dataesr/scientific-paragraphs-categorization huggingface dataset:
To further characterize gene expression patterns of PCC/PGL tumours, we assayed miRNA expression levels in our cohort by Illumina sequencing. Unsupervised classification using the miRNome data of 172 PCCs/PGLs revealed 7 homogeneous subgroups (Mi1 to Mi7, Fig. 3a ) strongly associated with mRNA expression subgroups (P ¼ 1. 8e À 63, w 2 -test) and mutations in known driver genes (P ¼ 6. 1e À 39, w 2 -test, Fig. 3b ). Clusters Mi1 and Mi2 were more closely associated and corresponded to mRNA expression Cluster C1, whereas clusters Mi3 to Mi7 corresponded to mRNA expression Cluster C2. Clusters Mi1 and Mi2 comprised SDHx-and VHL-mutated tumours, respectively, whereas cluster Mi3 was enriched in sporadic tumours belonging to the C2B mRNA expression cluster. We used the DESeq package  26  to identify differentially expressed miRNAs between the subgroups (Supplementary Data 6).
Germline DNA was extracted from leukocytes using standard protocols. Tumour samples (30-50 mg) were powdered under liquid nitrogen. DNA was extracted and purified using a QIAamp DNA Mini Kit or an Allprep Kit (Qiagen) and RNA was extracted using an RNeasy Mini Kit (Qiagen). miRNA was extracted using a miRNeasy Mini Kit (Qiagen). Concentration of nucleic acids was determined using a Nanodrop ND-1000 instrument (Nyxor Biotech). RNA quality was assessed by electrophoresis on a Bioanalyzer 2100 (Agilent Technologies). A 28S/18S ratio above 1. 5 was considered to rule out degradation. Genetic testing and validation of somatic mutations. Mutation analysis of PCC/PGL susceptibility genes was performed as previously described  [4] [5] [6]. Screening of CDKN2A gene and hotspot mutations in HRAS (exons 2, 3, 4 and 5), TP53 (exons 4, 5, 6, 7, 8, 9, 10 and 11) and MET (exons 2, 14, 16 and 19) genes in the whole cohort, as well as validation of 20 mutations identified by exome sequencing in cancer genes (Supplementary Data 2) were performed by Sanger sequencing.
The somatic nature of mutations was confirmed by sequencing tumour DNA and their paired blood DNA. The primer sequences and PCR conditions are available on request. Analysis of copy-number data. DNA samples from 150 PCC/PGL (145 primary tumours and 5 relapses) were analysed with Illumina HumanCNV610-Quad v1. 0. Hybridization was carried out by Integragen SA (Evry, France), following the manufacturer's recommendations. Raw fluorescent signals were launched into BeadStudio software (Illumina) and normalized  54  to obtain log R ratio and B allele frequency values. Genomic profiles were divided into homogeneous segments by applying the circular binary segmentation algorithm  55  to both log R ratio and B allele frequency values. The ploidy of each sample, the level of contamination with normal cells and the allele-specific copy number of each segment was determined using the Genome Alteration Print method  56.
The overall genomic instability of each sample was quantified as the fraction of aberrant arm score  57. When several samples were available from the same patient, tumour progression trees were reconstructed using the TuMult algorithm  24. Sequencing data processing and mutation detection. Whole-exome sequencing was performed in 30 pairs of PCCs/PGLs and matched normal samples (from blood), and 1 trio with a primary tumour and metastasis. In all tumour samples, the ratio of tumour cells to non-tumour cells was determined to be 450% and this value was confirmed by SNP array analysis. Sequence capture and exome sequencing were performed by Integragen as described elsewhere  57. Briefly, Agilent in-solution enrichment (SureSelect Human All Exon Kit v4 þ UTR) with the provided biotinylated oligonucleotide probe library (Human All Exon v4 þ UTR-70 Mb) was used for DNA sequence capture, enrichment and elution from the tumour and matched blood DNA samples.
