dataesr/scientific-paragraphs-categorization huggingface dataset:
1  and supplementary material S1, Supplementary Material online). PBLE vs. PGBD PBLE transposases were aligned with AliView (version 1. 17. 1; Larsson 2014) using Muscle with default parameters. Blocks with a conservation level higher than 30% (relative to the consensus given by AliView) with no long indels (>20aa) were retained for phylogenetic analyses (supplementary material S3, Supplementary Material online). The phylogenies (Maximum likelihood, but different methods led to similar topologies), based on the previous blocks, were inferred with MEGA6  (Tamura et al. 2013 ) after a search for the best evolutionary scenario with ProtTest 2. 4 server (AIC, matrix LG + F+G). For the phylogeny of PGBD and all copies (PBLE, PGBD, and PGBD like), the same procedure was followed FIG. 1. -Data mining of piggyBac element.
Search of elements belonging to the piggyBac superfamily was done using known copies (literature or database) as BLAST queries. Ten PGBD and 107 PBLE copies were used as queries. After a first run, 295 sequences annotated as PGBD (PGBD1, 2, 3, 4, and 5) and 151 new sequences, annotated as PGBD-like element, belonging to 58 species were retrieved. TIR were looked for in the 5 0 and 3 0 flanking regions (5 kb in both directions) of these new sequences. Fifty of them were found with two TIR and 101 with one (5 0 or 3 0 ) or no TIR. In fine, a total of 563 sequences were available. The sequences with more than 100 N were eliminated. (supplementary materials S4 and S5, Supplementary Material online).
3. -Phylogenetic tree of PBLE. This phylogeny is based on amino-acid sequences covering about 237 residues (the alignment is given in supplementary material S3, Supplementary Material online). For simplicity, only 29 sequences are represented, but the complete tree is available in supplementary material S7, Supplementary Material online. After a search of the best evolutionary scenario (ProTest 2. 4), this unrooted tree was generated in MEGA6 with the maximum likelihood (ML) method, using LG + F + G matrix. Only bootstrapping values (100 replications) higher than 70% are mentioned on the branch. Red dots, green squares, yellow lozenges and blue triangles refer to the different SGs, that is, SG1, SG2, SG3, and SG4, respectively. Sequence names: the term PBLE (piggyBac-Like Element) is used for sequences extracted from literature, or copies newly characterized in this study, while the term piggyBac is restricted to sequences extracted from Repbase (real name of these sequences in this database).
The human PGBD5 and the last two sequences (Pma and Bfl) are orthologous, suggesting an ancient domestication  (Pavelitz et al. 2013). These eight genes were used as query against general database to extract new PGBD sequences. The objective was to get a better idea of the specific distribution of these sequences. This allowed us to retrieve 295 PGBD sequences. All these sequences can be clustered as eight groups. The orthology was verified from a detailed analysis of the 5 0 and 3 0 flanking regions (as shown in supplementary material S8, Supplementary Material online). All these sequences are found in single copies. They present a coding region in which all or a part of the piggyBac transposase is identified. Within each group, the similarity level between the sequences is higher than 85% (see below), and between groups no alignment can be made except for the parts corresponding to the piggyBac transposase.
In the PGBD2 group (n = 62), a single uninterrupted exon corresponding to the piggyBac transposase is found. As mentioned by  Sarkar et al. (2003)  and  Pavelitz et al. (2013) , we observed two exons in 5 0 of the transposase sequence. Based on all PGBD2 sequences extracted from the database, the length of the encoded proteins (uninterrupted exon) varies from 586 (Pal-PGBD2) to 759 (Lve-PGBD2) amino acids. The average similarity between the members of this group is 88%. The PGBD3 (n = 15) transposase is inserted into the fifth intron of the Cockayne Syndrome group B gene (CSB). Indeed, unlike other PGBD, the PGBD3 transposase is flanked by a potential 3 0 splicing site in the 5 0 region and a polyadenylation signal in the 3 0 region. Thus, an alternative splicing of this region leads to a regular CSB product or to the CSB-PGBD3 fusion protein which has been conserved since the common ancestor of human and marmoset lineages, that is, 43 Ma  (Newman et al.
