dataesr/scientific-paragraphs-categorization huggingface dataset:
Genome Sequencing and Sequence Analysis The 11 isolates were sequenced using Illumina NextSeq, which generates 150 bp pairedend reads, with a mean coverage of 80×. The de novo assembly of the genomes of all isolates was performed using SPAdes v. 3. 11. 1. Antimicrobial resistance determinants were identified using Resfinder (version 1. 1. 4) [14]  and The Comprehensive Antibiotic Resistance Database (Card)  [15]. These analyses were realized with an alignment greater than 100 bp and a similarity threshold value of 95%. The search for virulence genes was carried out with virulence factor database (Virulence factors of Pathogenic Bacteria, www. mgc. ac. cn/VFs, accessed on 23 June 2021).
Molecular Typing Methods Spa typing was achieved through spatyper (https://cge. cbs. dtu. dk/services/spatyper, accessed on 23 June 2021). SCCmec types were determined for the four MRSA genomes with SCCmecFinder (https://cge. cbs. dtu. dk/services/SCCmecFinder, version 1. 2, accessed on 23 June 2021) with a threshold at 90% and minimum length at 60%. Prophage was initially detected with the PHASTER software (https://phaster. ca, accessed on 23 June 2021). We further confirmed the presence of ϕSa3 and ϕMR11 using in silico PCR with previously described primers  [16]. We also investigated the presence of the IEC genes (sak, scn, and chp) by in silico PCR according to Van Wamel et al. [17]. Genomes were annotated with the Prokka program  [18]. We extracted the ϕSa3 sequences from S. aureus ST398 genomes with a homemade script, using the biopython library (https://github.
Phylogeny of S. aureus ST398 To understand the population structure of the clone, we downloaded all available S. aureus genomes from NCBI in June 2020 (n = 11,560). The ST398 genomes (n = 861) were then identified by in silico MLST (with a homemade pipeline pyMLST, https://github. com/bvalot/pyMLST, accessed on 25 June 2021) and extracted from this collection. Using genes of a reference genome (CP003045. 1), pyMLST identified 2621 core genes further used to build a minimum spanning tree. Isolates of which the genomes had less than 15 alleles of difference were clustered. We kept one isolate per group (n = 443) that shared the same mecA status, host, and geographic origin. The phylogenetic tree was built with genes present in at least 95% of the strains and with less than 5 mutations per 100 base pairs.
The genes were aligned using Mafft (https://mafft. cbrc. jp/alignment/software, accessed on 23 June 2021) and only the regions with SNPs were used, i. e. , 15,680 positions. From these data, we built a phylogenetic tree using the Bayesian approach with the GTR + G model using MrBayes (http://nbisweden. github. io/MrBayes, accessed on 23 June 2021). The presence of the mecA gene, of prophage ϕSa3, and the host were annotated using itol (https://itol. embl. de, accessed on 23 June 2021).
Firstly, we had few clinical isolates, especially MRSA ST398. However, we compared our isolates with others published in NCBI. Secondly, long-read sequencing data were not analyzed; however, the selection method of isolates downloaded from NCBI permits for a large comparison of genomes with reliable data. This study focuses only on virulence, resistance genes, the presence or absence of prophage, and genetic difference in the ϕSa3 prophage. The ϕSa3 prophage is an origin-specific marker of S. aureus ST398 and the cornerstone of the virulence of the bacterium. Finally, the clinical data of strains causing infections and sequenced, were retrospectively collected; thus, exposure data are limited. Indeed, one patient (patient 7) was a farmer with no data on his specific occupation (e. g. , the presence of pig or poultry farming).
tabaci s. l. genomes. All assemblies generated in this study were deposited in the European Nucleotide Archive (ENA) at the European Molecular Biology Laboratory (EMBL-EBI) under project accession numbers PRJEB28507, PRJEB35304, PRJEB39408 (https:// www. ebi. ac. uk/ ena); genomic PacBio data is described in Additional file 1: Table  S1. See Table  2 , Additional file 1: Table  S3  and Additional file 2: Figs. S2-S7 for additional assembly processing and taxonomic contamination evaluation.
