dataesr/scientific-paragraphs-categorization huggingface dataset:
Only those individuals with at least one other individual of their species in the database were tested against databases containing all the available samples, except the query individual. N. : number, sp. : species, ge. : genera, ind. : individual. Note that six morpho-species belonging to unknown genera were excluded from the reference databases for genus-level identification but were kept for species-level identification. doi:10. 1371/journal. pone. 0054921. t002 the 50 ha forest dynamics plot at Korup in Cameroon. With this database, we test the performances of DNA barcodes in the identification of individual trees to species or genus under the assumption that the database is exhaustive in terms of species content but not necessarily in terms of haplotype diversity within species. We focus on the official standard barcodes rbcL and matK, and on the noncoding intergenic spacer trnH-psbA.
There are many analytical methods available for the identification of specimens using barcoding  [21, 22]. In this study we compare two methods, one requiring a global multiple alignment and the other based on a basic local alignment search tool (BLAST) which is easy to apply and widely used. Specifically, we address the following questions: (1) How do rbcL, matK and trnH-psbA differ in sequence recovery (first pass comparison)? (2) Are there strong differences in species or genus identification success according to identification methods and markers? (3) Does the gain in information obtained when combining two markers justify the extra cost and labor necessary to sequence a second marker? (4) Does the identification success decrease among closely related species or with intra-species nucleotide polymorphism? (5) Is intraspecies polymorphism correlated to local clade richness, as we would expect if related species hybridize, and is it increasing from the local scale to the regional scale? (6) How is barcoding success for species and genus identification affected when a fraction of the species is missing from the reference database, or in the absence of a local database?.
Study sites and sampling The samples were collected in lowland evergreen forest, in five permanent forest plots where most trees had previously been measured and morphologically identified  [23, 24]. A 50 ha plot located in Korup (Cameroon), provided the dataset used for the evaluation of the barcoding resolution at the local scale. Four 1 ha plots located in the Monts de Cristal  [24] , provided a dataset to study the genus level barcoding success at a regional scale, in absence of a local DNA barcode database. These two areas are particularly interesting for barcoding studies because (i) they have been the focus of intense taxonomic investigation by experienced botanists, (ii) permanent plots are used for ongoing long-term studies and will benefit from our barcoding efforts in future, (iii) the two study areas are located in regions of high tree alpha diversity, where biodiversity monitoring could particularly benefit from a barcoding approach (see Figure  1b  in  [25] ).
Only those individuals that belonged to genera present in our Korup dataset were included in this study, i. e. 148 samples belonging to 86 species, 50 genera and 28 families (Table  S1 ). Herbarium voucher specimens were collected for each sample and deposited at BRLU and the National Herbarium of Gabon (LBV). All necessary permits were obtained for the described field studies. Twenty four of the sampled species were shared between the two sites. In the whole dataset, 18% of the individuals were not identified to a scientifically described species: they belong to morpho-species. These morpho-species represent 17% of the species-level taxa in our dataset in Cameroon and 30% in our dataset in Gabon.
Sequences with a lot of ambiguous bases (. 4%) were not included in the dataset. For trnH-psbA we used the trnHf-05 and psbA3f primers. For rbcL, two primer pairs were used for different samples in different laboratories: rbcLaF/rbcLaR and rbcL1F/rbcL724R. Amplification of matK required the use of two sets of primers (matK1RKimf/matK3FKim-r and matK390f/matK1326r) to obtain sequences for more than half of the samples. To increase the sequencing success for samples that failed to amplify, we repeated the PCR with the same primer pairs up to four times. Assembling, editing and preliminary alignment of the sequences were done in CodonCode Aligner software (version 3. 6. 1, CodonCode Corporation). The sequences were checked in GenBank for possible contaminations. For matK and rbcL, we also checked the position of the sequence in a neighbour joining tree realised with the PAUP software (v.
4. 0b10,  [26] ) based on what would be expected from the phylogenetic position of the species (APG III phylogeny,  [27] ). We found several contaminations of Lejeuneaceae species (leafy liverworts) and these sequences were discarded. All sequences have been deposited in the Barcode of Life Data Systems (BOLD,  [28] ) and will be available on GenBank. We aligned the sequences for individuals belonging to the same species, and we checked the traces to detect and confirm intraspecies sequence polymorphism (note that ambiguous bases were not considered as polymorphism). In the presence of intra-species polymorphism, we carefully compared all reference herbarium vouchers for that species and if we were not certain that the field identification of a sample was correct, this sample was discarded (1% of the samples).
