dataesr/scientific-paragraphs-categorization huggingface dataset:
We then compared the A. pompejana predicted proteins in the MPI + JGI + Genoscope dataset to those already available in the JGI + Genoscope dataset. We found 13,301 sequences (>100 aa) in the MPI + JGI + Genoscope dataset with no identical BLAST hit (fraction identical <90%) to the JGI + Genoscope dataset. We then performed BLASTP searches with these sequences in the predicted proteome of the annelid Capitella teleta, the closest relatives of A. pompejana with complete genome information (phylogenetic tree in Additional file 7). We found that 3,897 C. teleta sequences had one or more significant hits (e-value 1e-5) among the novel A. pompejana proteins. These represent newly identified, conserved A. pompejana proteins. In addition, our data also extended the length of many truncated protein sequences in the JGI + Genoscope dataset.
In a BLASTP comparison of the predicted protein datasets, 2,776 query sequences from the MPI + JGI + Genoscope dataset were at least 40 amino acids longer than their corresponding fragments in the JGI + Genoscope dataset. Next, we annotated the combined predicted A. pompejana proteome using BLASTP (Additional file 8). We defined the sequences based on their first hit in the SwissProt database, and further annotated them with the first hit in the C. teleta, human, Danio rerio and Drosophila melanogaster proteomes. We also identified 775 sequences with no BLASTP hit in 26 eukaryotic genomes (including 18 animals) but significant hits in prokaryotes (in the UniRef90 database). These were annotated as potential contaminants or genes that originated by recent lateral gene transfer. A future A. pompejana genome sequencing project could distinguish between these two possibilities.
In order to estimate the completion of the A. pompejana datasets, we performed extensive BLASTP searches in the proteome of 15 animal and 8 additional eukaryotic species. We used C. teleta protein sequences as query and performed BLASTP searches in the 23 eukaryotic datasets, including the A. pompejana datasets. We then counted the number of C. teleta proteins that had a significant hit (e-value 1e-5) in all animals, but not outside animals, or all eukaryotes. These two subsets of proteins (general animal and general eukaryotic) are also expected to be present in A. pompejana. We therefore counted how many of these two subsets had a BLAST hit in the A. pompejana datasets (Table  2 ). We found that the combined A. pompejana resource is about 74-99% complete, depending on the subset of proteins examined.
Animalspecific proteins were highly covered, between 74-94%, depending on whether we considered all sequences or only full-length sequences. Proteins present in all eukaryotes had even higher percent coverage (87-99%). This is probably due to the higher expression levels of genes with general eukaryotic cellular functions. When we considered only full-length A. pompejana proteins, the MPI + JGI + Genoscope dataset was 74% complete for animalspecific proteins, compared to the 55% completion of the JGI + Genoscope dataset. These searches show that the A. pompejana resource is about 74-99% complete at the level of eukaryotic paralogous families and protein domains. Overall, our sequencing efforts greatly extended the known A. pompejana transcriptome and proteome.
Generation of an orthologous set of protein sequences As a prerequisite for a thorough assessment of the thermoadaptation of the A. pompejana proteome we generated a large set of orthologous protein sequences from A. pompejana and nine other eukaryotic species. Importantly, our orthologous set also included three other lophotrochozoan species, the annelids C. teleta and Helobdella robusta and the mollusk Lottia gigantea. To be able to identify general features of thermoadaptation in eukaryotes, we also included the thermotolerant fungus, Chaetomium thermophilum, and a mesophilic yeast, Saccharomyces cerevisiae in the orthologous set. The orthologous set was generated using an all-againstall BLASTP search and the reciprocal best-hit approach, Number of full-length (with start and stop codon), partial (with stop codon), and total number of predicted protein sequences in the three datasets clustered at 100%, 98% and 90% identity.
pompejana or C. thermophilum as the most thermostable species (Figure  3A-D ). In the THERMORANK analysis, for example, A. pompejana ranks behind H. robusta and L. gigantea, also highlighting the importance of broad taxon-sampling when performing these comparisons. Additionally, the length of the protein sequences in the trimmed multiple alignments (an indication of the length of surface loops)  [24]  did not identify A. pompejana proteins as thermostable, and ranked the two fungi as the species with the most compact proteins Number of BLAST hits in the different A. pompejana datasets using two subsets of the Capitella proteome: sequences that only give hits in all of the 14 animals included, but not outside metazoa (hits to all 14 animals only), and sequences that have hits in all 14 animals included and 8 other non-metazoan eukaryotes.