The eluted fraction was amplified by PCR and sequenced on an Illumina HiSeq 2000 sequencer as paired-end 75-bp reads. Image analysis and base calling were performed using Illumina Real-Time Analysis Pipeline version 1. 12. 4. 2 with default parameters. Illumina CASAVA 1. 8. 2 software was used to align reads against the hg19 genome build (GRCh37) and to detect SNVs and indels. We used the ELANDv2 algorithm for alignment. Targeted regions were sequenced to an average depth of 80 Â. We applied a previously described pipeline  51  , with some modifications, to generate a list of somatic variants located in coding regions, plus four intronic bases corresponding to consensus splice sites. Quality-control filtering removed variants sequenced in o10 reads, with o3 variant calls or with QPHRED of o10.
Variants were considered to be of somatic origin when the frequency of variant reads was Z10% in the tumour and o5% in the normal counterpart, with a significant enrichment of variant cells in the tumour as assessed through a Fisher's exact test. Common polymorphisms (with reported frequency of 41%) were removed by comparison with dbSNP135, the 1000 Genomes Project database and a proprietary database of exomes from normal tissues. Missense variants were classified as 'benign', 'possibly damaging' or 'probably damaging' using PolyPhen-2 software (http://genetics. bwh. harvard. edu/ pph2/). AlamutVisual Software version 2. 4. 6 (http://www. interactive-biosoftware. com/software. html) was used for nomenclature of gene variations. Somatic mutations occurring in at least two samples were validated visually using the Integrative Genomics Viewer  58.
8. 2 software. Adaptor sequences were removed using mirExpress software  60  and FASTA files for each sample were processed by miRanalyzer0. 3 software 61 , to quantify read counts for each miRNA referenced in mirBase74 v18. In total, 763 miRNAs were expressed (410 reads) in at least 2 samples and were used for unsupervised classification. The miRNA count data were log2 transformed, divided by the total number of reads in each sample and centred on the mean expression level of each gene. Consensus clustering was then performed  62  and the optimal number of clusters (K ¼ 7) was determined from cumulative distribution function (CDF) curves. We used the Bioconductor DESeq package  26  to identify differentially expressed miRNAs. For validation of the expression of miRNA cluster 182/96/183 in tumour tissues and murine cell lines, we used the following TaqMan assays (Applied Biosystems): mmu-miR-183 (ID 002269), mmu-miR-96 (ID 000186), hsa-miR-182 (ID 002334) and mmu-miR-182 (ID 002599), hsa-miR-324-3p (ID 002161), has-miR-320 (ID 002277) and U6 snRNA (ID 001973).
Briefly, 10 ng of RNA previously treated with Turbo DNase was reverse transcribed. Amplifications were performed in a StepOne Plus Real-Time PCR system. The expression of U6 or the mean expression of miR-320 and miR-324 were used as normalization control in murine cells and tumours, respectively. Gene expression data. Gene expression profiles were available from a previous study, in which we identified five stable PGL/PCC subgroups, significantly associated with clinical features and gene mutations  6. HG-U133 Plus 2. 0 Affymetrix GeneChip data from this previous study are available online as ArrayExpress entry E-MTAB-733 (http://www. ebi. ac. uk/arrayexpress/). DNA methylation arrays. Whole-genome DNA methylation was analysed in 145 tumour samples using the Illumina Infinium HumanMethylation27 assay as previously reported  13.
Data from this previous study are accessible through the NCBI's Gene Expression Omnibus series accession number GSE43298 (http:// www. ncbi. nlm. nih. gov/geo). Cellular assays. imCCs  13  were cultured in medium DMEM þ Glutamax (GIBCO) supplemented with 10% fetal bovine serum (GIBCO) and maintained in a humidified 5% CO 2 atmosphere at 37 °C. For stable transfections of miRs, we used the Origen plasmid mmu-miR-183 (catalogue number SC400806), which also contains the mmu-miR-96 sequence, whereas both scrambled and mmu-miR-182 sequences were cloned in pCMV6-A-Puro Vectors. MegaTran 1. 0 was used as transfection reagent (CliniSciences). Clones expressing one of the miRs belonging to the cluster 182/96/183 were obtained by limited dilution assays. For immunofluorescence, microscope slides (Thermo scientific) were coated with Poly-D-lysine (100 mg ml À 1 ) for 3 h at 37 °C and washed with PBS 1 Â without calcium.
