dataesr/scientific-paragraphs-categorization huggingface dataset:
The EukRef pipeline automatically assembles the complete set of sequences from National Center for Biotechnology Information (NCBI) that are associated with the clade of interest, but manual curation is required to vet the resulting phylogenetic tree, classify sequences, and transform the free text retrieved from GenBank into MIxS and EnvO standard inputs. Additionally, curators are encouraged to maximize the information attached to each reference sequence by using relevant literature to fill in missing metadata and flesh out fields retrieved from GenBank to maximize the information attached to each reference sequence. Altogether, these annotated datasets and the accompanying outputs are meant to provide a reliable tool for interpreting high-throughput sequencing surveys. The generated data will be available at the project website (eukref.
g. , API3_2). • Never reuse the same number-even if a lineage later disappears-to avoid confusion (e. g. , MAST-5 no longer exists). • Do not name isolated sequences, especially long branches. These are potentially chimeras or low-quality sequences. When isolated sequences look genuine (are not chimeras, upon detailed inspection), they can be kept in the reference alignment and database because they may carry useful environmental information. These sequences should be identified simply by their clone name. taxonomic framework implemented at European Bioinformatics Institute (EBI) (www. ebi. ac. uk) and provide phylogenetic evidence for internal nodes and environmental clades of significance. EukRef will also provide reference trees to inform two other efforts within UniEuk, EukMap, and EukBank, which aim to improve eukaryotic taxonomic framework and to create a repository of properly annotated high-throughput environmental sequencing (HTES) reads, respectively  [21].
In the long term, these datasets will also be transferred to existing reference databases for eukaryotes, including SILVA, PR 2 , and eventually INSDC. The annotations will also be freely available to other databases that are currently taxonomically restricted but might wish to expand to eukaryotes, such as the Ribosomal Database Project  [39]. Ongoing curation and incorporation of newly available sequences will be facilitated by using Pumper  [40] , which allows an automatic sequence retrieval and tree building, and Sativa  [41] , which automatically annotates sequences in a tree. Both depend on the quality of the initial input, highlighting the need for high-quality initial annotation, as implemented in EukRef.
Heterotrichea ciliates as a case study To illustrate both the curation process and why it is important, we annotated a well-known group of ciliates, the Heterotrichea, as a case study. The initial dataset imported into the pipeline consisted of only nine small subunit ribosomal RNA gene (SSU rRNA) sequences published by Rosati and colleagues in 2004  [42]  (Fig  3A ). After six cycles of the sequence retrieval script, we obtained 412 sequences, which were combined with outgroup sequences and used to build an initial tree (Fig  2 ). After discarding all the sequences that fall outside of the Heterotrichea, were of low quality, or were shorter than 500 bp, we were left with 258 heterotrich sequences (Fig  3B ), representing 37 operational taxonomic units (OTUs) clustered at 97% (Fig.
• specific_host: For symbiotic lineages (including parasites). Host taxonomy ID (taxid) from INSDC. • geo_loc_name: The geographical origin of the sample as defined by the country or sea name, followed by specific region name. 3C). Of these 258 sequences, 74% corresponded to isolates (previously cultured and/or isolated taxa) 26% corresponded to sequences known only through environmental surveys. The classification of each of the 258 sequences was annotated based on the phylogeny, and the metadata were curated using GenBank records and associated literature, according to the EukRef guidelines (Fig  3C ). The classification was improved or corrected compared to the initial GenBank record for 25% of the sequences, and metadata were added for the majority (70% of the sequences). This example shows the efficiency of the EukRef pipeline in collating both known isolate sequences and unidentified environmental sequences, thus increasing the taxon sampling, diversity coverage, and phylogenetic resolution of the targeted clade.
Outreach and training Yearly week-long intensive curation workshops organized in different parts of the world represent the core of the curation effort. These workshops bring together advisers (taxonomic experts) with curators-typically students and postdocs-who are actively investigating the taxonomy and diversity of a particular eukaryotic lineage in order to curate a reference database for a particular eukaryotic group that will further their research efforts. The participants acquire the expertise in using the provided workflow and tools to gather and curate a ribosomal DNA database, and they gain experience working in a UNIX-based command line environment. The process of curating the classification and metadata for their retrieved sequences requires participants to delve deeply into the literature for their lineage, improving their taxonomic knowledge as well.
