@article {715, title = {Uniform genomic data analysis in the NCI Genomic Data CommonsAbstract}, journal = {Nature Communications}, volume = {12}, year = {2021}, month = {Jan-12-2021}, doi = {10.1038/s41467-021-21254-9}, url = {http://www.nature.com/articles/s41467-021-21254-9}, author = {Zhang, Zhenyu and Hernandez, Kyle and Savage, Jeremiah and Li, Shenglai and Miller, Dan and Agrawal, Stuti and Ortuno, Francisco and Staudt, Louis M. and Heath, Allison and Grossman, Robert L.} } @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 {664, title = {Using mechanistic models for the clinical interpretation of complex genomic variation}, journal = {Scientific Reports}, volume = {9}, year = {2019}, month = {Jan-12-2019}, doi = {10.1038/s41598-019-55454-7}, url = {http://www.nature.com/articles/s41598-019-55454-7http://www.nature.com/articles/s41598-019-55454-7.pdfhttp://www.nature.com/articles/s41598-019-55454-7.pdfhttp://www.nature.com/articles/s41598-019-55454-7}, author = {Pe{\~n}a-Chilet, Maria and Esteban-Medina, Marina and Falco, Matias M. and Rian, Kinza and Hidalgo, Marta R. and Loucera, Carlos and Dopazo, Joaquin} } @article {474, title = {Using activation status of signaling pathways as mechanism-based biomarkers to predict drug sensitivity.}, journal = {Sci Rep}, volume = {5}, year = {2015}, month = {2015 Dec 18}, pages = {18494}, abstract = {

Many complex traits, as drug response, are associated with changes in biological pathways rather than being caused by single gene alterations. Here, a predictive framework is presented in which gene expression data are recoded into activity statuses of signal transduction circuits (sub-pathways within signaling pathways that connect receptor proteins to final effector proteins that trigger cell actions). Such activity values are used as features by a prediction algorithm which can efficiently predict a continuous variable such as the IC50 value. The main advantage of this prediction method is that the features selected by the predictor, the signaling circuits, are themselves rich-informative, mechanism-based biomarkers which provide insight into or drug molecular mechanisms of action (MoA).

}, keywords = {Algorithms, Antineoplastic Agents, biomarkers, Cell Line, Tumor, Cell Survival, gene expression, Humans, Lethal Dose 50, Neoplasms, Phosphorylation, Proteins, Signal Transduction}, issn = {2045-2322}, doi = {10.1038/srep18494}, author = {Amadoz, Alicia and Sebasti{\'a}n-Leon, Patricia and Vidal, Enrique and Salavert, Francisco and Dopazo, Joaquin} } @article {565, title = {Understanding disease mechanisms with models of signaling pathway activities}, journal = {BMC systems biology}, volume = {8}, year = {2014}, month = {10}, pages = {121}, doi = {10.1186/s12918-014-0121-3}, author = {Sebasti{\'a}n-Leon, Patricia and Vidal, Enrique and Minguez, Pablo and Conesa, Ana and Tarazona, Sonia and Amadoz, Alicia and Armero, Carmen and Salavert Torres, Francisco and Vidal-Puig, Antonio and Montaner, David and Dopazo, Joaquin} } @article {1093, title = {Understanding disease mechanisms with models of signaling pathway activities.}, journal = {BMC systems biology}, volume = {8}, year = {2014}, month = {2014 Oct 25}, pages = {121}, abstract = {BackgroundUnderstanding the aspects of the cell functionality that account for disease or drug action mechanisms is one of the main challenges in the analysis of genomic data and is on the basis of the future implementation of precision medicine.ResultsHere we propose a simple probabilistic model in which signaling pathways are separated into elementary sub-pathways or signal transmission circuits (which ultimately trigger cell functions) and then transforms gene expression measurements into probabilities of activation of such signal transmission circuits. Using this model, differential activation of such circuits between biological conditions can be estimated. Thus, circuit activation statuses can be interpreted as biomarkers that discriminate among the compared conditions. This type of mechanism-based biomarkers accounts for cell functional activities and can easily be associated to disease or drug action mechanisms. The accuracy of the proposed model is demonstrated with simulations and real datasets.ConclusionsThe proposed model provides detailed information that enables the interpretation disease mechanisms as a consequence of the complex combinations of altered gene expression values. Moreover, it offers a framework for suggesting possible ways of therapeutic intervention in a pathologically perturbed system.