genomics.}, journal = {Artif Intell Med}, volume = {45}, year = {2009}, month = {2009 Feb-Mar}, pages = {97-107}, abstract = {

OBJECTIVE: The ultimate goal of any genome-scale experiment is to provide a functional interpretation of the results, relating the available genomic information to the hypotheses that originated the experiment.

METHODS AND RESULTS: Initially, this interpretation has been made on a pre-selection of relevant genes, based on the experimental values, followed by the study of the enrichment in some functional properties. Nevertheless, functional enrichment methods, demonstrated to have a flaw: the first step of gene selection was too stringent given that the cooperation among genes was ignored. The assumption that modules of genes related by relevant biological properties (functionality, co-regulation, chromosomal location, etc.) are the real actors of the cell biology lead to the development of new procedures, inspired in systems biology criteria, generically known as gene-set methods. These methods have been successfully used to analyze transcriptomic and large-scale genotyping experiments as well as to test other different genome-scale hypothesis in other fields such as phylogenomics.

}, keywords = {Biological Evolution, Computational Biology, Genomics, Genotype, Models, Theoretical}, issn = {1873-2860}, doi = {10.1016/j.artmed.2008.08.003}, author = {Dopazo, Joaquin} } @article {582, title = {Gene set internal coherence in the context of functional profiling.}, journal = {BMC Genomics}, volume = {10}, year = {2009}, month = {2009 Apr 27}, pages = {197}, abstract = {

BACKGROUND: Functional profiling methods have been extensively used in the context of high-throughput experiments and, in particular, in microarray data analysis. Such methods use available biological information to define different types of functional gene modules (e.g. gene ontology -GO-, KEGG pathways, etc.) whose representation in a pre-defined list of genes is further studied. In the most popular type of microarray experimental designs (e.g. up- or down-regulated genes, clusters of co-expressing genes, etc.) or in other genomic experiments (e.g. Chip-on-chip, epigenomics, etc.) these lists are composed by genes with a high degree of co-expression. Therefore, an implicit assumption in the application of functional profiling methods within this context is that the genes corresponding to the modules tested are effectively defining sets of co-expressing genes. Nevertheless not all the functional modules are biologically coherent entities in terms of co-expression, which will eventually hinder its detection with conventional methods of functional enrichment.

RESULTS: Using a large collection of microarray data we have carried out a detailed survey of internal correlation in GO terms and KEGG pathways, providing a coherence index to be used for measuring functional module co-regulation. An unexpected low level of internal correlation was found among the modules studied. Only around 30\% of the modules defined by GO terms and 57\% of the modules defined by KEGG pathways display an internal correlation higher than the expected by chance.This information on the internal correlation of the genes within the functional modules can be used in the context of a logistic regression model in a simple way to improve their detection in gene expression experiments.

CONCLUSION: For the first time, an exhaustive study on the internal co-expression of the most popular functional categories has been carried out. Interestingly, the real level of coexpression within many of them is lower than expected (or even inexistent), which will preclude its detection by means of most conventional functional profiling methods. If the gene-to-function correlation information is used in functional profiling methods, the results obtained improve the ones obtained by conventional enrichment methods.

}, keywords = {Algorithms, Breast Neoplasms, Carcinoma, Intraductal, Noninfiltrating, Computational Biology, Databases, Nucleic Acid, Female, Gene Expression Profiling, Genomics, Humans, Oligonucleotide Array Sequence Analysis, Papillomavirus Infections, Reproducibility of Results}, issn = {1471-2164}, doi = {10.1186/1471-2164-10-197}, author = {Montaner, David and Minguez, Pablo and Al-Shahrour, F{\'a}tima 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 {598, title = {PhylomeDB: a database for genome-wide collections of gene phylogenies.}, journal = {Nucleic Acids Res}, volume = {36}, year = {2008}, month = {2008 Jan}, pages = {D491-6}, abstract = {

The complete collection of evolutionary histories of all genes in a genome, also known as phylome, constitutes a valuable source of information. The reconstruction of phylomes has been previously prevented by large demands of time and computer power, but is now feasible thanks to recent developments in computers and algorithms. To provide a publicly available repository of complete phylomes that allows researchers to access and store large-scale phylogenomic analyses, we have developed PhylomeDB. PhylomeDB is a database of complete phylomes derived for different genomes within a specific taxonomic range. All phylomes in the database are built using a high-quality phylogenetic pipeline that includes evolutionary model testing and alignment trimming phases. For each genome, PhylomeDB provides the alignments, phylogentic trees and tree-based orthology predictions for every single encoded protein. The current version of PhylomeDB includes the phylomes of Human, the yeast Saccharomyces cerevisiae and the bacterium Escherichia coli, comprising a total of 32 289 seed sequences with their corresponding alignments and 172 324 phylogenetic trees. PhylomeDB can be publicly accessed at http://phylomedb.bioinfo.cipf.es.

}, keywords = {Base Sequence, Escherichia coli, Genes, Genomics, History, Ancient, Humans, Phylogeny, Proteins, Saccharomyces cerevisiae, Sequence Alignment}, issn = {1362-4962}, doi = {10.1093/nar/gkm899}, author = {Huerta-Cepas, Jaime and Bueno, Anibal and Dopazo, Joaquin and Gabald{\'o}n, Toni} } @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} }