Genomic annotations of the six new B. tabaci s. l. genomes were performed using the 'Ensembl gene annotation system'  [46] , which produced transcript models based on empirical evidence; to the exclusion of any abinitio prediction methods. High quality standards were achieved by ensuring model accuracy that derives solely via integration of alignment of expressed protein, cDNA and other types of biological sequences, such as highthroughput RNA-seq. The Ensembl gene annotation pipeline also facilitates the identification of alternative splice patterns, allowing for multiple alternate splice variants to be captured per transcript model. Finalized gene sets obtained for all eight B. tabaci s. l. genomes were recovered with only small variances in total PCG counts. The number of PCGs captured across the majority of genomes were considerably fewer than previous estimates, decreasing from ~ 15 k (B.
6% (Uganda-1) to 83. 2% (SSA1-SG1-Ug). Structural annotation was achieved using all combined RNA-Seq data from other B. tabaci s. l. generated as part of this study. Given the close evolutionary relationships of these B. tabaci populations, the transcriptomic evidence readily aligned and was therefore useful for capturing Uganda-1 gene features. The caveat, however, is that the Uganda-1 annotation likely has reduced gene recovery, maximization of complete ORFs, exon-intron boundaries and species-specific features. Structural annotation of the B. tabaci s. l. genomes revealed a marked reduction in total gene models recovered, compared to previously published gene sets of B. tabaci s. s. and B. argentifolii. Despite this, we posit that use of a uniform annotation methodology employed across contiguous genome assemblies, and without use of ab-initio methods, provides a well-supported and conservative estimate of B.
Integrative systematics: a consilience of evidence Phylogenomic analysis of B. tabaci s. l. and other insects Phylogenomic analyses on an alignment of 655 OGCs or 131,953 amino acids (average % missing data = 10. 34%) were conducted using the best fitting evolutionary model (LG + Γ + I + F) under both maximum likelihood (ML) and Bayesian inference (BI) methods using RAxML and Mr. Bayes, respectively. Only amino acid sequences of the longest canonical protein-coding transcript were considered. Results from both ML and BI phylogenetic analyses recovered near universal support of the exact same topology, providing high support for monophyletic whitefly (Aleyrodidae) and monophyletic B. tabaci s. l. (Fig. 4a ). Overall topological support recovered from ML and BI analyses was similar with linear log-likelihood values of lnl-2.
tabaci s. l. populations from Sub-Saharan Africa occupied a monophyletic clade with Uganda-1 positioned as the earliest branching member. Although the partial mtCO1 marker provides a good initial framework for identifying putative biological species within B. tabaci s. l. , analysis of mtCO1 alone has limitations. There is a need, therefore, to continue the integrative approach  [6]  for identifying biological species within the group which involves combining evidence from biological and molecular datasets. An integrative approach, as applied herein, supports that the seven African cassava populations of B. tabaci s. l. represented in Fig. S8 , can be reduced to three biological species. Hence, the SSA1-SG1 Nigeria, SSA1-SG1 Uganda and SSA1-SG2 Kayingo populations were grouped as "B. tabaci SSA1-SG1 ∪ SG2" and SSA2 Kiboga, SSA2 Nigeria and SSA3 Nigeria were grouped as "B.
ENSSSA1UGT025164 and ENSSSA1NGT004694 respectively. Two SSA1-SG1-Ug genes (ENSSSA1UGT010306 & ENSSSA1UGT014032) were annotated as alpha-amylase, an enzyme that hydrolyze alpha bonds of large polysaccharide such as starch and glycogen. Alpha-amylases have also been previously reported in B. tabaci  [114]. Among the studied insects, these genes were found only in T. urticae, R. prolixus and D. melanogaster. S14. The α-glucosidase genes related to sucrose hydrolysis are located in cluster 2, highlighted in purple (**) To classify the α-glucosidases in B. tabaci SSA1-SG1-Ug and B. tabaci SSA1-SG1-Ng based on the approximate specificity, selected protein sequences were submitted to the protein structure homology-modeling server (Expasy webserver-SWISSMODEL)  [115]  to identify the best protein database (PDB) template for their three-dimensional structure modeling.
