dataesr/scientific-paragraphs-categorization huggingface dataset:
RNA was prepared for sequencing following the Illumina TruSeq mRNA protocol. Sequencing was carried out on the Illumina MiSeq (2 Â 250 bp paired-end) platform. Illumina reads were qualitytrimmed and filtered using Trimmomatic v. 0. 32  (Bolger et al. , 2014)  with default settings. Filtered reads, paired and unpaired, were used for de novo transcriptome assembly using Trinity v. 2. 1. 0 (Grabherr et al. , 2011)  following published protocols  (Haas et al. , 2013). Candidate coding regions within transcript sequences from the final assembly were identified and translated using Transdecoder 2. 1. 0 (Haas et al. , 2013). Raw reads were submitted to the NCBI Short Read Archive database (Bioproject PRJNA385088). As a basis for selecting nuclear protein-coding genes for analysis, we used a previously compiled alignment from  Irisarri et al.
(2017) , in the following called reference alignment. For detailed methods of obtaining this reference alignment, see  Irisarri et al. (2017). In brief, the reference alignment was assembled by first grouping 20 vertebrate proteomes into putative orthology using USEARCH  (Edgar, 2010)  and OrthoMCL  (Li et al. , 2003)  and discarding orthogroups with missing data for major clades of jawed vertebrates. After aligning and custom paralog-splitting, the resulting protein clusters were complemented with 80 additional published genomic and transcriptomic data sets of vertebrates using the software Forty-Two (or ''42"; D. Baurain; https://bitbucket. org/dbaurain/42/) that controls for orthology using several proteomes in strict three-way reciprocal best BLAST hit tests. Subsequently, following the methods outlined in  Irisarri et al.
(2017) , the reference alignment went through several decontamination steps to remove: (1) all human and non-vertebrate sequences; (2) crosscontaminations; (3) highly incomplete genes; (4) genes with poor alignment or frame shifts; (5) genes resulting in extremely long branches in some taxa suggesting the possibility of contamination or undetected paralogy. The final vertebrate reference data set as used for the analysis of  Irisarri et al. (2017)  contained 4593 genes. Sequences of Salamandra transcriptomes were aligned to this reference data set using the software Forty-Two. The resulting alignment was then submitted to a pipeline of thorough filtering and decontamination, composed of the following eight steps: (1) Sequences from non-vertebrate sources (e. g. Bacteria or Platyhelminthes) were detected by BLAST searches and 21,265 sequences were removed (almost exclusively from the previously published S.
The corresponding retained nucleotide sequences were both recovered and aligned according to the amino acid alignments using the software Leel (or ''1331"; D. Baurain; https://bitbucket. org/dbaurain/42/). All subsequent analyses were based on these nucleotide alignments. (  6 ) We checked for remaining contaminating or paralogous sequences by comparing the branch lengths in gene trees and in the concatenation tree as in  Irisarri et al. (2017) , and removed 445 sequences having a branch length ratio (gene tree vs. concatenation tree) >7. (  7 ) To reduce stochastic error in gene trees, we removed all the codons that were present in less than 50% of the species and any sequence having less than 30 nucleotides. (  8 ) The resulting nucleotide data set (available as Mendeley Research Data) was then concatenated using SCaFoS  (Roure et al.
First, node support was assessed using a gene jackknife approach  (Delsuc et al. , 2008)  to determine what proportion of the data would need to be sampled to resolve the maximal number of nodes: one hundred alignment replicates were generated by randomly sampling genes up to ca. 10,000, 50,000, 100,000, 500,000, 1,000,000 and 3,000,000 nucleotide positions, respectively. For each replicate, unpartitioned ML trees were estimated using RAxML with a GTR + C model defined for the whole data set, and gene jackknife proportions estimated for each node. Second, we also used ASTRAL II  (Mirarab and Warnow, 2015) , a statistically consistent algorithm to estimate the species tree topology under the multi-species coalescent model  (Mirarab and Warnow, 2015). Clade support was evaluated by computing the local posterior probability, a feature of ASTRAL II that has shown high precision compared with multi-locus bootstrapping on a wide set of simulated and biological datasets  (Sayyari and Mirarab, 2016).
