dataesr/scientific-paragraphs-categorization huggingface dataset:
These observations suggest that once inserted in the GBS genome, Tn916 and Tn5801 are maintained, as they seem to impose a limited fitness cost to GBS. Indeed, it has been suggested that due to the intricate mechanism of Tn916 regulation, Tn916 gene expression likely results in minimizing the biological cost to the host  31, 32. It should be noted that the dominance of the tet(M) gene carried by these ICEs is not specific to GBS  31. Indeed, 133 of 271 Enterococcus faecalis genome sequences available in GenBank as of 15 May, 2014 contain a tet(M) gene, while only 11 contain tet(S) and one tet(O). Furthermore, TcR genes remain the most abundant antibiotic resistance genes identified in human gut microbiota as recently reported  33. Therefore, the strong selective pressure that tetracycline usage has exerted is more widely observed, but in the case of GBS it has led to the emergence of pathogenic clones.
Finishing was performed by Sanger sequencing of PCR products corresponding to gaps and to low-quality regions. The COH1 genome was annotated as previously described  43. Briefly, coding sequences were defined by combining Genmark predictions with visual inspection of each open-reading frame for the presence of a start codon with an upstream ribosome-binding site and BLASTp similarity searches on Uniref 90, Trembl and Swissprot databases. Function predictions were based on blastp similarity searches and on the analysis of motifs using the Pfam databases (http://pfam. xfam. org/). The five other reference genomes were obtained after ordering the Velvet assembled contigs by using the MAUVE software  44  and the genome of 2603V/R as reference  45. They were automatically annotated by using the RAST server  46.
De novo assembly and sequence analysis Genome sequences were assembled using the Velvet software  47  with an optimized k-value and a minimal coverage of 10. MLST types were determined by extracting the sequences of the seven genes of the GBS MLST system  5  and comparing them with the known STs from the GBS MLST web server (http://pubmlst. org/sagalactiae/). Serotypes were determined by BLASTn similarity search using as query the nucleotide sequences of the ten cps loci corresponding to the 10 known GBS serotypes. Antibiotic resistance genes were searched by BLASTx search with the protein sequences of 38 resistance genes from Gram-positive bacteria for tetracycline, macrolides, streptomycin, kanamycin, spectinomycin, streptothrycin, lincosamides and chloramphenicol. Genomic islands encoding antibiotic resistance genes were analysed by extracting the sequences surrounding the antibiotic resistance genes.
Phylogenetic analysis A phylogeny of the 229 GBS isolates sequenced here and the fish isolate SS1219 (ref. 48)  was constructed by considering nucleotides corresponding to all variable positions located in the core genome for example, in regions present in all isolates. Individual phylogenies of the six CCs were performed after removal of the genomic islands not shared by all isolates, and recombined regions. Automatic detection of recombined regions using the Gubbins software  14  was used to predict genome variation that had arisen by homologous recombination. The ability of this method to robustly predict recombination across the whole population was limited due to the observed genetic diversity of the population, and the corresponding deep branches of the phylogeny. We therefore combined this automatic detection with the visual inspection of variation in each of individual CCs in SyntView 49 viewer (http://genopole.
pasteur. fr/SynTView/flash/Streptococcus_agalactiae), to identify clusters of SNPs likely to have arisen by recombination. Phylogenetic relationships were inferred using the Maximum Likelihood (ML) method in MEGA5 version 5. 10 (ref. 50) and the BEAST software v1. 7. 5 (ref. 51). The confidence of the ML tree was estimated using the bootstrap method with 200 bootstrap replications. The trees of the six individual CCs were rooted by using the sequence of an isolate belonging to another CC. The species tree is unrooted. The Bayesian phylogenetic software of BEAST v1. 7. 5 (ref. 12) was used to investigate the temporal evolution of CC1, CC17, CC19 and CC23. The non-recombined variable sites as defined above for each CC was used as markers of ancestral polymorphisms and the year of isolation for each isolate (Supplementary Table  1 ) was used to calibrate the clock rate.
Therefore, final phylogenetic analyses were run in triplicate for 100 million Markov Chain Monte Carlo generations with 10% burn-in using the above parameters. Overall congruency of Bayesian models were observed for all four CCs analysed by BEAST; however, the lack of a wide temporal sampling of strains for CC1 resulted in the lack of an accurate root to accurately define a clock rate, hence the broad confidence values observed in Fig. 1e. Path-O-Gen (http://tree. bio. ed. ac. uk/software/pathogen/) was used to identify a 'molecular clock' evolutionary signal as defined by a linear association between the ML root-to-tip branch lengths and year of strain isolation for the five major TcR sub-lineages. This also enabled an alternative method to the Bayesian approach of BEAST to date the expansion event of the five major TcR sub-lineages.
