dataesr/scientific-paragraphs-categorization huggingface dataset:
irregularis SPs of the fungal secretome database (classes: SP, SP 3 , and SL-  Choi et al. , 2010). To identify putative RiSPs and GrSPs orthologs in other fungi, BLAST-P analyses were performed against the RefSeq with a e-value < 10 -5 with exclusion of glomeromycota proteins  (Altschul et al. , 1990). The 220 R. irregularis and 64 R. clarus candidate effectors described in Sędzielewska Toro and Brachmann (2016) were compared to RiSPs through BLAST-P analysis; a e-value < 10 -5 was selected to define putative orthologous proteins. G. margarita genes were analyzed through our pipeline to identify SPs with a similar approach and compared through BLAST-P analysis to GrSPs; a e-value < 10 -5 was selected to define putative orthologous proteins. The same analysis was performed with P. indica  (Zuccaro et al.
For expression analyses, trimmed pairended reads were mapped onto the transcripts of 872 and 2633 SP genes of R. irregularis and G. rosea, respectively, using CLC Genomics workbench with stringent settings (similarity and length read mapping criteria at 98 and 95% respectively, maximum number of hits for a read on different genes limited to 10). We used the settings "one reference sequence per transcript" and "Maximum number of hits for a read = 10" to define unique and total reads mapping on each transcript, allowing the differenciation of expression patterns of close sequences. The mapped reads for each transcript were calculated and normalized as RPKM for calculating gene expression (reads per kilobase of transcripts per million reads mapped-  Mortazavi et al. , 2008). Intact and broken pairs were both counted as one.
The RPKMs of each transcript in different conditions were compared using proportion-based test statistics  (Baggerly et al. , 2003)  implemented in CLC genomic Workbench suite. This beta-binomial test compares the proportions of counts in a group of samples against those of another group of samples. Different weights are given to the samples, depending on their sizes (total counts). The weights are obtained by assuming a Beta distribution on the proportions in a group, and estimating these, along with the proportion of a binomial distribution, by the method of moments. The result is a weighted t-type test statistic. We then calculated False Discovery Rate (FDR) correction for multiple-hypothesis test  (Benjamini and Hochberg, 1995). Only genes showing a difference of 10 reads between compared conditions were considered as significantly expressed.
Definition of a Consensus Set of SPs of Rhizophagus irregularis DAOM 197198 Two independent genomic assemblies of the same strain of R. irregularis  (DAOM 197198)  were previously published  (Tisserant et al. , 2013; Lin et al. , 2014). Due to incomplete assembly, the deduced catalogs of putative SPs from these two sets of data were partial and very few SPs were found totally identical by direct blast (Table  S1C , see column B). In order to get a more robust global dataset, we performed a bidirectional Genomic mapping and alignment program (GMAP) analysis  (Wu and Watanabe, 2005)  of the two gene repertoires. GMAP allowed the identification of common genes (perfect match), genes unique to each dataset, and genes partly similar, i. e. , truncated or differently defined in each gene repertoire.
clarus available secreted proteins (Table  S1C ). We compared the SPs that we identified from the Gloin1 gene models (726 Gloin1-originating RiSPs) to already characterized R. irregularis Gloin1-SP identified in the Fungal Secretome Database (FSD;  Choi et al. , 2010; Kim et al. , 2016). These proteins were classified in various categories according to their signal peptide predictions (SP, SP 3 , SL, Table  S1C ). Due to differences in pipeline definition (e. g. ,: SPs containing a NLS are discarded in the FSD), 216 out of 726 of our Gloin1-originating RiSPs are absent in the FSD. Within the 872 RiSPs, different sequence features were identified: Presence of PFAM domains, nuclear localization sequences (NLS), cysteine rich regions (SCRs), organization as repeat contain proteins (RCPs), and distribution in protein families (tribes).