Low serine content  [26]  ranks A. pompejana and C. thermophilum first and second in cumulative rankings, although C. thermophilum receives very similar scores to human (Figure  3G ). The CvP-bias  [17]  was the only measure that ranked A. pompejana and C. thermophilum as the two most thermostable species (Figure  3H ). We validated all the measures used on prokaryotic thermophile-mesophile test datasets. All measures robustly ). The species are as in Figure  2. discriminated a self-compiled Thermus thermophilus (a thermophilic bacterium) vs. Deinococcus radiodurans (an extremophilic but not thermophilic bacterium) dataset. Other published datasets were also tested, and most measures distinguished thermophiles from mesophiles  [20, 27, 28]  (Figure  4 ). We also calculated the GC-content for the nucleotide sequences corresponding to the protein sequences in our trimmed orthologous set (Table  3 ).
We found that C. thermophilum had the highest GC-content, A. pompejana had a GC-content below the average of the dataset, and L. gigantea, H. robusta and S. cerevisiae had the lowest GCcontent values. We plotted the ratio of GC-rich codons (GARP residues) against the ratio of AT-rich codons (FYMINK residues; Figure  5A )  [29]  across the ortologous set and found a very strong correlation, indicating that GC-content strongly influences amino acid composition. We also tested how individual amino acids are influenced by GC-content and found that the frequency of GARP residues as well as of Trp, Ile and Lys correlates with GCcontent (Additional file 10). The thermostability measures we applied, however, do not correlate with GC-content (Figure  5B-F ), indicating that the observed ranking is not influenced by GC-content.
We identified 26 proteins where A. pompejana ranked lowest, including the exosome component Rrp4. We expressed and purified recombinant Rrp4 proteins from A. pompejana,    and (B-E ) four published datasets were tested with the different thermostability measures to discriminate thermophiles from mesophiles. Dataset 1 (B) is from ref. [20] , dataset 2 (C) is from ref. [27] , dataset 3 and 4 (D, E) are from ref. [28]  (containing hyperthermophiles versus mesophiles and thermophiles versus mesophiles respectively). human, and yeast and performed thermal denaturation experiments. As predicted, we found that human Rrp4 was more thermoresistant than the A. pompejana protein (Figure  6A  and Table  4 ). Overall, the difference in the thermal stability values between the thermophilic and mesophilic orthologous pairs and the difference in the CvP-bias showed a consistent trend.
Discussion Taking advantage of an extended sequence dataset, we searched for global signals of thermoadaptation in the proteome of the hydrothermal vent annelid A. pompejana. We used a broad phylogenetic framework, comparing orthologous eukaryotic proteins across vast evolutionary distances. For a detailed understanding of thermoadaptation, one would ideally focus on several closely related thermophilic and mesophilic species. However, there are no similar large-scale sequence resources available from other alvinellids, and such analyses are also confounded by the uncertain evolutionary history of thermoadaptation within the group. A recent analysis compared A. pompejana to a closely related mesophilic species, Paralvinella grasslei  [29]. This study revealed a higher proportion of Ala residues in A.
All quality trimmed and vector screened 454 and Illumina reads were assembled using Velvet version 1. 1. and Oases  [31]. The k-mer length used was 25. The resulting contigs and singletons were joined with all published A. pompejana ESTs from NCBI dbEST, and with our EST sequences, and passed to the CAP3 assembler with default parameters  [32]. To assess the amount of newly obtained transcripts from our own sequencing effort, our sequencing data and the JGI + Genoscope ESTs (218,458) were also assembled separately with CAP3. Contigs and singletons were joined for each assembly to compose the final set of transcript sequences. Transcript sequences were translated with ESTScan  [33]  and trimmed to the longest stop codon-free protein fragment. If a 5' stop-codon was present, the sequence was scanned to the next methionine and that was considered as the protein start.
