%0 Journal Article %J Mathematics %D 2021 %T Deciphering Genomic Heterogeneity and the Internal Composition of Tumour Activities through a Hierarchical Factorisation Model %A Carbonell-Caballero, José %A López-Quílez, Antonio %A Conesa, David %A Dopazo, Joaquin %B Mathematics %V 9 %P 2833 %8 Jan-11-2021 %G eng %U https://www.mdpi.com/2227-7390/9/21/2833https://www.mdpi.com/2227-7390/9/21/2833/pdf %N 21 %! Mathematics %R 10.3390/math9212833 %0 Journal Article %J Brief Bioinform %D 2019 %T A comparison of mechanistic signaling pathway activity analysis methods. %A Amadoz, Alicia %A Hidalgo, Marta R %A Cubuk, Cankut %A Carbonell-Caballero, José %A Dopazo, Joaquin %K Algorithms %K Humans %K Postmortem Changes %K Signal Transduction %K Systems biology %K Transcriptome %X

Understanding the aspects of cell functionality that account for disease mechanisms or drug modes of action is a main challenge for precision medicine. Classical gene-based approaches ignore the modular nature of most human traits, whereas conventional pathway enrichment approaches produce only illustrative results of limited practical utility. Recently, a family of new methods has emerged that change the focus from the whole pathways to the definition of elementary subpathways within them that have any mechanistic significance and to the study of their activities. Thus, mechanistic pathway activity (MPA) methods constitute a new paradigm that allows recoding poorly informative genomic measurements into cell activity quantitative values and relate them to phenotypes. Here we provide a review on the MPA methods available and explain their contribution to systems medicine approaches for addressing challenges in the diagnostic and treatment of complex diseases.

%B Brief Bioinform %V 20 %P 1655-1668 %8 2019 09 27 %G eng %N 5 %1 https://www.ncbi.nlm.nih.gov/pubmed/29868818?dopt=Abstract %R 10.1093/bib/bby040 %0 Journal Article %J NPJ Syst Biol Appl %D 2019 %T Differential metabolic activity and discovery of therapeutic targets using summarized metabolic pathway models. %A Cubuk, Cankut %A Hidalgo, Marta R %A Amadoz, Alicia %A Rian, Kinza %A Salavert, Francisco %A Pujana, Miguel A %A Mateo, Francesca %A Herranz, Carmen %A Carbonell-Caballero, José %A Dopazo, Joaquin %K Computational Biology %K Computer Simulation %K Drug discovery %K Gene Regulatory Networks %K Humans %K Internet %K Metabolic Networks and Pathways %K Models, Biological %K Neoplasms %K Phenotype %K Software %K Transcriptome %X

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.

%B NPJ Syst Biol Appl %V 5 %P 7 %8 2019 %G eng %1 https://www.ncbi.nlm.nih.gov/pubmed/30854222?dopt=Abstract %R 10.1038/s41540-019-0087-2 %0 Journal Article %J Nat Commun %D 2018 %T The effects of death and post-mortem cold ischemia on human tissue transcriptomes. %A Ferreira, Pedro G %A Muñoz-Aguirre, Manuel %A Reverter, Ferran %A Sá Godinho, Caio P %A Sousa, Abel %A Amadoz, Alicia %A Sodaei, Reza %A Hidalgo, Marta R %A Pervouchine, Dmitri %A Carbonell-Caballero, José %A Nurtdinov, Ramil %A Breschi, Alessandra %A Amador, Raziel %A Oliveira, Patrícia %A Cubuk, Cankut %A Curado, João %A Aguet, François %A Oliveira, Carla %A Dopazo, Joaquin %A Sammeth, Michael %A Ardlie, Kristin G %A Guigó, Roderic %K Blood %K Cold Ischemia %K Death %K Female %K gene expression %K Humans %K Models, Biological %K Postmortem Changes %K RNA, Messenger %K Stochastic Processes %K Transcriptome %X

Post-mortem tissues samples are a key resource for investigating patterns of gene expression. However, the processes triggered by death and the post-mortem interval (PMI) can significantly alter physiologically normal RNA levels. We investigate the impact of PMI on gene expression using data from multiple tissues of post-mortem donors obtained from the GTEx project. We find that many genes change expression over relatively short PMIs in a tissue-specific manner, but this potentially confounding effect in a biological analysis can be minimized by taking into account appropriate covariates. By comparing ante- and post-mortem blood samples, we identify the cascade of transcriptional events triggered by death of the organism. These events do not appear to simply reflect stochastic variation resulting from mRNA degradation, but active and ongoing regulation of transcription. Finally, we develop a model to predict the time since death from the analysis of the transcriptome of a few readily accessible tissues.

