dataesr/scientific-paragraphs-categorization huggingface dataset:
e. , datasets including closely related morpho-species; and (iii) datasets with overall homogenous sequence length. Indeed, the reduced size of the dataset might explain failure to reject the null model for ''Spinose B'' and ''Microperforate'' species delimitations and calls for more sequences from a larger set of morpho-species. In the case of the tree ''Spinose B'', integration of the divergent morpho-species Turborotalia quinqueloba might also be at stake in putative species delimitation failure. Finally, the GMYC-delimitated clusters within the Type III of O. universa (Fig. 3 ), which apparently does not correspond to a distinct genuine species, show that sequence length heterogeneity may strongly influence GMYCbased species delimitation.
The peculiar color of G. ruber (pink) reinforces the species status of this genetic type. Types IIa and IIb NCBI+new: number of sequences available on NCBI and number of new sequences added. Patristic literature: number of sequences included in the dataset based on literature species definitions; inter, intra, intraG: number of sequences used for inter-genetic type, intra-genetic type and intra-genomic distances calculations, respectively. *: number of sequences used for total distances calculation (for the morpho-species for which no cryptic species have been identified). ABGD: number of sequences included in the dataset used for ABGD species delimitation. GMYC: number of sequences included in the dataset used for GMYC species delimitation. doi:10. 1371/journal. pone. 0104641. t003 Figure  5.
Geographic distribution of genetic types of Globigerinella siphonifera and Globigerinoides ruber. Gray shading indicates the relative abundance of each morpho-species in planktonic foraminiferal assemblages from surface sediments, interpolated from data in the MARGO database  [2, 79]. The species delimitations are according to the literature and the method noted in red in the left corners of the maps. The fourth map corresponds to a synthesis. Geographic location data from  [9, 13, 16, 17, 29, 31, 32, 33, 36, 39, 45, 60, 80, 81]. doi:10. 1371/journal. pone. 0104641. g005 of G. ruber, together re-named Globigerinoides elongatus after  [29] , are distinguished from other G. ruber genetic types thanks to their flattened final chamber. Morphological criteria can further be argued to reinforce the species status of Type IIb, which has significantly smaller shells than other G.
Nevertheless, our analyses show that heterogeneity of the sequence dataset is detrimental for objective species delimitation. The ABGD method requires long enough and strictly overlapping sequences. Consequently, the design of the ABGD dataset excluded part of the original information, i. e. , potential cryptic species. Even if the GMYC method requirements are less strict, our study shows that heterogeneity in length and/or quality of the sequences can lead to non-significant results pointing to unreliable species delimitations. Furthermore, in the case of GMYC, more sequencing effort is required to get significant species delimitations or to conclude on the taxonomic status of some of the singletons defined by this method. We note in addition that the extent of intra-genomic diversity, which may mask recent speciation events, remains unfortunately poorly investigated in planktonic foraminifera.
Our study shows that running automatic delimitation of planktonic foraminiferal cryptic species requires a high level of dataset quality. Sequences should ideally cover the six variable regions of the 1200 bp of the end part of the SSU. For each genetic type, a number of clones should be sequenced from a few individuals in order to estimate the extent of intra-genomic distances and to detect possible species over-splits whenever the automatic methods separate clones from the same individuals into different putative species. Yet, the development of automatic species delimitation methods is still at the beginning, and new and user-friendly methods are flourishing. Fast and accurate species delimitation methods have to be expected in the near future, that would, in particular, integrate non-concerted evolution as a parameter.
The species distribution of modern planktonic foraminifera is complex, with some species being cosmopolitan, and other being narrower specialists. Sampling sites remain in most cases limited, studies of seasonal distribution variations are rare, and the vertical distribution of species in the water column remains largely unknown, preventing a finer understanding of the ecology (e. g. , niche partitioning) and biogeographical history of these species, especially when they are found sympatrically. From that point of view, sequencing of environmental DNA and RNA represent a great opportunity to better characterize the distribution of planktonic foraminiferal cryptic species. Such datasets are bound to multiply as Next Generation Sequencing (NGS) methods are becoming more commonplace. Nevertheless, the correct interpretation of environmental NGS datasets first needs accurate species definitions, i.