}, keywords = {Disease mechanism, pathway, signalling, Systems biology}, issn = {1752-0509}, doi = {10.1186/s12918-014-0121-3}, url = {http://www.biomedcentral.com/1752-0509/8/121/abstract}, author = {Sebasti{\'a}n-Leon, Patricia and Vidal, Enrique and Minguez, Pablo and Ana Conesa and Sonia Tarazona and Amadoz, Alicia and Armero, Carmen and Salavert, Francisco and Vidal-Puig, Antonio and Montaner, David and Joaqu{\'\i}n Dopazo} } @article {900, title = {Using GPUs for the Exact Alignment of Short-read Genetic Sequences by Means of the Burrows{\textendash}Wheeler Transform.}, journal = {IEEE/ACM transactions on computational biology and bioinformatics / IEEE, ACM}, volume = {9}, number = {4}, year = {2012}, month = {2012 Mar 20}, pages = {1245-1256}, abstract = {General Purpose Graphic Processing Units (GPGPUs) constitute an inexpensive resource for computing-intensive applications that could exploit an intrinsic fine-grain parallelism. This paper presents the design and implementation in GPGPUs of an exact alignment tool for nucleotide sequences based on the Burrows-Wheeler Transform. We compare this algorithm with state-of-the-art implementations of the same algorithm over standard CPUs, and considering the same conditions in terms of I/O. Excluding disk transfers, the implementation of the algorithm in GPUs shows a speedup larger than 12x, when compared to CPU execution. This implementation exploits the parallelism by concurrently searching different sequences on the same reference search tree, maximising memory locality and ensuring a symmetric access to the data. The article describes the behaviour of the algorithm in GPU, showing a good scalability in the performance, only limited by the size of the GPU inner memory.}, keywords = {Burrows-Wheeler transform, CPU execution, GPGPU, NGS}, issn = {1557-9964}, doi = {10.1109/TCBB.2012.49}, url = {http://ieeexplore.ieee.org.sire.ub.edu/xpl/articleDetails.jsp?reload=true\&arnumber=6175888}, author = {Salavert Torres, Jose and Blanquer Espert, Ignacio and Tomas Dominguez, Andres and Hernendez, Vicente and Medina, Ignacio and Terraga, Joaquin and Dopazo, Joaquin} } @article {570, title = {Using GPUs for the Exact Alignment of Short-Read Genetic Sequences by Means of the Burrows-Wheeler Transform}, journal = {IEEE/ACM Transactions on Computational Biology and Bioinformatics}, volume = {9}, year = {2012}, month = {Jan-07-2012}, pages = {1245 - 1256}, issn = {1545-5963}, doi = {10.1109/TCBB.2012.49}, url = {http://ieeexplore.ieee.org/document/6175888/http://xplorestaging.ieee.org/ielx5/8857/6202798/06175888.pdf?arnumber=6175888}, author = {Torres, J. S. and Espert, I. B. and Dominguez, A. T. and Garcia, V. Hernendez and Castello, I. Medina and Gimenez, J. Terraga and Blazquez, J. Dopazo} } @article {522, title = {Using GPUs for the exact alignment of short-read genetic sequences by means of the Burrows-Wheeler transform.}, journal = {IEEE/ACM Trans Comput Biol Bioinform}, volume = {9}, year = {2012}, month = {2012 Jul-Aug}, pages = {1245-56}, abstract = {

General Purpose Graphic Processing Units (GPGPUs) constitute an inexpensive resource for computing-intensive applications that could exploit an intrinsic fine-grain parallelism. This paper presents the design and implementation in GPGPUs of an exact alignment tool for nucleotide sequences based on the Burrows-Wheeler Transform. We compare this algorithm with state-of-the-art implementations of the same algorithm over standard CPUs, and considering the same conditions in terms of I/O. Excluding disk transfers, the implementation of the algorithm in GPUs shows a speedup larger than 12, when compared to CPU execution. This implementation exploits the parallelism by concurrently searching different sequences on the same reference search tree, maximizing memory locality and ensuring a symmetric access to the data. The paper describes the behavior of the algorithm in GPU, showing a good scalability in the performance, only limited by the size of the GPU inner memory.