Testing the barcoding accuracy at the local scale The local scale analysis was conducted using the Korup dataset. We tested barcoding success when assigning samples to a genus or to a species with our local DNA barcoding reference database under the assumption that the database is exhaustive in terms of species (i. e. , the sample to test belongs to a species represented in the database) but not necessarily in terms of within species diversity (i. e. , the sample to test may bear an haplotype different from all the conspecific individuals represented in the database). To this end, we excluded the query sample from the database because in a real case study the probability that a query sample comes from an individual already sampled to construct the database is almost zero. In fact, the probability that a query sample bears an haplotype represented in conspecific individuals from the database depends on (i) the haplotypic diversity of the species (number of haplotypes and their relative frequencies at the studied scale), and (ii) the number of conspecific individuals represented in the database.
Moreover, keeping a query sample in a database would imply that there is always a sample in the database with a perfect sequence match even for ambiguous or missing bases, which is unrealistic. Consequently, we evaluated the barcoding success only on species represented by at least two samples, so that at least one sample is still in the database when another is the query one. For the coding genes rbcL and matK, sequences were aligned using CLUSTALX implemented in the software Mega4  [29]. We verified and modified the alignment where inconsistencies were found, and we translated the sequences into amino-acid sequences to guide the alignment. Sequences were trimmed at both end of the alignment in order to avoid too many missing data at the ends, keeping 705 characters in the alignment for rbcL and 1413 for matK.
We did not align trnH-psbA because it was too variable with such a diversity of families. Two methods were used to evaluate barcoding identification success (i. e. the proportion of individuals assigned to the correct species or genus only): the genetic distance between sequence pairs (GD) after a global multiple sequence alignment and the percentage identity (PI) following a basic local alignment search tool (BLAST). As the GD method is based on a global sequence alignment, it was only applied to rbcL and matK. The PI method was applied to the three markers. Details for the two methods are as follows. The GD method relied on an analysis of Kimura's 2-parameter (K2P) genetic distance (  [30] ) matrix among all barcode sequences in the Korup dataset. Analyses were run with the PAUP software (v.
The commands are given in Supporting Information S1. An individual was matched to the species or genus of the individuals with which it shared the highest percentage identity in the matrix. We considered only those sequence pairs that had a minimum of 80% percentage identity. We also excluded the sequence pairs with an alignment length inferior to 250 base pairs for matK and rbcL, and 100 base pairs for trnH-psbA. To calculate the barcoding success for combination of markers, we used the sum of the genetic distances between sequence pairs in the GD method and the sum of the percentage dissimilarity between sequence pairs in the PI method. The significance of differences of barcoding success between markers and/or combination of markers was evaluated with chi-square tests (x 2 ). We investigated the relationships between the barcoding success and the number of samples per species in the reference database with Spearman correlations.
Mean values of the barcoding success were calculated for each species, based on the barcoding success scores with the PI method of all samples belonging to that species. Barcoding success scores were defined as: 1 = the sample is assigned to the correct species only, 0 = the sample is assigned to several species including the right one, -1 = the sample is assigned to one or several species not including the right one. These scores are justified because for a sample that could not be assigned unambiguously to its species, we can consider that a method performs better if it assigns it to multiple species including the correct one rather than to one or several wrong species. With the same method, we tested the relationship between the barcoding success and the species richness of the clade of the query sample in the database.
Clade richness was evaluated in two ways: the number of species belonging to the genus of the query sample in the database and the number of species represented by samples sharing at least 99% percentage identity in a BLAST with the query sample. We also tested if the presence of intra-species polymorphism observed in a species (the number of haplotypes) Table  3. Determinants of the barcoding success of African rain forest trees: Spearman's correlation coefficients between the proportion of individuals correctly identified at the species level and the number of individuals (N. indiv), number of haplotypes (N. haplotypes), or clade richness (Clade R. genus, Clade R. 99% PI) per species in the database. The barcoding success is calculated for each species as the mean barcoding success of all individuals belonging to that species (1: assigned to the correct species only, 0: assigned to several species including the correct one, 21: assigned to one or several species not including the right species).
Clade richness is either measured as the number of species in the database belonging to the same genus as the query individual (Clade R. genus), or as the number of species in the database that have samples with a percentage identity in a BLAST $99% with the query sample (Clade R. PI). doi:10. 1371/journal. pone. 0054921. t003 was decreasing the barcoding success for the samples belonging to that species. Finally, we assessed the influence of the completeness of the database on the species and genus identification success. To this aim, 10%, 20% and 50% of the species were randomly excluded from the reference database. This was done with the PI method and with rbcL, using the same query samples as for the analysis with the full database. We did five trials with different random species selections.
Testing the barcoding accuracy at the regional scale We evaluated the decrease of accuracy in genus identification when a database from another study area is used, and when this database includes the genus, but not necessarily the species, of the query samples. To this end, we selected the 148 samples in the dataset from Gabon that belonged to a genus represented by at least one individual in the database from the Korup plot in Cameroon. We the PI method to assign a genus to the specimens in Gabon, using our local database in Cameroon as reference database. The significance of differences of barcoding success between markers and/or combination of markers was evaluated with chi-square tests.
