dataesr/scientific-paragraphs-categorization huggingface dataset:
Construction of the "lax" database The GenBank database structured under the ACNUC format  [84]  is the source of the sequences and their annotations. The sequence and annotations of each gene of interest are extracted in a GenBank format flat file using the RAA_Query communication protocol  [85]. For each nucleotide sequence longer than 300 bp, the name of the species and other information about the nature of the strain, like the NCBI TaxId, the strain collection Id or the taxonomic rank are extracted from flat files. Next, a script extracts relevant information concerning the nomenclature compliance and the TS status of each sequence. Species names are checked against the DSMZ (http://www. dsmz. de) and LPSN databases  [86]  (http:// www. bacterio. net) devoted to prokaryotic nomenclature.
In some cases, the TaxId allows to correct species names. If a name is validly published, the strain is marked as nomenclature compliant (tag v for valid). In the opposite case, the name is marked as not compliant to nomenclature (tag ?). A list of GenBank Ids that have an erroneous species name has been constructed by hand since 2007 and extended when the evidence of error is reported. GenBank Ids of each sequence are compared to this list, and the occurrence of the erroneous species assignment is flagged (tag X). Tags corresponding to the Nomenclature information for a given sequence are therefore [v/?/X]. The TS status of each sequence is deduced by comparison of the collection Id of the corresponding strain to a database constructed by using the current DSMZ "Prokaryotic Nomenclature up-to-date" Excel file, the LPSN website and a home-made list locally maintained since 2004.
The TS status of the strain may be expressed by a T following the strain Id (for example CIP8828T). If this tag is found, and if the name is grammatically correct (two words, the first being capitalized, or four words, one being "subsp. ") the sequence is considered to be that of a TS. In some cases the T flag is used without a grammatically correct name; this may be the preliminary indication of a new species, the sequence is therefore tagged t to prevent any loss of information. At the end of this process, a list of missing TS is built (type strains present in the above-mentioned TS database but not found at this point), and the StrainInfo database is used to manually correct the sequence TS status. A sequence strain quoted as "reference sequence" in LPSN is tagged TT. Other sequences are tagged N.
The tags corresponding to the TS information are therefore [TT/T/t/N]. A Fasta-formatted sequence file is then produced using a descriptor constructed according to this grammar: >Genus_species[_subsp_subspecies]∼[v/?/X] ∼[TT/T/t/N]∼GenBankId=NCBI taxonomic levels Other database flavors are constructed extracting corresponding sequences from the "lax" database by searching for regular expressions with the Unix command grep. For instance, the motif TT is used to collect sequences of the "superstringent" database. For each database, a BLAST database is then constructed. Databases for the other markers, for which only three flavors ("lax", "stringent", "TS-stringent") are considered, are constructed by the same procedure. Table  1  presents the databases flavors and specifications.
Quality control of reference databases The quality control of the SSU rDNA database is done by a script that searches missing TSs by comparing the DSMZ list of species and the TSs of the database. This is both a proof of the correct extraction of all genera, and an indicator of the exhaustivity of the database. As TSs of non-cultivated species are not defined, one sequence for each medically important, non-cultivated species is manually introduced. The increase of the number of sequences for each prokaryotic phylum between two extractions is also checked, as well as the global increase of all TS sequences. Because the other databases are not exhaustive in term of taxonomic coverage, we only check for them the global increase of the number of sequences between two extractions and the global increase of TS sequences.
Tree building The first step towards the construction of a phylogenetic tree including the query sequence consists in a BLAST search  [23]. BLASTN is run with the query against the selected database with an expectation value set to E ≤ 0. 1 and without filtering for low-complexity regions. The requested number of sequences with the highest similarity scores is extracted. This is not done if the number of BLAST hits is < 10 because it is the sign that something went wrong. Selected sequences are multiply aligned by MAFFT  [87]. Then, the BMGE program  [88]  with its default parameters is used to trim sequences in the multiple sequence alignment in order to select blocks of sites that are optimally suited for phylogenetic inference. FastTree  [89]  is then used to reconstruct the tree by approximate maximum likelihood.