%B Nat Commun %V 9 %P 490 %8 2018 02 13 %G eng %N 1 %1 https://www.ncbi.nlm.nih.gov/pubmed/29440659?dopt=Abstract %R 10.1038/s41467-017-02772-x %0 Journal Article %J Cancer Res %D 2018 %T Gene Expression Integration into Pathway Modules Reveals a Pan-Cancer Metabolic Landscape. %A Cubuk, Cankut %A Hidalgo, Marta R %A Amadoz, Alicia %A Pujana, Miguel A %A Mateo, Francesca %A Herranz, Carmen %A Carbonell-Caballero, José %A Dopazo, Joaquin %K Cell Line, Tumor %K Cluster Analysis %K Disease Progression %K Gene Expression Profiling %K Gene Expression Regulation, Neoplastic %K Gene Regulatory Networks %K Humans %K Kaplan-Meier Estimate %K Metabolome %K mutation %K Neoplasms %K Oncogenes %K Phenotype %K Prognosis %K RNA, Small Interfering %K Sequence Analysis, RNA %K Transcriptome %K Treatment Outcome %X

Metabolic reprogramming plays an important role in cancer development and progression and is a well-established hallmark of cancer. Despite its inherent complexity, cellular metabolism can be decomposed into functional modules that represent fundamental metabolic processes. Here, we performed a pan-cancer study involving 9,428 samples from 25 cancer types to reveal metabolic modules whose individual or coordinated activity predict cancer type and outcome, in turn highlighting novel therapeutic opportunities. Integration of gene expression levels into metabolic modules suggests that the activity of specific modules differs between cancers and the corresponding tissues of origin. Some modules may cooperate, as indicated by the positive correlation of their activity across a range of tumors. The activity of many metabolic modules was significantly associated with prognosis at a stronger magnitude than any of their constituent genes. Thus, modules may be classified as tumor suppressors and oncomodules according to their potential impact on cancer progression. Using this modeling framework, we also propose novel potential therapeutic targets that constitute alternative ways of treating cancer by inhibiting their reprogrammed metabolism. Collectively, this study provides an extensive resource of predicted cancer metabolic profiles and dependencies. Combining gene expression with metabolic modules identifies molecular mechanisms of cancer undetected on an individual gene level and allows discovery of new potential therapeutic targets. .

%B Cancer Res %V 78 %P 6059-6072 %8 2018 11 01 %G eng %N 21 %1 https://www.ncbi.nlm.nih.gov/pubmed/30135189?dopt=Abstract %R 10.1158/0008-5472.CAN-17-2705 %0 Journal Article %J Nature %D 2018 %T Genomics of the origin and evolution of Citrus. %A Wu, Guohong Albert %A Terol, Javier %A Ibañez, Victoria %A López-García, Antonio %A Pérez-Román, Estela %A Borredá, Carles %A Domingo, Concha %A Tadeo, Francisco R %A Carbonell-Caballero, José %A Alonso, Roberto %A Curk, Franck %A Du, Dongliang %A Ollitrault, Patrick %A Roose, Mikeal L %A Dopazo, Joaquin %A Gmitter, Frederick G %A Rokhsar, Daniel S %A Talon, Manuel %K Asia, Southeastern %K Biodiversity %K citrus %K Crop Production %K Evolution, Molecular %K Genetic Speciation %K Genome, Plant %K Genomics %K Haplotypes %K Heterozygote %K History, Ancient %K Human Migration %K Hybridization, Genetic %K Phylogeny %X

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.

