dataesr/scientific-paragraphs-categorization huggingface dataset:
Phylogenetic analyses We combined the protein sequences from both AVRK1 and AVRA10 analyses, aligned them with muscle  [59]  and edited the alignment using jalview  [61]. We selected the sequences that were at least 90 % of the length of AVRK1 and removed 27 sequences that were redundant in that region, resulting in 317 sequences for the phylogenetic analyses. We used the BEAUti/BEAST software package v. 1. 8. 1  [62]  to infer Bayesian phylogeny (the subsequent cited programs are also distributed with BEAST). We carried out two independent runs of 100,000,000 generations with sampling every 1000 generations. The runs were combined using Tracer v. 1. 5. 0. The Effective Sample Size (ESS) for the posterior distribution, likelihood and treeLikelihood showed satisfactory values, showing that the MCMC chain has been run for long enough to get a valid estimate of the parameter.
Positive selection analysis We used the program codeml from the package PAML 4. 8  [63]  to detect positive selection that may affect some sites in either AVRK1-like or AVRA10-like group of sequences. We used six different codon-based likelihood models (M0, M1a, M2a, M3, M7 and M8) to detect positively selected amino acid sites. The models are implemented in a maximum likelihood framework and tested using a likelihood ratio test (LRT).
EKA protein identification The database of 415 predicted EKA proteins including AVRK1 and AVRA10 (Table  3 ) was merged with the Bgh DH14 protein database on the Blugen website (http:// www. blugen. org/) in order to have a database search space that better reflects a complete proteome. The resulting database (of 6,885 sequences and 3,289,563 amino-acid residues) was used to query the mass spectrometry (MS) data sets from a large-scale analysis of the haustorial and hyphal proteomes of Bgh DH14  [44]. The original MS and Mascot data as well as associated metadata are publicly available and can be retrieved from the PRIDE database (Accession Numbers 15917-15924; http://www. ebi. ac. uk/pride/). The search parameters were the same as in  [44] , but using Mascot vs. 2. 4 (MatrixScience, London) as search engine.
Availability of supporting data The data sets supporting the results of this article are available in: INRA. http://doi. org/10. 15454/1. 4454357532232493E12. TE consensus library generated from Bgh genome assembly. INRA. http://doi. org/10. 15454/1. 44543859671938E12. Bgh genome: TEs annotation file. LabArchives. http://doi. org/10. 6070/H4X34VG0. Sequence alignment used to produce the phylogeny. PRIDE database (Accession Numbers 15917-15924; http://www. ebi. ac. uk/pride/). Original MS and Mascot data and associated metadata for protein identification.
2005). Three other M. oryzae rice isolates were recently newly sequenced  (Yoshida et al. 2009; Xue et al. 2012; Wu et al. 2015) , and their genome structure and composition was very close to the 70-15 reference genome. This genome is enriched in genes coding for SPs, in particular candidate effectors that are suspected to be key elements during the infection process by manipulating plant defense or deriving plant metabolism to the pathogen's benefit  (Chen et al. 2012; Giraldo and Valent 2013; Zhang and Xu 2014). To date, eight of the nine molecularly characterized avirulence genes in M. oryzae are small SPs  (Valent and Khang 2010). The M. oryzae rice isolate genome is also enriched in enzymes involved in secondary metabolite biosynthesis. To better understand how M. oryzae has adapted to different hosts, and especially to rice, we gathered a unique data set of eight isolates pathogenic to different plants.
Gene Annotation Gene structural prediction was performed using the EuGene software  (Foissac et al. 2008)  to combine results from 1) three ab initio prediction softwares: EuGeneIMM  (Schiex et al. 2001) , SpliceMachine  (Degroeve et al. 2005) , and FGENESH (http://linux1. softberry. com/berry. phtml, an ab initio gene predictor); and 2) homology-based comparison (using BLASTX and BLASTN) against Uniprot, fungal proteins obtained from 30 public genomes, and ESTs (expressed sequence tags) databases. The EuGene software was trained to generate Magnaporthe-specific matrix and to optimize parameters, using a set of 300 Magnaporthe genomic/full-coding cDNA pairs manually curated. Functional annotation of the coding sequences predicted with EuGene was carried out using InterProScan 5  (Quevillon et al.