ML trees were generated in the sub-lineages: CC1 Tn916-1 (n = 26); CC19 Tn916-17 (n = 20); CC17 Tn5801-11 (n = 41); CC17 Tn916-12 (n = 23) and CC23 Tn5801-2 (n = 18) from the same recombination-removed SNP alignments used for BEAST analyses. The maps were generated by using the SyntView software. Isolates were ordered according to the distance from the reference genome depicted at the inner circle. CC numbers are indicated in the centre. Recombined regions compared with the reference genome correspond to regions with a higher density of SNPs indicated by short lines on each circle corresponding to one strain. Around the outside circle are the relative positions of selected antigenic loci. The reference genomes were BG-NI-011 for CC1, DK-NI-008 for CC10, COH1 for CC17, RBH11 for CC19, CCH210801006 for CC23 and Bangui-IP-105 for CC26.
Acknowledgements This work was supported by the  French National Research Agency  (Grant  ANR-08-GENM-027-001  and  2010-PATH-004-02 ) and by the  Labex IBEID. The  Institut Pasteur Genopole  is a member of  France Génomique  ( ANR10-IBNS-09-08 ). M. R. D. is supported by the  National Health and Medical Research Council of Australia  ( 565526  and  635250 ). M. T. G. H. and J. P. were supported by  Wellcome Trust  grant  098051. This study was in part supported by the  European Commission Seventh Framework  (grant agreement number  200481 ) as part of the  DEVANI program. We would like to acknowledge  Dr Sri Sriprakash  and  Ms Karen Taylor  for their involvement with the Australian GBS strain collection and  Alexandre Almeida  for critical reading of the manuscript.
The stocking history of each stocked lake is recorded by the provincial government since 1900 in a central database. For each stocking event, a standardized stocking form is filed, compiled in the database and carefully verified to detect potential errors. Although the database is reliable, we performed additional validation of the data for the stocked lakes used in this study for further improvements. Lakes were categorized as 'unstocked' when no stocking was recorded and as 'stocked' when at least one stocking event was documented. The mean number of stocking events in stocked lakes was 18 (range 5-50). A source lake was a lake where broodstock was collected and used to produce juveniles in captivity, which were released in wild populations, usually at age 1+. Of the 72 sampled lakes, 10 were source lakes (n = 432), 40 were unstocked lakes (n = 1600) and 22 were stocked lakes (n = 971).
Genetic diversity The software MICRO-CHECKER  (Van Oosterhout et al. 2004)  was used to assess the potential presence of null alleles and large allelic dropout. F IS and number of alleles per locus (A) were estimated using FSTAT 2. 9. 3  (Goudet 2001 ). Allelic richness (A R ) adjusted for the smallest sample size (n = 15) was calculated using HP-RARE 1. 0  (Kalinowski 2005). We calculated mean A R for unstocked and stocked populations and tested for difference using Student's t-test. We used a linear model to assess the relationship between A R and the natural logarithm of lake area [ln(LakeArea)] for unstocked and stocked lakes. Strength of the correlation and significance were assessed with Pearson's product-moment correlation coefficient. Expected and observed heterozygosities (H E and H O, respectively) were obtained with GENETIX 4.
The last group was composed of the four lakes with historical and contemporary samples. Each STRUCTURE run started with a burn-in period of 50 000 followed by 300 000 Markov Chain Monte Carlo (MCMC) steps. For each lake group considered, we selected the best k according to the variation of likelihood  (Pritchard et al. 2000) , the Δk method  (Evanno et al. 2005 ) implemented in STRUCTURE HARVESTER  (Dent and vonHoldt 2012)  and consistency of STRUCTURE outputs. The software DISTRUCT  (Rosenberg 2004 ) was used to plot STRUCTURE outputs. To further investigate the impact of stocking, we quantified proportions of fish belonging to local clusters, nonlocal clusters, or to a category of individuals putatively admixed between local and nonlocal populations and representing putative hybrids. For this analysis, we used lakes sampled within the five stocked regions and excluded source lakes.