The analysis of the Ka/Ks ratio provides arguments in favor of the domestication of these sequences. Indeed, this ratio calculated within each PGBD group varies from 0. 123 ± 0. 003 in PGBD2 to 0. 432 ± 0. 004 in PGBD1 (PGBD3 = 0. 143 ± 0. 005, PGBD4 = 0. 150 ± 0. 009, PGBD5 = 0. 397 ± 0. 002). This suggests the existence of a purifying or stabilizing selection. In this context, the DDD catalytic domain of the piggyBac transposase is not conserved among the domesticated sequences, including those retrieved from databases and those described in literature  (Sarkar et al. 2003; Newman et al. 2008; Pavelitz et al. 2013) , suggesting that this  Molecular Evolution of piggyBac Superfamily.
In the complete tree inferred from PBLE, PGBD like, and PGBD, three distinct groups (G1, G2, and G3) are specific to Actinopterygii species (supplementary material S10, Supplementary Material online). On the one hand, the first two contain complete or partial transposase of PBLE or PGBD-like copies that can be present in several copies in each species. On the other hand, the third group corresponds to sequences found as single copy in 14 teleost fish species (supplementary material S1, Supplementary Material online). All these sequences are annotated PGBD-like4 in database. No ortholog is detected in other vertebrates. This group, here named NeoPGBD, presents a conserved putative ORF with few substitutions or gaps but with no frameshift or non-sense mutation (supplementary material S8, Supplementary Material online).
Discussion A large diversity of transposons belonging to the piggyBac superfamily has been documented in eukaryotes, except in plants, from data mining in databanks. This study provides a detailed characterization of these elements, their specific distribution and their possible evolution. A total of 157 PBLE, including 50 new sequences, were analyzed. The target site is TTAA. Rarely, for only five elements (unique copy), other TSD can be observed (supplementary material S2, Supplementary Material online). This is probably due to mutations, since the 5 0 TSD and 3 0 TSD sequences are not the same. In only two cases (Yabusame1, AcePBLE), the two TSD are identical, but Yabusame1 does not seem active in spite of a putative intact ORF  (Daimon et al. 2010). TIRs of PBLE appear to be divergent both in length and sequence except for the first highly conserved cytosine.
The evolutionary trajectory of PGBD is not systematically accompanied by modifications of transposase activity (see, for instance,  Sarkar et al. 2003; Pavelitz et al. 2013). In this FIG. 6. -Continued and 33 PBLE sequences. Maximum likelihood (ML) method, with LG + F + G matrix, is used to construct this tree. Only bootstrap values (100 replications) higher than 70% are labeled. Groups including PGBD elements and PBLE and/or PGBD-like, are framed in colors. Red dots, green squares, yellow lozenges, and blue triangles refer to the different PBLE SGs, that is, SG1, SG2, SG3, and SG4, respectively. The putative domesticated sequences (NeoPGBD), found only in Actinopterygii, are underlined in purple. Sequence names: the term PBLE is used for sequences extracted from literature, or copies newly characterized in this study, while the term piggyBac is restricted to sequences extracted from Repbase (real name of these sequences in this database).
Acknowledgments This work was financially supported by the  Centre National de la Recherche Scientifique  [ UMR 9191 ], the  University Paris-Sud , the  Tunisian Ministry of Higher Education and Scientific Research and the University of Tunis El Manar. Authors thank  Mireille Be  ´termier and  Julien Bischerour  for their helpful comments and  Malcolm Eden  for the English review of the manuscript.
Nucleotide sequence accession number The 16S r RNA gene sequence was deposited in Genbank under Accession number LT714171. 1. Phylogenetic tree showing the position of ''Veillonella massiliensis" strain Marseille-P3594 T relative to the phylogenetically closest species. Sequences were aligned using CLUSTALW, and phylogenetic inferences obtained using the maximum-likelihood method within the MEGA software. Numbers at the nodes are percentages of bootstrap values obtained by repeating the analysis 500 times to generate a majority consensus tree. Only the bootstraps scores superior to 75 were retained. The scale bar indicates a 1% nucleotide sequence divergence.