Acknowledgements This work is part of the  'Cartes d'Identitedes Tumeurs (CIT)  programme' funded and developed by ' La Ligue Nationale contre le Cancer ' (http://cit. ligue-cancer. net). We thank  Jacqueline Godet  and  Jacqueline Metral  for their strong support within the  CIT Program. We are grateful to  Professors X. Jeunemaitre  and  Xavier Bertagna  for continual support and helpful discussions. We also thank all members of the  Genetics Department and Biological Resources Center and Tumor Bank Platform, Hopital europeen Georges Pompidou  ( BB-0033-00063 ), as well as  Catherine Tritscher  for administrative assistance. The work leading to these results has received funding from the  Agence Nationale de la Recherche  ( ANR 08   GENOPATH 029 MitOxy  and  ANR-2011-JCJC-00701  MODEOMAPP), the  Programme Hospitalier de Recherche Clinique  grant  COMETE 3  ( AOM 06 179 ), the  Plan Cancer : Appel projets Epigntique et Cancer 2013  ( EPIG201303   METABEPIC ) and from the  European Union   Seventh Framework Program  ( FP7/2007-2013)  under grant agreement number  259735.
Introduction Numerical simulation experiments for weather research and operational purposes are among the most computationally demanding applications due to both the huge amount of data involved and the enormous requirements of computing power. Numerical climate models, in addition, require very long simulation times. The management of a weather or climate numerical simulation experiment is a challenging task. This task is often carried out in an ad hoc manner, which cannot be easily adapted to other types of experiments or even to a different computer system  (Redler et al. , 2012). This work presents the implementation and use of a framework, WRF4G, to manage the experiment workflow of a particular numerical weather and climate model, the Weather Research and Forecasting (WRF) modelling system.
Furthermore, WRF is a community model in the public domain and, therefore, benefits from the contributions from an active community (currently, there are more than 25 000 registered users worldwide). Unlike other application-oriented models, WRF provides a flexible and computationally-efficient framework which allows solving a variety of atmosphere simulation problems across different time-scales, from weather forecast to climate change projection. Section 2 provides a brief description of the components of this modelling system, which are essentially shared by other mesoscale atmospheric models. From a computational point of view, climate models are complex applications formed by several components (including pre-and post-processing modules) which are executed sequentially exchanging information through configuration files and data sets.
of simulation experiments found in the literature. The management of this experiment workflow is, again, a time consuming task which requires an execution/monitoring system to abstract and automate the process. This kind of system is usually referred to as a workflow execution framework. There are only few examples of these workflow execution frameworks in the literature. Since the needs are common to all models, the approach given for these frameworks is essentially the same. They provide mechanisms to easily configure, run and monitor different kinds of experiments with a given model, usually on HPC environments. The IC3 autosubmit 1  and WRFPortal 2  allow to execute the EC-Earth and WRF models, respectively. Other experiment workflows incorporate data pre-and post-processing as well. But, these frameworks are devised to work with specific resources (in most cases the one used in the developers institution) and cannot be exported to others.
Back Close Full the other for the scientific experiment to be carried out. Some configuration examples are provided in Sect. 6, covering those experiment workflows introduced in Sect. 3. As the number of simulations increase, users may need to use additional computer resources and there is an additional effort in configuring the model scripts to execute in the new infrastructures. Furthermore, if the user wants to use several computer infrastructures at the same time, new services are needed to schedule and dispatch the jobs according to the status of the resources. Section 5 shows how new computing and data resources can be easily added to WRF4G. In a recent work,  Fernández-Quiruelas et al. (2011)  describe the challenges and limitations posed by climate application workflows on distributed computing infrastructures.
WRF discretizes the vertical dimension into hybrid terrain-following eta levels. The interpolation in the vertical from the native levels of the meteorological data to the eta levels is performed by a program called real, which produces the initial and boundary conditions in the format (NetCDF with WRF metadata) required by the numerical core of the model. This numerical core (wrf) is the last step in the model workflow and solves the dynamics and physics of the atmosphere, producing high-resolution meteorological data. The programs geogrid, ungrib and metgrid are known as the WRF Preprocessing System (WPS) and are configured using a common file (namelist. wps). Likewise, the real and wrf programs are configured using a different, but common, file (namelist. input). This model workflow does not only apply to WRF, but also to many other mesoscale or regional climate models.
