dataesr/scientific-paragraphs-categorization huggingface dataset:
THE CLIMATE SYSTEM HISTORICAL FORE CAST PROJECT. Leading operational and research centers around the globe thus collectively possess a sizable database of past hindcasts that potentially represents an immensely valuable resource for the research community interested in questions pertaining to seasonal prediction and predictability. For this potential to be realized, the hindcast suites need to be freely and publicly available, using a common format to facilitate their manipulation and analysis. The World Climate Research Programme's (WCRP) Working Group on Subseasonal to Interdecadal Prediction (WGSIP) therefore initiated a project known as the Climate-System Historical Forecast Project (CHFP) to achieve these aims, with the project launched at the WCRP Workshop on Seasonal Prediction in June 2007  (Kirtman and Pirani 2009).
The CHFP invites leading centers to contribute their hindcast suite on a voluntary basis to a common database hosted at Centro de Investigaciones del Mar y la Atmósfera (CIMA) in Argentina. These hindcasts are made freely available for noncommercial purposes through a web portal, while advanced users can access files through wget or Open-Source Project for a Network Data Access Protocol (OPENDAP)-based scripts. A description of the contributing centers and models is given in Table  1. As a guide to data producers, a set of 27 atmospheric and 13 oceanic variables are requested as monthly averages. Although not all centers are able to fulfill the full complement, a common set of nine variables are available as monthly means from all centers. Daily data submission is also encouraged where resources allow, and as a minimum, daily (near) surface temperature and precipitation output are presently available for seven contributing systems (Table  1 , column 6).
(For a full list of up-to-date variables available the reader is referred to http://chfps. cima. fcen. uba. ar/DS/summary2. php. ) To provide guidance to the use of the database and also avoid duplication of efforts, WGSIP also supports specific sponsored subprojects. These have included the Seasonal Prediction Intercomparison Project (SMIP), the first and second Global Land-Atmosphere Coupling Experiments (GLACE), the Stratosphere-Resolving Historical Forecast Project (stratHFP), and the Sea Ice Historical Forecast Project (iceHFP). In addition, recently initiated WGSIP projects are the Long-Range Forecast Transient Intercomparison Project (LRFTIP); SNOWGLACE, which is evaluating the impact of realistic snow initialization on skill of subseasonal-to-seasonal forecasts; and WGSIP's teleconnections initiative that is aimed at diagnosing tropical-extratropical interactions at seasonal and subseasonal time scales.
Many of these projects were designed to examine the impact of a certain component of the Earth system (e. g. , land surface, stratospheric phenomena such as the quasibiennial oscillation, and sea ice) on prediction and predictability. WGSIP's projects frequently draw on the CHFP database, supplemented by additional sensitivity integrations when needed. Further details of the recently initiated WGSIP projects are available in  Merryfield et al. (2017) , with  Butler et al. (2016)  and  Osman et al. (2016)  reporting on the research deriving from stratHFP and SMIP, respectively. Osman and Vera (2017)  made an assessment of the predictability and prediction skill of climate anomalies over South America from CHFP models and confirmed that the multimodel ensemble performed on average better than any single model.
than the errors in the forecast, it implies these sources of error are underestimated, and the forecast is deemed "overconfident. " Likewise, in rare cases the opposite can occur, where models have too much spread and are underconfident  (Kumar et al. 2014; Eade et al. 2014 ). However, it is clear when examining Fig. 4 , which compares Niño-3. 4 ensemble spreads and root-meansquare errors (RMSE), that overconfidence is a common deficiency in the tropics for many of the CHFP  store forecast data in version 2 of the gridded binary (GRIB2) format, a strategic choice was taken to archive CHFP data in netCDF format more commonly used in the research and climate modeling community. Climate prediction protocols require the ability to handle multiple time axes (viz. , the hindcast start date associated with a particular realtime forecast and the hindcast time step).