Independent biological samples were produced to perform RT-qPCR on ten IRM up-regulated genes in medicago. All of them validated the RNAseq data (Table  S1F ). Considering the 137 up-regulated RiSPs, 84 are up-regulated in the IRM (Figure  2B  right panel), among them 33 are shared between the three hosts (Figure  2B  left panel, Table  S1D ), suggesting that R. irregularis produces a common set of RiSPs irrespectively of the colonized host. Among these 33 RiSPs, a majority (  19 ) are short proteins (< 200 aa) and include 12 SCRs (Table  S1D ), two (RiSP646 and RiSP734) are predicted to possess a NLS and display repeated motifs and six present a PFAM domain. Conversely, one third of RiSPs (28 among 84) are specifically up-regulated in a single host: 7, 9, and 12 have a specific up-regulation in M.
indica, T. melanosporum and L. bicolor (Table  S1C ). Intracellular isoforms were described to participate in the regulation of intercellular pH. For instance, Nce103, a Saccharomyces cerevisiae β-class of carbonic anhydrase, was shown to be important for the fungal growth under ambient air condition and play a role in CO 2 /HCO - 3 homeostasis  (Götz et al. , 1999; Amoroso et al. , 2005; Elleuche and Poggeler, 2010). Secreted fungal carbonic anhydrases are supposed to have a role also in soil acidification  (Thorley et al. , 2015) , thus allowing a better mineral acquisition in ERM. 688 among 872 RiSPs lack PFAM annotation and have no predicted function. We used the MEME software to identify motifs present as internal repeats or shared by different proteins that may unravel functional groups not yet described (Figure  4 ; Figure  S2 ), before sorting them by expression groups.
, 2009) , or internal repeats such as SP7 and Ss-RhS1  (Kloppholz et al. , 2011; Yu et al. , 2016). A total of 41 SP-coding genes containing a NLS motif were identified from the 872 RiSP candidates (Table  S1C ). Only four are up-regulated. RiSP668 and RiSP755 (Figure  7 ) are upregulated in reponse to root exudates, RiSP646 (Figure  5 ) in IRM and RiSP734 in both conditions (Figure  5 ). The software MEME did not identify any significant enriched putative translocation motif in these four proteins or in the 41 NLS-containing RiSPs. Throughout the 872 RiSPs, 141 can be assigned to Small Cysteine Rich (SCR) proteins. The analysis of the 872 RiSPs did not reveal an enrichment of Cysteine-containing motifs compared to the whole genome repertoire. In order to identify the motifs conserved in SCRs, we run the MEME software in this subgroup of proteins but no motif enrichment was observed.
Gigaspora rosea Secreted Proteins Up-Regulated during Symbiosis Previous RNA sequencing data were obtained from G. rosea cultivated in the same conditions as for R. irregularis, except that we were unable to obtain mycorrhized thalli of Lunularia (Figure  S1 ). The G. rosea transcriptome assembly consists of 86332 contigs (mean length: 643 bp; 13318 contigs > 1 kb, 57472 contigs > 300 bp)  (Tang et al. , 2016). Similarly to R. irregularis, we curated G. rosea secretome for the presence of NLS, PFAM domains, SCR and T-reks features and used Blastclust to identify protein tribes. We obtained 2633 GrSPs (Table  S5C ), a number that could be however overestimated as defined from a transcriptome assembly of 86332 non-redundant virtual transcripts that contain probable partial sequences  (Tang et al.
rosea. Secondly, SPs expressed while colonizing host plants were mostly fungal lineage specific. Third, a core set of symbiotic SPs shared by R. irregularis and G. rosea was identified, including putative effector proteins that could target conserved mechanisms in monocots and dicots. This work therefore paves the way to further functional analysis dedicated to proteins specifically or commonly expressed by different glomeromycotan fungi, underlying that data obtained from R. irregularis DAOM 197198 could not always be relevant for all AM fungi as often considered in litterature. Table  S1  | Sequences, structural features, and expression of R. irregularis gene-encoding secreted proteins. (A) Overall quality of RNAseq libraries from R. irregularis in culture or in symbiotic tissues. Triplicate variability (figure) was analyzed by principal component analysis with CLC Genomic Workbench.