Fragments shorter than 60 amino acids were discarded. The sequence assemblies can be queried by BLAST at http://jekely-lab. tuebingen. mpg. de/blast/. Amino acid composition of protein sequences and measures derived from amino acid composition were calculated with custom scripts in Python. The CvP bias was calculated as (C -P)/length × 100, where C and P represents the number of charged (EDKR) and polar residues (STNQ) respectively. For the multi-species BLAST comparisons Capitella teleta, Helobdella robusta, Lottia gigantea, and Daphnia pulex protein sequences were downloaded from JGI (http://www. jgi. doe. gov/, Filtered Models). Protein sequences for Caenorhabditis elegans, Drosophila melanogaster, Anopheles gambiae, Strongylocentrotus purpuratus, Branchiostoma floridae, Ciona intestinalis, Danio rerio, Gallus gallus, Homo sapiens, Hydra magnipapillata, Nematostella vectensis, Trichoplax adhaerens, Monosiga brevicollis, Neurospora crassa, Schizosaccharomyces pombe, Dictyostelium discoideum, Naegleria gruberi, Chlamydomonas reinhardtii, Paramecium tetraurelia, and Arabidopsis thaliana were downloaded from the NCBI RefSeq database (http://www.
Thermophile-mesophile benchmark datasets Protein sequences for Thermus thermophilus and Deinococcus radiodurans were downloaded from UniProtKB by filtering on taxonomy ids 262724 and 1299. The Li et al. [20]  dataset (Dataset 1, THERMORANK) was downloaded from http://www. abl. ku. edu/thermorank/suppl. html. The Montanucci et al. [27]  dataset (Dataset 2) was downloaded from http://lipid. biocomp. unibo. it/~ludovica/thermo-meso-MUT/ and only the first pair was considered from each cluster. The Taylor and Vaisman datasets (Datasets 3 and 4) were obtained from the supplementary material of ref. [28].
Thermorank analysis For local usage, the THERMORANK tool was reimplemented according to ref. [20] , using the Python programming language. Tripeptide residue accessible surface area values were used as described in ref. [38]. The 8 protein sequences in each orthologous set were ranked and the cumulative rank sums over the 457 sets calculated to assess the overall trend of ranking. Ranking calculations for all measures were performed using the R software environment (http://www. r-project. org/).
Thermal shift assay Solutions containing 5 μl of 2 mg/ml protein with 35x of Sypro Orange (Invitrogen) and 45 μl of buffer screen were added to the wells of a 96-well PCR plate (Eppendorf). The plate was sealed and heated in a real-time PCR system (Eppendorf) from 20°C to 80°C in increments of 0. 2°C. Fluorescence changes were monitored simultaneously. The wavelengths for excitation and emission were 470 and 550 nm, respectively. To obtain the temperature midpoint for the protein unfolding transition (Tm), a Boltzmann model was used to fit the fluorescence data  [39].
Although a full analysis in homeothermic metazoans is beyond the scope of this paper, we determined the average CvP values of the proteomes of several homeothermic vertebrates and found a strong correlation between body temperature and the CvP values, as shown in Table  5. This question can be addressed in more detail in the future when more bird genomes and genomes from vertebrates with low body temperature (e. g. dolphins, whales) become available. While the data is currently very limited, are there any possible explanations for the CvP being more successful as a discriminant than other measures in eukaryotes? One possible explanation is that some of the other measures are over-fitted to a training dataset (e. g. the Tm-Index uses dipeptide composition). The CvP-bias is a simple measure with few parameters combining information from two classes of amino acids, the changes of which have previously been linked to thermoadaptation.
CvP-bias also performs best on the prokaryotic datasets. A larger number of Alvinella and Chaetomium protein structures and their comparison to mesophilic ortologues will help to clarify the role of charged and polar residues in thermoadaptation. Using transcriptomics data, this paper examines whether there is a signal of thermoadaptation in proteins from the thermotolerant annelid worm, Alvinella pompejana. As well as significantly increasing the available sequence data for this species, which is a worthy effort in itself, the authors examine whether there is a compositional signature for thermoadaptation in proteins from this species and from the thermotolerant yeast Chaetomium thermophilum. In contrast to a comparison of two prokaryote species (here there is a large literature on thermotolerant proteins), one a thermophile, and the other not, the authors find only a relatively faint indication of thermotolerance for these two eukaryotes.