Protein modeling and design activities often require querying the Protein Data Bank (PDB) with a structural fragment, possibly containing gaps. For some applications, it is preferable to work on a specific subset of the PDB or with unpublished structures. These requirements, along with specific user needs, motivated the creation of a new software to manage and query 3D protein fragments. Fragger is a protein fragment picker that allows protein fragment databases to be created and queried. All fragment lengths are supported and any set of PDB files can be used to create a database. Fragger can efficiently search a fragment database with a query fragment and a distance threshold. Matching fragments are ranked by distance to the query. The query fragment can have structural gaps and the allowed amino acid sequences matching a query can be constrained via a regular expression of one-letter amino acid codes.
This problem is well-known to the chemoinformatics community, which has developed several efficient strategies to screen large databases of small molecules. For example, geometric embedding and locality sensitive hashing  14  , kd-trees  15  , a tree data structure (called µ-tree) with a heuristic  16  , bounds of similarity scores for chemical fingerprints  17  and a proximity filter based on the logical exclusive or operator  18  have all been developed to this end. Currently, several fragment pickers  [19] [20] [21] [22]  and protein fragment databases  [23] [24] [25] [26] [27] [28]  are available. Of particular interest is the Super method  20  that uses the lower bound of RMSD  29  to screen the whole fragment space. However, our research on protein design and refinement of protein decoys for crystallographic phasing required specific options and therefore a new fragment picker.
Methods Implementation for r j in R do d ← distance(q, r j ) d inf ← d -d q d sup ← d + d q {distance(f i , r j ) comes from the database index} M ← {∀f i ∈ M | distance(f i , r j ) ∈ [d inf , d sup ]} end for {exact query: refine the result of pruning} M ← {∀f i ∈ M | distance(f i , q) ≤ d q } return M Fragger exploits the triangular inequality of RMSD  30  to prune the fragment space (Figure  1  and Algorithm 1). RMSDs are computed efficiently via the QCP method  31. Fragger is written in OCaml 32 , except backbone RMSD computations which are performed with a new version of the C++ ranker tool from Durandal  33. Computations are parallelized on multi-core computers via the Parmap library  34. Fragger allows a database to be queried with a fragment and an RMSD threshold. Matching fragments are ranked by RMSD to the query.
Amendments from Version 1 Thanks to the referees' recommendations, we have improved our manuscript in this revised version. An extra paragraph was added in the introduction to describe the rationale underlying Fragger. Several citations to web servers and protein fragment databases were added to provide a broader overview of similar approaches. Some variables in the algorithm have been renamed to facilitate easy understanding. A new paragraph and one reference about choosing good reference fragments were added. Several changes were made to clarify the choice of reference fragments, and the impact of the effective cutoff values on search speed.
Results and discussion Tests were performed on one core of a 2. 4GHz Intel Xeon workstation with 12GB of RAM running Ubuntu Linux 12. 04. The PDB dataset is composed of all proteins determined by X-ray, without highly similar sequences (30% sequence identity cutoff) in order to create a challenging set of fragments to benchmark a protein design algorithm. It contains 13,554 PDBs. PDBs were extracted from the protein databank website using the advanced search tab and ticking the "Retrieve only representatives at 30% sequence identity" box. Querying with a three (resp. nine) residues fragment takes at least 6. 75s (resp. 5. 2s). Query times vary with the query fragment, reference fragments, indexed proteins and RMSD tolerance to the query. In general, the longer the required fragment length and the smaller the RMSD tolerance, the faster the query.
Reference fragments can be chosen randomly. Pruning of the search space is better if there are at least three reference fragments, far from each other. Once a RMSD index has been computed for a randomly chosen fragment (f i ), taking the furthest fragment from it (f j ) and the median fragment (f k ) would give three acceptable reference fragments. For interested contributors, some good heuristics can be found in the literature but were not implemented in Fragger, like Brin's greedy algorithm  35. For one time tasks, it is not necessary to create RMSD indices and actually query a database, as fragments extraction and RMSD computations are fast enough. For example, it takes only 15s to generate all (41,200) fragments of 13 residues starting with alanine and ending with glycine (middle of Figure  1 ).