To address this issue new protocols have been developed within the European Union-funded Seasonal-to-Decadal Climate Prediction for the Improvement of European Climate Services (SPECS) project. Once these protocols are in place, processing of large hindcast ensembles stored within a single netCDF file will be possible with standard software packages. An added advantage is that this will also permit the database to be migrated to the ESGF, which is already used to access the Earth system models that are assessed by the Intergovernmental Panel on Climate Change (IPCC) process. These actions will underpin the sustainability of the database. The final issue regards the choice of fields submitted to the database. On the one hand, the core set CHALLENGES AND SUSTAINABILITY. To ensure that the CHFP database is sustainable and well utilized by the research community over the longer term, a number of challenges need to be addressed.
First and foremost, the forecasting systems that have contributed to the CHFP are not static but are subject to intermittent upgrades with improvements to model physics and data assimilation systems. With each new release, a new set of hindcasts is conducted to ensure changing bias characteristics can be accounted for. If the CHFP is to remain relevant for research, it is imperative that each new state-of-the-art system is included in the database. Indeed, by retaining all earlier model releases, the intention is that the CHFP will serve to document the systematic improvement in seasonal forecasting systems over time. A key challenge to this endeavor is that, at present, both the submission of data and the maintenance of the CHFP database is conducted on a voluntary basis without external funding sources.
A second aspect that is critical to sustainability is the use of a data format that is self-describing and standard. While many operational centers The CHFP database therefore represents another piece of the meteorological open-access puzzle, making a vast set of seasonal forecasts freely available to the research community, facilitating the move toward of variables should not be too large to ensure that data volumes do not become too excessive and the submission process too onerous for contributing centers, which is particularly important for voluntary undertakings. On the other hand, many research questions or applications modeling undertakings require noncore model variables or data with daily frequency and must therefore make recourse to the subset of models for which such data are available.
Additional noncore fields are submitted on an ad hoc, center-by-center basis, with the result that the available model set changes according to the scientific question posed. The database would be more robust for research purposes if it adhered to a more rigid protocol of a core set of variables, supplemented by additional dataset tiers for modeling centers willing to provide them. Ideally, these additional variables and their respective archiving frequency would evolve in time in response to requests from database users, and feedback from the user community is thus strongly desirable and encouraged. OUTLOOK. We are presently experiencing an undeniable and inexorable evolution toward open data policies in support of research institutions and their undertakings. Open access ensures the scientific potential of data is maximized for the full benefit of society, increases scientific feedback concerning the strengths and drawbacks of the data, and allows fair and equal access to the scientific community, which is of particular importance to scientists in developing countries who face difficulties in participating seamless prediction.
Acknowledgements: The research team are grateful for the funding provided for this research by the  Ministry for Primary Industries (New Zealand ), and ongoing support of the  Department of Geography at the University of Canterbury , Plant & Food Research and the  Marlborough Wine Research Centre, Lincoln University , and the  COSTEL Laboratory at the University of Rennes 2 (France). James Sturman 's assistance with the final graphics is also much appreciated. We would also like to thank the organisers of the  ClimWine2016 Symposium  for the opportunity to present our work.
Ultrathin sections of 90 nm were observed using a FEI Tecnai G2 operating at 200 kV. Sequencing and bioinformatic analysis of Noumeavirus genome. The Noumeavirus genomic DNA was recovered from 2 Â 10 9 purified particles using the Purelink Genomic DNA Extraction Mini Kit (Life Technologies) according to the manufacturer's recommendations. Two micrograms of purified viral DNA were sent to Lausanne Genomic Technologies Facility (Lausanne, Switzerland) where they were used to generate paired-end sequence (2 Â 100 bp) reads using the HiSeq platform. We retained 28,912,450 paired reads (overall Q432). We used idba_ud v1. 1. 1 (ref. 56) to assemble this data set into a 376,295 bp long contig. The 88nt at 5 0 and 3 0 ends of this contig were identical and could be exchanged with one another suggesting a circular genome.