Finally, enrichment PCR was performed to amplify the library using forward and reverse RAD primers. Sequencing was conducted on an Illumina Next-Seq machine at Glasgow Polyomics to generate paired-end reads 75 bp in length. Raw reads were submitted to the NCBI Short Read Archive database (Bioproject PRJNA386146). Sequence reads were de-multiplexed, Illumina adaptors and barcodes removed, and reads truncated to 60 nucleotides using Stacks v. 1. 35  (Catchen et al. , 2013). Processed reads per sample ranged from 4. 9 to 16. 8 million, compared to 5-17 million raw reads per sample. Reads were assembled de novo into loci using pyRAD v. 3. 0. 6 (Eaton, 2014). Reads were first clustered within an individual at a minimum depth of 10 with a clustering threshold of 85%. The same clustering threshold was then used to assemble de novo loci across samples; final RAD loci ranged in length from 109 to 144 nucleotides, with an average of 111 nucleotides.
Eight prior operators (treeScaler; SubtreeSlide; RateAGScaler; RateATScaler; YuleModelTreeScaler; YuleModelSubtreeSlide; FixMeanMutationRatesOperator; and ucldStdevScaler) were optimised based on the output of this trial, and the analysis re-run. A maximum clade credibility tree was then generated from the output of the optimised analysis using TreeAnnotator 2. 4. 2. ''Gene" jackknifing (i. e. , jackknifing of RAD loci) was carried out as described for the RNAseq data, with replicates of ca. 10,000, 50,000, 100,000, 500,000 and 800,000 nucleotide positions. In addition to the concatenation approach, we also used the coalescent-based program SNAPP  (Bryant et al. , 2012)  to infer the species tree under a finite-sites model of mutation from unlinked biallelic SNPs extracted from the RADseq data set.
Mitogenome analyses We assembled mitogenomes of all Salamandra species from the quality-trimmed RNAseq data. We first randomly sampled 20% of the raw data and subsequently retrieved and assembled mitochondrial sequences with MIRA v4. 0  (Chevreux et al. , 1999)  and MITObim v1. 8  (Hahn et al. , 2013)  following  Machado et al. (2016)  and using default parameters. We used the complete mitochondrial sequence of Salamandra infraimmaculata (EU880331) as reference genome in the first MIRA step. Assemblies in CAF format were manually verified in Geneious software, v. 6 (Biomatters) to evaluate the coverage and quality of each mtDNA element. All positions with coverage lower than 4 were coded as ambiguous (''N"). Preliminary annotation of each sequence was done using the mitochondrial genome annotation server MITOS  (Bernt et al.
, 2013)  with default parameters. Validation of tRNA sequences were performed using tRNAscan-SE  (Lowe and Chan, 2016). The resulting automatic annotation was confirmed and edited manually by comparison to Salamandra infraimmaculata EU880331. All newly determined sequences were submitted to Genbank (accession numbers MF043386-MF043393). We also included complete or almost complete mitochondrial genome sequences of Chioglossa lusitanica (EU880308) and Mertensiella caucasica (EU880319) as outgroups, added species of Lyciasalamandra (EU880318, AF154053) as hierarchical outgroups, and furthermore added one species of Salamandra for which a full mitogenome sequence was available from Genbank (EU880331). The latter sample was originally analyzed as S. salamandra  (Zhang et al. , 2008) , but corresponds to a sample of S.
infraimmaculata from Turkey. We aligned mitochondrial sequences using MAFFT v. 7  (Katoh and Standley, 2013)  and determined the optimal among-gene partitioning scheme and model choice for dataset in PartitionFinder  (Lanfear et al. , 2012)  under the Bayesian Information Criterion (BIC). Bayesian phylogenetic inference was performed with MrBayes v. 3. 2. 6  (Ronquist et al. , 2012)  using two independent runs of eight chains. Chains were started from random trees and run for 10 million generations each, being sampled every 1000 generations. Twenty-five percent of the trees were discarded as 'burnin' before generating a consensus tree. The full mitogenomic data set was also analyzed under the ML optimality criterion in RAxML v. 8. (Stamatakis, 2014) , using the GTRGAMMA model of nucleotide substitution and a partitioned approach, with partitions and substitution models as defined by Partitionfinder  (Lanfear et al.
