dataesr/scientific-paragraphs-categorization huggingface dataset:
2014 ) transcripts to the vervet genome at an average coverage of 98. 8%  (Kapustin et al. 2008). To further assess the vervet ge-nome for quality of gene representation, we aligned with BLAT  (Kent 2002)  de novo assembled transcripts  (Grabherr et al. 2011) , derived from vervet blood, caudate, pituitary, fibroblast, and adrenal RNA-seq data, against the vervet genome reference at a 90% identity threshold, and found that >95% of vervet transcripts constructed in a reference-free manner were represented in the assembly at 95% of their length. Additionally, we produced an independent set of predicted protein-coding genes (19,165) using the Ensembl annotation process  (Flicek et al. 2014 ) that adds additional gene query options.
2004 ); we used these data because the MHC region of the rheMac7 reference  (Zimin et al. 2014 ) contains several errors due to the complex SDs that characterize the MHC of all rhesus macaques examined to date  (Daza-Vamenta et al. 2004; Watanabe et al. 2007). When compared to the rhesus macaque single haploid MHC region  (Daza-Vamenta et al. 2004 ), the vervet displayed highly conserved synteny with only minor structural variation (Fig. 5A ). However, gaps that flank the Chsa-A locus and another localized within the Chsa-B region (the analogous region in the rhesus macaque MHC included a tandem array of nineteen closely related Mamu-B loci) prevented us from drawing definitive conclusions for sequence orientation in some apparently syntenic regions. To assess MHC locus diversity in C. a. sabaeus, we sequenced MHC class I transcripts from 83 vervets.
Unlike other NHPs adopted for the study of human disease, vervets used in biomedical research descend mainly from small founder populations that have expanded from extreme bottlenecks, making them particularly suited for studying the phenotypic impact of structural variations. We have so far evaluated gene-containing deletions and duplications observed in the VRC extended pedigree. In this setting, observed sequence gain has considerably outpaced loss, a pattern similar to that observed in great apes  (Sudmant et al. 2013). Overall, CAE6, a chromosome that attracts a proportionally higher number of SINEs than other vervet chromosomes, displays the most frequent sequence losses. Intriguingly, human Chromosome 19, which is homologous to CAE6, also has, among human chromosomes, incurred the greatest number of losses, based on examination of numerous SV events in the database of genome variation  (Zarrei et al.
All 454 and ABI3730 reads were assembled with the Newbler algorithm (Roche); all Illumina reads were assembled with ALLPATHS  (Gnerre et al. 2011 ). These versions were merged  (Yao et al. 2012) , misassembled regions were identified with BES mapping and corrected, and then data from finished BAC clones (n = 143) were integrated where gaps remained  (Kurtz et al. 2004 ). This error-corrected assembly was used to construct individual chromosome files (Supplemental Methods). We utilized the fully described NCBI gene annotation pipeline (http://www. ncbi. nlm. nih. gov/ genome/annotation_euk/process/) for gene model predictions that were supported by RNA-seq samples not associated with this study: SAMN02356306, SAMN02439943, SAMN02665515, SAMN02665584, SAMN02665615.
Repeats The transposed element landscape was derived by a local version of RepeatMasker (http://www. repeatmasker. org/RMDownload. html) using the latest rhesus monkey transposon library (http://www. girinst. org/server/RepBase/index. php), as detailed in Supplemental Methods. Unique vervet sequences represent the best source for detecting new lineage-specific genomic elements, including active retrotransposons, and were extracted as described in Supplemental Methods. Coordinates of vervet lineage-specific sequences were then correlated with the two-way alignment and repetitive elements from the RepeatMasker report. Overlapped coordinates were then used to calculate the genome-wide coverage of lineage-specific repetitive elements, which were then used to populate a database containing unique vervet regions occupied by more than 69% of repetitive elements (purified from satellite and low complexity DNA) to compile lineage-specific TE insertions and activities (Supplemental Table  S3 ).