Genemark 4. 29 (ref. 60) was used to ab initio predict protein coding genes from the Noumeavirus genome sequence. The functional annotation of the 452 predicted proteins was achieved by combining different methods. Blast 61 search was performed versus the whole non-redundant protein database at NCBI  61. A domain search was performed against Pfam  62  28. 0, TIGRFAM  63  15. 0, SMART  64  6. 2, ProDom  65  2006. 1, PANTHER  66  9. 0, Prosite 67 20. 113, Hamap 68 201502. 04 using interproscan  69  5. 14-53. A CD-search  70  was achieved against the conserved domain database at NCBI. Potential (trans)membrane proteins were predicted using Phobius  71. Nuclear signal peptides were predicted by NucPred 72 , PredictNLS  73  , NLStradamus  74  and Nucleolar localization sequence detector  75  (NOD).
Peptides and proteins were identified and quantified using MaxQuant software 77 (version 1. 5. 3. 30). Spectra were searched against the Noumeavirus or Melbournevirus protein sequence databases, A. castellanii protein sequence database, and the frequently observed contaminants database embedded in MaxQuant. Minimum peptide length was set to seven aa. Maximum false discovery rates were set to 0. 01 at peptide and protein levels. Intensity-based absolute quantification (iBAQ)  78  values were calculated from MS intensities of unique þ razor peptides. Only proteins identified by a minimum of two peptides were further considered. iBAQ values from both analytical replicates were column-wise normalized and the protein ranking in particle was deducted from to the sum of the two normalized iBAQ values per protein.
Given the correlation between the two virion proteomes we considered that the use of biological replicates was superfluous. The ranking of A. castellanii proteins in uninfected cells was computed from a proteome analysis previously performed  37. Uninfected cells were grown in the exact same conditions as cells infected with Noumeavirus and Melbournevirus (growth medium, temperature, volume and number of cells). To compare the presence and abundancy of the proteins involved in transcription between the proteomes of exclusively cytoplasmic viruses and Marseilleviridae, we reanalysed published data  26  obtained on Pithovirus sibericum and Mimivirus virions using the method described in this study. Fluorescence study of infected A. castellanii cells. Two Mollivirus methyltransferases containing either a nucleolar or a nuclear localization signal (NoLS, ML216 or NLS, ML135) were cloned into the pGAPDH-GFP amoebal expression plasmid  79  to yield a C-terminally GFP-tagged protein targeted to the nucleolus or the nucleus.
Acknowledgements This work was partially supported by the  French National Research Agency   ANR-14-CE14-0023-01 , by a  DGA-MRIS  scholarship and by the  Provence-Alpes-Co  ˆte-d'Azur region (2010 12125). Proteomic experiments were partly supported by the  ProFi grant  ( ANR-10-INBS-08-01 ). Genome assembly and sequence analyses were performed on the  PACA-Bioinfo platform supported by France-Genomique  ( ANR-10-INBS-0009 ) and  Institut Franc¸ais de Bioinformatique  ( ANR-11-INSB-0013 ). We are grateful to  Dr Laurent Bordez  who collected the sample from which Noumeavirus was isolated and to  Pr. E. Bateman  for his kind gift of the original pGAPDH-GFP vector. We thank  Sylvain Poquet  for his help on Melbournevirus fluorescence studies,  Dr J. -P. Chauvin ,  Dr A. Kosta ,  F. Richard  and  A.
Analyses of high-throughput transcriptome sequences of non-model organisms are based on two main approaches: de novo assembly and genome-guided assembly using mapping to assign reads prior to assembly. Given the limits of mapping reads to a reference when it is highly divergent, as is frequently the case for non-model species, we evaluate whether using blastn would outperform mapping methods for read assignment in such situations (>15% divergence). We demonstrate its high performance by using simulated reads of lengths corresponding to those generated by the most common sequencing platforms, and over a realistic range of genetic divergence (0% to 30% divergence). Here we focus on gene identification and not on resolving the whole set of transcripts (i. e. the complete transcriptome). For simulated datasets, the transcriptome-guided assembly based on blastn recovers 94.