We conclude that the α-glucosidase GH13 are: (i) sucrose hydrolyzing enzymes (with PDB: 6Igg. 1. A, 6Iga. 1. A, 6Igf. 1. A as the best homologous PDB templates), (ii) toxin receptor proteins (PDB: 6K5p. 1. A), (iii) neutral and basic amino acid transporter protein rBAT (PDB: 6li9. 1. A) and (iv) alpha amylase (PDB: 1dhk. 1. A, 1kxt. 1. A). Of these, the largest numbers of proteins were toxin receptor proteins and sucrose hydrolyzing enzymes.
argentifolii contained a protein-ligand interaction of two magnesium ions, interacting with ASP 48, ASP 50 and ILE 52 (Additional file 1: Table  S15 ). The B. tabaci SSA1-SG1-Ug gene ENSS-SA1UGT001243 was identified using sequence-based methods. The gene encodes for a sucrase hydrolysis enzyme. Oligomeric modeling in SWISSMODEL identified PDB: 6k5p. 1. A (a binary toxin receptor protein) and 6lga. 1. A (sucrose hydrolase) as the two best quaternary structure annotations. The best model built for both ENSSSA1UGT001243 and its ortholog in B. argentifolii ENSMEAMV2T026084 ("Bta03818"  [32] ) sequence was that of a toxin receptor protein, while the second-best model predicted a sucrose hydrolase. Another study has reported some of the α-glucosidase GH13 acquire a secondary function, citing a toxin receptor as an example, primarily in mosquito species  [118].
We expand on results by providing a comparative genomics framework (i. e. , OGC gene tree generated via OrthoFinder) including specific reference to broader whitefly, and importantly T. vaporariorum. This approach was non-exhaustive, with further examination is warranted to verify these findings and fully elucidate these candidate gene family targets with potential for whitefly control. Evasion of the host immune system is crucial for successful host infection and propagation of virus particles. Here we have examined the presence and diversity of Table  8  Selection pressure analysis on the Bemisia tabaci SSA1-SG1 α-glucosidase (GH13) genes Analysis of selection pressure acting on the α-glucosidase (GH13) gene of B. tabaci SSA1-SG1-Ug and B. tabaci SSA1-SG1-Ng in "cluster 2". The adaptive Branch-site Random Effects Likelihood (aBSREL) software analyzed diversifying selection using a branch-site effects model  [116] , while Fixed Effect Likelihood (FEL) software analyzed the number of sites within a gene with pervasive selection  [117].
Gene Likelihood peptidoglycan recognition proteins (PGRP) (IPR015510, IPR002502, IPR006619, IPR036505). PGRPs are a widely distributed, diverse family of proteins conserved between invertebrates and mammals which facilitate maintenance of vector-pathogen homeostasis via innate immune response against invading pathogens  [123] [124] [125] [126]. PGRPs exhibit gene-copy variability across Arthropoda. D. melanogaster (thirteen genes) contains a rich repertoire while mosquitos (seven genes) and tsetse flies (six genes) genomes contain fewer PGRPs  [127] [128] [129] [130]. To date, only a single PGRP gene 'BtPGRP' (AJQ31845. 1) has been identified in B. argentifolii  [131]. In this study, comparative analysis revealed two independent PGRP-containing OGCs ('OG0000284' and 'OG0003013'). OGC 'OG0000284' , was composed of single copy PCGs across all genomes and contained proteins with high sequence identity to 'BtPGRP'  [131].