Tree visualization A Scalable Vector Graphic (SVG) version of the tree is computed by SeaView  [44]  and is modified by a set of Python scripts to decorate sequence labels (species name and GenBank IDs) with hypertext links to LPSN, a local copy of GenBank, and StrainInfo. The positions in the tree of the three best BLAST hits are shown by colored dots. LeBIBI QBPP also outputs another tree where the species name part of sequence labels is replaced by taxonomic information as follows: the full taxonomic classification string of each sequence (with all taxon levels from Bacteria or Archaea to family, genus and species) is computed, the part of these strings that is shared by all sequences of the tree is removed, and what remains is used to build sequence labels. Application of the "branch width as support" option of SeaView allows to graphically display branch support through branch widths: widest branches correspond to SH ≥ 0.
Tree analysis The sequence with the smallest patristic distance (that is the sum of the length of the branches connecting two leaves) from the query sequence is displayed. A query sequence at small topological distance of database sequences suggests a high evolutionary relatedness between them. This distance allows to define the proximal cluster, that is, all sequences at a topological distance of two nodes or less from the query. If the distance between the query sequence and the closest species is under the 75 % percentile of the distribution of intra-species distances, leBIBI QBPP outputs a message stating that the query sequence putatively belongs to this species. Next, if the same distance is under the 75 % percentile of the distribution of genus inter-species distances, the message reports the putative belonging of the query sequence to the genus of the closest species.
This information depends of course of which reference database is used as wrongly assigned sequences may destroy its accuracy. A warning message is issued when the closest sequence in terms of patristic distance does not belong to the proximal cluster. The system also issues a message when the closest sequence is not among the five best BLAST hits to point out the relatively frequent cases where the best BLAST hit does not match the evolutionary closest neighbor. Also, the taxonomic diversity within the tree is estimated by collating genera of the tree leaves. If they are all identical, a warning is returned indicating that no outgroup is available. In such case, it is advisable to repeat the analysis after having increased the number of BLAST hits retained for tree building.
Report The report (Fig. 1 ) summarizes the analysis and gives information relevant for the interpretation of the phylogeny and the taxonomic assignment of the query sequence. A summary of the database used is given along with statements about the consequences of the database stringency on the obtained phylogeny. The nucleotide composition of the query sequence is given. If it contains any undetermined bases, their amount is an indicator of the quality of the sequencing process. Too many undetermined bases have a negative impact on the quality of the phylogeny and this leads to a warning message. The length of the matching section of the first BLAST hit is expected to be close to the length of the query (≥ 95 %). Even if this does not impair phylogenetic reconstruction, it may indicate a global or local low quality of the query sequence.
Mitigation of BLAST-induced unexpected phylogenetic tree The BLAST algorithm searches a database for similar sequences, not for the phylogenetically closest ones. Consequently, a very loosely related sequence gets sometimes recruited. This kind of outlier sequences may lead to difficulties in tree interpretation and is frequently characterized by the apparition of a long branch. A "reverse QBPP" procedure may identify the problem: submitting the outlier sequence, easily accessed through the tree Fig. 1  LeBIBI QBPP report summarizes the analysis and gives additional informations that may be useful to interprete the phylogenetic tree hypertext links, to leBIBI QBPP will lead to a completely different set of selected species and tree.
Strategy of exploration and databases The leBIBI QBPP databases and web tools are designed to quickly reconstruct a phylogeny with a SSU rDNA (16S) or a housekeeping protein gene sequence. It also provides elements to help the biologist to interpret the tree and especially to place the query sequence within a known taxonomy rank. As underlined in the databases section above, several different databases are available and QBPP gives a more informative answer if an adapted querying strategy is followed. The optimal strategy is a trade-off between the advantages of a large number of recruited sequences (it increases the likelihood of having recruited all closely related sequences, and the quality of the coverage of diversity and of the phylogeny), processing speed, and ease in interpretation. The best protocol is to begin with a rather stringent database to maximize phylogenetic and taxonomic diversity.