For each replicates, the correlation matrix ("normalized" version of the covariance matrix) was calculated to define the orthogonal eigenvectors of the first and second major principal components, showing a simplified version of the variability of data. (B) RNAseq raw expression data of the 872 RiSPs obtained from Illumina Hiseq2000 sequencing (2 x 101 bp). Calculations were performed using CLC Genomics Workbench 9. 0 software (see methods). Control = in vitro germinated spores (except for root exudates assays: control = spores produced in same condition but without root exudates). (C) List of the 872 RiSPs, their structural features and expression level in different comparisons. Column A refers to the best gene models that were identified by comparing Gloin1 and Rir gene models by GMAP. GeneID listed in Column B refers to the non-selected SP sequences (if present in the other assembly) that have similarity with the best SP gene model in column A; GeneID in bold type in column B refer to sequences that are strictly similar (identity and length) to corresponding sequence in Column A.
05). (E) List, expression, and structural features of the 28 RiSPs up-regulated in a single host. Only statistically robust data (fold discovery rate-FDR ≤ 0. 05) are listed. RE, Root Exudates; ERM, Extra-Radical Mycelium; FDR, False Discovery Rate; NLS, Nuclear Localization Signal; PFAM, Protein Family; SCR, Small Cysteine Rich; SP, Signal Peptide. (F) Expression level of 10 RiSPs genes in mycorrhized M. truncatula roots compared to germinating spores of R. irregularis, as defined by RNAseq and RT-qPCR. RNA was extracted with the Qiagen RNAeasy mini kit and cDNA was synthesized using the Promega M-MLV RT enzyme, following the manufacturer instructions. RT-qPCR were performed on a Roche LightCycler 480. Total RNA were extracted from mycorrhized M. truncatula roots and germinating spores, in three independant replicates each.
(C)List of the 2633 GrSPs, structural features and expression level in different comparisons. "Grid"referstonumber of contig as defined inTang et al. (2016). Columns C to O list the sequence features. MEME motifs are described by their number (Figure4) and their occurrence (value within brackets). Expression data are listed in column P to T. Only fold change values statistically validated (fold discovery rate-FDR < 0. 05) are presented (see TableS5Bfor global expression data). Best orthologs with sequence of RiSPs are listed in column V. Putative isoforms are depicted with superscripts number in "Gr id" column, see footnotes. (D) List, expression, and structural features of the 289 GrSPs up-regulated in at least one symbiotic condition. Only statistically robust data (FDR ≤ 0. 05) are listed.
ACKNOWLEDGMENTS This work has been done in the  Plant Science Research Laboratory (LRSV) , part of the  "Laboratoire d'Excellence" (LABEX)  entitled  TULIP  ( ANR-10-LABX-41 ). LK is granted by  Agronutrition (Labège, France). We thanks  S. Roy  and  J. Loubradou  ( Agronutrition, Labège ) for fungal spore production,  P. M. Delaux  for providing the L. cruciata culture and  J. M. Prospéri  ( Centre de Ressources Biologiques M. truncatula, UMR AGAP 1334, Montpellier, France ) for M. truncatula A17 seeds. We are very thankful to  P. M. Delaux  and  G. Bécard  ( LRSV, UMR 5546 CNRS Université Toulouse III, Castanet-Tolosan, France ) for critical reading of the manuscript.
OpenMOLE is a scientific workflow engine with a strong emphasis on workload distribution. Workflows are designed using a high level Domain Specific Language (DSL) built on top of Scala. It exposes natural parallelism constructs to easily delegate the workload resulting from a workflow to a wide range of distributed computing environments. OpenMOLE hides the complexity of designing complex experiments thanks to its DSL. Users can embed their own applications and scale their pipelines from a small prototype running on their desktop computer to a large-scale study harnessing distributed computing infrastructures, simply by changing a single line in the pipeline definition. The construction of the pipeline itself is decoupled from the execution context. The high-level DSL abstracts the underlying execution environment, contrary to classic shell-script based pipelines.
INTRODUCTION 1. Problem Larger sample sizes increase statistical power by reducing the variance of the sampling distribution. With large datasets like the Human Connectome Project 1 (HCP) now freely available, one of the reasons why large studies are not more often conducted is the tremendous amount of computing power required. Distributed computing can offer this processing power but it can be hard to set up a distributed experiment for non-computer scientists. Another important aspect to increase the quality and impact of scientific results is their capacity to be reproduced, especially by a different scientist. Researchers are more and more encouraged to share their experiments and the source code that led to the results they present. In order to be usable by other researchers, experiments have to be organized in a certain way.