An UPGMA (unweighted pair group method with arithmetic mean) tree was constructed which reflects these relationships using the R software package "phangorn"  (Schliep 2011 ). To get from individual relationships to species relationships, the average within-species coalescent time was subtracted from the between-species coalescent time, i. e. , T split = 1/(2µl) × (π between -π within ) was estimated for each pair of subspecies. This estimate assumes that the effective size of each ancestral population before the split was an average of the current effective population sizes (see Supplemental Table  S16 , where a generation time of 8. 5 yr was assumed). These estimates assume instantaneous speciation and the absence of subsequent gene flow. MSMC software in two-haplotype mode  (Schiffels and Durbin 2014)  was used to estimate effective population size among vervet subspecies.
2013). PCR products were purified with AmPure-XP SPRI beads (Beckman Coulter), normalized, and pooled as MID-tagged amplicons. The resulting amplicon pools were subjected to pyrosequencing with a GS Junior instrument (Roche/454). After index binning of quality-filtered sequence data, and assembly of unidirectional contigs of identical sequence reads, BLAST was run to align contigs against a custom allele database of vervet class I sequences.
Data access The vervet genome assembly in this study has been submitted to NCBI GenBank (http://www. ncbi. nlm. nih. gov/assembly/) under assembly accession number GCA_000409795. 2. Deletion variants have been submitted to the NCBI database of genomic structural variation (dbVar; http://www. ncbi. nlm. nih. gov/dbvar) under accession number nstd114. The raw sequence data from this study have been submitted to the NCBI BioProject (http:// www. ncbi. nlm. nih. gov/bioproject) under accession number PRJNA240242.
Acknowledgments Funding to R. K. W. was provided by  NIH-NHGRI  grant  5U54HG00307907. Support for the Vervet Research Colony was provided by  NIH  grant  RR019963/OD010965  to  J. R. K. Funding  to N. B. F. was provided by  NIH  grants  R01RR016300  and  R01OD010980. The  French National Agency for Research on AIDS and Viral Hepatitis (ANRS)  provided funding to M. C. M. -T. Funding to M. R. and R. S. was provided by the  Ministero della Universita' e della Ricerca. Funding to K. D. was provided by  Genome Canada  and  Genome Quebec. B. A. and R. N. acknowledge support from the  Wellcome Trust  (grant number  WT095908 ) and the  European Molecular Biology Laboratory. We thank the following organizations for providing sampling permits and permissions: Ethiopian Wildlife Conservation Authority;  Zambia Wildlife Authority; Wildlife Division, Forestry Commission, Republic of Ghana ; and  Gambia Department of Parks and Wildlife Management.
Results: We present FlexFlux, an application implementing a new way to combine the analysis of both metabolic and regulatory networks, based on simulations that do not require kinetic parameters and can be applied to genome-scale networks. FlexFlux is based on seeking regulatory network steady-states by performing synchronous updates of multi-state qualitative initial values. FlexFlux is then able to use the calculated steady-state values as constraints for metabolic flux analyses using FBA. As input, FlexFlux uses the standards Systems Biology Markup Language (SBML) and SBML Qualitative Models Package ("qual") extension (SBML-qual) file formats and provides a set of FBA based functions.
Conclusions: FlexFlux is an open-source java software with executables and full documentation available online at http://lipm-bioinfo. toulouse. inra. fr/flexflux/. It can be defined as a research tool that enables a better understanding of both regulatory and metabolic networks based on steady-state simulations. FlexFlux integrates well in the flux analysis ecosystem thanks to the support of standard file formats and can thus be used as a complementary tool to existing software featuring other types of analyses.
Different FBA-based analyses are implemented in a number of available software including COBRA Toolbox  [3] , COBRApy  [4] , Cell-NetAnalyser  [5] , SurreyFBA  [6] , OptFlux  [7] , FASIMU  [8]  and SBRT  [9]. For a review of differences and specificities of some of these tools, see  [10]. However, metabolic network analysis alone cannot explain the differences observed between two differentiated cells of an organism, or the behaviour of a versatile micro-organism given a particular environmental condition. Indeed, cells have evolved regulatory networks to integrate environmental signals or acquired differentiated states that result in modulation of gene expression. Therefore this specific gene expression triggers specific phenotypes depending on the environmental constraints or cell differentiation.