For these analyses, source q-membership was used, as many sources could have been used for each stocked lake. Lastly, we assessed the effect of the time spent (in years) since the last stocking event on the populations' source q-membership. For this latter analysis, as stocking pressure may influences populations' source q-membership, we only used pairs of source and stocked populations that exchanged similar number of fish per hectare to eliminate potential bias originating from differences in stocking intensity. The only stocking intensity in number of fish stocked per hectare for which we had consistent data over the 40 years period was 17-22 fish stocked per hectare. Nine pairs of source and stocked populations corresponded to this criterion  (# unstocked-# source: 11-47, 11-68, 14-10, 20-68, 22-21, 23-68, 32-48, 33-68, 42-37).
In every stocked region, stocked populations shared various proportions of ancestry with their source(s), while unstocked populations corresponded to a unique cluster (except lakes 25 and 26; Fig. 4B ; Table  3 ). Finally, k = 4 was retained in the case of the comparison of historical and contemporary samples  (Fig 4C). Each unstocked population corresponded to the same unique cluster in both contemporary and historical samples. Admixtures analyses also revealed that stocking had a profound impact on the genetic integrity of lake trout populations. Considering the whole dataset, the proportion of fish assigned to the local cluster, a nonlocal cluster or a putatively admixed category differed between unstocked and stocked lakes (v 2 (2) = 991, P < 0. 0001; Fig  5A ). Over the 16 unstocked populations sampled in the five different stocked regions, 97.
Acknowledgements We are grateful to  Craig R. Primmer  and two anonymous reviewers for their constructive comments about the manuscript. We thank the numerous sport fishermen, outfitters, controlled harvested zones (ZECs), National parks, Makivik Corporation, GENIVAR and numerous biologists and technicians from  MDDEFP (Qu ebec ) who helped with the field work. We also want to thank  M. -H. Perreault ,  L. Papillon ,  G. Côt  e and  I. Gilbert  who helped with laboratory work, as well as  E. Normandeau  for insightful discussions about analyses and bioinformatics help. Research was supported by grants from the  Natural Sciences and Engineering Research Council of Canada (NSERC) , the  Fonds  qu eb ecois de recherche sur la nature et les technologies (FRQNT) and from  Qu ebec-Oc  ean to EV, from  R eseau Aquaculture Qu  ebec to CP, from the  Canadian Research Chair  in genomics and conservation of aquatic resources to LB and from MDDEFP.
Data analysis Microarray background subtraction and data analysis were conducted in R statistical environment using GenePix Pro 6. 0 and LIMMA as described previously  [30, 34]. The Benjamini-Hochberg method was used to control the false discovery rate to correct for multiple testing  [35]. Genes with a fold change equal to or above 1. 4 and an adjusted p#0. 05 were considered to be significantly differentially regulated. Annotation and putative protein functions were based on the National Center for Biotechnology Information Refseq database and Clusters of Orthologous Groups of protein (COG) database  [36]. Operon predictions, COG assignments and COG functional categories were obtained from the Microbes Online database  [37]. Microarray data are available in the ArrayExpress database (www.
Validation of microarray results using quantitative reverse transcription PCR (qRT-PCR) Differential expression of selected genes was validated by qRT-PCR of cDNAs using SYBR Green-based detection on a Rotor Gene 3000 system (Corbett Research, Sydney, Australia) as reported previously  [30]. To normalize the amount of mRNA in each reaction, a pool of six reference genes TDE2535, TDE0872, TDE1208, TDE1999, TDE1226 and TDE0002  [30]  was assessed for expression stability using the geNorm program  [38]. Analysis indicated that the geometric mean of the three most stable genes TDE2535, TDE1208 and TDE0872 with average pair-wise variations of 0. 146 was sufficient to calculate a normalization factor for each sample. Primers for the validation gene set TDE1624, TDE1625, TDE1626, TDE1627, TDE1259, TDE2119, TDE2120, TDE0405, TDE0762, TDE1669, TDE0387, TDE0627 and TDE0832 were designed using Primer3  [39]  (Table  S1 ).
denticola conditioned medium (which had no detectable free glycine) in preference to use of pure glycine standards to account for matrix effects from the medium. The difference in free glycine levels relative to that at t = 0 h for each replicate was expressed as a function of P. gingivalis cell numbers in different media. A regression line was fitted using a linear mixed modelling approach, which allowed the regression lines to be fitted with random slopes by considering the fixed effects for the different growth medium and time interaction (SPSS version 20, IBM, IL, USA). For the determination of the total glycine content of cell-free media samples were freeze-dried and hydrolyzed at 150uC for 2 h in the presence of 6 M HCl containing phenol (1% v/v).
