dataesr/scientific-paragraphs-categorization huggingface dataset:
7% (95% CI 5. 4%-8. 1%). This prevalence was almost 10 times higher than that in all blood donors (crude OR 9. 7; 95% CI 6. 0-15. 5) and was 7 times higher when compared to FTBDs only (crude OR 7. 0; 95% CI 4. 0-12. 5) (Fig. 3 ). However, demographic characteristics differed significantly between the two populations. Indeed, most of the blood donors were young men. The resulting mean age was 38. 4 years for the rural subgroup (range, 15-59 years) and 31. 0 years for blood donors (range, 17 to 59 years). The female-tomale ratio was 0. 62 for the rural subgroup and 0. 22 for the blood donor subgroup. In the rural subgroup, prevalence was significantly higher in women than in men  The data for the rural subgroup was extracted from a nationwide epidemiological survey in Gabon, previously published.
In West Africa, Gessain et al. reported an HTLV-1 seroprevalence of 1. 23% (22 of 1785) in Guinea,  20  whereas Diop et al. reported an overall HTLV-1 & -2 seroprevalence of 0. 16% (8 of 4900) at the CNTS of Dakar in Senegal (0. 14 and 0. 02%, respectively). 14 The seroprevalences of HTLV-1 The data for the rural subgroup was extracted from a published nationwide epidemiological survey in Gabon. 7 p < 0. 01. ‡ p < 0. 001. N = total number of individuals tested; n+ = number of HTLV-1 infected individuals; (%) = prevalence of HTLV-1. and HTLV-2 in the Ethiopian blood bank were 0. 19% (3 of 1600) and 0. 25% (4 of 1600), respectively,  21  and Gudo et al. reported a seroprevalence of HTLV-1 of 0. 89% (18 of 2019) in Mozambique. 15 The largest study among African blood donors was recently performed in South Africa; the prevalence of HTLV-1 was found to be 0.
These authors contributed equally to this work. This study received funding from the  CNRS  ( UMR 3569 ) (AG); the  Institut Pasteur, France (AG) , and through the " Investissement dʼAvenir" as part of a French "Laboratoire dʼExcellence" (LabEx) research program :  Integrative Biology of Emerging Infectious Diseases  ( ANR10-LBX-62 IBEID ) (AG). JLR received financial support from the  European Union  ( FOOD/2016/379-660 ).
Thanks to huge advances in sequencing technologies, genomic resources are increasingly being generated and shared by the scientific community. The quality of such public resources are therefore of critical importance. Errors due to contamination are particularly worrying; they are widespread, propagate across databases, and can compromise downstream analyses, especially the detection of horizontallytransferred sequences. However we still lack consistent and comprehensive assessments of contamination prevalence in public genomic data. Here we applied a standardized procedure for foreign sequence annotation to 43 published arthropod genomes from the widely used Ensembl Metazoa database. This method combines information on sequence similarity and synteny to identify contaminant and putative horizontally-transferred sequences in any genome assembly, provided that an adequate reference database is available.
easily propagate across databases in a self-reinforcing vicious circle. If a DNA sequence from species A is initially assigned to the wrong species B due to a contamination of B by A, it is likely to keep its incorrect status for a while, and may even be identified as a contamination of A by B when the genome of A is eventually sequenced  (Merchant et al. 2014). Despite all the possible problems stemming from contamination in genomic resources, most studies addressing this issue so far have focused on one particular genome (e. g. , tardigrades) and/or one particular source of contaminants (e. g. , humans). Only two studies that we are aware of have consistently screened more than one genome assembly. Merchant et al. (2014)  focused on the bovine genome but also applied their pipeline to eight randomly drawn draft genomes (five animals, two plants, one fungus), with contrasted results.
g. ,  Schönknecht et al. 2014 ), but a quantitative assessment of the prevalence of HGT in eukaryotes is difficult, and many HGT candidates were subsequently shown to result from contamination. Controversies over the confusion between HGT and contaminants have concerned the human genome  (Willerslev et al. 2002; Salzberg 2017) , the Nematostella vectensis sea anemone genome  (Starcevic et al. 2008; Artamonova et al. 2015) , and the Hypsibius dujardini tardigrade genome, among others. In H. dujardini, the initial estimate of 17% of genes being of foreign origin was revisited to 1% when contamination was properly accounted for  (Hashimoto et al. 2016; Koutsovoulos et al. 2016). A straightforward way to identify contamination in a newly sequenced genome is to compare the assembled sequences to existing databases using BLAST-like algorithms.