8% of genes irrespective of read length at 0% divergence; however, assignment rate of reads is negatively correlated with both increasing divergence level and reducing read lengths. Nevertheless, we still observe 92. 6% of recovered genes at 30% divergence irrespective of read length. This analysis also produces a categorization of genes relative to their assignment, and suggests guidelines for data processing prior to analyses of comparative transcriptomics and gene expression to minimize potential inferential bias associated with incorrect transcript assignment. We also compare the performances of de novo assembly alone vs in combination with a transcriptome-guided assembly based on blastn both via simulation and empirically, using data from a cyprinid fish species and from an oak species.
Likewise it was also demonstrated that evolved structural differences between reference and query (e. g. indels, inversions) can generate bias/error in transcript sequences inferred via guided assembly, due to their dependence on the reference as a template in mapping  [20, 21]. Finally, Jain et al. [22]  proposed a combination of de novo and genome-guided approaches wherein transcripts from Trinity (de novo) were augmented with those from TopHat1-Cufflinks (genome-guided). Although this approach increased the overall efficiency of transcriptome inference, it did not address the specific limitations of each method. The current challenge is therefore inferring transcriptomes while addressing these limitations, in particular when divergence with the closest related genome (or transcriptome) is high, as is typically the case for non-model organisms.
Guided assembly has considerable potential  [23] , but the effect of divergence on mapping efficiency may be a critical factor limiting this potential. We decided to address this issue by combining the de novo approach with a modified guided transcriptome step. We chose an assignment method that would be less sensitive to divergence, namely the widely used nucleotide BLAST (blastn) algorithm. Blastn finds regions of similarity between biological sequences and can accept more relaxed similarities than mapping procedures. In theory, this would make it a tool of choice in assigning reads to genes prior to assembly, and could overcome the limitations of mapping in the guiding step (any other software application with similar properties should also work as well). Here we first test for the performance of a read assignment pipeline based on blastn, using simulated reads generated from a well-characterized reference transcriptome.
Implementing read assignment with blastn We implemented a flexible assignment pipeline (Fig 1 ) as follows; note that all scripts are available at https://github. com/egeeamu/voskhod. Sequencing reads are filtered for over-representation (PCR duplicates or over-expression) using a custom Python script and then merged, when relevant (i. e. when using overlapping paired-end reads), with Pear  [27]  using the following parameters: minimum overlap -v 8, scoring method -s 2, p-value -p 0. 01, minimum assembly length -n 30, maximum assembly length -m 0. After merging-a step in the pipeline that is optional-all available reads are quality filtered using a custom script that first trims 5' and 3' ends on the basis of Phred quality score, trimming bases until a Phred score is ! 13 (i. e. < 0. 05 error rate) is encountered.
A read is further rejected (and replaced by a sequence made up by 50"Ns" to keep the parity between R1 and R2 files when it applies) if it displays one of the following conditions: 1. one base with a Phred quality score < 5; 2. more than 10% of bases with a Phred quality score < 13;  Next, Nucleotide-Nucleotide BLAST  [28]  is used to assign filtered reads to gene-id(s) by finding regions of similarity between the read and the reference transcriptome(s) stored in a local SQLite database  [29]  with Word-size = 9, HSP > 70% and similarity > 70%. This set of parameters allows for filtering chimeras and contaminants when applied to reference transcriptomes. With such parameters, it would be inadequate to use a genome as reference because individual reads could match multiple exons from the same gene while being erroneously rejected from the analysis.
In the following procedures, we therefore use reference transcriptomes only. Of course, when an annotated genome is available, one can extract transcripts to reconstruct the corresponding reference transcriptome. Each read is assigned to the gene-id corresponding to the best hit. When identical scores (overall quality of an alignment) are obtained for multiple gene-ids, this information is stored in the variable Hit_multigene. This variable is used to reflect the level of mismatches on paralogous genes or multigene families. As the pipeline is designed to accommodate multiple reference transcriptomes, when identical scores are obtained for a single gene-id from distinct reference transcriptomes in the local database, this information is stored in the variable Hit_multispecies along with the associated geneid.