In these types of models, the components display a finite number of possible states, and their values are updated via logical rules composed of the states of other components. This makes the search for steadystates easier than with continuous models. They have the advantage of not requiring any kinetic parameter for simulations, like FBA, and they provide more modelling precision than Boolean models. A growing number of qualitative networks is available to the community  [16]  through platforms like The Cell Collective  [17]. This sharing of qualitative models is facilitated by the development of SBML-qual  [18] , a standard XML (Extended Markup Language) based format designed to represent multi-state qualitative models based on the SBML format  [19]. Some software tools have integrated this format and can perform qualitative network analyses : The Cell Collective  [17] , CellNetAnalyser  [5] , GINsim  [20] , CellNOpt  [21]  and BoolNet  [22].
Flux variability analysis (FVA) The goal of this function is to compute the maximal and minimal values of all reaction fluxes, or a set of fluxes, when the objective function is optimized. To do that, FlexFlux performs a regulatory steady-state analysis, a FBA with the provided objective function. Then, the optimal value of the objective function is set as a constraint, and all reactions are consecutively minimised and maximised as objective functions of new FBA. FlexFlux's implementation of FVA uses the fastFVA approach  [31]. When applied to a metabolic network of around 2000 reactions, it can be performed in less than 15 s on a computer with one processor (Intel Core™ i5-4590). A comparison with the computation time of FVA in other FBA software is detailed in Additional file 1 (Table  S1 ).
Sensitivity to the number of simulations was assessed by simulating various number of initial conditions. We obtained a logarithmic curve suggesting a correct sampling for 100. 000 initial conditions (Fig. 5 ). This shows that, in the presence of a network for which it is not possible to test every possible initial condition (2 139 initial conditions are possible for this network), FlexFlux is able to pinpoint the dominant states that the network can reach. These dominant states may then be used to constrain a metabolic network and perform flux balance analyses. We checked the accuracy of the RSA method on this Boolean network using BoolNet, and found identical results. Also this result shows that FlexFlux can run a very high number of RSA in a limited time. We compared the computation time of steady-state analyses in different software in Additional file 1 (Table  S2 ).
This possibility, for the first time implemented in a flux analysis software, can highly refine the modelling of the system. The multi-state qualitative models supported by FlexFlux allow subtle analyses with variables containing more than two possible states, in particular reaction fluxes. Finally, to our knowledge, FlexFlux is the only available tool that uses standard exchange file formats for both networks (SBML and SBML-qual), allowing full compatibility with other FBA or regulatory network analysis tools. In order to determine the regulatory network steadystate, FlexFlux uses a synchronous update of the network state. As explained before, in the synchronous update case, a state completely determines the next one. This means that from a given initial network state, the same attractor will always be found.
For example, negative auto-regulation, which is very common in biological systems  [46] , as well as feedback loops in general, cannot be included in a linear optimisation problem where all equations must be true at the same time. With the FlexFlux steady-state algorithm, feedback loops can be included in the regulatory network. Indeed, in the case of a negative auto-regulation, FlexFlux will reach a cyclic attractor of the regulatory network. FlexFlux already offers 13 functions. All of them support a Regulatory Steady-state Analysis (RSA). FlexFlux code is open-source and thus can be used by other developers to create new functions that use the capabilities described in this paper. In addition, as we show in Additional file 1 (Tables  S1  and S2 ), FlexFlux implementations of successive FBAs and steady-states analysis are among the fastest compared to other software.
Conclusions FlexFlux is a free, open source Java software that joins two types of steady-state based analyses that are usually performed separately: FBA and qualitative multi-state simulations. FlexFlux is the first tool to support both SBML and SBML-qual standards file formats to describe metabolic and regulatory networks respectively. This support ensures compatibility with other FBA software and qualitative network analysis software. Regulatory network simulations are based on synchronous updates of the network state and the result can be translated into continuous intervals used as constraints in the FBA. This allows to easily constrain a flux analysis depending on regulatory network reprogramming. FlexFlux also contains different methods to analyse in detail regulatory and metabolic networks in interaction.