If a sequence's best match is assigned to a species that is phylogenetically distant from the target organism, then the sequence is annotated as a contaminant. There are several problems with this simple strategy. First, this does not allow one to distinguish contaminants from HGT. Second, this approach is entirely dependent on the correctness of the reference database. A best-BLAST-hit survey can only propagate, not correct, pre-existing taxonomic mis-assignments, as discussed above. Third, such an approach is also dependent on the completeness of the reference database, and on the phylogenetic position of the target organism. If the reference database is imbalanced and dominated by one or a few particular taxa (typically model organisms), then its power to properly discriminate genuine sequences from contaminants will be maximal for newly sequenced organisms closely related to the dominant taxa, and much lower for organisms distantly related to the dominant taxa.
Solutions to these problems exist, and include (i) considering multiple BLAST hits, not just the "best" one, (ii) using an appropriately balanced reference database, (iii) incorporating information on synteny (i. e. , physical co-localization of loci on the same scaffold), and ultimately phylogeny, in addition to sequence similarity. Here we collated these ideas in an integrated framework aiming at properly quantifying the prevalence of contamination in genomic data based on reliable, existing tools. We applied this pipeline to 43 published genomes of arthropods distributed in the Ensembl database. We report that data quality is highly heterogeneous across species in this widely used database, some genomes being heavily affected by contamination. Our results also show that a careful annotation of contaminant sequences is mandatory in any subsequent attempt to detect HGT.
Foreign sequence annotation We developped a dedicated pipeline for the simultaneous detection of contaminants and HGT candidates in published genome assemblies. This pipeline was optimized and benchmarked in arthropods, but can be applied to any other taxa, provided that an adequate reference database is available. The outline of the pipeline is presented in Figure  1. It takes as input a genome assembly and a set of predicted coding sequences (CDS). It returns a set of CDS annotations with the following categories: genuine arthropod gene, HGT candidate, contaminant candidate, orphan gene, uncertain. Five non-metazoan taxonomic groups are considered as potential sources of contaminants and HGT: eubacteria, archaea, fungi, viridiplantae and 'protists'. Each investigated genome is processed independently and without any a priori on the source(s) of contamination.
As discussed below, the power of this pipeline to detect foreign sequences depends on the level of fragmentation of the considered assembly. The first step of the pipeline is a preliminary taxonomic assignment of CDS based on sequence similarity. Using DIAMOND BLASTP (v0. 8. 22, "more-sensitive" mode, otherwise default parameters;  Buchfink et al. 2015) , each CDS was blasted against a custom protein reference database (see below). Hits with identity greater than 40%, alignment length greater than 75 amino-acids and E-value lower than 10 210 were retained. A minimum of two such hits to two different species was required for taxonomic assignment. CDS not matching this criterion were regarded as orphan genes ('no reliable taxonomic assignment') and not considered further. For each CDS, the 10 hits with the smallest E-values were consideredor less if less than 10 hits had an e-value below 10 210.
A CDS was assigned to a given taxonomic group (i. e. , eubacteria; archaea; viridiplantae; fungi; protists) if at least 70% of its best hits fell within this group. These were called "foreign CDS candidates". In addition, a CDS was assigned to the "confident-arthropod" group if 100% of its best hits were to a species of Metazoa, among which at least 70% to a species of Arthropoda. Finally, a CDS was assigned to "other metazoa" if at least 70% of its best hits were to species of non-arthropod metazoa, and none to a species of arthropods. CDS not matching any of these criteria were considered taxonomically unassigned. Using the 10 best hits instead of just the best one provides a robust way to account for potential contaminations and other sources of taxonomic mis-assignment in the reference database.