Acknowledgements We are thankful to  Elizabeth Williams  and  Steffen Schmidt  for the critical reading of the manuscript. We thank also  Yunqi Li  and  Jianwen Fang  for providing scripts. Capitella, Helobdella, Lottia and A. pompejana sequence data were produced by the  US Department of Energy Joint Genome Institute  http://www. jgi. doe. gov/ in collaboration with the user community. We gratefully acknowledge the captains and crew of the  R/V'L'Atalante' , and the pilots and co-pilots of the  ROV Victor6000 and 'Nautile ' groups for collecting material, and are particularly grateful to  F. Gaill  and  N. LeBris , chief scientists of the  HOT96  and  PHARE02  cruises, who allowed us to conduct this work. This work was supported by a  Sequencing Grant  ( M. IF. A. ENTW8050 ) from the  Max Planck Society.
in MPI and 9,639 in MPI + JGI + Genoscope compared to 3,634 in JGI + Genoscope). Annotation of predicted A. pompejana proteins Next we searched the nucleotide datasets for potential open reading frames and analyzed the predicted protein sequences (Additional file 4, 5 and 6). We identified A 20,000 B 8,000 MPI MPI MPI+JGI+Genoscope MPI+JGI+Genoscope JGI+Genoscope JGI+Genoscope Number of contigs 5,000 10,000 15,000 Number of predicted proteins 2,000 4,000 6,000 0 0 200-300 400-500 600-700 800-900 1000-1100 1200-1300 1400-1500 >1500 60-80 100-120 140-160 180-200 220-240 260-280 300-320 340-360 380-400 420-440 460-480 >500 Length of contigs (bp) Length of predicted proteins (aa) Figure 1 Size distribution of cDNA contigs and predicted protein sequences in three A. pompejana datasets. (A) Histograms representing the number of contigs in the different size-ranges for the MPI (black), MPI + JGI + Genoscope (red) and JGI + Genoscope (cyan) assemblies.
Animals were treated in a humane manner, in accordance with the guidelines of the American Society of Mammalogists  [28]. Madagascar has no ethics committee overseeing animal experimentation, and the IRD has no ethics board for the review of animal experimentation protocols. However, the ANR-SEST (Agence Nationale pour la Recherche, Sante-Environnement et Sante-Travail) program on plague diffusion, which provided some of the funding for this project, has been approved by the Managing Director of the IRD. In addition, regional approval was obtained from the regional Head of Veterinary Service (Herault, France), for the sampling and killing of rodents and the harvesting of their tissues (approval no. B 34-169-1) carried out during this study.
Molecular methods We carried out molecular analyses on a maximum of 44 rats per trap site, the rats from a given trap site being considered as a subpopulation (Table  2 ). Genomic DNA was extracted from ethanol-stored tissues with the PUREGENE DNA purification kit. Genotyping was conducted for 15 microsatellite loci. Six of these loci were originally developed for Rattus norvegicus (D7Rat13, D10Rat20, D11Mgh5, D11Rat56, and D16Rat81;  [31] ). Samples from Mandoto had already been genotyped for these markers  [29] , but some were re-analyzed for the purposes of comparison. Ten additional microsatellite loci were recently developed specifically for R. rattus (Rr14, Rr17, Rr21, Rr22, Rr54, Rr67, Rr68, Rr93, Rr107, and Rr114:  [32] ), and were genotyped for the entire dataset. Every individual successfully genotyped at some loci but not at some others underwent re-amplification once by simple PCR (to avoid primer competition), for each failed locus.
Data analyses Deviations from Hardy-Weinberg equilibrium (HWE) within subpopulations and genotypic linkage disequilibrium (LD) between pairs of loci were tested using the Markov chain method implemented in GENEPOP v. 4  [33]. We corrected for multiple testing by the False Discovery Rate (FDR) approach  [34]  implemented in the QVALUE package of R. Some loci displayed significant heterozygote deficiencies in several subpopulations (see Results). In these loci, some null genotypes were found. We used MICRO-CHECKER v. 2. 2. 3  [35]  to test whether heterozygote deficiencies could be accounted for by the existence of null alleles. We then used FREENA (  [36] , available from www. montpellier. inra. fr/URLB), to assess the need to correct for null alleles. The genetic diversity of each subpopulation was assessed by calculating Nei's unbiased genetic diversity (H S ,  [37] ), allelic richness (r, evaluating the number of alleles independent of sample size, calculated for a minimum of 7 individuals by the rarefaction procedure  [38]  implemented in FSTAT v.
2. 9. 3. 2  [39] ), and F IS (tested for significance with FSTAT). Effective population size (N E ) was estimated with LDNE  [40]. The method used is based on linkage disequilibrium and assumes random associations of alleles at different loci. Alleles with a frequency $0. 02 were used to minimize possible bias  [40]. We investigated whether plague epizootics had left detectable traces on genetic diversity and population size, by assessing the relationship between r, H S , F IS or N E and plague seroprevalence in each subpopulation within areas by carrying out Spearman's rank non parametric correlation analyses in SAS v. 9. 3  [41]. We compared r, H S , and F IS between areas with FSTAT (10,000 permutations), and N E using nonparametric Kruskal-Wallis tests in SAS. Genetic structure was examined in several ways within the four study areas.