Back Close Full 2013). Most of them deal with the model ability to represent the observed climate and to project future changes at regional scale, in response to increased concentrations of greenhouse gases. Regional climate simulations can use a computer resource during months to produce several decades of simulated atmospheric states. This results in huge output data sets. Furthermore, since regional models require not only initial, but also boundary conditions along the whole simulated period, the input files for this type of experiment are also very large. Since a computer resource can hardly be available uninterruptedly for several months, WRF (and all models) has the possibility to be restarted from special files which are created according to the user needs. Therefore, the climate simulation can be divided into parts but, unlike the previous experiment type, these parts are dependent on the previous one, which will produce the restart file required to run the next.
Understanding WRF4G: new concepts The execution of a single simulation with WRF is very vulnerable to a large amount of errors and problems, such as missing or corrupt input data, errors editing the namelist files, etc. These problems can make the model crash in any step of the workflow or, even worse, let the model work with an improper configuration or input data that might affect the scientific results. In the case of ensembles or multi-parameter experiments, if the simulations are handled separately, it is easy to find differences in the configuration of the members, what leads to errors in the output. As the number of simulations of an experiment increases, users may need to use more computer resources. The effort of configuring new infrastructures to run WRF experiments is very high.
WRF execution workflow When a single computing resource is used to run an experiment, data is usually stored locally and data transfer time is negligible compared to the overall job execution time. When more than a single computing resource is used, data has to be accessible from all computing resources. If data repositories and computing resources are not connected through a fast network, data transfers become a bottleneck. To minimize data transfers, WRF4G integrates the postprocessing step (WRF output is filtered to reduce its size) after the simulation execution in the computing node. For the same reason, the geogrid program is not run inside the WRF workflow of WRF4G (see Fig. 1b ). The output generated by geogrid can be recycled in different model runs, as the geographical data are independent from the driving model, time range covered and model configuration (except the resolution).
These data must be produced by the user, and provided to WRF4G to specify the model spatial domain. A preprocessor step has also been included in the WRF4G workflow to convert the input data used as initial and boundary conditions to a format readable by ungrib. The WRF workflow execution in the computing node is driven by a wrapper script in charge of preparing the environment and orchestrating the run. The wrapper contains a monitor that tracks the events occurred during the model execution and updates them in a central database. This wrapper also uploads the restart and post-processed output files to the data respositories as their are being produced.
Back Close Full WRF4G is an open-source and publicly available software that can be downloaded from the Santander Meteorology Group website 6  and deployed in any supported Linux system. Again, the only requirement for the WUI is Python. In order to simplify the learning process, WRF4G provides some examples ready to run in the WUI. In the default WRF4G configuration, the WUI also acts as a WCR and as a WDR. A small repository with the input and boundary datasets needed to run the examples together with a pre-compiled MPI version of WRF is included in the installation. Thus, users can get familiar with WRF4G by running small experiments in their PC. WRF4G runs a database that persists all the experiment information and status (namelist files, toolchain used to create the executables, preprocessor,.
), thus facilitating the reproducibility of the results. Although, the database used by default is MySQL installed with the WRF4G, other instances of MySQL or any other relational database could be used. With the information stored in the database, the status of the experiment execution can be monitored in real-time. WRF4G is layered to separate the experiment design from the execution environment. An atmospheric simulation experiment is defined through two configuration files: one contains the scientific configuration of the experiment (start and end dates, model configuration, experiment setup, input data, postprocessing to apply, etc. ) and the other, the execution environment (number of MPI process to run, memory required, data repositories, etc. ). Below we describe how to configure different kinds of experiments with WRF4G.
Managing experiments with WRF4G Once the configuration files defining the experiments have been created (experiment. wrf4g and resources. wrf4g), the user is ready to prepare the WRF4G environment to run the experiment using the wrf4g_prepare command. In this phase, the experiment. wrf4g configuration is analysed and the details about the resulting realizations and chunks are recorded in the database. WRF configuration files for each of the chunks are also created and transfered to the data repositories pointed by the "WRF4G_BASEPATH" variable in the resources. wrf4g file. Figures.