Retaining at least 50 sequences around the query reduces the risk of not recruiting phylogenetically closest strains because they are far in the BLAST hit list. If a broad variety of taxa is obtained (i. e. , with external groups, especially genera), it is possible to reduce the number of extracted sequences for better readability, but the user will have to verify that there is no change in the closest clusters. On the contrary, if the tree does not contain enough species diversity, it is necessary to increase the number of extracted sequences or to try a more stringent database. It is always very important to test the "lax" database because some sequences of important uncultured species are present in this collection only. The "lax" SSU rRNA database is the broadest, so its processing is the slowest.
This database contains a lot of sequences that are approximately or wrongly identified or of poor quality and short (albeit > 300 bp in length). It should be used to build a phylogeny of the query sequence versus any prokaryote (cultivated, environmental), but often the taxonomy will be difficult to interpret. It is exhaustive, but the phylogenetic signal may be blurred by a swarm of approximately identified sequences, of low quality and possibly redundant. The "lax" SSU rRNA database is therefore essentially exploratory, more suited for research than for routine analyses. The "stringent" SSU rRNA database is the best when it comes to the quality of the phylogenetic reconstruction because it contains less sequences and has globally better characterized items. It contains sequences of strains that are validly denominated and represent all the biological diversity of species (besides another diversity created by errors in naming or publishing of sequences).
Some strains belonging to a species whose members can be phylogenetically very distant (such lack of homogeneity is mostly due to lack of strains or taxonomical studies) cannot be processed without using this "stringent" database. On the contrary it may be impossible to use it in the case of species that are highly represented in GenBank because the phylogeny cannot be computed due to a large number of nearly identical sequences. In such a case the tree is saturated by one species (or genetic variant) and cannot be interpreted. This database is also the only one allowing to compute the distribution of the distances between sequences within one species because it contains often more than one sequence for a given species. Unfortunately it also contains incorrectly identified sequences, introducing uncertainty or errors in the interpretation of the phylogeny.
The "TS-stringent" database (only available for SSU rDNA sequences) is less contaminated by erroneous species names and generally the identification of the sequences is of high quality. This is obtained by a decrease of the variety (mostly one strain per species, the TS being present) that may lead to poor phylogenetic reconstructions in the case of high genomic variations among the species and a TS that is not representative of this diversity. The technical uncertainties in sequencing or possible contaminations or tube-switching explains the observed incoherences of the position of multiple sequences of the same type strain in the phylogenies. Unknown species are also more difficult to position among the already validated species clusters. Uncultured species are mostly missing as their TS are not defined.
The "superstringent" database (also only available for SSU rDNA sequences) is the smallest and fastest to run. The sequences are selected to be representative of a given species and are of high quality. Neither the diversity within a species, nor technically induced biodiversity is represented. As in the similar BLAST database entitled "rRNA typestrains/prokaryotic 16S ribosomal RNA" developed at the NCBI, uncultivated prokaryotes are not present because of the absence of TSs for these species. This is a database giving accurate phylogenies but that may be sometimes incorrect or not representative of the biological reality. The uncultured species are missing as in the "TS-stringent" database. The "genus-level" database (also only available for SSU rDNA sequences) is the oddest of all. Its sequences are selected to represent all recognized genera.