There are also a series of web servers focusing on this goal > that are not cited. We have added several citations to web servers and protein fragment databases which are using various methods. > -The way the algorithm is described makes it rather uneasy to > understand. There could first be some awkwardness in the notations. For > instance, in the algorithm description, blanks between d and b and > between r and f could be discarded. Secondly, it could be difficult > for a reader to understand the role of the representative fragments, > the way they are identified and used from the present description of > the algorithm. Probably an additional flowchart or figure to explain it > would be welcome. We have renamed some variables in the algorithm to bypass typographic problems introduced by the journal's style-sheet.
We have also added a new paragraph and one reference about choosing good reference fragments. > -The critical parameters of the procedure are not really identified. What > are the effective cutoff values, how do they impact on the search ? This is quite complex: the search speed is influenced by the protein database, the fragment length and the query RMSD tolerance. In the manuscript, we now summarize the general trend as: "In general, the longer the required fragment length and the smaller the RMSD tolerance, the faster the query. ". > -It seems Fragger offers possibilities to constrain amino acidd > sequences. Is it a prior or a posterior filtering?.
Indeed, good reference fragments can improve the search performance. We have added a new paragraph and one reference about choosing good reference fragments. No competing interests were disclosed. Competing Interests: 11 October 2017 Referee Report doi:10. 5256/f1000research. 13520. r26264 Pierre Tuffery Molécules Thérapeutiques In Silico (MTi) (UMR-S 973), French National Institute of Health and Medical Research (INSERM), Sorbonne Paris Cité, Paris Diderot University, Paris, France.
It seems Fragger offers possibilities to constrain amino acidd sequences. Is it a prior or a posterior filtering ? Is the rationale for developing the new software tool clearly explained? Partly Is the description of the software tool technically sound? PartlyAre sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others? YesIs sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool? YesAre the conclusions about the tool and its performance adequately supported by the findings presented in the article? Partly No competing interests were disclosed. Competing Interests:I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.
- It seems Fragger offers possibilities to constrain amino acidd sequences. Is it a prior or a posterior filtering ? Is the rationale for developing the new software tool clearly explained? Partly Is the description of the software tool technically sound? PartlyAre sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others? YesIs sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool? YesAre the conclusions about the tool and its performance adequately supported by the findings presented in the article? Partly No competing interests were disclosed. Competing Interests:I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.
We focused on the mitochondrial marker cytochrome oxidase I (COI). The results obtained using the new method were compared with individual larval sequencing. We validated the ability of the method to identify Amazonian catfish larvae at the species level and to estimate the relative abundance of species in batches of larvae. Finally, we applied the method and provided evidence for strong temporal variation in reproductive activity of catfish species in the Ucayalı ´River in the Peruvian Amazon. This new time and cost effective method enables the acquisition of large datasets, paving the way for a finer understanding of reproductive dynamics and recruitment patterns of tropical fish species, with major implications for fisheries management and conservation.
Understanding the reproductive cycles and recruitment dynamics of commercially important migratory species is thus essential for the sustainable management of fisheries in Amazonia. Regular, standardized plankton net sampling of the larvae of these species is a promising way to achieve these goals. One of the technical limits of this approach is the specific identification of freshwater fish larvae, particularly those of Siluriformes, given the great number of species in this order. Morphological criteria alone in early development stages only enable differentiation of families, and sometimes of genera, but very rarely of species  [19, 20]. With the development of dedicated databases and the standardization of molecular tools, molecular approaches like DNA barcoding offer a solution to this problem.