These early career scientists also form connections to the community of researchers studying protist classification and environmental distribution, allowing them to expand their network and establish collaborations beyond the context of EukRef. So far, we have organized two workshops in Vancouver, Canada, in 2015 and Barcelona, Spain, in 2016, and we have one more planned for 2018 in Roscoff, France. A total of 60 scientists were involved in the first two workshops; 40 of them were students in charge of curating a part of the tree of the eukaryotes. The first workshop in 2015 was focused on two well-known groups of eukaryotes, ciliates and excavates. The ciliate curation has been recently published  [22]  and is available at github (https://github. com/eukref/curation), while the excavates curation team is currently preparing their curated database for publication.
Conclusions and future perspectives EukRef brings together members of the community with expertise in the taxonomy of different eukaryotic lineages to curate available ribosomal DNA sequences from cultured isolates and morphologically identified organisms, together with those from environmental surveys, all within a phylogenetic framework. In the long term, EukRef aims to assemble a curated reference database of 18S rRNA gene sequences covering all eukaryotes. Taxonomists have the greatest knowledge of eukaryotic groups but are rarely involved in curating sequence databases and seldom use existing environmental data. However, these are exactly the people needed to make sense of the vast diversity revealed in these studies. Bringing together taxonomists and microbial ecologists will provide better reference databases, which in turn will improve the automatic annotation of the numerous eukaryotic environmental sequencing surveys increasingly being conducted by the broader research community.
(C) Taxonomic agreement on the annotation of the 20 most abundant OTUs within BioMarKs using each database, listed on x-axis as GenBank, SILVA, and PR 2. Full taxonomic annotation available in S1Table. INSDC, International Nucleotide Sequence Database Collaboration; OTU, operational taxonomic unit; PR 2 , Protist Ribosomal Reference Database; SAR, Stramenopiles, Alveolates, and Rhizaria; 18S rRNA, small subunit ribosomal DNA.
Column Ann: proportion of sequences for which annotation was unchanged (black), improved (pink), or corrected (red). Metadata are added to the curated database for each sequence based on GenBank record and/or information in publications associated with the sequences. Column Env: portion of sequences in the cluster found in marine, freshwater, brackish, or unknown environment. Column Scr: portion of sequences derived from environmental sequencing or known isolates (either cultures or morphologically identified cells). Fully curated reference database of Heterotrichea available in S3 Data. Ann, annotation; Env, environment; Scr, source. https://doi. org/10. 1371/journal. pbio. 2005849. g003.
Gordon and Betty Moore Foundation  moore. org. Received by JdC and LWP. National Science Foundation www. nsf. gov(grant number  1545931 ). Received by MB,  JdC , and LWP. International Society of Protistologists protistologists. org. Received by JdC and LWP. Tula Foundation tula. org. Received by JdC, VB, MK, and  PJK. Marie Skłodowska-Curie  Actions European Comission https://ec. europa. eu/research/mariecurieactions/ (grant number  FP7-PEOPLE-2012-IOF -331450 CAARL ). Received by  JdC. Czech Academy of Sciences ,  Czech Republic Fellowship Purkyne. Received by MK. European Regional Development Fund http://ec. europa. eu/regional_policy/en/funding/erdf/ (grant number  CZ. 02. 1. 01/0. 0/0. 0/16_019/0000759CePaViP ). Received by MK. National Science Foundation www. nsf. gov(grant number  OCE1435515 ).
Received by  LS. Investissements d'Avenir  (grant number  ANR-11-BTBR-0008OCEANOMICS ). Received by CB and CdV. NSERC-DG http://www. nserc-crsng. gc. ca/. Received by LWP. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Table. INSDC, International Nucleotide Sequence Database Collaboration; OTU, operational taxonomic unit; PR 2 , Protist Ribosomal Reference Database; SAR, Stramenopiles, Alveolates, and Rhizaria; 18S rRNA, small subunit ribosomal DNA. https://doi. org/10. 1371/journal. pbio. 2005849. g001.