Measuring blastn efficiency and performance for read assignment Based on the published transcriptome of Danio rerio (extracted from genome version Zv10), we used the longest transcript for each gene (i. e. 31,953 transcripts)  [30]  and simulated RNAseq reads of known variability. We simulated reads for four length classes (100, 150, 200 and 350 bases, see S1 Protocol for details), with each dataset corresponding to the whole transcriptome at 10X coverage for each gene. Varying read lengths allows studying erroneous assignments caused by conserved homologous regions (e. g. causing assignment to multiple values for gene-id), the expected outcome being that longer reads will decrease ambiguity. Sequence divergence between simulated reads and the reference transcriptome was used as a proxy to simulate confounding processes such as sequence error rate (estimated at 0.
Transcriptome assembly approaches De novo assembly pipeline. In a recent study, Lu et al. [35]  compared various assembly software, including: Trinityrnaseq_r2012-04-27  [9] , Oases  [36]  and trans-ABySS  [37]. The authors demonstrated that Trinity produces assemblies with the highest completeness and contiguity (using default parameters). Wang and Gribskov  [38]  also rank Trinity among the de novo assembly programs producing fewer chimeras. Our de novo assembly (Fig  1B ) is built therefore on Trinity  [9, 39]. Both single-end and paired-end reads can be used. When pairedend reads are used, merging the R1 and R2 reads is unnecessary as Trinity uses the information from separate R1 and R2 paired files. The quality-based filtering procedure was the same as for the read assignment pipeline, as implemented specifically for non-merged reads.
The de novo assembly was done using Trinity 2. 2. 0 with normalize_reads and stranded_library options, using the combination of R1 and R2 reads when available. The output of the assembly is a collection of generated contigs stored in the local database and exported in a single FASTA file. Each contig was first identified/annotated by alignment against the transcriptome of the reference species using blastn (Word-size = 9, HSP > 70% and similarity > 70%), and used to populate a reference database. Only the best HSP was conserved and converted to its reversecomplement when a strand differed from the reference. When multiple hits occurred (i. e. the same blastn score), the contig was assigned to the corresponding gene-ids and denoted with the variable Hit_multigene in the database. This step is crucial to remove gap effects in some paralogous sequences with conserved regions.
The output of this step is a collection of assigned contigs stored in the local database (i. e. the reconstructed de novo assembly) and exported as FASTA files, one for each gene-id. These files define a de novo inferred transcriptome of the non-model species that was stored in the local database. Developing a transcriptome-guided assembly pipeline based on blastn. Given the impact of divergence between the reference transcriptome and sequencing reads on assignment, and "because the resulting assembly can be biased towards the closely related genome rather than the focal genome"  [21]  (i. e. constraining the annotation to the reference structure of transcripts), it is interesting to include as reference the collection of annotated contigs issued from a de novo assembly produced with Trinity, in combination with the related reference transcriptome.
With this design, the assignment profits both from sequence proximity with the de novo contigs, thereby decreasing the effect of sequence divergence, while simultaneously allowing for assigning reads to genes not reconstructed de novo, hence increasing transcriptome coverage. First, the de novo approach was used as previously described, producing a list of assigned contigs in a local database that also contains the related reference transcriptome (Fig  1B ). The reads, merged with Pear when working with paired end sequencing, are filtered on quality and assigned to the local reference database. Each read (merged or not) is assigned with blastn to the gene-id corresponding to its best hit as described in the read assignment pipeline. Each assigned read is stored in the local database and appended to a FASTA file (one file per gene) before assembly.
To avoid circularity with the de novo assembly, we used successively Spades (v3. 7. 1. 8; careful option and cov-cutoff = auto;  [40]  with the auxiliary BayesHammer error correction algorithm  [41] , and then CAP3  [42]  default parameters). We used a combination of these two established software solutions because they rely on different assumptions with different behaviors regarding read length, quantity of reads and transcript diversity  [16, 35, 43, 44]. The output of this step (one SQLite file; output from each assembly program concatenated into a single file) is a collection of contigs. A final validation step was used to prevent chimeric or mis-assembled reconstructions generated during the assembly step. All contigs were annotated using blastn and the related reference transcriptome stored in the local database.