Back Close Full Then, users can submit the whole experiment (by default) or just some realizations by running the wrf4g_submit command. In this phase, the database is queried to obtain a list of the chunks that need to be run. These chunks are efficiently scheduled to the computing resources taking into consideration the dependencies among them. The experiment (or just some realizations) can be killed at any moment using wrf4g_kill and its status can be monitored with wrf4g_status. Figure  6  shows the output of wrf4g_status when the second Chunk of a experiment with a single realization is downloading the boundary conditions. The granularity of the wrf4g commands allows to handle separately realizations and chunks. In this way, if, for example, one output file generated by a chunk is corrupt, we can rerun just this chunk again.
Conclusions In this paper we present a public domain WRF workflow execution framework (WRF4G 8  ) that automatically manages the above mentioned problems by means of execution, monitoring and data management frameworks which provides transparent access to heterogeneous resources  (local clusters, grid, clouds, HPC, etc. ). Thus, the definition and execution of typical experiments commonly undertaken with WRF is highly simplified. This leads to an enormous save of time and facilitates the access to new infrastructures with no additional overhead. The WRF4G framework is based on layers to separate the experiment design from the execution environment, including a monitoring and management system to easily restart broken simulations until the experiment is completed, and the ability of running these experiments on heterogeneous distributed computing resources concurrently in a transparent way.
Thus, the WRF4G capability to harness powerful computing resources such as grids and supercomputers, allow the WRF community to undertake more ambitious problems with higher impact in society. files for each of the chunks are also created and transfered to the data repositories pointed by the "WRF4G_BASEPATH" variable in the resources. wrf4g file. Then, users can submit the whole experiment (by default) or just some realizations by running the wrf4g_submit command. In this phase, the database is queried to obtain a list of the chunks 365 that need to be run. These chunks are efficiently scheduled to the computing resources taking into consideration the dependencies among them. The experiment (or just some realizations) can be killed at any moment using wrf4g_kill and its status can be monitored with wrf4g_status.
Acknowledgements. This work has been supported by the  Spanish National R&D Plan  under projects  WRF4G  ( CGL2011-28864 , co-funded by the  European Regional Development Fund -ERDF-)  and  CORWES  ( CGL2010-22158-C02-01 ) and the IS- ENES2  project from the  7FP of the European Commission  (grant agreement no. 312979 ). C. Blanco  acknowledges financial support from programa  de Personal Investigador en Formación Predoctoral  from  Universidad de Cantabria , co-funded by the  regional government of Cantabria. The authors are thankful to the developers of third party software (e. g. GridWay, WRFV3, python and NetCDF), which was intensively used in this work.
Introduction The comprehensive elucidation of the multi-level, dynamic molecular networks that underlie the processes of living cells requires the generation of dynamic data at different levels of biological organization, the integration of complementary data types, and the incorporation of prior knowledge  (Ideker et al, 2011; Bar-Joseph et al, 2012). Currently large-scale, dynamic experimental datasets are still scarce, but it can be anticipated, based on rapidly advancing technology, that such data will become abundantly available in the near future. Consequently, the primary challenges for the comprehensive analysis of dynamic molecular networks will shift from data generation toward data and knowledge integration. At present, most molecular network analyses consider a single data type such as transcript, protein, or metabolite abundances.
Functional relations between genes and metabolites were recently inferred with Bayesian networks from metabolome, transcriptome, and other datasets  (Bradley et al, 2009; Zhu et al, 2012). However, simple Bayesian networks cannot consider feedback and are therefore unable to describe the exact nature of most metabolic signaling systems. Consequently, despite the frequent observations that nutritional stimuli modulate signaling and transcriptional networks, the exact nature of most metabolic signals is elusive and their identification remains a challenge  (Wilson & Roach, 2002; Dechant & Peter, 2008; Watson et al, 2013). To infer causal relationships between potential metabolic signals and the kinases and/or transcription factors they regulate, we codesigned a perturbation matrix for the generation of dynamic multi-omics datasets and a probabilistic, model-based analysis method that systematically incorporates prior knowledge.