Burkholderia Filtering Bacterial sequences were identified in four assemblies by both BLAST and GOHTAM  (Menigaud et al. 2012)  analyses. BLASTX analyses against NCBI (National Center for Biotechnology Information) nr protein database showed strong identity with Burkholderia fungorum. The detection and characterization of the sequences potentially originated from Burkholderia were performed by oligonucleotide usage profile discrimination and manual curation (see supplementary material S1, Supplementary Material online, for the detailed method). Transposable Elements (TE) Annotation TE were detected, classified, and annotated using pipelines from the REPET package (https://urgi. versailles. inra. fr/Tools/ REPET). A library of consensuses representing TE families was first characterized on the reference genome of M.
oryzae 70-15 using the TEdenovo pipeline  (Flutre et al. 2011). Classification of consensuses was based on structural features (LTR, TIR, polyA queue, etc. ) and on similarities using PFAM HMM domains  (Sonnhammer et al. 1998 ) and the Repbase Update database  (Jurka et al. 2005) ; we followed the unified classification system  (Wicker et al. 2007). All the consensus TE sequences were manually reviewed and curated from chimeras, duplicates, and nested elements to build the TE reference library of M. oryzae. This library was then used to annotate the genome TE copies, even those nested and degenerated, on the ten genomes analyzed here using the TEannot pipeline  (Quesneville et al. 2005).
Gemo Web Resource All the annotation information acquired in this project is gathered and accessible through a web resource designed for the Magnaporthe community and available at http://genome. jouy. inra. fr/gemo. The database and Genome Browser (Gbrowse) system run on the international open source project Generic Model Organism Database (GMOD: http://www. gmod. org). The database was populated with the nine Magnaporthe isolates of the GEMO project along with their genome scaffold sequences as reference and several features predicted or mapped to these sequences: Predicted genes and associated functional annotations (Interpro, predicted SP, secondary metabolism, and CAZy) and annotated transposable elements. Bacterial regions from Burkholderia were also added in a dedicated track for the four concerned genomes.
Evolutionary Analyses between the Ten Genomes Each putative single-copy protein ortholog cluster recovered from orthology prediction was aligned using the T_Coffee software version 8. 93  (Notredame et al. 2000). The corresponding nucleic alignment was obtained using the tranalign program of the EMBOSS package  (Rice et al. 2000). We kept only ortholog clusters for which nucleic alignments were nonambiguous (this reduced the set of analyzed orthologs from 6,976 to 6,878). Aligned ortholog gene families were postprocessed to filter putative nonreliable positions using Gblocks  (Castresana 2000).
Network Reconstruction To evaluate the presence of conflicting signals in the data set, and especially in the rice lineage, we built a distance matrix (F84 model) from the concatenation of the individual filtered alignments of the 6,878 putative orthologs. We excluded the BR29 M. grisea strain from this analysis, as well as the 70-15 isolate because of its hybrid origin; the alignments used for network reconstruction thus included eight M. oryzae sequences. We then apply the NeighborNet  (Bryant and Moulton 2004 ) method implemented in the SplitsTree software  (Huson 1998). The NeighborNet distance method is an extension of the Neighbor Joining algorithm allowing the representation of a network of conflicting signals in data, regardless of their sources.
Diversity Analysis and Recombination Detection To detect recombination in the single-copy ortholog gene families and into the rice lineage, we used a simple and robust statistical test: The Pairwise Homoplasy Index (PHI) test  (Bruen et al. 2006 ) based on detection of phylogenetically incompatible site-pairs. We used the PhiPack software, that also computes two diversity indices: The mean Pi values (i. e. , the nucleotide diversity defined as the average number of nucleotide differences per site between any pair of DNA sequences randomly chosen from the sample) and the mean number of informative sites (IS) per alignment. A site of an alignment is informative only when it includes at least two different kinds of nucleotides, each of which is represented in at least two of the sequences under study.
The PHI recombination test was applied on one hand to the 6,878 aligned orthologs, and on the other hand to 128 genomic aligned regions larger than 50 kb and present both in the 70-15 reference genome and in scaffolds of the rice isolates. These regions were obtained from a multiple genome alignment performed with the Mugsy software (default parameters)  (Angiuoli and Salzberg 2011)  and including the eight 70-15 scaffolds (used as reference genome) and all scaffolds of the five isolates of the rice lineage (see supplementary material S6, Supplementary Material online).