A foreign CDS candidate was considered as "uncertain" if it did not reliably map to any scaffold or if it was the only CDS to map to a given scaffold. When present, the "confidentarthropod" tag was propagated across all scaffolds linked by chimeric alignments. This synteny-based step can also be performed at the contig scale in case of doubts regarding the scaffolding process; this should increase the proportion of foreign candidates classified as "uncertain". The corresponding script is available on GitHub (https://github. com/ClementineFrancois/Foreign-CDS-detection). The analysis of the 43 arthropod assemblies of this study took around 48 hr to run on 50 CPU.
Evaluated genome assemblies The 43 arthropod genomes available in Ensembl Metazoa (Release 37, as of October 2017;  Kersey et al. 2017)  were investigated using our dedicated pipeline. This included 36 insects, two crustaceans, four chelicerates and one myriapod (see Supplementary Table  S1 ). For each species, the set of masked genomic scaffolds ("dna_rm. toplevel") as well as the set of all predicted coding sequences ("cds. all") were retrieved from Ensembl Metazoa. Depending on the species, the set of annotated CDS was either generated by Ensembl or imported from other reference databases relying on different annotation pipelines. Scaffolds shorter than 200 bp were discarded. The longest transcript was selected for each gene. Coding sequences shorter than 150 bp were discarded.
Custom reference database A custom protein reference database was built to cover all domains of life and included 937 species (4,622,809 sequences). The proteomes of 100 eukaryotic species were retrieved from Ensembl (Release 90;  Zerbino et al. 2017)  and Ensembl Metazoa (Release 37;  Kersey et al. 2017). These included 40 metazoa (of which 20 arthropods), 20 fungi (of which 10 fungi known to infect arthropods), 20 Viridiplantae and 20 'protists'. The proteomes of 837 prokaryotic species, of which 748 eubacteria and 89 archae, were retrieved from the Microbial Genome Database for Comparative Analysis (mbgd_2016-01;  Uchiyama et al. 2014 ) selecting one species per genus. An additional 11 known symbionts of arthropods were subsequently included. Within each proteome, redundant sequences (.
Validation of the HGT candidates We took a phylogenetic approach to validate / invalidate HGT candidates in one species of interest, the pea aphid Acyrthosiphon pisum. All HGT candidates detected in the pea aphid assembly were clustered into families with Silix (v1. 2; Miele et al. 2011) , requiring a minimum of 60% of identity (default parameters otherwise). For each family, a protein alignment of the candidate HGT sequence(s) and its (their) 50 best BLAST hits in the custom reference database was generated with MAFFT (v7;  Katoh and Standley 2013). Only BLAST hits with identity greater than 40%, alignment length greater than 75 amino-acids and E-value lower than 10 210 were considered. The alignements were cleaned using HMMcleaner (stringency parameter = 12). Phylogenetic trees were inferred using RAxML (v8.
Statistical analyses According to the recommendations of  Warton and Hui (2011) , all proportion data were logit-transformed prior to statistical analyses, using the 'car' R package  (Fox and Weisberg 2011). The normality of the residuals was checked for all models reported in this article. All data analyses were performed with R 3. 4 software (R Core Team 2018) using the vegan  (Oksanen et al. 2018 ) and seqinr  (Charif and Lobry 2007)  packages. Table  S1  describes the genomic features of the 43 arthropod species from EnsemblMetazoa investigated in this study. Table  S2  details the composition of the custom reference database. Table  S3  describes the categorization of all CDS in the 43 arthropod genomes. Table  S4  indicates the inferred function and potential donor for the six validated HGT families in the pea aphid assembly.
Indeed, all CDS encoded on a given contaminant scaffold are expected to derive from the same organism, thus to be assigned to the same non-metazoan group (e. g. , eubacteria). Out of 408 detected contaminant scaffolds, only one was taxonomically inconsistent. This 20kb scaffold from the Lucilia cuprina (blowfly) assembly encoded one eubacterial and two fungal CDS. It could be a chimera between two contaminant sequences. The great majority of detected contaminations originated from eubacteria (1,796 out of 1,849 contaminant candidates for the 43 species), except in blowfly Lucilia cuprina which was mostly contaminated by fungal sequences (41 CDS; Figure  2  and Suppl. Table  S3 ). The fact that no archaeal contamination was detected in any assembly (Suppl. Table  S3 ) might at least in part reflect a taxonomic gap in the public reference databases.