Wang et al. [131]  reported that BtPGRP contained an Arg 106 which is associated with recognizing meso-diaminopimelic acid (DAP)-type peptidoglycans, as previously shown for D. melanogaster PGRP-LE and PGRP-LC  [132]. Our results confirm the presence of BtPGRP in whitefly, while further demonstrating its complete sequence conservation of Arg 106 between all Aleyrodidae species examined and D. melanogaster PGRP-SB1 (FBtr0075348). The second PGRP-like OGC 'OG0003013' , contained proteins which exhibited functional domains linked to the 'N-acetylmuramoyl-L-alanine amidase/PGRP domain' superfamily (IPR002502). 'OG0003013' contained B. tabaci s. l. members harboring two gene copies each (SSA3-Ng, Uganda-1, B. tabaci s. s). However, not all Hemiptera examined (A. pisum, M. persicae) had detectable homologs within either of these two PGRPlike OGCs and highlights a possible PGRP gene loss over time within whitefly and more broadly Hemiptera.
We instead present a targeted examination of hsp70 protein domains (IPR018181, IPR013126, IPR043129) in B. tabaci s. l genomes. A total of five OGCs ('OG0000087' , 'OG0002826' , 'OG0004004' , 'OG0004227' , 'OG0004931') with homology to HSP70 functional protein-domains were identified. Between 58-75% of the total gene members, across all hsp70-like OGCs identified across all B. tabaci s. l. genomes, were located within a single OGC 'OG0000087'. Published hsp70 'BtHSP70' (XP_018908958. 1) previously identified in B. argentifolii recovered significant CRBB hits to 'OG0000087' members. Our results showed hemipteran species exhibit the largest diversity of hsp70like genes, with OGC 'OG0000087' containing up to fourteen HSP70 homologs per species; while B. tabaci s. l. genomes contained between 9-14 compared to 10 in T.
vaporariorum. Recent studies have demonstrated that for B. tabaci s. l. , the vesicle trafficking system of midgut cells plays an important role in the transport of begomoviruses across the midgut basal membrane to the haemolymph  [142, 143]. Further work by Zhao et al. [144]  identified the vesicle-associated membrane protein-associated protein B (VAPB) in B. argentifolii, which showed an inhibitory role in the transmission of the begomovirus tomato yellow leaf curl virus (TYLCV). Our investigation of VAPB-like proteins in B. tabaci s. l. genomes recovered six OGCs with homology to VAPB protein domains related to "Vesicle-associated membrane-protein-associated protein" (IPR016763), "PapD-like superfamily" (IPR008962) and "Major sperm protein" (MSP) domain (IPR000535). One of the six OGCs 'OG0002935' harbored members with high sequence identity to that of B.
argentifolii VAPB (XP_018905345. 1). All species were represented in 'OG0002935' by a single VAPB gene member, to the exclusion of B. tabaci SSA1-SG1-Ng and B. tabaci Asia II-5 which both had two copies. VAPB was not identified in B. tabaci s. s. Notably then, VAPB is potentially not vital for successful TYLCV transmission, given B. tabaci s. s. Is known to be an efficient vector of this virus  [145]. Another crucial protein related to begomovirus transmission is found in B. tabaci midgut proteins (MGP). MGP is crucial for successful begomovirus transmission, where the viral coat protein interacts with the MGP to facilitate transfer into hemolymph and salivary glands  [146]. MGP is characterized by the presence of a secretory signal-peptide and transcription activator MBF2 (IPR031734) domain.
tabaci s. l. The second OGC 'OG0000655' , had the highest overall homology to the MGP reported previously for B. argentifolii (AIK97534. 1) [146]. Both OGCs represent excellent gene family targets for possible expansion of whitefly control mechanisms given their high degree of importance for begomovirus transmission. Lastly, we examined the diversity of B. tabaci s. l. genes with homology to cyclophilins (CyPs). CyPs are a diverse superfamily of proteins found in both prokaryotes and eukaryotes. Cyps are involved in protein folding  [147] , and function in many cellular processes, such as protein-protein interactions and chaperone functionality (reviewed in  [148] ). CyPs have been implicated in luteovirus transport and transmission by aphids  [149]. Whole genome comparative analysis based on query proteins with functional annotations linked to the cyclophilinlike domain superfamily (IPR029000), among others (IPR002130, IPR024936, IPR020892) resulted in recovery of fourteen OGCs containing CyP-like proteins.