}, keywords = {Algorithms, Animals, Computational Biology, Computer Graphics, Data Compression, Drosophila melanogaster, Genes, Insect, Image Processing, Computer-Assisted, Models, Genetic, Sequence Alignment, Sequence Analysis, DNA}, issn = {1557-9964}, doi = {10.1109/TCBB.2012.49}, author = {Salavert Torres, Jose and Blanquer Espert, Ignacio and Dom{\'\i}nguez, Andr{\'e}s Tom{\'a}s and Hern{\'a}ndez Garc{\'\i}a, Vicente and Medina Castell{\'o}, Ignacio and T{\'a}rraga Gim{\'e}nez, Joaqu{\'\i}n and Dopazo Bl{\'a}zquez, Joaqu{\'\i}n} } @article {584, title = {On the Use of Functional Module Definitions in the Analysis of Genomic Experiments}, journal = {Molecular and Cellular Toxicology}, volume = {5}, year = {2009}, month = {09}, pages = {47-47}, author = {Dopazo, Joaquin} } @article {17935148, title = {Use of estimated evolutionary strength at the codon level improves the prediction of disease-related protein mutations in humans}, journal = {Hum Mutat}, volume = {29}, number = {1}, year = {2008}, note = {Capriotti, Emidio Arbiza, Leonardo Casadio, Rita Dopazo, Joaquin Dopazo, Hernan Marti-Renom, Marc A Evaluation Studies Research Support, Non-U.S. Gov{\textquoteright}t United States Human mutation Hum Mutat. 2008 Jan;29(1):198-204.}, pages = {198-204}, abstract = {Predicting the functional impact of protein variation is one of the most challenging problems in bioinformatics. A rapidly growing number of genome-scale studies provide large amounts of experimental data, allowing the application of rigorous statistical approaches for predicting whether a given single point mutation has an impact on human health. Up until now, existing methods have limited their source data to either protein or gene information. Novel in this work, we take advantage of both and focus on protein evolutionary information by using estimated selective pressures at the codon level. Here we introduce a new method (SeqProfCod) to predict the likelihood that a given protein variant is associated with human disease or not. Our method relies on a support vector machine (SVM) classifier trained using three sources of information: protein sequence, multiple protein sequence alignments, and the estimation of selective pressure at the codon level. SeqProfCod has been benchmarked with a large dataset of 8,987 single point mutations from 1,434 human proteins from SWISS-PROT. It achieves 82\% overall accuracy and a correlation coefficient of 0.59, indicating that the estimation of the selective pressure helps in predicting the functional impact of single-point mutations. Moreover, this study demonstrates the synergic effect of combining two sources of information for predicting the functional effects of protein variants: protein sequence/profile-based information and the evolutionary estimation of the selective pressures at the codon level. The results of large-scale application of SeqProfCod over all annotated point mutations in SWISS-PROT (available for download at http://sgu.bioinfo.cipf.es/services/Omidios/; last accessed: 24 August 2007), could be used to support clinical studies.}, keywords = {Algorithms Codon/genetics Computational Biology/*methods *DNA Mutational Analysis Databases, Human Humans Iduronic Acid/analogs \& derivatives/metabolism *Point Mutation Polymorphism, Molecular *Genetic Predisposition to Disease Genetic Variation Genome, Protein *Evolution, Single Nucleotide Proteins/chemistry/*genetics Tumor Suppressor Protein p53/genetics}, url = {http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve\&db=PubMed\&dopt=Citation\&list_uids=17935148}, author = {E. Capriotti and Arbiza, L. and Casadio, R. and Dopazo, J. and H. Dopazo and M. A. Marti-Renom} } @article {600, title = {Use of estimated evolutionary strength at the codon level improves the prediction of disease-related protein mutations in humans.}, journal = {Hum Mutat}, volume = {29}, year = {2008}, month = {2008 Jan}, pages = {198-204}, abstract = {