This problem has already been evidenced from the study of mammalian gut microbiota  (Raymann et al. 2017)  and likely impacts all database-dependent studies. In summary, out of 43 published genome assemblies, 15 (i. e. , 35%) presented at least some traces of non-metazoan contamination, including four which were substantially contaminated. These figures are likely an underestimation of the actual prevalence of contamination because of the limitation due to incompleteness of reference genomic databases, as discussed above. Moreover, the overall prevalence of contamination is expected to be even higher as we did not consider metazoan contaminants. Yet contamination from wet lab technicians as well as from model organisms extensively used in research facilities (e. g. , mouse, zebrafish,. ) is likely to occur in any sequencing project.
Contaminant CDS were scattered across many small scaffolds harboring only a few CDS each, a pattern similar to most of the screeened assemblies (Suppl. Figure  S4 ). 99. 5% of the contaminant scaffolds (201 out of 202) were from eubacterial origin. An examination of the taxonomy of BLAST hits indicated that a vast majority of contaminant sequences originated from donors of the order Enterobacterales, and showed closest matches to species of the families Enterobacteriaceae and Erwiniaceae. Interestingly, these two families contain several well-described bacterial symbionts of aphids, such as the obligate endosymbiont Buchnera aphidicola or the facultative symbionts Hamiltonella defensa and Serratia symbiotica  (Oliver et al. 2010). However, none of the detected contaminant CDS blasted reliably on the genome of Buchnera aphidicola nor on the genomes of common aphid secondary symbionts (Hamiltonella defensa, Serratia symbiotica, Spiroplasma, Cardinium and Rickettsia), although these species were represented in our reference database.
Symbiont-derived sequences were likely present in the raw dataset and subsequently removed from the assembly, which is a common procedure in sequencing projects (e. g. , see International Aphid Genomics Consortium 2010). This targeted cleaning approach can only be applied to well-known symbionts of the focal organism. The remaining contaminant sequences might thus correspond to less studied aphid symbionts, such as species of the genera Pantoea or Erwinia, which showed strong BLAST matches with contaminant CDS and have been identified as aphid gut symbionts  (Harada et al. 1997; Gauthier et al. 2015). The 75 HGT candidates detected in the pea aphid assembly clustered in 70 gene families, from which gene phylogenies were constructed. The 70 trees were inspected by eye, and only six of them were considered as reliably supporting an instance of HGT (Suppl.
The 827 contaminant CDS were concentrated in only 30 contaminant scaffolds harboring up to 108 CDS each, a pattern strikingly different from the other assemblies we analyzed (Suppl. Figure  S4 ). All contaminant sequences were of eubacterial origin, and ca. 97% (799 out of 827) consistently showed high sequence similarity with two species of the Orbaceae family present in our reference database, namely Gilliamella apicola and Frischella perrara. These 799 Orbaceae CDS correspond to just 25 contaminant scaffolds, the lengths of which sum up to 2,157,077 bp. Gilliamella apicola is known to be a gut symbiont of bumblebees and its genome size is 2. 2 Mb  (Kwong and Moran 2013) , suggesting at first sight that the whole genome of this species could be included in the bumblebee assembly. However,  Martinson et al.
(2014)  described a new bumblebee gut symbiont sequenced concurrently with the genome of its host. This symbiont, Candidatus Schmidhempelia bombi, is another good candidate as it was not present in our reference database, has a genome size of at least 2 Mb, and shares significant sequence similarity with Frischella perrara and Gilliamella apicola. All contaminant CDS were blasted against the three assemblies available in NCBI (Candidatus Schmidhempelia bombi str. Bimp; Gilliamella apicola str. WkB30; Frischella perrara str. PEB0191). 769 CDS out of 827 showed 100% nucleotide similarity with sequences of Candidatus Schmidhempelia bombi. The maximum sequence similarity with Frischella perrara and Gilliamella apicola were 95% et 87%, respectively. We conclude that almost the entire genome of Candidatus Schmidhempelia bombi is present in the bumblebee assembly distributed by Ensembl Metazoa, although this symbiont was described and its genome sequence published in 2014  (Martinson et al.