Ethics statement The field collections carried out for the purpose of this paper did not involve endangered or protected species. The sampling was carried out in the open ocean and followed the regulations for the exclusive economic zones (EEZ) of the coastal countries, provided for each expedition by the respective authorities (for exact location of sampling stations, see Table  S2 ). No specific permission was required to collect the analysed plankton. Geographic distribution of genetic types of Hirsutella hirsuta and Globigerinita glutinata. Gray shading indicates the relative abundance of each morpho-species in planktonic foraminiferal assemblages from surface interpolated from data in the MARGO database  [2, 79]. The species delimitations are according to the literature and the method noted in red in the left corners of the maps.
For G. glutinata, only delimitations according to the ABGD method are reported as genetic type delimitation by the GMYC method lead non-significant results. Geographic location data from this study and from  [13, 17, 16, 32, 81]. doi:10. 1371/journal. pone. 0104641. g006 Material Planktonic foraminiferal SSU rDNA data were extracted from the NCBI query portal (http://www. ncbi. nlm. nih. gov/gquery) on January 17 th , 2013, representing a total of 1232 SSU rDNA sequences. Additional SSU rDNA data were gathered from living foraminifera we collected with plankton tows (63 to 100 mm mesh sizes) in the North Atlantic (cruises C-MarZ and FORCLIM7), Pacific (cruises REVELLE, KT-06 and GYRAFOR-A) and Indian (cruises OISO-4, OISO-19, Melville and GYRAFOR-B) Oceans (Fig. 1 ). During these cruises, specimens were taxonomically identified at the morpho-species level before being individually isolated into the GITC* DNA extraction buffer  [37].
To evaluate intra-individual variability, 2 to 7 clones were selected per individual for sequencing. For the morpho-species Globigerina bulloides, Beella digitata, Orbulina universa and Sphaeroidinella dehiscens, the amplified PCR products were directly sequenced. The clones and PCR products were sequenced using an ABI prism 3100 sequencer (Applied Biosystems) at the Station Biologique de Roscoff and an ABI prism 3730XL sequencer (Applied Biosystems) from Biofidal service provider (Vaulx-en-Velin, France). The new sequences obtained in this study were deposited in Genbank with accession numbers KJ633126 to KJ633260.
Synonymy, ambiguous assignation and dataset assembly From the 1232 SSU rDNA sequences downloaded from NCBI, 5 had no taxonomic assignation at a species rank and were not retained for further analyses. The 12 sequences labeled Globigerinella aequilateralis were renamed Globigerinella siphonifera following  [5]. The 9 sequences corresponding to the right-coiled morphotype of Neogloboquadrina pachyderma were renamed Neogloboquadrina incompta following  [46]. Finally, because of the polyphyletic nature of sequences from specimens identified as Globigerinoides ruber  [45] , we clustered Globigerinoides ruber and Globigerinoides conglobatus as a single taxonomic unit here termed ''G. ruber+conglobatus''. Several sequences were found to be highly divergent from other sequences attributed to the same morpho-species and/or identical to sequences attributed to a different morpho-species.
Following  [17]  and due to ambiguous identification, we also excluded from our analyses the 3 sequences as G. scitula. We thus retained 1217 sequences from NCBI. Adding the 135 newly obtained sequences, we finally constituted a dataset of a total of 1352 sequences belonging to 25 morpho-species (Table  S2 ; Table  3 ). Due to differences in scientific objectives and amplification strategies, the assembled dataset contains sequences of various lengths (from 196 to 3412 bp) which do not overlap systematically. As a consequence, we defined a block of sites representing the best compromise between sequence length and number of sequences. This block of ,700 bp, located between the universal primers SR2 (  [63] ; position 1277-1263 in Saccharomyces cerevisiae, 59-GGTGGTGCATGGCCGG-39) and BMB-CR (  [64] ; position 1624-1646 in S.
Patristic distances For each morpho-species, sequence alignments were separately and automatically generated using the Mafft software  [66]. Best substitution models were determined using Modeltest (File S1;  [67] ), and most likely phylogenetic relationships were reconstructed using PHYML  [68]. Patristic distance (path length between tips in the most likely topology) matrices were then calculated using the APE package of R software  [69, 70]. Patristic distances were preferred over pairwise distances to better account for multiple substitutions at the same site. For some of the studied morphospecies, as for example G. ruber and G. sacculifer, many of the available sequences were identical while for others, as for example Turborotalia quinqueloba and Neogloboquadrina pachyderma, almost all the deposited sequences were different.