Acknowledgements We acknowledge funding from the  Institut National de la Recherche Agronomique  (grant  31000363 ) and the  Région Midi-Pyrénées  (grant  13050322 ) as well as by the  French Laboratory of Excellence  project " TULIP"  ( ANR-10-LABX-41 ;  ANR-11-IDEX-0002-02 ). RP was supported by  EMBO (Long-Term Fellowship   ALTF 1627-2011 ) and  Marie Curie Actions  ( EMBOCOFUND2010 ,  GA-2010-267146 ). We thank  Stéphane Genin  and  Thomas Schiex  for support and fruitful discussions in apprehending regulatory network features. We would also like to thank  Fabien Jourdan  and  Susana Rivas  for reading the manuscript and providing helpful comments. Received: 18 May 2015 Accepted: 27 November 2015.
Model description ORCHIDEE is a process-based ecosystem model built for simulating global carbon, water and energy fluxes  [30]. ORCHIDEE-GM is a version of this model that has been developed to explicitly represent grassland management such as mowing and livestock density. Grassland management was developed by implementing the management module from PaSim  [28, [31] [32] [33] , a grassland model developed initially for site applications, into ORCHIDEE. The equations that differentiate ORCHIDEE-GM from the standard version of ORCHIDEE are described by Chang et al. [29] , together with a comparison of both model versions at 11 sites equipped with eddy-covariance and biometric measurements. The model assumes the simple principle that farmers will produce the maximum number of animals that can be fed locally by above ground productivity, optimally combining grazing and mowing (local forage production)  [28]  as well as nitrogen fertilizer application, although the model does not explicitly simulate the nitrogen cycle  [29].
Simulation set-up ORCHIDEE-GM is integrated on a grid over Europe using the harmonized climate forcing data from the ERA-WATCH reanalysis for the period 1901-2010 and at a spatial resolution of 25' by 25'  [45]. Mean and standard deviation of the ERA-Interim time series  [46]  were adjusted according to the WATCH time series  [47, 48]  by using the overlapping period 1989-2001. The harmonized dataset was spatially downscaled to 25' by overlapping CRU CL2. 0  [49]  monthly means to the spatial anomaly of the harmonized datasets for each single climatic variable. An altitude-based correction was applied for downscaling surface pressure according to a digital elevation map from CRU CL2. 0. This resolution (25' by 25') is sufficient to represent regional meteorological regimes accurately in low lying regions, but not in mountainous areas.
Besides nitrogen fertilizer application, nitrogen deposition was considered as nitrogen addition as well. Gridded nitrogen deposition rates for Europe was originated from European Monitoring & Evaluation Programme (EMEP) dataset, and was a product of EU-PF7 project (GHG-Europe; data are available at http://gaia. agraria. unitus. it/ ghg-europe/data/others-data) where the decadal means were linear interpolated to annual values. It is noteworthy that the N fixation by legumes was not accounted for in this study. A series of simulations was carried out, and the illustration of the simulation protocol is shown in Fig 2. ORCHIDEE-GM was first run for a spin-up (simulation E1) without management using the first 10 years of the period 1901-1910 recycled in a loop, and atmospheric CO 2 concentration for 1900 (296 ppm) until all carbon pools reached equilibrium (long term Net Ecosystem Exchange, NEE = 0 at each grid point).
Regional census statistics (grassland area, livestock numbers) The European statistical database, Eurostat  [41] , divides the European Union (EU) into regions at three different levels abbreviated as NUTS (Nomenclature des Unités Territoriales Statistiques  [55] ). Above NUTS-1 (referred as NUTS-0 hereafter) is the country level of the Member State. Eurostat  [41]  provides data on grassland area and livestock numbers for NUTS regions  [55]. A full scope Farm Structure Survey (FSS; Council Regulation (EEC) No 571/88, and Regulation 1166/2008) is carried out every 10 years since 1990, with sample surveys every 2 or 3 years. In this study, we compiled data from 30 countries (EU28 plus Norway, and Switzerland) and 279 regions (NUTS-2 level except for Germany, where only NUTS1-level data are available) of Europe.