Independence of data sets To understand whether the RNAseq and RADseq datasets were independent, we calculated sequence overlap between them. For each individual, sequences representing the RAD loci, were aligned to the RNAseq data set using Bowtie2 v. 2. 2. 9. An overall alignment rate of 1. 23% was found: of 56,987 paired reads that mapped, none aligned concordantly, 46 (0. 08%) aligned discordantly once, and when single-end reads were aligned independently (113,882 in total), 817 (0. 72%) aligned one time and 497 (0. 44%) aligned >1 time. This confirms that the loci used for the RNAseq and RADseq analyses were almost completely non-overlapping and that the two analyses can be considered independent subsamples of the same underlying genomes. We also confirmed that no genes encoding mitochondrial proteins were present in the final RNAseq alignment used for analysis.
Gene ontology analyses Given that mitochondrial and nuclear DNA sequences gave a contradicting signal regarding the monophyly of black or alpine salamanders (i. e. a grouping of S. atra + S. corsica vs. S. atra + S. lanzai; see Results) we tested whether this discordance could be explained by differences in the functional categories of genes. We first counted the total number of genes supporting a certain topology using Phylosort, v. 1. 3  (Moustafa and Bhattacharya, 2008). We then tested whether incongruencies among analyses were due to particular nuclear genes coding for proteins whose functions interact with mitochondria and might have thus coevolved with mitochondrial genes  (Hill, 2016). We first created a consensus protein sequence for each of the 3070 RNAseq genes and used BLAST to compare them to the UniProtKB/Swiss-Prot database (http:// www.
Phylotranscriptomic analysis of shallow phylogenetic relationships In this study, we used nucleotide sequences of nuclear proteincoding genes derived from various phylogenomic data sets to reconstruct shallow phylogenetic relationships among closely related species of amphibians. Among these were sequences of nuclear protein-coding genes obtained by RNAseq, a kind of data set typically used to resolve deep phylogenies, with amino acid sequences as phylogenetic characters and taxa often separated for hundreds of millions of years (e. g. ,  Misof et al. , 2014; Jarvis et al. , 2014; Chen et al. , 2015). Here we followed an approach that recovered the nucleotide sequences of those transcripts that passed a stringent decontamination pipeline at the amino acid level (to remove sequences and genes potentially affected by sample contamination, paralogy, sequencing errors, or other artefacts; see Methods), and used the resulting concatenated nucleotide alignment of expressed genes for phylogenetic reconstruction.
The results fully agreed with those derived from a concatenated alignment of RADseq-derived SNPs (Fig. 1 ), which represented a  largely independent subsample of the salamander's genomes and is a kind of data set typically used for shallow phylogenetic inferences. This validates our usage of a phylotranscriptomic approach, and confirms that such RNAseq-derived data hold promise for reconstructing not only deep nodes of the tree of life but also shallow phylogenetic relationships among taxa probably characterized by recent gene flow. It is remarkable that with double-digest RAD sequencing it was possible to obtain a substantial number of homologous RADseq loci, despite the enormous genome size of the target species, with haploid C-values estimated between 27 and 41 pg  (Gregory, 2016). The overall number of nucleotide positions and phylogenetically informative sites in the RNAseq data set was almost fourfold that of the RADseq data set, but the RADseq data sets still led to a highly resolved tree and most nodes stabilized at shorter gene jackknife replicates as compared to the RNAseq dataset (Fig.
2 ). RADseq loci represent a relatively random subsample of the entire genome, potentially capturing a wider range of evolutionary signals than protein coding genes; i. e. it should contain fast as well as slowly evolving loci that might broaden the phylogenetic spectrum cov-ered. By contrast, the RNAseq transcript sequences used in our analysis were restricted to those loci that are consistently expressed and conserved across vertebrates, and hence potentially more limited in phylogenetic resolution. Another potential shortcoming is that alleles at heterozygous positions are not called in the RNAseq analysis pipeline and a considerable amount of nucleotide variation is therefore neglected, with possible influences on the RNAseq-derived species tree. Interestingly, despite these very different characteristics of the two data sets, the proportion of phylogenetically informative sites was identical in both of them (0.
Conflict between concatenation and species tree approaches Massive phylogenomic data sets, such as those obtained from RNAseq, certainly have the potential to lead to improved phylogenetic inference. However, simply adding more sequences to the data set is not enough  (Philippe et al. , 2011). Our analysis clearly exemplifies the limitations of large amounts of sequence data, as different analysis methods can result in opposing phylogenetic hypotheses, each with strong support using classical statistical metrics such as non-parametric bootstrap or Bayesian posterior probabilities. For the RADseq data, the species tree analysis with SNAPP (Fig. 3 ) placed S. corsica away from the atra-lanzai clade, with maximum posterior probability, conflicting with the analysis of the concatenated alignment that placed these three taxa in one clade (Fig.