%B Nature %V 554 %P 311-316 %8 2018 02 15 %G eng %N 7692 %1 https://www.ncbi.nlm.nih.gov/pubmed/29414943?dopt=Abstract %R 10.1038/nature25447 %0 Journal Article %J Biol Direct %D 2018 %T Models of cell signaling uncover molecular mechanisms of high-risk neuroblastoma and predict disease outcome. %A Hidalgo, Marta R %A Amadoz, Alicia %A Cubuk, Cankut %A Carbonell-Caballero, José %A Dopazo, Joaquin %K Computational Biology %K Gene Expression Regulation, Neoplastic %K Humans %K JNK Mitogen-Activated Protein Kinases %K Models, Theoretical %K Neuroblastoma %K Signal Transduction %X

BACKGROUND: Despite the progress in neuroblastoma therapies the mortality of high-risk patients is still high (40-50%) and the molecular basis of the disease remains poorly known. Recently, a mathematical model was used to demonstrate that the network regulating stress signaling by the c-Jun N-terminal kinase pathway played a crucial role in survival of patients with neuroblastoma irrespective of their MYCN amplification status. This demonstrates the enormous potential of computational models of biological modules for the discovery of underlying molecular mechanisms of diseases.

RESULTS: Since signaling is known to be highly relevant in cancer, we have used a computational model of the whole cell signaling network to understand the molecular determinants of bad prognostic in neuroblastoma. Our model produced a comprehensive view of the molecular mechanisms of neuroblastoma tumorigenesis and progression.

CONCLUSION: We have also shown how the activity of signaling circuits can be considered a reliable model-based prognostic biomarker.

REVIEWERS: This article was reviewed by Tim Beissbarth, Wenzhong Xiao and Joanna Polanska. For the full reviews, please go to the Reviewers' comments section.

%B Biol Direct %V 13 %P 16 %8 2018 08 22 %G eng %N 1 %1 https://www.ncbi.nlm.nih.gov/pubmed/30134948?dopt=Abstract %R 10.1186/s13062-018-0219-4 %0 Journal Article %J PLoS One %D 2018 %T The modular network structure of the mutational landscape of Acute Myeloid Leukemia. %A Ibáñez, Mariam %A Carbonell-Caballero, José %A Such, Esperanza %A García-Alonso, Luz %A Liquori, Alessandro %A López-Pavía, María %A LLop, Marta %A Alonso, Carmen %A Barragán, Eva %A Gómez-Seguí, Inés %A Neef, Alexander %A Hervás, David %A Montesinos, Pau %A Sanz, Guillermo %A Sanz, Miguel Angel %A Dopazo, Joaquin %A Cervera, José %K Adult %K Aged %K Cytodiagnosis %K Female %K Gene Regulatory Networks %K Genetic Association Studies %K Genetic Heterogeneity %K Humans %K Karyotype %K Leukemia, Myeloid, Acute %K Male %K Middle Aged %K mutation %K Neoplasm Proteins %K Nucleophosmin %K Prognosis %K whole exome sequencing %X

Acute myeloid leukemia (AML) is associated with the sequential accumulation of acquired genetic alterations. Although at diagnosis cytogenetic alterations are frequent in AML, roughly 50% of patients present an apparently normal karyotype (NK), leading to a highly heterogeneous prognosis. Due to this significant heterogeneity, it has been suggested that different molecular mechanisms may trigger the disease with diverse prognostic implications. We performed whole-exome sequencing (WES) of tumor-normal matched samples of de novo AML-NK patients lacking mutations in NPM1, CEBPA or FLT3-ITD to identify new gene mutations with potential prognostic and therapeutic relevance to patients with AML. Novel candidate-genes, together with others previously described, were targeted resequenced in an independent cohort of 100 de novo AML patients classified in the cytogenetic intermediate-risk (IR) category. A mean of 4.89 mutations per sample were detected in 73 genes, 35 of which were mutated in more than one patient. After a network enrichment analysis, we defined a single in silico model and established a set of seed-genes that may trigger leukemogenesis in patients with normal karyotype. The high heterogeneity of gene mutations observed in AML patients suggested that a specific alteration could not be as essential as the interaction of deregulated pathways.

%B PLoS One %V 13 %P e0202926 %8 2018 %G eng %N 10 %1 https://www.ncbi.nlm.nih.gov/pubmed/30303964?dopt=Abstract %R 10.1371/journal.pone.0202926 %0 Journal Article %J Oncotarget %D 2017 %T High throughput estimation of functional cell activities reveals disease mechanisms and predicts relevant clinical outcomes. %A Hidalgo, Marta R %A Cubuk, Cankut %A Amadoz, Alicia %A Salavert, Francisco %A Carbonell-Caballero, José %A Dopazo, Joaquin %K Computational Biology %K gene expression %K Gene Regulatory Networks %K Humans %K mutation %K Neoplasms %K Precision Medicine %K Sequence Analysis, RNA %K Signal Transduction %X