NPP is a crucial variable in vegetation models. However, high quality measurements of grassland NPP are sparse due to the difficulty of measuring some NPP components such as fine-root production  [57]. A new version of the Luyssaert et al. [58]  database comprising nonforest biomes (Campioli, unpublished) was obtained. NPP data from seven European temperate grassland sites of this database were compared with NPP of cut grassland simulated with ORCHIDEE-GM at their corresponding locations on the grid. The temporal evolution of grassland productivity is not available for most regions. However for some countries, the Eurostat statistical database provides long-term grassland yield data from 1973 onwards, which are directly calculated as harvested production per unit area  [59]. NDVI has been shown to be positively correlated with temperate grassland productivity  [60].
Newly sown temporary grassland has high productivity, and thus is assumed to support the simulated potential livestock density, whereas less productive rough grazing grasslands are assumed to receive little management and to support only 10% of D reg (these grasslands are usually located in mountainous areas with steep slopes and limited accessibility). Combining modeled livestock density with statistical grassland-area data, we produced a simulated number of animals for each region, N sim that can be compared with N obs available from the statistics. Long-term regional time series of grassland area and livestock numbers (N obs ) are not available for the Eurostat NUTS regions. Therefore, country average N obs (also called NUTS-0) from 30 countries from the FAO statistical database  [5]  were used to evaluate modeled trends of N sim over the period from 1961 to 2009.
Grassland productivity Statistical productivity  [41] , assembled by Smit et al. [20]  GIMMS NDVI  [61]  Net primary productivity (NPP) Site NPP observations NPP database (Campioli, unpublished) Livestock density Global livestock distribution maps FAO  [62]  Regional grass-fed livestock numbers Grassland area  [41]  Livestock numbers  [41]  Livestock diet  [3]  doi:10. 1371/journal. pone. 0127554. t001 (slope) close to unity with a high coefficient of determination (R 2 ) and with P < 0. 05 indicates a good model-data agreement. All the statistics in this study were calculated using Interactive Data Language (IDL) software (Version 8. 3 (linux x86_64 m64). (c) 2013, Exelis Visual Information Solutions, Inc. http://www. exelisvis. com/ProductsServices/IDL. aspx).
03% per year for each of them). It implies that the maximum response of photosynthetic capacity to nitrogen addition and the shape of saturating curve (Eq 3) have very limited effects on the trends of potential productivity estimate. The larger uncertainties come from parameters representing photosynthetic and morphological plant traits (Vcmax opt / Jmax opt , and SLA max ), which contribute a productivity uncertainty of 0. 05% per year for each of them. The uncertain values of these parameters could be one of the sources for model-data disagreement when simulating C fluxes at sites  [29]. However, these PFT-specific average plant functional traits in ORCHIDEE, in reality, are highly site-specific, but not with large variation on average. To narrow the uncertainty of the traits-related parameters, improved observation datasets on both mean value at community level (contrary to species level) and on spatial distribution are required.
Meanwhile, these traits are tightly correlated with leaf nitrogen concentrations  [88]  suggesting a possible way to reduce the uncertainty by fully coupling nitrogen and carbon cycles in terrestrial ecosystems  [39]. Livestock density and its ratio with grassland productivity Spatial distribution of grass-fed livestock density estimated by ORCHIDEE-GM is in agreement with the general pattern of actual ruminant livestock distribution given by FAO  [62]  (Fig  5 ). However, because FAO data are densities expressed as livestock per unit of land area suitable for livestock production, data from regions with reduced grassland cover, or with low intensity of grassland utilization by livestock, should not be compared with ORCHIDEE-GM output. The actual livestock density is also restricted by soil quality, terrain slope, and by socioeconomic factors.
Regional numbers of grass-fed livestock Grass-fed livestock numbers simulated by ORCHIDEE-GM are comparable with the data inferred from Eurostat and FAO statistics, yet with some discrepancies (Figs 9 and 10). Modeldata misfits in the numbers of grass-fed livestock units could be caused by: i) the assumption used in Eqs 4 and 5 that grasslands are either cut or grazed, whereas a number of grassland areas in Europe have a mixed use (e. g. , combining a spring or summer cut and several grazing periods per year  [7, 75] , ii) the fact that cut grasslands sustain more head of livestock per hectare than grazed grasslands (since the fraction of the total aboveground dry matter harvested by mowing is greater than with grazing; see  Fig 3 and [32] ). The proportion of cut grasslands (1-F opt , in Eq 4) in the real world could be higher than our optimal value in some regions (e.