Predicting the functional impact of protein variation is one of the most challenging problems in bioinformatics. A rapidly growing number of genome-scale studies provide large amounts of experimental data, allowing the application of rigorous statistical approaches for predicting whether a given single point mutation has an impact on human health. Up until now, existing methods have limited their source data to either protein or gene information. Novel in this work, we take advantage of both and focus on protein evolutionary information by using estimated selective pressures at the codon level. Here we introduce a new method (SeqProfCod) to predict the likelihood that a given protein variant is associated with human disease or not. Our method relies on a support vector machine (SVM) classifier trained using three sources of information: protein sequence, multiple protein sequence alignments, and the estimation of selective pressure at the codon level. SeqProfCod has been benchmarked with a large dataset of 8,987 single point mutations from 1,434 human proteins from SWISS-PROT. It achieves 82\% overall accuracy and a correlation coefficient of 0.59, indicating that the estimation of the selective pressure helps in predicting the functional impact of single-point mutations. Moreover, this study demonstrates the synergic effect of combining two sources of information for predicting the functional effects of protein variants: protein sequence/profile-based information and the evolutionary estimation of the selective pressures at the codon level. The results of large-scale application of SeqProfCod over all annotated point mutations in SWISS-PROT (available for download at http://sgu.bioinfo.cipf.es/services/Omidios/; last accessed: 24 August 2007), could be used to support clinical studies.

}, keywords = {Algorithms, Codon, Computational Biology, Databases, Protein, DNA Mutational Analysis, Evolution, Molecular, Genetic Predisposition to Disease, Genetic Variation, Genome, Human, Humans, Iduronic Acid, Point Mutation, Polymorphism, Single Nucleotide, Proteins, Tumor Suppressor Protein p53}, issn = {1098-1004}, doi = {10.1002/humu.20628}, author = {Capriotti, Emidio and Arbiza, Leonardo and Casadio, Rita and Dopazo, Joaquin and Dopazo, Hern{\'a}n and Marti-Renom, Marc A} } @inbook {491, title = {Use of GO Terms to Understand the Biological Significance of Microarray Differential Gene Expression Data}, booktitle = {Microarray data analysis III}, year = {2003}, pages = {233-247}, publisher = {Kluwer Academic, K. F. Johnson and S. M. Lin}, organization = {Kluwer Academic, K. F. Johnson and S. M. Lin}, author = {D{\'\i}az-Uriarte, R and Fatima Al-Shahrour and Dopazo, J.} } @inbook {492, title = {Using Gene Ontology on genome-scale studies to find significant associations of biologically relevant terms to group of genes}, booktitle = {Neural Networks for Signal Processing XIII}, year = {2003}, pages = {43-52}, publisher = {IEEE Press}, organization = {IEEE Press}, address = {New York, USA}, keywords = {babelomics}, author = {Fatima Al-Shahrour and Herrero, J. and A. Mateos and J. Santoyo and D{\'\i}az-Uriarte, R and Dopazo, J.