Comparison with other functionally close solutions LeBIBI QBPP is somewhat similar to other webtools combining the selection of sequences similar to the query (by BLAST or other approaches), and a pipeline that performs multiple alignment, and finally computes a phylogeny. The closest equivalent is the NCBI BLASTN using the "16S ribosomal RNA sequence (Bacteria and Archaea)" database. This database is similar to our "superstringent" database and it is possible to compute a phylogenetic tree using a distance matrix built with BLAST pairwise alignments and either the Fast Minimum Evolution  [92]  or Neighbor Joining (NJ)  [93]  algorithms. The alignment between the query sequence and sequences issued from the BLAST search is not a multiple alignment and may only partially cover the query sequence.
This is not a true phylogenetic reconstruction unlike done by leBIBI QBPP where a global alignment is computed and the tree is built with the ML approach which outperforms distance methods. The same research on the NCBI site may use the whole GenBank database with the option of suppressing "environmental samples". This database is then close to our "lax" database but this does not repair the absence of true phylogenetic reconstruction, and in many situations the tree is overcrowded by very similar sequences. The RDP web site also offers the possibility to load a query sequence, find the closest neighbours in terms of 7-mer sharing percentage (by using Sequence Match) and to build a phylogenetic tree (via Tree Builder). The databases provided by this service are known to be of high quality and it is possible to restrict it to cultured bacteria, uncultured or both as well as to TSs, non TSs or both.
These selections thus correspond to our "lax" (selection of cultured and non-cultured) or "superstringent" (selection of type-strain sequences only) databases and to other, intermediate choices. The maximum number of matches is limited to 20, but it is possible to select more closest taxa by another procedure (Hierarchy Browser or Sequence Match), and to proceed to their phylogenetic analysis. Alignment is done by the fast, rRNA secondary-structure aware Infernal aligner  [94] , and the phylogeny is obtained by distance methods such as NJ or Weighbor  [95]  with bootstrap support computation. At most 50 sequences can be put in the tree. A good knowledge of bacterial taxonomy is required to select the more phylogenetically related neighbours and to select a pertinent outgroup if wanted. Apart from these requirements, the phylogeny obtained is subject to the intrinsic limitations of distance reconstruction methods.
This website requires numerous selection and data transfer steps that are not needed in leBIBI QBPP The selection of the recruited sequences that will be used for alignment and phylogeny is not needed in leBIBI QBPP , where this is done by choosing the reference database and the number of retained matching sequences. The last similar tool is provided by the Phylogeny. fr web site  [96]. This system allows to perform a BLASTN search and then to compute a phylogeny on a set of homologous sequences. The first main difference with leBIBI QBPP is the fact that the submission of several sequences is not possible. Also, the database choice is limited to GenBank. Consequently, ill-identified sequences and large numbers of nearly identical sequences are often recruited in the resulting phylogenetic trees.
In its simplest protocol, this web tool performs multiple sequence alignment computation by Muscle, alignment trimming by GBlocks, phylogenetic reconstruction by PhyML and tree rendering with TreeDyn  [97]. Many options allow to customize this process. The main differences between the service provided by Phylogeny. fr and the present tool is that leBIBI QBPP performs all its analyses in one step from the user's viewpoint, and that its databases are optimized for microbial phylogeny.
Case Studies A short SSU rDNA gene fragment of an unknown bacterium was recently sequenced, and studied in our laboratory. Using the "stringent" database with 50 recruited sequences led to an unexpected phylogenetic tree with multiple warnings (Additional file 1). The interpretation was that the tree was unbalanced due to a large number of Mycobacterium tuberculosis sequences. M. lepromatosis was the closest species in terms of patristic and node distances. As this species has not been validly published yet (this is denoted by the t after the name), the "superstringent" database could not be used. The chosen solution was to increase the number of recruited sequences to 100. The resulting phylogenetic tree was considerably improved (Additional file 2), especially through the recruitment of an outgroup sequence.