denticola. The increased expression of genes encoding components of the glycine cleavage and glycine reductase systems was confirmed by qRT-PCR using TDE2535, TDE0872 and TDE1208 as stable reference genes. There was a significant correlation between the expression ratios determined by both microarray and qRT-PCR (R 2 = 0. 9839) (Figure  S1 ). This comparison revealed a slight compression of the gene expression data from the DNA microarray analysis and gave a higher up-regulation of components of the glycine cleavage and glycine reductase systems with a. 1. 4 fold up-regulation of TDE1627, the T-protein of the glycine cleavage system and TDE2120, the GrdE2 of the glycine reductase system (Table  2 ). The COG category with the most differentially expressed genes was O (posttranslational modification, protein turnover, chaperones), where 10 genes (18.
However, free glycine production by P. gingivalis in OBGM/T. denticola conditioned medium was 0. 54960. 090 mmole/10 9 cells, which was more than three times that observed in OBGM or OBGM/PBS (p,0. 01). To determine the source of this increased free glycine the free and total glycine concentrations were determined prior to and 48 h after P. gingivalis inoculation in OBGM/T. denticola conditioned and OBGM/PBS media. Peptide-bound glycine (totalfree) decreased by 1. 26460. 143 mmole/10 9 cells in the OBGM/T. denticola conditioned medium, significantly (p,0. 01) more than the 0. 73360. 056 mmole/10 9 cells decrease in OBGM/PBS. These data indicate a higher rate of peptide hydrolysis and release of free glycine by P. gingivalis in the OBGM/T. denticola conditioned medium. Total glycine decreased by 0.
Transmembrane helices were found in 1,271 (20. 8%) proteins. An Enzyme Commission (EC) number was assigned to 676 (11. 1%) genes. We assigned 884 (14. 4%) genes to 98 metabolic pathways present at the Kyoto Encyclopedia of Genes and Genomes (KEGG) with the highest number of genes related to purine metabolism. Blast2GO BLASTx alignments using the NCBI NRPEP database confirmed that the closest matches to A. adeninivorans genes were very often found in Y. lipolytica (Additional file 5). Spliceosomal introns are more frequent than in Saccharomycetaceae or in Debaryomycetaceae, but in the same range as reported for Y. lipolytica (914 versus 1119, Table  2 ). In this latter species, introns are characterized by a very short distance between the 3′ splicing site and the branch point, but have in contrast retained the ancestral consensus hemiascomycete 5′ splicing site (GTAAGT).
Phylogeny and synteny conservation A phylogenetic tree was reconstructed for each A. adeninivorans protein-coding gene, the so-called phylome, and used to identify orthology and paralogy relationships among related species  [34]. This comprehensive collection of evolutionary histories is publicly available at PhylomeDB snRNA, small nuclear ribonucleic acid; snoRNA, small nucleolar ribonucleic acid; CDS, coding DNA sequence; G + C, guanine and cytosine; aa, amino acids; i-genes, intron-containing genes; ND, not-determined. Data from S. cerevisiae, Lachancea thermotolerans, D. hansenii and Y. lipolytica were taken from  [30] ; data on intron-containing genes from  [31]. [35]. Species phylogenies were computed on a set of concatenated orthologs and using a super tree approach combining all individual gene phylogenies.
Gene families: expansion and contraction The gene trees in the phylome were scanned to detect and date duplication events  [38]. With an average of 0. 253 duplications per gene in the specific lineage leading to Arxula, this genome does not seem to contain a large amount of duplications. This is nevertheless greater than the 0. 015 value found in the common ancestor of Y. lipolytica and A. adeninivorans (Additional file 9). Most Arxulaspecific expansions are not very large (between three and nine sequences) and correspond to peptidases, transporters, dehydrogenases and some proteins related to nitrogen metabolism (Additional file 9). One expansion, however, contains over 100 members of unknown function and no homologs in any database, which is to our knowledge the largest gene family described in yeast (Figure  2  and Additional file 9).
The reads were assembled using the whole genome shotgun assembler ARACHNE and the chromosome sequences were individually reassembled using the Phred/Phrap/Consed software package. For the finishing step, we used primer walking of clones, PCR amplifications and in vitro transposition technology [Template Generation System™ II Kit (Fisher Scientific, Illkirsh, France) or Hypermu < Kan-1 > (Tebu-Bio, Le Perray-en-Yvelines, France), corresponding to 814, 33 and 17,975 reads, respectively. The final assembly consisted of four scaffolds larger than 1 Mb, hereafter referred to as chromosomes, and nine shorter contigs of a size ranging from 4 to 120 kb, including four mitochondrial scaffolds. Four of the remaining contigs were later incorporated at chromosome ends in the final assembly using data obtained from the 454 assembly.