Understanding the aspects of the cell functionality that account for disease or drug action mechanisms is a main challenge for precision medicine. Here we propose a new method that models cell signaling using biological knowledge on signal transduction. The method recodes individual gene expression values (and/or gene mutations) into accurate measurements of changes in the activity of signaling circuits, which ultimately constitute high-throughput estimations of cell functionalities caused by gene activity within the pathway. Moreover, such estimations can be obtained either at cohort-level, in case/control comparisons, or personalized for individual patients. The accuracy of the method is demonstrated in an extensive analysis involving 5640 patients from 12 different cancer types. Circuit activity measurements not only have a high diagnostic value but also can be related to relevant disease outcomes such as survival, and can be used to assess therapeutic interventions.

%B Oncotarget %V 8 %P 5160-5178 %8 2017 Jan 17 %G eng %N 3 %1 https://www.ncbi.nlm.nih.gov/pubmed/28042959?dopt=Abstract %R 10.18632/oncotarget.14107 %0 Journal Article %J Bioinformatics %D 2017 %T Reference genome assessment from a population scale perspective: an accurate profile of variability and noise. %A Carbonell-Caballero, José %A Amadoz, Alicia %A Alonso, Roberto %A Hidalgo, Marta R %A Cubuk, Cankut %A Conesa, David %A López-Quílez, Antonio %A Dopazo, Joaquin %K Animals %K Genetic Variation %K Genome %K Genomics %K Genotype %K Humans %K Models, Statistical %K Quality Control %K Reproducibility of Results %K Software %X

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.

%B Bioinformatics %V 33 %P 3511-3517 %8 2017 Nov 15 %G eng %U https://academic.oup.com/bioinformatics/article-lookup/doi/10.1093/bioinformatics/btx482 %N 22 %1 https://www.ncbi.nlm.nih.gov/pubmed/28961772?dopt=Abstract %R 10.1093/bioinformatics/btx482 %0 Journal Article %J Nucleic acids research %D 2016 %T Actionable pathways: interactive discovery of therapeutic targets using signaling pathway models. %A Salavert, Francisco %A Hidago, Marta R %A Amadoz, Alicia %A Cubuk, Cankut %A Medina, Ignacio %A Crespo, Daniel %A Carbonell-Caballero, José %A Joaquín Dopazo %K actionable genes %K Disease mechanism %K drug action mechanism %K Drug discovery %K pathway analysis %K personalized medicine %K signalling %K therapeutic targets %X The discovery of actionable targets is crucial for targeted therapies and is also a constituent part of the drug discovery process. The success of an intervention over a target depends critically on its contribution, within the complex network of gene interactions, to the cellular processes responsible for disease progression or therapeutic response. Here we present PathAct, a web server that predicts the effect that interventions over genes (inhibitions or activations that simulate knock-outs, drug treatments or over-expressions) can have over signal transmission within signaling pathways and, ultimately, over the cell functionalities triggered by them. PathAct implements an advanced graphical interface that provides a unique interactive working environment in which the suitability of potentially actionable genes, that could eventually become drug targets for personalized or individualized therapies, can be easily tested. The PathAct tool can be found at: http://pathact.babelomics.org. %B Nucleic acids research %8 2016 May 2 %G eng %U http://nar.oxfordjournals.org/content/early/2016/05/02/nar.gkw369.full %R 10.1093/nar/gkw369 %0 Journal Article %J PLoS One %D 2016 %T The Mutational Landscape of Acute Promyelocytic Leukemia Reveals an Interacting Network of Co-Occurrences and Recurrent Mutations. %A Ibáñez, Mariam %A Carbonell-Caballero, José %A García-Alonso, Luz %A Such, Esperanza %A Jiménez-Almazán, Jorge %A Vidal, Enrique %A Barragán, Eva %A López-Pavía, María %A LLop, Marta %A Martín, Iván %A Gómez-Seguí, Inés %A Montesinos, Pau %A Sanz, Miguel A %A Dopazo, Joaquin %A Cervera, José %K Exome %K Gene Regulatory Networks %K Genome, Human %K Humans %K INDEL Mutation %K Leukemia, Promyelocytic, Acute %K mutation %K Mutation Rate %K Polymorphism, Single Nucleotide %K Reproducibility of Results %X