Researchers are thus faced with two major problems in order to produce top quality studies: the necessity to provide a reproducible experimental protocol, and the technical challenge to upscale their implemented solutions to cope with large datasets. The whole solution must be made available in a relatively standard way so that other groups can pick up the experiment and re-run against their own set of resources and data. What is the best way to describe experiments so that they can easily be reproduced by other researchers? Workflow, or pipelines, are a common way to model scientific problems involving different tools along multiple distinct stages. Although some initiatives try to unify workflow description  (Amstutz et al. , 2016) , a majority of researchers still compose their pipelines using plain shell scripts.
Proposed Solution OpenMOLE is a generic workflow management solution not targeting a particular community. It allows users to embed their own application, rather than limiting them to a set of prepackaged tools made available for a specific usage. Although this approach requires more involvement from the user's side, it also gives them more flexibility. Further down the line, a pipeline solution tailored for a specific field might not be suitable for multidisciplinary studies. In the specific case of neuroimaging projects, it is not rare to also collect genetics data in order to combine it with the information extracted from the images. Reproducibility and sharing of OpenMOLE workflows start with its Domain Specific Language (DSL) that is used to describe the workflow steps and connections.
The OpenMOLE DSL is an embedded DSL, written as a set of extensions to the Scala programming language. As a superset to Scala, it benefits from all the constructs available in this high-level programming language and harnesses Scala's strong type system to make workflow descriptions more meaningful and less error-prone. As a Scala application, OpenMOLE runs in the Java Virtual Machine (JVM) runtime. This makes it agnostic to its underlying Operating System (OS) and is another step toward sharing OpenMOLE workflows from one user to another, regardless of their work environment. OpenMOLE is built with a strong focus toward the distribution of a pipeline workload to remote computing environments. Pipelines defined within the OpenMOLE framework are totally decoupled from the environments on which they are executed.
It has not been built with this kind of applications in mind and focuses on providing low-level elements ensuring re-executability of a command line on any other Linux machine. However, its possibilities can be harnessed to form the base of a new OpenMOLE task re-executable on multiple environments. Medical imaging pipelines are ideal candidates to evaluate our solution as they typically involve an heterogeneous software ecosystem. These software pieces usually come with a broad set of dependencies that are hard to track manually. They also manipulate large datasets that cannot be embedded in the software container and have to be transferred separately to the execution node running the current stage of the pipeline. The same remark applies to the pipeline's results as can be seen in  Parisot et al.
Community-Tailored Workflow Engines On the other hand, some communities have seen the emergence of tailored workflow managers. For example, the bioinformatics community has developed Taverna  (Oinn et al. , 2004)  and Galaxy  (Goecks et al. , 2010)  for the needs of their community. In the specific case of the neuroimaging field, two main solutions emerge: NiPype  (Gorgolewski et al. , 2011)  and LONI  (Rex et al. , 2003). NiPype is organized around three layers. The most promising one is the top-level common interface that provides a Python abstraction of the main neuroimaging toolkits (FSL, SPM,. ). It is extremely useful to compare equivalent methods across multiple packages. NiPype also offers pipelining possibilities and a basic workload delegation layer only targeting the cluster environments SGE and PBS.
), harnesses efficient middlewares such as Dirac, and automatically manages endto-end data transfer even across heterogeneous computing environments. Compared to other workflow processing engines, OpenMOLE promotes a zero-deployment approach by accessing the computing environments from bare metal, and copies onthe-fly any software component required for a successful remote execution. OpenMOLE also encourages the use of software components developed in heterogeneous programming PSOM Matlab No Generic MIT OpenMOLE Domain Specific Language, Scala Yes Generic AGPL 3 Information was drawn from the web pages in footnote when present, or from the reference paper cited in the section otherwise. languages and enables users to easily replace the elements involved in the workflow.