} } @conference {556, title = {Unsupervised reduction of the dimensionality followed by supervised learning with a perceptron improves the classification of conditions in DNA microarray gene expression data}, booktitle = {Neural Networks for Signal Processing XII. 2002 IEEE Signal Processing Society WorkshopProceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing}, year = {2002}, publisher = {IEEE}, organization = {IEEE}, address = {Martigny, Switzerland}, doi = {10.1109/NNSP.2002.1030019}, url = {http://ieeexplore.ieee.org/document/1030019/http://xplorestaging.ieee.org/ielx5/8007/22134/01030019.pdf?arnumber=1030019}, author = {Conde, L. and Mateos, A. and Herrero, J. and Dopazo, J.} } @article {12471146, title = {Use of single point mutations in domain I of beta 2-glycoprotein I to determine fine antigenic specificity of antiphospholipid autoantibodies}, journal = {J Immunol}, volume = {169}, number = {12}, year = {2002}, note = {Iverson, G Michael Reddel, Stephen Victoria, Edward J Cockerill, Keith A Wang, Ying-Xia Marti-Renom, Marc A Sali, Andrej Marquis, David M Krilis, Steven A Linnik, Matthew D GM54762/GM/NIGMS NIH HHS/United States Research Support, Non-U.S. Gov{\textquoteright}t Research Support, U.S. Gov{\textquoteright}t, P.H.S. United States Journal of immunology (Baltimore, Md. : 1950) J Immunol. 2002 Dec 15;169(12):7097-103.}, pages = {7097-103}, abstract = {Autoantibodies against beta(2)-glycoprotein I (beta(2)GPI) appear to be a critical feature of the antiphospholipid syndrome (APS). As determined using domain deletion mutants, human autoantibodies bind to the first of five domains present in beta(2)GPI. In this study the fine detail of the domain I epitope has been examined using 10 selected mutants of whole beta(2)GPI containing single point mutations in the first domain. The binding to beta(2)GPI was significantly affected by a number of single point mutations in domain I, particularly by mutations in the region of aa 40-43. Molecular modeling predicted these mutations to affect the surface shape and electrostatic charge of a facet of domain I. Mutation K19E also had an effect, albeit one less severe and involving fewer patients. Similar results were obtained in two different laboratories using affinity-purified anti-beta(2)GPI in a competitive inhibition ELISA and with whole serum in a direct binding ELISA. This study confirms that anti-beta(2)GPI autoantibodies bind to domain I, and that the charged surface patch defined by residues 40-43 contributes to a dominant target epitope.}, keywords = {Amino Acid Substitution/genetics Antibodies, Antibody/genetics Binding, Antiphospholipid/blood/*metabolism Antibodies, Competitive/genetics/immunology Enzyme-Linked Immunosorbent Assay/methods Epitopes/analysis/*immunology/metabolism Glycine/genetics Glycoproteins/biosynthesis/*genetics/*immunology/isolation \& purification/metabolism Humans Models, Molecular Peptide Fragments/genetics/immunology/isolation \& purification/metabolism *Point Mutation Protein Structure, Monoclonal/blood/metabolism Antiphospholipid Syndrome/immunology Arginine/genetics *Binding Sites, Tertiary/genetics Recombinant Proteins/biosynthesis/immunology/isolation \& purification/metabolism Static Electricity beta 2-Glycoprotein I}, url = {http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve\&db=PubMed\&dopt=Citation\&list_uids=12471146}, author = {Iverson, G. M. and Reddel, S. and Victoria, E. J. and Cockerill, K. A. and Wang, Y. X. and M. A. Marti-Renom and Sali, A. and Marquis, D. M. and Krilis, S. A. and Linnik, M. D.} } @inbook {496, title = {Using perceptrons for supervised classification of DNA microarray samples: obtaining the optimal level of information and finding differentially expressed genes}, booktitle = {ICANN 2002, LNCS 2415}, year = {2002}, pages = {577-582}, publisher = {J.R. Dorronsoro}, organization = {J.R. Dorronsoro}, author = {A. Mateos and Herrero, J. and Dopazo, J.} }