Preliminary Acute Promyelocytic Leukemia (APL) whole exome sequencing (WES) studies have identified a huge number of somatic mutations affecting more than a hundred different genes mainly in a non-recurrent manner, suggesting that APL is a heterogeneous disease with secondary relevant changes not yet defined. To extend our knowledge of subtle genetic alterations involved in APL that might cooperate with PML/RARA in the leukemogenic process, we performed a comprehensive analysis of somatic mutations in APL combining WES with sequencing of a custom panel of targeted genes by next-generation sequencing. To select a reduced subset of high confidence candidate driver genes, further in silico analysis were carried out. After prioritization and network analysis we found recurrent deleterious mutations in 8 individual genes (STAG2, U2AF1, SMC1A, USP9X, IKZF1, LYN, MYCBP2 and PTPN11) with a strong potential of being involved in APL pathogenesis. Our network analysis of multiple mutations provides a reliable approach to prioritize genes for additional analysis, improving our knowledge of the leukemogenesis interactome. Additionally, we have defined a functional module in the interactome of APL. The hypothesis is that the number, or the specific combinations, of mutations harbored in each patient might not be as important as the disturbance caused in biological key functions, triggered by several not necessarily recurrent mutations.

%B PLoS One %V 11 %P e0148346 %8 2016 %G eng %N 2 %1 https://www.ncbi.nlm.nih.gov/pubmed/26886259?dopt=Abstract %R 10.1371/journal.pone.0148346 %0 Journal Article %J Scientific reports %D 2016 %T The pan-cancer pathological regulatory landscape. %A Falco, Matias M %A Bleda, Marta %A Carbonell-Caballero, José %A Joaquín Dopazo %X Dysregulation of the normal gene expression program is the cause of a broad range of diseases, including cancer. Detecting the specific perturbed regulators that have an effect on the generation and the development of the disease is crucial for understanding the disease mechanism and for taking decisions on efficient preventive and curative therapies. Moreover, detecting such perturbations at the patient level is even more important from the perspective of personalized medicine. We applied the Transcription Factor Target Enrichment Analysis, a method that detects the activity of transcription factors based on the quantification of the collective transcriptional activation of their targets, to a large collection of 5607 cancer samples covering eleven cancer types. We produced for the first time a comprehensive catalogue of altered transcription factor activities in cancer, a considerable number of them significantly associated to patient’s survival. Moreover, we described several interesting TFs whose activity do not change substantially in the cancer with respect to the normal tissue but ultimately play an important role in patient prognostic determination, which suggest they might be promising therapeutic targets. An additional advantage of this method is that it allows obtaining personalized TF activity estimations for individual patients. %B Scientific reports %V 6 %P 39709 %8 2016 Dec 21 %G eng %U http://www.nature.com/articles/srep39709 %R 10.1038/srep39709 %0 Journal Article %J Scientific Reports %D 2016 %T The pan-cancer pathological regulatory landscape %A Falco, Matias M. %A Bleda, Marta %A Carbonell-Caballero, José %A Dopazo, Joaquin %B Scientific Reports %V 6 %8 Jan-12-2016 %G eng %U http://www.nature.com/articles/srep39709http://www.nature.com/articles/srep39709.pdfhttp://www.nature.com/articles/srep39709.pdfhttp://www.nature.com/articles/srep39709 %N 1 %! Sci Rep %R 10.1038/srep39709 %0 Journal Article %J Nucleic acids research %D 2015 %T Babelomics 5.0: functional interpretation for new generations of genomic data. %A Alonso, Roberto %A Salavert, Francisco %A Garcia-Garcia, Francisco %A Carbonell-Caballero, José %A Bleda, Marta %A García-Alonso, Luz %A Sanchis-Juan, Alba %A Perez-Gil, Daniel %A Marin-Garcia, Pablo %A Sánchez, Rubén %A Cubuk, Cankut %A Hidalgo, Marta R %A Amadoz, Alicia %A Hernansaiz-Ballesteros, Rosa D %A Alemán, Alejandro %A Tárraga, Joaquín %A Montaner, David %A Medina, Ignacio %A Dopazo, Joaquin %K babelomics %K data integration %K gene set analysis %K interactome %K network analysis %K NGS %K RNA-seq %K Systems biology %K transcriptomics %X Babelomics has been running for more than one decade offering a user-friendly interface for the functional analysis of gene expression and genomic data. Here we present its fifth release, which includes support for Next Generation Sequencing data including gene expression (RNA-seq), exome or genome resequencing. Babelomics has simplified its interface, being now more intuitive. Improved visualization options, such as a genome viewer as well as an interactive network viewer, have been implemented. New technical enhancements at both, client and server sides, makes the user experience faster and more dynamic. Babelomics offers user-friendly access to a full range of methods that cover: (i) primary data analysis, (ii) a variety of tests for different experimental designs and (iii) different enrichment and network analysis algorithms