The two methods and the markers also differed in the degree of ''wrong'' identifications, i. e. when the method matched the query sample to one or several species not including the correct one (see Table  2 ). The percentages of wrong species identifications were always lower with the GD method (between 0 and 2. 3%) than with the PI method (between 1. 4 and 10%). The success rate for species identification was not affected by the number of samples per species, but it was strongly influenced by the clade richness of the query sample (Spearman correlations 20. 364 to 20. 627, P. 0. 001, Table  3 ). The correlation between the number of haplotypes of a species within the reference database and the mean barcoding success of the samples belonging to that species was significant for matK (Spearman correlation 20.
178, P = 0. 04) but not for the other markers (Table  3 ). Unsurprisingly, the success rate for species and genus identification decreased when the reference database did not include all the species of the query samples. For rbcL, the species identification success dropped from 71. 2% with the full database, to 67. 161. 6 %, 61. 761. 7 % and 42. 562. 4 % using a database with 10, 20 or 50% missing species, respectively. The genus identification success dropped from 96. 8% to 92. 061. 3 %, 87. 560. 7 % and 71. 062. 8 % using a database with 10, 20 or 50% missing species, respectively. Table  4. Barcoding success of African rain forest trees at the regional scale for genus identification using the PI method. Barcoding accuracy at the regional scale At the regional scale, we tested the effectiveness of the PI method for genus identification when blasting the 148 trees from the dataset in Gabon belonging to genera present in our local database of barcode sequences from Cameroon.
Here, the genera, but not all species, of the query samples were present in the reference database. Identification success reached 84% with rbcL, 88% with trnH-psbA and 90% with matK but these differences are non significant (Table  4 ; x 2 tests, P. 0. 05). The percentages of wrong genus identifications varied between 8 and 12%. Combining the markers did not improve the success rate compared to using matK alone.
Sequence recovery and practical considerations Among the three barcode markers, matK required much more effort to retrieve the sequences than rbcL and trnH-psbA. Because we used different protocols in different labs and for different makers, we were not able to make a statistical analysis to measure the lab effect and the marker effect, and this result is therefore only indicative. However, a lower amplification and sequencing success of matK has been reported in several other studies (i. e. 42 % of the species  [12]  or around 70%  [5, 18, 31, 32] ). In contrast, the CBoL Plant Working Group  [16] , on a dataset of 367 angiosperms samples, reported that 84% of the Angiosperm species were successfully amplified and sequenced using a single matK primer pair. We used the same primer pair on our dataset, and on the first trial we obtained reliable sequences for only 63% of the species.
6 in Panama, 1. 7 in Cameroon, 1. 8 in French Guiana). However, differences in the methods applied might also affect the results. For example, in their BLAST approach, Kress et al. [18, 31]  did not exclude the query sample from the database tested and considered the highest Bit-Score rather than the highest Percentage Identity. Applying this approach to our dataset using all available samples per pDNA sequence, we obtain 75. 3%, 81. 1% and 92. 1% species identification success for rbcL, matK and trnH-psbA, respectively, which is 4% to 8% better than the values reported in Table  2a. However, we believe that these results are too optimistic. Indeed, even if two sequences from different species are identical except for a few unresolved bases or a slight difference in length, the Bit-Score will be higher between a sequence and itself than between the two sequences (see Table  5  for an example).
This difference substantially increases the percentage of wrong identifications with the PI method. If a query sequence is identical to the sequences of say two species in the database, an ambiguous base occurring in the sequence of the correct species will cause the PI method to match the query sequence to the wrong species while the GD method will conclude that there are multiple possible species assignments (i. e. that there are multiple species with equal genetic distance to the query). This problem with the PI method could probably be solved through a modification of the BLAST algorithm. Using the Bit-Score rather than the PI as criterion for species identification worsens the problem because, as stated above, the Bit-Score also depends on sequence length (Table  5 ). Further improvements of identification success could possibly be obtained with new methods, like the one designed by Little  [21] , incorporating taxon hierarchy and within-taxon variability, or character-based approaches (e.
g. [37] ). Nevertheless, simulation results involving closely related species report only a marginally higher performance of diagnostic-based methods over distance or BLAST methods  [38]. The likely reason is that identification success is mostly limited by the occurrence of shared haplotypes between species, a limit that no method can circumvent. For all barcode markers, there was a significant decrease in successful species identification when the clade richness increased (Table  3 ). This is unfortunate because it is precisely for closely related species difficult to identify in the field without fertile specimens (e. g. in the genera Beilschmiedia, Cola, Rinorea, Trichoscypha. ) that barcoding identification would be most useful. This lower performance of barcoding in species-rich clades might in part be explained by an under or over-estimation of the diversity in the field.