Estimation of the Fraction of 70-15 Genome that Belongs to the Rice Lineage The phylogenetic analysis package ETE  (Huerta-Cepas et al. 2010 ) was used to determine the proportion of different lineages found within the 6,878 topologies analyzed. Specifically, we looked for 1) the number of trees where 70-15 was placed within the rice lineage, 2) the number of trees where 70-15 was placed outside the rice lineage as a direct sister lineage, and 3) the number of trees where 70-15 was placed outside the rice lineage and was not a sister lineage to the lineage.
Genome Sequencing and Assembly Hybrid de novo assemblies of the nine M. oryzae/M. grisea genomes were performed independently for each isolate with Newbler 2. 6. Resulting genome depths of coverage exhibited important variations (table 1a) ranging from 4 Â (FR13 isolate) to 80 Â (US71 isolate). Total assembly sizes (before Burkholderia filtering) varied between isolates: Six of the nine assembly sizes were comparable to the reference 70-15 reference strain (41-43 Mb) but three isolates (GY11, PH14, and TH12) exhibited higher assembly sizes of 46, 49, and 50 Mb, respectively (table  1a ). Scaffolding was challenging for two genomes (FR13 and GY11) due to low yield of the sequencing process (FR13) or fragmentation of assembly (GY11, contig N50 < 9 kb). In brief, this new genome data set was somehow heterogeneous and comprised two highly fragmented genomes (FR13 and GY11), two medium-quality genomes (PH14 and TH12), and five high-quality assembled genomes (BR29, BR32, CD156, TH16, and US71) according to their assembly metrics.
The BUCKy software  (Larget et al. 2010 ) was used to perform BCA and to analyze the primary concordance tree, which is composed of lineages supported by the largest proportion of genes. The topology of the primary concordance tree obtained from the 6,878 individual trees (fig. 3 ) was consistent with the biological knowledge we had about the ten analyzed isolates, especially genetic groups based on microsatellites  (Tharreau et al. 2009; Saleh et al. 2014 ) and host specificity. All rice isolates clustered together as a monophyletic lineage sister to the foxtail millet lineage (US71). This relatedness confirms the result previously obtained by  Couch et al. (2005)  on the basis of ten nuclear genes. Within this rice lineage, we observed that, as expected, the 70-15 and GY11 isolates grouped together, an observation consistent with the genealogy of 70-15.
To further investigate evolutionary history of the M. oryzae genomes, we also adopted a network representation, allowing the integration of different conflicting phylogenies. We applied the NeighborNet method  (Bryant and Moulton 2004)  implemented in the SplitsTree software  (Huson 1998 ) on the matrix of pairwise genetic distances calculated from the concatenated alignment of the 6,878 orthologs, without taking into account the BR29 M. grisea isolate. We removed the reference genome 70-15 as it is a hybrid between rice and nonrice isolates. The resulting network (fig. 4 ) confirmed that conflicting signal existed in our data set, but indicated that it was mostly found within the rice lineage (GY11, FR13, TH12, TH16, and PH14 isolates). Conversely, evolution of the three nonrice isolates (US71, BR32, and CD156) was more supported by a tree-like topology, but conflicting signal was not completely absent along these nonrice branches.
Footprints of Interlineage Hybridization in the 70-15 Genome As outlined above, the 70-15 reference strain is not a field isolate, and has a complex laboratory pedigree  (Chao and Ellingboe 1991). It originates from an in vitro cross between FIG. 3. -Primary concordance tree obtained from 6,878 M. oryzae/ grisea single-copy orthologs. The primary concordance tree topology features relationships inferred to be true for the largest proportion of genes according the BUCKy software. CFs are indicated at internal branches and represent the proportion of individual gene phylogenies out of 6,878 in which this branch is resolved, providing a measure of branch support. Colors of leaves indicated host of origin of isolates (for M. oryzae, blue: rice, purple: foxtail millet, green: wheat, orange: goose grass; for M.
grisea, red: crab grass). a rice isolate and an Eragrostis isolate, followed by several backcrosses, three of them performed with the GY11 isolate (fig. 5a ). We therefore expected footprints of hybridization between rice and nonrice lineages in the 70-15 genome. We used the ETE package (Huerta-Cepas et al. 2010) to estimate the proportion of genes among the 6,878 single-copy orthologs that support (or not) an assignment of 70-15 genome within the rice lineage. Out of the 6,878 topologies analyzed, only 1,676 topologies were recovered with a completely resolved rice lineage (grouping the five rice isolates in a monophyletic clade) (fig. 5b ). In 1,610 of those 1,676 topologies (96%), 70-15 clustered together with the rice lineage, indicating that they shared a common ancestor. In 39 topologies (2% of the 1,676 trees with a resolved rice lineage), 70-15 formed a distinct but sister lineage of the rice lineage.