for the interpretation of the results of such tests in the proper functional context. In addition to the public server, local copies of Babelomics can be downloaded and installed. Babelomics is freely available at: http://www.babelomics.org. %B Nucleic acids research %V 43 %P W117-W121 %8 2015 Apr 20 %G eng %U http://nar.oxfordjournals.org/content/43/W1/W117 %R 10.1093/nar/gkv384 %0 Journal Article %J Nature methods %D 2015 %T Combining tumor genome simulation with crowdsourcing to benchmark somatic single-nucleotide-variant detection. %A Ewing, Adam D %A Houlahan, Kathleen E %A Hu, Yin %A Ellrott, Kyle %A Caloian, Cristian %A Yamaguchi, Takafumi N %A Bare, J Christopher %A P’ng, Christine %A Waggott, Daryl %A Sabelnykova, Veronica Y %A Kellen, Michael R %A Norman, Thea C %A Haussler, David %A Friend, Stephen H %A Stolovitzky, Gustavo %A Margolin, Adam A %A Stuart, Joshua M %A Boutros, Paul C %E ICGC-TCGA DREAM Somatic Mutation Calling Challenge participants %E Liu Xi %E Ninad Dewal %E Yu Fan %E Wenyi Wang %E David Wheeler %E Andreas Wilm %E Grace Hui Ting %E Chenhao Li %E Denis Bertrand %E Niranjan Nagarajan %E Qing-Rong Chen %E Chih-Hao Hsu %E Ying Hu %E Chunhua Yan %E Warren Kibbe %E Daoud Meerzaman %E Kristian Cibulskis %E Mara Rosenberg %E Louis Bergelson %E Adam Kiezun %E Amie Radenbaugh %E Anne-Sophie Sertier %E Anthony Ferrari %E Laurie Tonton %E Kunal Bhutani %E Nancy F Hansen %E Difei Wang %E Lei Song %E Zhongwu Lai %E Liao, Yang %E Shi, Wei %E Carbonell-Caballero, José %E Joaquín Dopazo %E Cheryl C K Lau %E Justin Guinney %K cancer %K NGS %K variant calling %X The detection of somatic mutations from cancer genome sequences is key to understanding the genetic basis of disease progression, patient survival and response to therapy. Benchmarking is needed for tool assessment and improvement but is complicated by a lack of gold standards, by extensive resource requirements and by difficulties in sharing personal genomic information. To resolve these issues, we launched the ICGC-TCGA DREAM Somatic Mutation Calling Challenge, a crowdsourced benchmark of somatic mutation detection algorithms. Here we report the BAMSurgeon tool for simulating cancer genomes and the results of 248 analyses of three in silico tumors created with it. Different algorithms exhibit characteristic error profiles, and, intriguingly, false positives show a trinucleotide profile very similar to one found in human tumors. Although the three simulated tumors differ in sequence contamination (deviation from normal cell sequence) and in subclonality, an ensemble of pipelines outperforms the best individual pipeline in all cases. BAMSurgeon is available at https://github.com/adamewing/bamsurgeon/. %B Nature methods %8 2015 May 18 %G eng %U http://www.nature.com/nmeth/journal/vaop/ncurrent/full/nmeth.3407.html %R 10.1038/nmeth.3407 %0 Journal Article %J Molecular biology and evolution %D 2015 %T A phylogenetic analysis of 34 chloroplast genomes elucidates the relationships between wild and domestic species within the genus Citrus. %A Carbonell-Caballero, José %A Alonso, Roberto %A Ibañez, Victoria %A Terol, Javier %A Talon, Manuel %A Dopazo, Joaquin %K chloroplast %K citrus %K Phylogeny %K WGS %X Citrus genus includes some of the most important cultivated fruit trees worldwide. Despite being extensively studied because of its commercial relevance, the origin of cultivated citrus species and the history of its domestication still remain an open question. Here we present a phylogenetic analysis of the chloroplast genomes of 34 citrus genotypes which constitutes the most comprehensive and detailed study to date on the evolution and variability of the genus Citrus. A statistical model was used to estimate divergence times between the major citrus groups. Additionally, a complete map of the variability across the genome of different citrus species was produced, including single nucleotide variants, heteroplasmic positions, indels and large structural variants. The distribution of all these variants provided further independent support to the phylogeny obtained. An unexpected finding was the high level of heteroplasmy found in several of the analysed genomes. The use of the complete chloroplast DNA not only paves the way for a better understanding of the phylogenetic relationships within the Citrus genus, but also provides original insights into other elusive evolutionary processes such as chloroplast inheritance, heteroplasmy and gene selection. %B Molecular biology and evolution %V 32 %P 2015-2035 %8 2015 Apr 14 %G eng %U http://mbe.oxfordjournals.org/content/early/2015/04/27/molbev.msv082.full %R 10.1093/molbev/msv082 %0 Journal Article %J Mol Syst Biol %D 2014 %T The role of the interactome in the maintenance of deleterious variability in human populations. %A García-Alonso, Luz %A Jiménez-Almazán, Jorge %A Carbonell-Caballero, José %A Vela-Boza, Alicia %A Santoyo-López, Javier %A Antiňolo, Guillermo %A Dopazo, Joaquin %K Alleles %K Exome %K Gene Library %K Genetic Variation %K Genetics, Population %K Genome, Human %K Genomics %K Humans %K Models, Genetic %K mutation %K Phenotype %K Protein Conformation %K Protein Interaction Maps %K Sequence Analysis, DNA %K Whites %X