However, only three eubacterial HGT candidates were detected by our pipeline, even though four Wolbachia strains were present in our reference database. Besides, none of these HGT candidates showed any good match with Wolbachia sequences when blasted against NR NCBI. This discrepancy could have been explained if these HGT occurred a long time ago, causing horizontally-transferred sequences to degenerate beyond the point where they would be recognized as CDS, and thus would not have been screened in our pipeline. However, at least 28 of these Wolbachia horizontally-transferred sequences seem to be expressed at low abundance in D. ananassae  (Hotopp et al. 2007) , suggesting that they are not too degenerate to be transcribed. Another explanation would be an excessive cleaning of Drosophila ananassae assembly causing all foreign (HGT and contaminant) sequences to be systematically removed, regardless of their physical integration into the fly genome.
05 from FlyBase), including some with high similarity to Wolbachia sequences. This example illustrates the potential downstream impacts of the cleaning and annotation procedures implemented in genome sequencing projects, which can result in bona fide genes of interest being discarded, and therefore taken away from the genomic databases and literature. Moreover, the lack of specific documentation on the procedures implemented for each assembly makes the (frequent) successive versions changes hardly tractable for the users, although these can have substantial impact on the distributed genomic data.
CONCLUSIONS Identifying genes of foreign origin in a genome is a goal of major biological interest, which is required to properly account for the problem of contamination in published genome assemblies. Applying a consistent, automated, reproducible foreign sequence annotation pipeline, we revealed considerable heterogeneity among arthropod genomes from the Ensembl Metazoa database in terms of prevalence of contaminants. Of the 43 arthropod assemblies we analyzed, 28 were completely devoid of contaminant sequences (including the 12 Drosophila species), 11 included a few, while four of them were heavily affected (. 150 contaminant CDS). The highest level of contamination was detected in the bumblebee assembly which contained 827 contaminant CDS likely originating from a single endosymbiont.
This disparity between entries of a single, widely used database is worth noting, beyond the heterogeneity of annotation procedures among genome assemblies. Some of the Ensembl Metazoa assemblies were "cleaned" to the point that previously documented HGT have been removed, whereas others included hundreds of contaminant genes. Most of the detected foreign sequences proved to be contaminants, while very few HGT were confirmed. Therefore any analysis of HGT solely based on existing gene annotations would presumably yield results of little, if any, biological relevance. Contamination is in large part unavoidable and a major confounder of all downstream genomic analyses. While researchers should be accountable for the cleaning of their NGS datasets prior to distribution, there is inevitably some heterogeneity among labs and consortiums in terms of procedures and scientific goals.
ACKNOWLEDGMENTS These analyses largely benefited from the  Montpellier Bioinformatics Biodiversity  computing cluster platform. The authors would particularly like to thank  Dr. I. Uchiyama  for his help in extracting microbial genomes from the MBGD database. We are grateful to  Céline Scornavacca ,  Paul Simion  and  Yoann Anselmetti  for valuable help with bioinformatic scripts. We thank the editors and the anonymous reviewers for their valuable comments on an earlier version of this manuscript. This work was supported by  Swiss SNF  grant number  PP00P3_170627.
Fierstine & Voigt  [14]  concluded that sailfish have a larger area of dorsal micro-teeth than striped marlin, but no detailed investigations have been made to link their characteristics to differences in feeding strategies between species. In this study, we collected behavioural and morphological data on two species of Istiophoridae, striped marlin (Kajikia audax) and sailfish. This included the first high-speed footage of striped marlin, providing a unique opportunity to compare both species at a fine behavioural and morphological scale. We chose the striped marlin for comparison with the sailfish for two main reasons. Firstly, while sailfish and striped marlin are closely related, with comparable body sizes and diet (see electronic supplementary material for more details)  [17] [18] [19] [20] , their rostra are known to be shaped differently.