Acknowledgments The authors acknowledge  J. Milazzo  and  H. Adreit  for DNA preparation, and  S. Cros-Arteil  for help in Burkhoderia filtering and manual curation. They also thank  C. Cruaud ,  A. Couloux ,  A. Roy , and  P. Wincker  from the  Genoscope, Centre National de Sequencage (Evry, France ), for performing sequencing and automatic assembly of the nine genomes. Finally, they thank the Migale platform for hosting the project data and the.
Patients This study was carried out on baseline isolates from new cases of pulmonary TB patients in Guinea recruited in 2 different clinical trials in the capital Conakry: the Oflotub Study (registration number = NCT00216385)  (Merle et al. , 2012 (Merle et al. , , 2014) )  and the Study C (registration number = NCT00216333)  (Lienhardt et al. , 2011). A sub-set of isolates had been stored at À80 °C at the Institute of Tropical Medicine (ITM), Belgium. Both studies recruited consecutive adult smear positive patients without prior history of TB treatment (in the Oflotub study, patients with prior treatment more than 3 years ago were also eligible).
Genotype database comparison and analysis The genotyping results were compared to the international spoligotyping (SITVIT) database of the Pasteur Institute of Guadeloupe (http://www. pasteur-guadeloupe. fr:8081/SITVITDemo)  (Brudey et al. , 2006) , and the newly revised publicly available international multimarker database (SITVITWEB)  (Demay et al. , 2012). A SIT (spoligotype international type) was assigned when two or more patient isolates in the database shared the identical spoligotype-patterns, while spoligotype-patterns that had not been described before were defined as ''orphan''. To assign mycobacterial lineages and families to the genotyped isolates, spoligotypes were analyzed using TBlineage (http://tbinsight. cs. rpi. edu/about_tb_lineage. html) and Spotclust (http://tbinsight.
Statistical analysis Statistical analysis was performed using SPSS Statistic 17 (SPSS Inc. , USA). Proportions of drug susceptibility test profiles for both MTBC strains were compared using Chi-Square analysis. The two sided Pearson's Chi-Square test was used to assess associations of drug-resistance profiles with spoligotype families. Furthermore, an association of first-line anti-tuberculosis drug resistance profiles with MTBC genotypes was estimated and expressed as the odds ratio (OR) and 95% confidence interval (95% CI). A p-value of less than 0. 05 was considered statistically significant.
Acknowledgments We would like to acknowledge the staff members of the  Mycobacteriology Laboratory in Guinea-Conakry and of the Mycobacteriology Unit, Institute of Tropical Medicine, Belgium. We are grateful to the  VLIR-UOS scholarship program  and the staffs of the  Interuniversity Program of Molecular Biology (IPMB)  ( Vrije University Brussel, Katholic University of Leuven, and Antwerp University ). The study was partially funded by  European Research Council (ERC)   starting grant ''INTERRUPTB'', Grant  agreement No. :  311725.
Data collection and availability Rhodnius prolixus assembled genomic sequences (RproC1) were downloaded from VectorBase (htps://www. vectorbase. org/organisms/rhodnius-prolixus). We analyzed TEs in the whole genome using RepeatMasker with default parameters (http://www. repeatmasker. org) and a library of Metazoan TEs extracted from Repbase (http://www. girinst. org/repbase/) [11]. Python scripts and raw data including TE sequences, consensus, alignments and phylogenetic trees… are available at: http://echange. legs. cnrs-gif. fr:5000/fbsharing/LUGs8EBq Library based method for Tc1-mariner Element searches TBLASTN searches for Tc1-mariner elements  [12]  was run on the R. prolixus genome, using 8 mariner transposase protein sequences, representative of the major subfamilies and 15 non-mariner transposases (Additional file 1: Table  S1 ).
We obtained 51,271 and 2711 hits respectively. A suite of python scripts was then used for: i) Reconstitution of copies by associating hit distant of less than 1000 bp, in correct orientation ii) Filtering out any copies less than 400 bp-long iii) Extraction of all the sequences with or without 500 bp flanking sequences each side to get full copies iv) Clustering copies (without flanking sequences) with Usearch (-id 0. 8, -rev) [13] v) Aligning sequences (with flanking sequences) in each cluster with MAFFT  [14]  and refined by hand using AliView  [15]  allowing to identify the complete sequences vi) Filtering out sequences with "N", assemblytruncated copies, and duplicated copies (resulting from segmental duplication and not from transposition, as determined by the flanking sequences. vii)Trimming flanking sequences and generating nucleotide consensus (majority rule with keeping the longest elements), then protein consensus.