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.

%B Mol Syst Biol %V 10 %P 752 %8 2014 Sep 26 %G eng %1 https://www.ncbi.nlm.nih.gov/pubmed/25261458?dopt=Abstract %R 10.15252/msb.20145222 %0 Journal Article %J Fungal Genet Biol %D 2014 %T Sequencing and functional analysis of the genome of a nematode egg-parasitic fungus, Pochonia chlamydosporia. %A Larriba, Eduardo %A Jaime, María D L A %A Carbonell-Caballero, José %A Conesa, Ana %A Dopazo, Joaquin %A Nislow, Corey %A Martín-Nieto, José %A Lopez-Llorca, Luis Vicente %K Animals %K Ascomycota %K Female %K Gene Expression Regulation, Fungal %K Gene ontology %K Genome, Fungal %K Hordeum %K Host-Pathogen Interactions %K Nematoda %K Ovum %K Phylogeny %K Plant Roots %K Sequence Analysis, DNA %K Signal Transduction %K Transcriptome %X

Pochonia chlamydosporia is a worldwide-distributed soil fungus with a great capacity to infect and destroy the eggs and kill females of plant-parasitic nematodes. Additionally, it has the ability to colonize endophytically roots of economically-important crop plants, thereby promoting their growth and eliciting plant defenses. This multitrophic behavior makes P. chlamydosporia a potentially useful tool for sustainable agriculture approaches. We sequenced and assembled ∼41 Mb of P. chlamydosporia genomic DNA and predicted 12,122 gene models, of which many were homologous to genes of fungal pathogens of invertebrates and fungal plant pathogens. Predicted genes (65%) were functionally annotated according to Gene Ontology, and 16% of them found to share homology with genes in the Pathogen Host Interactions (PHI) database. The genome of this fungus is highly enriched in genes encoding hydrolytic enzymes, such as proteases, glycoside hydrolases and carbohydrate esterases. We used RNA-Seq technology in order to identify the genes expressed during endophytic behavior of P. chlamydosporia when colonizing barley roots. Functional annotation of these genes showed that hydrolytic enzymes and transporters are expressed during endophytism. This structural and functional analysis of the P. chlamydosporia genome provides a starting point for understanding the molecular mechanisms involved in the multitrophic lifestyle of this fungus. The genomic information provided here should also prove useful for enhancing the capabilities of this fungus as a biocontrol agent of plant-parasitic nematodes and as a plant growth-promoting organism.

%B Fungal Genet Biol %V 65 %P 69-80 %8 2014 Apr %G eng %1 https://www.ncbi.nlm.nih.gov/pubmed/24530791?dopt=Abstract %R 10.1016/j.fgb.2014.02.002