This phenomenon is probably an artifact reflecting the number of sequences available, the quality of sampling and/or an arbitrary selection performed by the authors for sequences to be deposited. As a consequence, in our calculations, we retained a unique sequence per pool of identical sequences. We calculated patristic distances within each morpho-species and extracted distances within the defined genetic types and among individuals from different genetic types. We also calculated intra-genomic distances (i. e. , distances observed among clones of the same individual) for each morphospecies. Since this calculation only requires individual and morpho-species identifications, sequences that lack information on their genetic type in NCBI were added to the analysis. Considering the lack of several genetic type labels in NCBI and the shortness of many sequences, it appears finally that the dataset used for calculation of patristic distance values was reduced to less than 40% (395 sequences on a total of 1352) of the available sequences (Table  3 ).
Automatic genetic type delimitation methods Genetic type delimitations used for the patristic distance approach evolved from the application of a body of various methods displaying advantages and pitfalls (e. g. those methods rely on prior species delimitation hypotheses). To avoid this, we used the Automatic Barcode Gap Discovery (ABGD;  [42] ) and General Mixed Yule Coalescent (GMYC;  [43] ) methods as independent and complementary approaches for species delimitation in modern planktonic foraminifera. ABGD and GMYC newly-defined delimitations were evaluated by running the patristic distance approach again; finally, we consider as most probably accurate the delimitations that are confirmed with the patristic distance approach. Automatic Barcode Gap Discovery (ABGD). ABGD is an automatic procedure that sorts sequences into putative species based on a barcode gap, i.
e. , the gap in genetic distances distribution between intraspecific and interspecific diversity  [42]. This barcode gap is observed whenever the divergence among organisms belonging to the same species is smaller than divergence among organisms from different species. ABGD is more efficient when the number of species is not too large and when the interspecific evolutionary rate differences are limited  [42]. Consequently, given the particularly heterogeneous rates of molecular evolution in planktonic foraminifera  [13, 72] , we ran ABGD for each morpho-species alignment independently, on the same SSU rDNA region as treated for patristic distances calculations. We used the software default settings for gap detection and the K-80 pairwise distance  [73]. As this method does not rely on a prior definition of taxonomic units, the analyzed dataset also includes the sequences that lacked genetic type label.
In some cases, the ABGD method identifies several MOTUs plateaus corresponding to decreasing number of genetic types delimitated within the dataset  [42]. First, when necessary, we discarded the delimitation hypotheses that assigned clones from the same individual to distinct genetic types. Among the remaining hypotheses, we considered the first MOTUs plateau (i. e. maximum number of MOTUs) as corresponding to the putative species delimitation  [42, 44]. Indeed, this first plateau is most likely marking the distance gap between intra-species distances (i. e. , the shortest distances within the dataset) and the distances between the closest putative species  [44] , whereas the second plateau usually reflects the distance gap between clades of putative species. General Mixed Yule Coalescent (GMYC).
Multiple species alignments were automatically generated using the Mafft software  [66]  and checked manually alignment errors. We retained a unique sequence per pool of identical sequences and the best substitution model was selected using Modeltest  [67]  under AIC criterion  [76]. Ultrametric trees were generated with the BEAST software using a lognormal relaxed molecular clock  [77]  without calibration point. These trees were then used as an input for GMYC species delimitation using the Splits package  [78]  for R software  [69].
Acknowledgments We thank two anonymous reviewers for their valuable comments. We are grateful to the  Institut Polaire Francais Paul Emile Victor (IPEV)  for the organization of the OISO and GYRAFOR-B cruises and to the  Institut de Recherche pour le Developpement (IRD)  for the GYRAFOR -A cruise. Support from the  OSU-Institut Pythe  ´as is acknowledged.
The authors declare funding from the following programs (see details for the role of each funders): Grants from the  FR41 of UniversiteLyon 1  (molecular analyses);  INSU INTERRVIE program  (sampling and molecular analyses);  ANR-06-JCJC-0142   PALEO-CTD (sampling) ;  ANR Blanc FORCLIM (sampling) ;  ANR-11-BTBR-0008  OCEANOMICS (molecular analyses). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
uvula* II G. uvula* G. glutinata n. d. inconclusive I G. glutinata* II G. glutinata* III G. glutinata* IV G. glutinata* C. nitida n. d. C. nitida inconclusive C. nitida* (1): delimitation invalidation due to improbable inter-species distances; (2): delimitation invalidation due to overlapping of intra-and inter-species distances; n. d. : no genetic type defined in the literature; n. a. : genetic type not included in the dataset; *: non-significant likelihood ratio test. doi:10. 1371/journal. pone. 0104641. t001Figure3. Ultrametric trees ''Spinose A'', ''Spinose B'' and ''Microperforate''with GMYC delimitations. Delimitations are significant only for ''Spinose A'' (see Table2). Colored branches correspond to GMYC clusters and outer circles correspond to the names of the morpho-species (outer arc) and plausible biological species (inner arc) (see Table1.