Indeed, in our dataset, we had 17% morphospecies that have not yet been matched to a scientifically described taxon. Some of these morpho-species might belong to species complexes with little morphological differences between individuals that could have been grouped under the same name in our dataset. On the other hand, it is possible that within some genera, morpho-species have been created that do not represent real species, but rather variants of the same species. However, the lower performance of barcoding in species-rich clades might also reflect a limitation of DNA barcoding based on plastid markers. There are many examples of poor resolution of the barcode sequences for sister species (e. g. in Crocus  [13]  and Quercus  [39] , but sister species were well differentiated in Acacia  [40] ).
Sequences availability in the database is a major limiting factor of DNA barcoding. Databases like BOLD or GenBank are general databases (not local), and will probably never be complete. These databases will however be used for barcoding unidentified specimens. In a real case study, it is not unrealistic that a non negligible proportion of samples will belong to species absent from the reference database, which will increase the rates of unassigned samples and of wrong identifications. Our results indicate that it is still reasonable to use a reference database including only 80% of the species present in the study area for a genus level identification (87% identification success with rbcL alone), which can be very useful in highly diverse forests. This is confirmed by our study conducted at the regional scale (without local database) where 90% correct genus identification was achieved using samples from genera, but not necessarily species, included in the database.
However, in several cases, the intra-species polymorphism was not distinguishing samples from Gabon and from Cameroon. Moreover, taxonomic identification errors were limited in our dataset because we checked the herbarium samples carefully. We have good reasons to think that even well-delimited species can be polymorphic at rbcL and/or matK in the African flora. This was observed for example in Santiria trimera, a complex of species where rbcL polymorphism was detected even within a well-defined morphotype in Gabon  [36]. Further population genetics studies for this morphotype demonstrated that it forms a well-defined gene pool matching the biological species concept  (Koffi et al. , unpublished). An increase of plastid genetic diversity from the local scale to the regional scale was also observed in other studies (i.
e. [43, 44, 45] ). Therefore, a few percent of species showing rbcL or matK polymorphism at the local scale does not necessarily reflect taxonomic identification problems. Classical taxonomic studies screen numerous individuals from multiple localities across the range of a given species to distinguish variation within a species from variation between species, in order to identify those characters that are uniquely shared among all members of that species  [7]. Similarly, a reference database for the barcoding of African rain forest trees should include sufficient for each species distributed over all its distribution range in order to be representative of its intra-specific variability.
Acknowledgments We thank  Peter Mambo ,  Sainge N. Moses ,  Raymonde Mboma ,  Raoul Niangadouma  and  Rhiannon Thomas  who helped MP and IP during fieldwork and the lab technician Frederic Walmacq who helped IP. We would like to acknowledge the  Centre de la Recherche Scientifique et Technologique (Gabon)  for logistic support to MP. We thank four anonymous reviewers who provided useful comments on a previous version of the manuscript. We are also grateful to the  Ontario Ministry of Economic Development and Innovation  and to  NSERC  for their support of the BOLD informatics platform.
Distribution of unannotated NXS/T motif in protein secondary structure elements All UniProtKB/Swiss-Prot records are manually curated. Even the prediction results of every protein are manually checked before they are entered into the database. Therefore, annota-tions available from UniProtKB records are considered 'gold standard' in terms of functional annotation. It is expected that unannotated NXS/T motifs not known to carry a glycan (and therefore functionally comparable to any N), should reflect the overall distribution of asparagines. If we consider proteins with crystallographic structures, there are 3779 unannotated NXS/T motifs in human proteins, 739 sites for mouse, 1163 sites for yeast, 103 sites for fly and 223 sites for plant. According to Figure  2A , the percentage of asparagines in the unannotated NXS/T motif is slightly higher than that of all asparagines (Figure  1B ) in a turn conformation.
Distribution of annotated NXS/T motif in protein secondary structure elements There are 2284 annotated NXS/T sites in 592 proteins which have PDB structures in the human dataset. Among these, 1779 sites are in turn (78%), 222 sites in the a-helix (9. 7%) and 283 sites in the b-sheet (12. 3%) (Figure  2B ). This distribution, based on analysis of the entire set of available structures, is consistent with recent results in the mouse showing 75% of NGSs in turns and 15% in b-sheets  [10]. The same tendency is observed in our analysis for mouse, fly, plant and yeast. In all  the species the percentages of asparagine that are part of the annotated motif is higher in loops/turns. The distribution of annotated NXS/T sites is significantly different from the distribution of the unannotated NXS/T in all the species.