Estimated null allele frequencies were low  [36]  (from 0. 05 to 0. 13; mean frequency for all loci = 0. 037). All analyses yielded similar results with and without loci not at HWE: those presented here were obtained with the whole dataset. LD was significant for 25 of the 4200 tests performed (0. 6% of comparisons), so the 15 loci were considered to be independent. The 15 microsatellite loci considered in the analyses were polymorphic in all subpopulations (Table  2 ). In Ambositra and Moramanga, the subpopulations with positive plague seroprevalence also had low levels of genetic diversity and a high F IS (Table  2 ) Subpopulation-specific N E values were below 100 individuals, except in three subpopulations with infinite confidence intervals for which estimations were thus, by definition, imprecise (Table  2 ).
Distribution of plague seroprevalence in rat populations In Madagascar, the distribution of plague seroprevalence levels and reported plague cases was shown to differ between plateau and mountain areas in humans  [11]. Our aim was to investigate whether the same pattern was observed in rat populations. In Ambositra and Moramanga, only one rat subpopulation per area had a positive seroprevalence for plague. This made it impossible to examine the relationship between topographic relief and seroprevalence distribution in these areas. The very small number of seropositive rats found in Ambositra (2 rats) was surprising, as this area reported the largest number of human cases during the 1990s  [10]. Given the lifespan of wild R. rattus (about one year:  [21] ), this may indicate a low incidence of the disease during the year preceding sampling: indeed, six and five human cases were reported in the villages studied in Ambositra and Moramanga, respectively (Plague Laboratory database, Madagascan Ministry of Health).
The positive correlation clearly suggests that rat dispersal has an influence on plague distribution in this area, except in BAtnV which was characterized by the highest plague seroprevalence level (Table  2 ). BAtnV had no spurious effects in IBD analyses. This village is located at immediate proximity of the main road of the area. BAtnV may thus be more related to distant human communities than the other villages of Betafo, which are all located at more than two kilometers from the asphalt road. As in Mandoto, few infected rodents, fleas, or humans (one human case reported in BAtnV in 2005; Plague Laboratory database, Madagascan Ministry of Health) may have reach BAtnV from a geographically distant location, and trigger an epidemics in local rats. Empirical studies investigating the genetic consequences of infectious diseases in animal have shown that some populations lose genetic diversity  [61, 62, 63] , whereas others do not  [64, 65].
Acknowledgments We thank the staff of the  Plague Laboratory (Institut Pasteur de Madagascar and Ministry of Heath)  for their excellent assistance during the fieldwork, and  Karine Berthier  and the anonymous reviewers for helpful comments on the manuscript. English editing was done by  Julie Sappa  ( Alex Edelman  and Associates). Data were generated thanks to the technical facilities of the  SFR MEB ''Montpellier Environnement Biodiversite. ''.
Funding was provided by the  IRD (Institut de Recherche pour le Developpement , http://www. ird. fr/), the  IPM (Institut Pasteur de Madagascar , http:// www. pasteur. mg/),the program  RAMSE (Recherche appliquee aMadagascar sur la santeet l'environnement)  and an  ANR-SEST (Agence Nationale pour la Recherche, Sante-Environnement et Sante-Travail , http://www. agence-nationale-recherche. fr /) program  ( Nu06 SEST 10 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Based on information in the Germplasm Resource Information Network (i. e. , GRIN) database, the five introductions originated from Tunisia  (PI 115055) , Eritrea  (PI 115056, PI 115058) , Arabia (PI 115057), and India (PI 115059). The dwarf line was true-breeding and used in crosses with an adapted pearl millet line to form the highly successful synthetic variety 'Starr'  (Hein 1953). Although there are no records confirming that Starr millet carried the d2 gene, the described morphology makes this a plausible hypothesis  (Kumar and Andrews 1993). Around the same time, in India,  Kadam et al. (1940)  obtained dwarf phenotypes after inbreeding local pearl millet lines. The dwarfs had shortened internodes, overlapping leaf sheaths, and shortened peduncles and were attributed to a recessive mutation.