COI amplification and sanger sequencing A 680 bp fragment of the COI gene was amplified using universal primers Fish F1 and Fish R1 and PCR conditions as previously described by Hubert et al. [29]. PCR was performed with a Phusion1 High-Fidelity PCR Master Mix (ThermoFisher, 1040-2678) using the following program: 3 minutes at 98˚C followed by 30 cycles of 80 seconds at 98˚C, 45 seconds at 55˚C and one minute at 68˚C, with a final extension of 10 minutes at 72˚C. PCR products were sequenced using the BigDye Terminator 3. 1 kit (Applied Biosystems). Sequencing was performed on an ABI prism 3130 (Applied Biosystems), at IRD Montpellier, (France) using the Fish F1 primer. Sequences are available on Dryad doi:10. 5061/dryad. 117tn (see S2 Table ).
Library preparation Preparation of biotinylated PCR probes for capture. COI probes were produced by PCR using the same conditions as above but with 5' biotinylated primers synthesized by Eurogentec SA (Angers France). Amplification was performed on four adults from four different species: Oxydoras niger (oni), Pimelodella sp. (pgra), Pinirampus pirinampu (ppi) and Pseudoplatystoma punctifer (ppu). The four species were selected to cover the panel of siluriform species present in our database, spanning a nucleotide divergence range of 20%. PCR probes were purified and pooled at equimolar concentrations for capture. Library preparation and capture were performed as described in Mariac et al. [33]. Libraries were tagged using 6-bp oligonucleotides (TAG) to allow for multiplexing, and then hybridized with COI-targeted probes as described in Mariac et al.
Data analysis We built a database with sequences of COI genes from 86 adult siluriform species, referenced in different collections or publications (see S5 Table ) and for which the phylogenetic relationships are established and discussed in Carvajal et al. [35]. The 86 sequences of COI used were deposited in GenBank (see S5 Table ). This database contains only species of economic importance in Amazonia and consequently does not include all the genera of Siluriformes. The database allowed species identification of the samples by mapping sequences to those in the reference database. The sequences produced using the Sanger method were examined with Geneious Pro 4. 8. 5 software  [36]. Automatic filtering involved trimming regions with more than a 5% chance of error per base. Sequences were manually checked and any ambiguities corrected.
The sequences were aligned with the reference database using MUSCLE (100 iterations,  [37] ). Finally, species assignment was computed by building a neighbor-joining tree using Tamura-Nei genetic distances. The sequences obtained with the NGS method were demultiplexed using the script "demultadapt" (available at https://github. com/Maillol/demultadapt), that sorts reads according to their TAG (6 bp, 0 mismatch threshold). Adapters were removed using Cutadapt 1. 2. 1 software with the following parameters: length overlap = 7 bp, insert size = 35 bp. Reads were trimmed based on their nucleotide mean quality (Q < 30) using the script "filter-mean-quality" (available at https://github. com/SouthGreenPlatform/arcad-hts/blob/master/scripts/ arcad_hts_2_Filter_Fastq_On_Mean_Quality. pl). Sequences were then mapped on the reference database using the BWA mem v.
0. 7. 12 package  [38, 39]. For each enriched library, we calculated 1) the percentage of useful reads, i. e. the ratio of the number of reads mapped on the database to the total number of reads, and 2) X-fold enrichment, which corresponds to the ratio of the number of reads from the enriched library mapped to the reference library to the number of reads from the non-enriched (genomic) library mapped to the reference library. X-fold measures the effectiveness (the enrichment) of the experiment. The percentage of useful reads measures the specificity of the enrichment. As we had built up the bulk DNA from species we were able to identify using Sanger sequencing, we knew which species we expected to find using the NGS approach. We were thus able to identify false positives, i. e. species found using the NGS approach that were not present in the bulk DNA.
Only properly paired reads with a maximum of one mismatch, no clipping, and no secondary alignment with the reference were used. Different filters were applied on the alignments: only the sequences for which both the R1 and R2 reads were mapped to the same species on the database were kept, and we kept only those mapped with quality > 0 (MAPQ>0), only sequences with strictly less than two mismatches, and an alignment over 100 bp. A given species was identified by listing which reference sequences the filtered reads were mapped to with a minimum coverage of 60%, otherwise we considered that it was not sufficiently precise for species level identification and kept only the genus taxonomic rank. The frequency of each identified species was calculated using the relative number of mapped reads divided by the total number of mapped reads across species.