This paper presents the results of the second edition of the Wind Farm Layout Optimization Competition, which was held at the 22nd Genetic and Evolutionary Computation COnference (GECCO) in 2015. During this competition, competitors were tasked with optimizing the layouts of five generated wind farms based on a simplified cost of energy evaluation function of the wind farm layouts. Online and offline APIs were implemented in Cþþ, Java, Matlab and Python for this competition to offer a common framework for the competitors. The top four approaches out of eight participating teams are presented in this paper and their results are compared. All of the competitors' algorithms use evolutionary computation, the research field of the conference at which the competition was held. Competitors were able to downscale the optimization problem size (number of parameters) by casting the wind farm layout problem as a geometric optimization problem.
Conclusion This paper presented the results of the 2015 competition on wind farm layout optimization. With this event, we were able to propose innovative algorithms to optimize large wind farms with a strong computational constraint. Thanks to this competition, we were also able to compare these approaches with state-of-the-art algorithms and observe the potential improvement of the optimization algorithms used to generate the wind farm layouts. This competition also provides a framework to compare future algorithms with existing ones on a comparative basis. The competition framework is freely available in multiple programming languages (Cþþ, Java, Matlab and Python) with a set of randomly generated wind scenarios. Because solutions were obtained with acceptable computational costs in this competition, we can now imagine targeting new optimization objectives.
Acknowledgments D. Wilson  is supported by  ANR-11-LABX-0040-CIMI , within programme  ANR-11-IDEX-0002-02. C. Segura acknowledges the financial support from  CONACYT  through the " Cienca B asica " project no. 285599. The work of  A. Kheiri  and  E. Keedwell  was funded by  EPSRC  grant no. EP/K000519/1.
Conserved core domain Class 2 members, which include TnpA REP proteins, were initially retrieved with PF01797 (121 amino acids long, http://pfam. xfam. org/family/Y1_Tnp) which was built uniquely for the IS200/IS605 family. However, the profile of most class 2 subfamilies aligns only partially with TnpA REP sequences (see Additional file 1: Figure  S2A ) and in some cases, two alignments are obtained for the N-and C-term regions respectively. This is also true if the length of the aligned part of the protein with the profile is analyzed (see Additional file 1: Figure  S2B ). For better core domain coverage, we constructed a specific HMMER TnpA REP -like family profile (Methods). The multiple alignments were manually edited to remove highly divergent sequences and to extract the conserved core domain as defined in PF01797.
The TnpA REP -like profile obtained with hmmbuild from 428 aligned sequences is 115 amino acids long. This new profile allows excellent coverage of class 2 sequences: a complete alignment of the profile with the protein sequences is observed for all the subfamilies (see Additional file 1: Figure  S2C ) despite the sequence length variability of class 2 proteins (see Additional file 1: Figure  S2D ). Class 3 includes only 10 members, 5 of which do not have conserved HuH or Y motifs. To augment this number, we searched Genbank for other instances of this class using BlastP. A sample of 24 sequences was retrieved and used to construct a specific class 3 profile with HMMER. Compared to class 1 and 2, class 3 proteins have an extended sequence conservation in the N-term region leading to a profile length of 155 AA.
HuH and Y motifs An ab initio search for conserved motifs with MEME confirmed the conservation of subclass-specific motifs overlapping the ubiquitous HuH, Y together with a W residue (Fig. 1d  and see Additional file 1: Figure  S3 ). S3 ). The downstream motifs share conserved serine residues and are part of a larger conserved domain (see below). The absence of conserved core motifs in certain sequences suggests that these correspond to non-functional proteins. To retain proteins that are most likely to be functionally active, we selected those that included a complete core domain defined by PF01797 or TnpA REP -like profiles encoding both the HuH and Y motifs. We obtained a sample of 722 from the original 924 sequences: 172 class 1, 545 class 2 and 5 class 3. In spite of the variations from class to class, the conservation of key catalytic HuH and Y residues suggest that these proteins are all involved in cleavage and rejoining ssDNA  [1].