7 ) contains most, but not all, PNW VGIIb strains along with strains FIG 6 fineStructure analysis of dominant VGII subtypes. To identify the population structure and possible recombination events within VGII, fineStructure analysis was performed using the SNP matrix developed for ϾFig. 2. Using one representative from each of the 32 VGII subtypes, whole-genome SNP data were reduced to a pairwise similarity matrix. The population structure is identified using this similarity matrix, where the underlying model assumes individuals within populations will share more regions of their genome with each other and have a similar amount of admixture with individuals from different populations. Identified populations are merged one at a time, at each step using the most likely population merge, to relate populations to each other through a tree.
MATERIALS AND METHODS Cryptococcus gattii isolates and Illumina sequencing. One hundred eighteen C. gattii isolates were included in the analyses (Table  1 ). Information for clinical strains was collected with the approval and oversight of the institutional ethic committee. Additionally, the two near neighboring taxa, C. neoformans var. neoformans strain JEC21 and C. neoformans var. grubii strain H99, were obtained as whole-genome assemblies from NCBI (http://www. ncbi. nlm. nih. gov/Genbank/index. html) and the Broad Institute of Harvard and MIT (http://www. broadinstitute. org/annotation/ genome/cryptococcus_neoformans_b), respectively. The 118 C. gattii isolates were selected based on previously established MLST sequence types (Table  1 ) to ensure global genomic diversity. The previously published VGIIa reference genome (R265) (see the Broad Institute link above) was resequenced for this analysis, due to the relatively high number of likely errors in the existing Sanger-based reference genome  (17).
Prior to sequencing, the libraries were quantified with quantitative PCR (qPCR) on the ABI 7900HT (Life Technologies Corporation, Carlsbad, CA, USA) using the Kapa library quantification kit (catalog no. KK4835; Woburn, MA, USA). Libraries were sequenced to a read length of 100 bp or 150 bp on the Illumina HiSeq system, to 75 bp or 100 bp on the GAIIx system, or to 250 bp on the MiSeq system. All WGS data files have been deposited in the NCBI Sequence Read Archive (http:// www. ncbi. nlm. nih. gov/bioproject; project PRJNA244927). SNP detection and phylogenetic comparison. (i) Genome assembly. All sequenced genomes were de novo assembled using the SPAdes assembler v2. 5. 0  (46). Raw contigs were filtered by length and coverage, as SPAdes tends to produce many short and low-coverage contigs.
For isolates assembled from 100-bp or 150-bp paired-end data, contigs shorter than 200 bp were filtered and removed, while contigs shorter than 500 bp were filtered/removed in genomes assembled from 250-bp paired-end MiSeq data. Additionally, for every genome, contigs with coverage below 20% of the average coverage of the 20 largest contigs were filtered out. (ii) MLST typing. The seven MLST loci, CAP59, GPD1, LAC1, SOD1, URA5, PLB1, and intergene sequence (IGS), were aligned to the contigs according to the consensus MLST scheme for the C. neoformans/C. gattii species complex  (10). Allele types (ATs) and sequence types (STs) were assigned according to the MLST database at http://mlst. mycologylab. org, and new ATs and STs were added to this database. A maximum likelihood tree was constructed based on ST locus scores using the program MEGA v.
5. 05  (47). (iii) SNP variant detection. Illumina read data were aligned against the herein newly obtained R265 (high virulent VGIIa Vancouver Island outbreak strain) assembly using Novoalign 3. 00. 03 (Novocraft Technologies, Selangor, Malaysia), and then SNP variants were identified using the GATK Unified Genotyper v2. 4  (48). SNP calls were then filtered (http://tgennorth. github. io/NASP/) to remove positions with less than 10ϫ coverage, with less than 80% variant allele calls, or identified by Nucmer as being within duplicated regions in the reference. In the cases of the VGIIb and VGIIc analyses, the read data were aligned by the same methods using the genome assemblies for B9563 and B8825, respectively. In total, five SNP matrices with different taxa were produced: (iv) Phylogenetic analysis.