, 2009; Leaché and Rannala, 2011; Mirarab et al. , 2016). On the other hand, strong advocates for concatenation approaches in phylogenomics are often concerned with resolving deep nodes in the tree of life where ILS should be less of an issue  (Gatesy and Springer, 2014). Still, concatenated data sets also appear to perform well for shallower nodes  (Wang et al. , 2017)  and have correctly recovered relationships in studies with simulated sequence data sets  (Rubin et al. , 2012; Cariou et al. , 2013; Tonini et al. , 2015; Rivers et al. , 2016). Based on these previous studies, we assume that in principle, both coalescence and concatenation approaches should be effective in reconstructing Salamandra relationships, given sets of DNA sequences appropriate for the respective method. However, it is questionable whether the Salamandra data sets are equally appropriate for being analyzed with the two methods.
It has been contended that coalescence methods should not be applied to complete protein-coding loci because they amalgamate potentially recombining genomic regions with different evolutionary histories, therefore violating important assumptions of the multispecies coalescent model  (Springer and Gatesy, 2016). In our analyses, it is obvious that most of the Salamandra RNAseq derived loci corresponded to multiple exons that are distant from each other in the genome, separated by long intronic sequences that were not included in our data set (as they are not translated into mRNA). Furthermore, coalescence methods can be less accurate than concatenation when the gene trees have poor phylogenetic signal  (Mirarab et al. , 2016). The example of S. atra relationships indicates that only a minority of all gene trees supports the favored placement of the species, in agreement with other examples of lacking phylogenetic congruence among single-gene trees (e.
g. ,  Dikow and Smith, 2013). This indicates that the protein-coding nuclear loci derived from RNAseq might not be sufficiently informative for reconstructing single-gene trees. Also the RADseq loci correspond to very short sequences, each with few SNPs, that might not be suitable for calculating single-locus trees with adequate phylogenetic resolution among the various species. For these loci we therefore used a SNP-based species tree approach which how-ever has not yet been extensively tested and thus might require methodological refinement. Because our data sets might thus not be optimal for being analyzed with species tree approaches, we consider the phylogenies obtained by the concatenated analyses (Fig. 1 ) to be more reliable. As a further cause for the conflicting topologies, we emphasize that in Salamandra, the phylogenetic signal supporting relationships within the atra-corsica-lanzai clade is at best very weak, given that in RNAseq gene jackknifing, over 1 million base pairs are needed to stabilize the preferred concatenated topology with bootstrap support values >60%.
Evolutionary history and biogeography of Salamandra We hypothesize that the phylogenies placing S. atra sister to S. lanzai (e. g. , Fig. 1 ) represent most accurately the evolutionary history of the genus Salamandra. We base this hypothesis on: (1) the congruence of the trees obtained from separate concatenated analyses of the RNAseq and RADseq datasets (Fig. 1 ); and (2) the phenotypic similarity of atra and lanzai in many key traits. Both are species occurring in the Alps, entirely black-colored (except the subspecies S. atra aurorae and S. atra pasubiensis), and pueriparous, i. e. , giving birth to fully metamorphosed juveniles (Fig. 5 ). In contrast, S. corsica has a geographic distribution restricted to the island of Corsica, is larviparous and yellow-black colored. If our preferred phylogenetic hypothesis is correct, then the alternative clade (atracorsica) as strongly supported by the mitogenomic data probably reflects ancient hybridization, with introgression of the mitochondrial genome of an ancestral S.
This topology is stable across all  of the nuclear phylogenomic trees we resolved (Figs. 1  and 3 ) and was previously recovered in a phylogeny based on a small set of nuclear genes  (Vences et al. , 2014). In gene jackknifing this topology (i. e. , the clade grouping all Salamandra to the exclusion of S. infraimmaculata) received support of 60% with a data set of 500,000 bp (RNAseq) and of 72% with 100,000 bp (RADseq), confirming it is highly supported by the data. Accepting this relationship of S. infraimmaculata as the sister species of all other Salamandra, the consensus topology of the nuclear gene data (Fig. 5 ) also suggests an origin of the genus in the Near East, considering that the two earliest branching clades (Lyciasalamandra and S. infraimmaculata) are restricted to this region.