Subclass domain organization To better define the organization of protein domains, a multiple-alignment of the core domain was performed (Fig. 5 ) using the sequence order from the maximum likelihood tree (Fig. 3 ) and trimmed for sites with rare insertions. This figure shows that core domain length and the distance between the HuH and Y motifs (Fig. 5  top) is quite different between families but is conserved Fig. 3  Tree of the TnpA Y1 representative sequences. The tree is rooted to the branch that separates class 1 and 2. Color code for the bootstrap value (b) intervals: 0. 7 < b ≤ 0. 8 in green, 0. 8 < b ≤ 0. 9 in blue and 0. 9 < b ≤ 1 in red. The MCL subclasses obtained with IF = 3. 0 are colored and subclass numbers are added in the corresponding segments. Based on the tree topology, subclass 2.
1 can be further divided in groups 2. 1. 1 and 2. 1. 2 and three subsets of sequences appear misclassified by MCL (highlighted by black and grey segments) according to their tree location. The taxonomic origin of the sequences is shown as a colored code in the outer ring (NCBI taxonomy phyla level). The phyla with less than three sequences on the tree are not colored. The Phyla are color-coded at the bottom of the figure. The tree display was obtained with online iTOL  [83]  (https://itol. embl. de/) within each family. To facilitate further analyses the core domain was split into upstream and downstream sub-domains (annotated in blue; Fig. 5 top ). The upstream sub-domain ends a few residues after the HuH motif and the downstream sub-domain starts several residues before the conserved W residue.
coli TnpA REP  [5, 23]  strongly supports this hypothesis. To determine whether the other tnpA REP -like genes are also associated with REPs, we searched for putative REP sequences in each genome with RNAMotif  [41]  (Methods). Canonical REP sequences, with a 5′ GT[AG]G tetranucleotide sequence at the foot of a secondary structure, were found in the close neighborhood (+/-1000 nt) of the large majority of the subclasses 2. 2, 2. 4, 2. 5, 2. 7 and 2. 8 genes. The results are shown according to the reference phylogenic tree (Fig. 7a ). This program recovered the vast majority of REP sequences which had been identified manually (Fig. 7b , red rectangles). However, in a few instances the program did not detect manually identified putative REP sequences (Fig. 7b , violet rectangles). The detailed organization of REP sequences found by automatic annotation and belonging to the more frequent REP sequence family are shown on Fig.
These differences could be explained by the different methodological workflows implemented. First for the identification of the TnpA REP (RAYT) and TnpA IS200 protein candidates, Bertels et al. (2017) used two independent tblastn searches based on two queries, one for each protein family (TnpA IS200 and TnpA REP ) while we undertook a more sensitive profile based search (hmmsearch) using the transposase IS200 like (Y1_Tnp) profile from Pfam. At this step, an e-value threshold of 0. 01 was used to maximize detection of remote homologs. Sequences of subclasses 2. 3, 2. 7, 2. 8 and 3 could be too distant from the Bertels query sequence (Pseudomonas fluorescens SBW25, PFLU_RS20900 belonging to subclass 2. 5) to be identified by their tblastn approach (e-value < 0. 02). In addition, we then reduced the sequence redundancy (due to recent gene duplications and the unbalanced representation of bacterial strains in the dataset) by clustering closely related sequences (cut off of 70% of identity), each cluster was then further reduced to one representative sequence, the medoid.
Exploration of sub-family organization was performed in both analyses using MCL graph partitioning but the similarity relationships between pairwise sequences were calculated differently. We generated a reciprocal all-against-all BlastP comparison (e-value <1e -5 ) on our data set including both TnpA IS200 and TnpA REP (RAYTs) proteins and the BlastP results were reformatted to an undirected and weighted graph (log of e-value). Different MCL inflate factor values were tested to select the optimal partition. Our approach is inspired by TRIBE-MCL  [51]  (https://micans. org/mcl/), a method for detecting protein families in large databases. Bertels et al. (2017)  computed relationships between proteins of the TnpA IS200 or TnpA REP (RAYTs) family independently by pairwise sequence comparisons obtained with a global alignment algorithm (Needleman-Wunsch).
Building TnpA Y1 dataset Completely sequenced genomes of 172 archaea and 2178 bacteria covering 1351 distinct species were downloaded from EBI (http://www. ebi. ac. uk/genomes/). The complete genomes of these 2355 strains, their proteomes and EMBL features were managed with an in house mySQL database. We also downloaded the collection of hidden Markov models (HMM) from Pfam (http:// pfam. xfam. org, release 29. 0). To retrieve transposase IS200-like proteins, we adopted a two step approach. First, the Pfam entry Y1_Tnp (PF01797), covering the transposase IS200-like protein family, was used as query with hmmsearch from the HMMER3 package (http://hmmer. org/; [69] ) against the 7,223,104 protein sequences from our sample. Only proteins showing an alignment with an e-values < 0. 01 (recommended threshold) were retained.