The M. leprae cluster is phylogenetically positioned close to M. lepromatosis and M. haemophilum, as expected. The query sequence is that of a new species of Mycobacterium  [98]. This was confirmed by analysis of the rpoB sequence obtained from the same bacterial extract and the rpoB "stringent" database (Additional file 3). A set of 44 partial SSU rDNA sequences (1200-1450 bp) have been obtained from bacteria cultivated from filtrated ion-exchanged tap water. The 44 sequences have been processed batch-wise by leBIBI QBPP with the "TSstringent" database as reference in a five-minute run (Additional file 4). In most cases, the closest sequence to the query belongs to its proximal cluster. Therefore, the taxonomic assignment of 41 strains was highly reliable according to the criteria presented above.
Conclusion LeBIBI QBPP is a unique tool helping biologists to build phylogenies involving prokaryotic species, in order to achieve the taxonomic assignment of sequences of interest. It is unique in the sense that it provides nomenclature-driven specialized databases, as well as a set of efficient programs allowing to reconstruct robust phylogenies, without sacrificing the speed required for an on-line service. First, leBIBI QBPP is different due to the use of a broad range of databases devoted to prokaryotes, and the careful selection of relevant sequences. Also, the emphasis on annotation quality for prokaryotic taxonomy cannot be found elsewhere. Second, multiple alignment trimming, as well as the use of an ML approach improve the quality of reconstructed trees. It could be argued that there are programs that perform better than FastTree for reconstructing phylogenies, but this comes with an important price in terms of processing speed.
Metagenomic analyses are challenging in metazoans, but high-copy number and repeat regions can be assembled from lowcoverage sequencing by "genome skimming," which is applied here as a new way of characterizing metagenomes obtained in an ecological or taxonomic context. Illumina shotgun sequencing on two pools of Coleoptera (beetles) of approximately 200 species each were assembled into tens of thousands of scaffolds. Repeated low-coverage sequencing recovered similar scaffold sets consistently, although approximately 70% of scaffolds could not be identified against existing genome databases. Identifiable scaffolds included mitochondrial DNA, conserved sequences with hits to expressed sequence tag and protein databases, and known repeat elements of high and low complexity, including numerous copies of rRNA and histone genes.
Next, we attempted to annotate the resulting scaffolds against existing databases, including collections of known repeats, and identify potential conserved coding regions, such as gene families and tandemly repeated genes. Mapping of scaffolds against the two available reference genomes can further provide information on the intragenomic organization and their intergenomic distribution across evolutionary lineages. Vice versa, the number and distribution of scaffolds mapped against full genome sequences can contribute a new approach to comparative genomics, and specifically to the analysis of the repetitive fraction that is notoriously difficult to characterize with standard genome sequencing methods. Finally, the scaffolds may represent the associated fauna and flora, including the microbiome and potential food sources, which provide information on the wider ecosystem in which the specimens partake.
This is reflected in the genetic distances based on existing COI data for both pools, which showed a much smaller range of divergences in Weevil than Canopy (supplementary table  S1 , Supplementary Material online). Each pool was generated by combining total genomic DNA extracted from individual specimens. For the Weevil pool, DNA for each individual (species) was added in equal amounts where possible, whereas for the Canopy pool equal volumes from each extraction were added, which resulted in greatly different concentrations due to the different body sizes and relative abundances of species. The Illumina libraries were generated with TruSeq kits (v. 2. 0, PE) and insert sizes of 850 bp (Canopy_Long) and 480 bp (Canopy_Short), respectively, or with the INTERMEDIATE LOW TO INTERMEDIATE VERY LOW X X X X X X X X X X X X , , , X , , , Y Y Y Y DDp FIG.
Sequence Quality Control and Assembly Remaining library adapter sequences were removed with Trimmomatic using default parameters  (Lohse et al. 2012). All libraries were assembled with version 7. 0 of the Celera package using default settings  (Miller et al. 2010 ). Read quality control was part of the Celera pipeline and was based on default parameters. Assemblies required 128 Gb RAM and two 2. 40 GHz Intel Xeon cores. Assembly time was up to 6 days (Weevil library).