Structural and functional annotation of B. tabaci s. l. genomes Genomic annotations of B. tabaci s. l. were generated via the EMBL-EBI Ensembl Gene Annotation pipeline  [46]. Ensembl transcript models generated are supported by experimental evidence in the form of aligned short-read RNA-seq data, then verified and filtered using secondary protein-to-genome alignments of proteins from whitefly or closely related species. The Ensembl Gene Annotation pipeline gave precedence to gene models supported by well-aligned transcriptomic evidence  [46, 161]. The genomes of B. argentifolii and B. tabaci s. s. were also reannotated (utilizing their original published RNA-Seq datasets). Bacteriocyte-specific RNA-seq libraries were applied to ensure bacteriocyte-specific gene expression contributed to structural annotation.
Finalized gene builds contain multi-transcript PCG structures, where each transcript can contain overlapping exon boundaries; excluding any redundant transcripts where the splicing pattern is completely redundant when comparing to a longer model  [46]. See Additional file 4 for complete detail of genomic annotation methods. Core databases of the six newly generated B. tabaci s. l. genomes presented herein were first released in April-2021, via Ensembl Metazoa (Ensembl release version e103) (https:// metaz oa. ensem bl. org/). Re-annotations of both B. argentifolii and B. tabaci s. s. are available as a standalone 'Figshare' dataset (FastA, GFF3)  [162]  as part of this study. Interproscan  [163]  version 5. 40-77. 0 (https:// github. com/ ebi-pf-team/ inter prosc an) was used to generate functional annotations of canonical PCG gene models.
TE annotation Transposable element (TE) libraries generated RepeatModeler (https:// www. repea tmask er. org/ Repea tMode ler/) v1. 73. To avoid 'over-masking' of protein encoding genomic loci, de novo TE repeat libraries were screened with BLASTp and BLASTn MegaBLAST  [173, 174]. The protocol for filtering potential protein containing repeat sequences is described here (https:// blaxt er-lab-docum entat ion. readt hedocs. io/ en/ latest/ filter-repea tmode ler-libra ry. html) and was implemented via bash script. Genome-wide copy number variation and overall sequence divergence on TE repeat types was examined using the Perl utility 'OneCodetofindthemall'  [175]  v. 1. 0 (https:// doua. prabi. fr/ softw are/ one-code-to-findthem-all), and statistics reported on a per genome scaffold basis. To summarize TE repeat content 'OneCodetofindthemall' output was parsed using an in-house Perl script taking '.
copynumber. csv' files as input. Kimura divergence scores were extracted from RM generated '. out' output file using the protocol as described  [48] , using custom Kimura divergence scripts (bash, R, Python) freely available via  [48]. Reciprocal cross-mating B. tabaci s. l populations from Nigeria and Uganda were subjected to reciprocal crossing experiments in sympatric and allopatric crosses. Sympatric populations cohabitated in the same field or had overlapping geographic ranges, while allopatric populations had separate, nonoverlapping ranges or were separated by more than 500 miles  [31, 59]. The following population combinations were classified as sympatric (i) SSA1-SG1-Ng X SSA3-Ng, (ii) SSA2-Ug X SSA1-SG1-Ug, (iii) SSA2-Ng X SSA3-Ng and (iv) SSA1-SG1-Ng X SSA2-Ng. Allopatric population combinations were (i) SSA1-SG1-Ng X SSA1-SG1-Ug, (ii) SSA1-SG1-Ng X SSA2-Ug and (iii) SSA2-Ug X SSA3-Ng.