A first set of 4081 proteins was obtained. Second, we reduced the sequence redundancy inherent to our sample due to i) the multiple replicates from strains of the same species (e. g. E. coli includes 56 strains in our sample), ii) the unbalanced distribution of bacterial phyla in public databases, leading to the overrepresentation of some closely related species and iii) finally the presence of a high number of identical copies in some genomes due to the repetitive nature of the element (e. g. 99 copies of IS200 in Yersinia pestis Angola). To minimize this bias which can affect the performances of multiple alignment and phylogenetic methods, closely related sequences were represented by one sequence, the medoid. To identify this, we performed all-against-all BlastP comparisons of our initial set of proteins.
HMM profiles To characterize the different protein families and domain organization, we used the HMMER package (http:// hmmer. org). This implements methods using probabilistic models, profile hidden Markov models (profile HMMs), that assign a position-specific scoring system for substitutions, insertions, and deletions  [71] [72] [73]. This is used for construction of protein domain models and is the basis of the protein domain model database, Pfam  [74, 75]. As seeds, we used the representative sequences described above. Alignments were obtained with mafft version 7  [76]  with default parameters "except -localpair -maxiterate 1000" parameters to ensure a high accuracy. The multiple alignments were edited with Jalview  [77]  and the core domain was retained following deletion of left and right flanking regions.
Partial or highly divergent sequences were removed. The HMMER profiles were built with hmmbuild from the input multiple alignments and assembled in an HMM database with hmmpress. Protein sequences were scanned against this database with hmmscan. The domain annotation file was parsed with a Perl program to select the best non-overlapping domains in each sequence. The residues which aligned with the conserved motifs in the HMMER profiles were extracted and the results of the domain annotation saved in the database.
Motif identification The search for conserved motifs used MEME program (Multiple Em for Motif Elicitation, http://meme-suite. org/) [84]. Individual MEME motifs do not contain gaps. The unaligned sequences were submitted to MEME with a motif width between 3 and 10 amino acids. We searched for a maximum of 20 motifs with an occurrence of zero or one motif per sequence since we did not expect that each motif would be present in all sequences. Repeats were predicted in tnpA REP genes with 'any number of repetitions' option of MEME. The results of MEME were used by MAST (Motif Alignment & Search Tool) program to annotate the motifs in sequences of our database.
REP sequence prediction REPs are not conserved in sequence between species (species-specific) and within a genome copies change slightly in sequence and length (imperfect/partial repeats). However, they share a common structural feature: a tetranucleotide guide sequence located 5′ to the foot of a short hairpin. We used the RNAMotif program  [41]  to search for REP sequence candidates in TnpA REP -encoding genomes. This program requires a file that describes the secondary structure interactions and the set of rules used to filter matching sequences which was constructed from the analyses of REPs reported in  [36]. It includes 66 previously described REP sequence families found mostly in Proteobacteria. All include a GTAG or GTGG tetranucleotide located between 0 to 2 nt 5′ from a secondary structure and include a basal stem of fully paired 7 to 20 nt and a loop of 2 to 5 nt (short hairpins) or an additional fully paired stem of 1 to 10 nt connected to the first by a bulge of 1 to 3 nt (long hairpins) (see Fig.
7e ). In addition, the first four basal stem nucleotides include at least three G or C bases (short hairpins) or the GC content of the stem is > = 50% (long hairpins). Only four REP sequences out of 66 do not follow these rules. The RNAMotif program was applied. The REP sequence candidates predicted in DNA sequences of complete genomes by RNAMotif were clustered into families with BlastN and MCL  [70]. For each family a multiple alignment was obtained with mafft  [76]  and the secondary structure computed with RNAalifold  [85]. Only families with at least one occurrence located in the vicinity (1000 nt upstream and downstream) of a tnpA REP gene were retained. The predicted REPs were assembled in dimers (BIMEs) or clusters, if the distance between consecutive REPs on the genome is less or equal to 150 nt.