3. This solution is evaluated with a workflow exploring the performance of different parameter initializations for decoding fMRI acquisitions from a canonical dataset  (Haxby et al. , 2001)  (Section 4). The decoder is taken from the NiLearn 4 https://wiki. galaxyproject. org/. 5 https://taverna. incubator. apache. org/introduction/taverna-features. 6 http://pipeline. loni. usc. edu/explore/features/. 7 https://code. kepler-project. org/code/kepler-docs/trunk/outreach/ documentation/shipping/2. 5/UserManual. pdf. 8 https://pegasus. isi. edu/documentation/execution_environments. php. 9 https://github. com/adraghici/openmole/tree/aws-env. tutorials  (Abraham et al. , 2014)  and demonstrates how a workflow made of a complex combination of Python and native binary dependencies can be successfully reproduced on different computing platforms without any prior knowledge regarding the state of their software stack.
Tasks execution depends on inputs variables, which are provided by the dataflow. Each task produces outputs returned to the dataflow and transmitted to the input of consecutive tasks. OpenMOLE exposes entry points to inject data in the dataflow (sources) and extract useful results at the end of the experiment (hooks). As shown in Figure  1 , OpenMOLE revolves around three main elements: the Applications, the exploration Methods and the support of Massively parallel environments. These three components are put together in a common DSL to describe the workflows. We will give a quick overview of these different components in the subsections. For more details regarding the core implementation and features of OpenMOLE, interested readers can refer to  Reuillon et al. (2010 Reuillon et al. ( , 2013 Reuillon et al.
A DSL to Describe Workflows According to  Barker and Van Hemert (2008) , workflow platforms should not introduce new languages but rely on established ones. OpenMOLE's DSL is based on the high level Scala programming language  (Odersky et al. , 2004 ). OpenMOLE's DSL introduces new operators in the Scala programming language to manage the construction and execution of the workflow. The advantage of this approach lies in the fact that workflows can exist even outside the OpenMOLE environment. As a high-level language, the DSL can be assimilated to an algorithm described in pseudo-code, easily understandable by most scientists. Moreover, it denotes all the types and data used within the workflow, as well as their origin. This reinforces the capacity to reproduce workflow execution both within the OpenMOLE platform or using another tool.
This feature proves very useful when considering the limited availability of a particular resource (shared cluster) or its suitability to process a particular problem 10 https://github. com/openmole/gridscale. (necessity to be processed on a GPU or another type of hardware accelerator). The final workflow description can thus connect tasks using different software components but also running on heterogeneous execution environments thanks to GridScale's large support of HPC platforms. The execution platform of OpenMOLE has proved to be robust enough to manage no less than half a billion instances  (Schmitt et al. , 2015)  of a task delegated to the European Grid Infrastructure (EGI).
EVALUATION OF THE REPRODUCIBILITY OF A NEUROIMAGING WORKFLOW We will evaluate the reproducibility enabled by the CARETask using an fMRI decoder on the Haxby dataset  (Haxby et al. , 2001). The goal of this experiment is to show that a pipeline intended to run on a local machine and requiring a set of preinstalled dependencies can be re-executed on various distributed computing environments using the CARETask. It validates the choice of the CARE technology to package applications and demonstrates the OpenMOLE integration that enables CARE to be used to reproduce scientific experiments.
Parameter Space Exploration of a Classifier This experiment is based on a tutorial 15  for the NiLearn package  (Abraham et al. , 2014). The example compares different classifiers on a visual object recognition decoding task using the Haxby dataset  (Haxby et al. , 2001). The Haxby dataset consists in the fMRI activity recorded for 6 subjects exposed to various stimuli from different categories. The example evaluates the performance of different parameter initialization of a logistic regression classifier to predict the category the subject is seeing from the fMRI activity. Significant prediction shows that the signal in the region contains information about the corresponding category. We have slightly modified the online example to focus on well-known classifier: the logistic regression. In the NiLearn tutorial, two input parameters vary for this algorithm.
The same parameter ranges are tested for this classifier as detailed in Table  2. In order to obtain comparable results, we have set the seed of the pseudorandom number generator used in the logistic regression to 0. The OpenMOLE workflow for this experiment is made of multiple tasks running both locally and on remote execution nodes as depicted in Figure  3. The initial task asks NiLearn to download the whole dataset from an online repository. An ExplorationTask then determines the parameter space that will be explored in parallel by OpenMOLE. The processing task takes a specific tuple of initialization parameters for the logistic regression from the exploration, along with a single subject as in the original example. Each instance of the processing task computes a leave-one-out cross-validated score for the logistic regression classifier initialized with the given input parameters.