Portiera genome assembly and comparative genomics Portiera genomes were obtained using an iterativeassembly approach (flye_iterative. sh available as part of data file deposited in 'Figshare'  [176] ). Briefly, Kraken2 v2. 1. 2 [154]  with the database PlusPF-16 (standard Kraken2 database plus the protozoa, fungi, and plant RefSeq databases, available from (https:// genome-idx. s3. amazo naws. com/ kraken/ k2_ pluspf_ 16gb_ 20210 517. tar. gz) was used to perform an initial selection of Portiera sequences from the pre-processed PacBio sequences (non-taxonomically filtered). Kraken-tools v1. 2 was used to extract any classified sequence below the Oceanospirillales rank. Primary assemblies of Portiera sequences were produced with flye  [177]  v2. 8. 3-b1695 (-pacbioraw -genome-size 0. 35 m -meta).
Primary assemblies were used to recover possible missing Portiera reads with minimap2 v2. 17-r941 (-x map-pb)  [155]  and the preprocessed PacBio sequences. Secondary assemblies were obtained using all sequences recovered with minimap2 and re-assembled with flye (-pacbio-raw -genome-size 0. 35 m -meta). Finally, secondary assemblies were polished with fly (-pacbio-raw -genome-size 0. 35 m -meta -polish-target -iterations 10). Polished Portiera assemblies were annotated with prokka v1. 14. 6  [178]  (-gram neg -rfam), using all available Portiera genomes as primary annotation source (proteins). Obtained annotations were manually inspected in Artemis v1. 5  [179]. Annotations and genome sequences provided in GenBank format (. gbk), available as part of a 'Figshare' dataset  [176]  deposited as part of this study.
Orthologous clusters of proteins (OCPs) were computed with OrthoFinder v2. 4. 0 (-M msa -T iqtree -I 1. 5)  [51]. Encoding potential of Portiera strains was manually assessed using Portiera MED-Q1 (CP003835) as reference. Genomic synteny among Portiera genomes was plotted with genoPlotR  [180]  based on 202 core OCPs and the three proteins encoded in the variable region (yidC, mnmE, and mnmG). Pairwise Average Nucleotide Identity values were calculated with the enveomics toolbox  [181]  using USE-ARCH v11. 0. 667 (ublast -id 0. 1 -maxhits 1,000-acceptall -evalue 1e-5 -accel 1) as alignment algorithm  [182]. The heatmap and hierarchical clustering (Euclidean distances and complete clustering) were obtained with the gplots package from R  [183].
Genome assembly statistics and comparisonThe Bemisia tabaci s. l. genome assembly statistics in comparison to selected (publicly available) insect genomes with INSDC accession numbers. Whitefly genome assembly sizes ranged from 615 to 787 Mb, which was consistently larger than the genomes of Acyrthosiphon pisum, Anopheles gambiae, Drosophila melanogaster and Tribolium castaneum.
Acknowledgements Our sincere thanks go to the farmers in  Uganda, Nigeria and India  who kindly provided access to their fields for our whitefly collections. We thank international internship students  Sarah Mieulet ,  Louise Cachenaut  and  Clara Menanteau  ( École d'Ingénieurs de Purpan ) for their support with insect rearing and collection. We thank  Xiao-Wei Wang ,  Shu-Sheng Liu  and  Andrew Armitage  for useful technical discussions. We thank  Simon Springate  and  Natalie Morley  for their support with insect culture maintenance.
Availability of data and materials The genomes, transcriptomes, and predicted protein-coding sequences are available from Ensembl Metazoa (http:// metaz oa. ensem bl. org) and are included within the references. Raw RNA-Seq datasets generated and/or analyzed during the current study are available from the European Nucleotide Archive database repository (https:// www. ebi. ac. uk/ ena) under the parent project accessions: PRJEB28507, PRJEB36965, PRJEB35304, PRJEB39408. All data generated during the analyses of these datasets are included in this published article, supplementary information files, and figshare repository (https:// doi. org/ 10. 6084/ m9. figsh are. 23666 799; https:// doi. org/ 10. 6084/ m9.