The mitochondrial genome sequence was assembled as a circular map using Sanger and 454 contigs and manually validated using single reads obtained with the Sanger technology. A. adeninivorans genome sequence data have been deposited at EMBL under the accession number PRJEB4557 [EMBL:PRJEB4557]. The shotgun library of A. adeninivorans for sequencing on Roche 454 GS FLX Titanium sequencer was prepared using 5 μg of genomic DNA. Based on random cleavage of the genomic DNA with subsequent removal of small fragments with Agentcourt AMPure SPRI beads (Beckman Coulter, Krefeld, Germany), the resulting single stranded DNA (ssDNA) library showed a fragment distribution between 300 and 1000 bp. The optimal amount of ssDNA library input for the emulsion (emPCR) was determined empirically through 4 small-scale titrations with one, two, four and eight copies per bead (cpb).
Finally, one cpb was used for the large-scale experiment. One individual emulsion PCR (two cups, one full emPCR-Kit LV (Roche Applied Science, Mannheim, Germany) was carried out to generate 5. 7 million DNA-carrying beads for two-region sized 70 × 75 PicoTiterPlates (Roche Applied Science, Mannheim, Germany) and each region was loaded with 2 million DNA-carrying beads. Two read sets were thus generated totaling 1,074,025 reads. This resulted in 542. 3 Mb of sequence data (45-fold genome sequence coverage) with an average read length of 505 bp. Assembly was performed using the Newbler software (v2. 3) within the Roche 454 suite package, MIRA multi-pass DNA sequence data assembler/mapper (v3. 0. 2) and CLC Bio assembler. To allow comparisons between the assemblies of different assembly programs, singletons and contigs shorter than 100 bp were discarded before subsequent analysis.
Mapping of GS FLX shotgun reads and contigs to assembled chromosomes To assess the quality of the final assembled genome, the 454 reads were mapped onto the chromosomes using the Burrows-Wheeler Alignment tool BWA  [53]. Two statistics were extracted from the mappings using Samtools  [54] : the percentage of reads that mapped on the assembly and the percentage of reads that mapped to each chromosomes. The quality of the final assembled genome was estimated using the dot-plot analysis which was performed using Nucmer software (NUCleotide MUMmer v3. 1  [55] ). The dot-plot alignment was generated by comparison of all assembled chromosomes and contigs. Mapping of 454 reads was used to estimate the gene copy number by computing the number of tags mapping to unique regions of the genome. For this purpose, sequences of 21 A.
adeninivorans genes, deposited in GenBank (NCBI), were used in BLASTn searches together with the set of all 454 reads using three BLAST e-value cutoff = e-10, e-50 and e-100 to improve search specificity. The analysis of the gene copy number was performed using the formula: GeneCopy-Number = (Number of BLASTn hits * Average read length)/ (Gene length * 454 sequence coverage).
At any phase, curators could add or modify gene models. Circos  [58]  was used for illustration of nuclear genome data such as: chromosome structure (position of centromeres, tRNA and rRNA genes), density of genes across the genome, content of repeat DNA, 454 reads mapped to chromosomes, syntenic blocks between A. adeninivorans and genomes of Y. lipolytica, K. pastoris and S. cerevisiae. Functional annotation of genes according to the GO terms, EC numbers and the KEGG pathway were performed for each A. adeninivorans CDS using the Blast2GO software suite. Protein domains were detected by Inter-ProScan with various databases (BlastProDom, FPrintScan, HMM-PIR, HMM-Pfam, HMM-Smart, HMM-Tigr, PatternScan, SuperFamily, HMM-Panther and Gene3D) through the European Bioinformatics Institute Web Services.
Protein families The classification of A. adeninivorans protein sequences into protein families was performed along two procedures. First, protein sequences were tentatively incorporated into protein families defined in the previous round of Génolevures genome annotation using PSI-BLAST with relaxation factors based on family dispersion  [30]. Second, the sequences rejected by the procedure were pooled with the sequences of the nine species already present in the Génolevures database which are not members of any protein family and a clustering with OrthoMCL  [60]  was applied to define new families.