De novo identification of MITEs We used a suite of python scripts gathered under the name AutoMitaur (Hua-Van, unpublished) and available at http://www. egce. cnrs-gif. fr/wp-content/uploads/2014/ 04/AutomitAur. v1. 0. 1. zip. Briefly, in this suite of script, BLASTN is used to compare a genome against itself for short hits at least 11 bplong, distant of 750 bp at most, and in inverted orientation (TIRs). The TIRs, the intervening sequence plus 60 bp flanking sequences on each side are then extracted. Sequences are then clustered and copies with similar flanking sequences are removed. Several filters are applied and only groups with at least ten independent sequences that reach a certain level of homogeneity between the sequences and display bona fide TIRs are kept. A consensus sequence is then determined for each cluster.
The pipeline also includes a step consisting of searching (BLASTN-SHORT) for putative autonomous partners, by using the defined TIR sequences as queries against the input genome, keeping only sequences larger than 1 kb. The putative longer elements are then searched against the RepBase protein database (31/01/2014 version) using BLASTX, to automatically identify the potential associated superfamily. In parallel, a BLASTX search was realized with the MITE consensus sequences as a query, against the database. Out of a raw output of 107 clusters, we could then select 41 MITE clusters for further analysis.
HT identification We compared R. prolixus mariner consensus sequences to Genbank and WGS NCBI databases (ftp://ftp. ncbi. nlm. nih. gov/) using BLASTN searches  [12]. Candidate elements for HT were identified as sequences with more than 75 % of nucleotide identity over more than 90 % of the query sequences. To discard potential cases of contamination with foreign DNA, each genomic context of the putative elements was carefully examined: each 50 kbp adjacent segment was inspected with a BLASTN procedure and only elements within a conserved synteny block were conserved. Cases of HTs were then validated using phylogenetic analyses.
Indeed, no more of 23 lineages > 5 independent copies have been identified within one single Drosophila genome  [17]. The R. prolixus genome then appears so far the most comfortable ecological niche for mariner elements. All these lineages fully covered the known mariner diversity and possibly formed at least one new subfamily. We first performed a classification of the R. prolixus nucleotide sequences using a clustering method (UPGM-VM) based on the whole nucleic sequences of 309 Tc1-mariner sequences. This classification allows the use of a large dataset in a reasonable calculation time, including distantly related Tc1 and Tc3 sequences found in animals, plants, fungi and bacteria (Additional file 2: Figure  S1 ). The resulting classification revealed the clustering of R. prolixus sequences within known clades/subfamilies with the exception of four lineages that may define a new subfamily called nosferatis (Nos in Additional file 2: Figure  S1 ).
Evidence of multiple HT of Mariner elements We screened the complete GenBank and WGS databases with the mariner and MITE mariner consensus in order to document the propensity of mariner TEs in R. prolixus to generate HTs. We detected ten putative cases of HT (Table  3 ). One case concerns a MITE element found in the genome of the flatworm Schmidtea mediterranea, a free-living freshwater planarian that preys on small arthropods and gastropods. Despite a time divergence of about 792 Ma between this species and R. prolixus, the level of similarity between the two elements is 97 % (http://www. timetree. org/index. php) [41]. Interestingly, in S. mediterranea, sequences homologous to MITE_185 of R. prolixus correspond to a complete mariner element present in numerous copies. This would indicate that MITE_185 is an internal, deleted derivative of an autonomous transposon still present in S.
For horizontal transfers two scenario of transmission could be examined: direct transmission or indirect via intermediate hosts. Interestingly, the implication of parasitoid insects as vector of HT of hAT and Ginger MITEs between R. prolixus and the silkworm B. mori has been proposed  [10]. A similar situation has been reported between R. prolixus and the twisted wing parasite Mengenilla moldrzyki that are known to infect a large variety of insects  [42]. In our dataset, we have detected a possible HT between the parasitic wasp S. oculata and R. prolixus. Eggs of Triatomine bugs as Rhodnius species are effectively infected by diverse parasitic wasps  [43]. Another example of HT of a mariner element between the parasitic wasp Ascogaster reticulatus and its host the moth Adoxyphyes honmai has also been evidenced  [44].