To understand relationships between isolates, we conducted whole-genome SNP typing (WGST) as previously described  (17, 39, 49). Maximum-parsimony SNP trees, based on each of the above five SNP matrices, were constructed, using PAUP* v. 4. 0b10  (50)  and visualized in FigTree v. 1. 3. 1 (http://tree. bio. ed. ac. uk/software/ figtree/). The C. gattii VGII tree was rooted using the large C. gattii tree, while the VGIIa, VGIIb, and VGIIc trees were each rooted using the other VGII subtypes as outgroups. Roots are shown simply as the midpoint on the branch determined by outgroup rooting, because the exact number of SNPs assigned to each branch varies slightly with the inclusion of different taxa, due to the SNP filtering strategies employed. The phi test for recombination was conducted on the whole VGII SNP data set, as well as the same data set broken into 500-bp nonoverlapping windows, using the PhiPack software  (51).
Moderate BSR scores occurred in a few cases when limited read data successfully aligned to a putatively missing gene (e. g. , is-cDNA 9 in VGIIb), or when read data aligned to truncated genes. In an effort to be conservative in identifying differential gene content using short-read data, gene differences that could not be confirmed with read data are not reported. This most often occurred in the case of putative genes that contained highly repetitive sequences, which cause assembly and definitive alignment issues in short-read data. (iii) Putative gene characterization. is-cDNAs, which were confirmed as variable with read data (see above), were translated into amino acid sequences and then searched against the Pfam database (http:// pfam. sanger. ac. uk/) to help identify potential protein functions.
ACKNOWLEDGMENTS We thank all our colleagues from around the world, who have submitted isolates for this study. This study was supported by a grant from the  National Institutes of Health  ( R21AI098059 [P. K. , D. M. E. , G. R. T. , S. R. L. , and W. M. ]) and an  NHMRC grant  ( APP1031943  [W. M. , T. C. S. and G. H. ]). The findings and conclusions of this article are those of the authors and do not necessarily represent the views of the  Centers for Disease Control and Prevention.
1 9. 90EϪ14 is-cDNA 11 Major facilitator superfamily PF07690. 11 7. 20EϪ23 is-cDNA 12 Phosphatidylethanolamine-binding protein domain PF01161. 15 5. 70EϪ18 is-cDNA 13 Fungus-specific transcription factor domain PF04082. 13 2. 90EϪ05 is-cDNA 14 Glycosyl hydrolase family 32 N-terminal domain, glycosyl PF00251. 15, PF08244. 7 1. 8EϪ51 4. 8EϪ10 hydrolase family 32 C-terminal domain is-cDNA 15 No significant matches is-cDNA 16 Alcohol dehydrogenase GroES-like domain, zinc-binding PF08240. 7, PF13602. 1 8. 5EϪ6, 2. 8EϪ19 dehydrogenase is-cDNA 17 No significant matches Present in PNW VGIIb is-cDNA 18 E1-E2 ATPase, haloacid dehalogenase-like hydrolase 3. 6EϪ47, 3. 1EϪ21 is-cDNA 19 No significant matches is-cDNA 20 No significant matches Present in PNW VGIIa is-cDNA 21 Sugar (and other) transporter PF00083.
19 1. 20EϪ94 is-cDNA 22 Alcohol dehydrogenase GroES-like domain, zinc-binding PF08240. 7, PF00107. 21 1. 6EϪ28, 1. 6EϪ16 dehydrogenase is-cDNA 23 Major facilitator superfamily PF07690. 11 1. 20EϪ17 is-cDNA 24 No significant matches is-cDNA 25 Aldo/keto reductase family PF00248. 16 1. 30EϪ52 is-cDNA 26 Protein of unknown function, putative collagen-binding domain PF13204. 1, PF12904. 2 2. 1EϪ54, 7. 4EϪ19 of a collagenase is-cDNA 27 Sugar (and other) transporter PF00083. 19 3. 70EϪ93 is-cDNA 28 Alcohol dehydrogenase GroES-like domain, zinc-binding PF08240. 7, PF00107. 21 5. 3EϪ28, 1. 2EϪ15 dehydrogenase is-cDNA 29 Fungus-specific transcription factor domain, fungal Zn(2)-Cys(6) PF04082. 13, PF00172. 13 2EϪ16, 1. 8EϪ4 binuclear cluster domain is-cDNA 30 No significant matches is-cDNA 31 No significant matches is-cDNA 32 Aspartyl protease PF09668.