Conclusion The data presented here have shown the potential of phylogenomic data sets to elucidate shallow relationships among closely related taxa, even if these have probably been characterized by past episodes of introgression. It is encouraging that different commonly used phylogenomic approaches, such as RNAseq and RADseq, result in data sets that yield congruent results, despite having very distinctive properties. Yet, our results also confirm the need for caution in interpreting high bootstrap proportions or Bayesian posterior probability values: with an increase in quantity of phylogenomic data, high values of these classical support metrics can be misleading as they do not necessarily reflect a strong phylogenetic signal for a certain branch. We are convinced that further improvement of analytical tools is of highest importance to deal with phylogenomic and phylotranscriptomic data sets, because model violations are to some degree inherent to all the methods used herein.
Acknowledgments We are grateful to  Marion Ballenghien  and  Nicolas Galtier  for locality information on the sample used for their published S. salamandra transcriptome, to  Martin Llewellyn  for making computing resources available, to  Alan Templeton  for support with our research on S. infraimmaculata, to  Franco Andreone  and  Philine Werner  for help with obtaining crucial samples, and to  David Donaire  for continued support. We acknowledge the use of the  Altamira Supercomputer (IFCA-CSIC) , and support of the  Alexander von Humboldt Foundation  to II and AR, the  European Molecular Biology Organization (EMBO)  to II, the  German Research Foundation (DFG)  ( STE1130/8-1  and  BL 1271/1-1 ) as part of a  German-Israeli project cooperation (DIP)  grant to SS, AN and LB, and of the  TULIP Laboratory of Excellence  ( ANR-10-LABX-41 ) to HP.
Fig. 3. Results of species tree analyses of Salamandra. (A)Species tree obtained from ML gene trees of each of the 3070 orthologous loci from the RNAseq analysis, summarized with ASTRAL II. Branch support was estimated by computing the local posterior probability (not calculated by ASTRAL II for the basalmost node which however is strongly supported in an analysis including Lyciasalamandra as outgroup; see Fig. S2). (B) Maximum clade credibility tree (cladogram representation) obtained from a SNAPP analysis of 3586 unlinked SNP loci identified from RADseq data. A. RNASeq (ASTRAL II) B. RADSeq (SNAPP) S. infraimmaculata S. infraimmaculata 1 S. algira 1 S. algira S. salamandra S. salamandra 0. 62 1 S. lanzai S. lanzai 0. 94 0. 99 1 S. atra S. atra S. corsica S. corsica 1/100 L. atifi (GenBank) L.
flavimembris (GenBank) S. infraimmaculata (GenBank) 1/100 1/100 S. infraimmaculata 1/90 1/100 S. infraimmaculata S. algira 0. 56/63 S. salamandra 1/100 1/100 S. salamandra 0. 04 1/100 S. corsica S. atra 1/100 S. lanzai.
, 1993; Waldor et al. , 1997). Strains named "atypical El Tor" have traits of both classical and El Tor  (Nair et al. , 2002). Recently, several atypical El Tor strains have been reported, including Matlab variants  (Safa et al. , 2006) , Mozambique variants  (Ansaruzzaman et al. , 2004) , altered El Tor  (Nair et al. , 2006) , and hybrid El Tor strains, harboring the classical CT allele ctxB1  (Goel et al. , 2008; Safa et al. , 2008). Safa et al. (2010)  proposed the term "atypical El Tor" for all V. cholerae O1 El Tor that harbor classical traits. Vibrio cholerae O1 strains isolated in Africa are known to be resistant to many antibiotics  (Ceccarelli et al. , 2006; Quilici et al. , 2010). Drug-resistance of bacteria is mainly linked to the mobilization and the dissemination of resistance genes through genetic determinants such as plasmids, integrons, and transposons.
Detection of Antibiotic Resistance and Virulence Molecular Markers PCR analysis was performed for detection of genetic determinants of antibiotic resistance and virulence. Amplification was carried out and virulence with primers described elsewhere  (Ploy et al. , 2000; Hochhut et al. , 2001; Shi et al. , 2006) , and GenBank accession number AF 099172. For the detection of virulence genes, primers used as those described by  Ogawa et al. (1990) ,  Keasler (1993), and Shi et al. (1998)  (Table  1 ) and GenBank accession numbers (AF262318, GQ485654). Chromosomal integration was detected by amplification of the right SXT element chromosome junction (attP-prfC gene sequence) (Table  1 ). All amplified DNA fragments were resolved by conventional electrophoresis in 1% agarose gel, stained with ethidium bromide and visualized under UV light.