dataesr/scientific-paragraphs-categorization huggingface dataset:
MuGLasso selects both task-specific and global LD-groups For both datasets, the LD-groups selected by MuGLasso are a mixture between populationspecific LD-groups (identified as those with near-zero regression coefficients for one task) and LD-groups that are shared between both populations. Table  4  shows the number of LDgroups/SNPs in each of these categories for MuGLasso. By contrast, the adjusted approaches do not provide population-specific LD-groups or SNPs. Finally, we report on Figure  3  the precision and recall of MuGLasso and the stratified approaches on the population-specific SNPs. MuGLasso outperforms all other approaches in both precision and recall.
Discussion and Conclusions We presented MuGLasso, an efficient approach for detecting disease loci in GWAS data from diverse populations. Our approach is based on a multitask framework, where input tasks correspond to subpopulations, and feature selection is performed at the level of LD-groups. Assigning samples from PCA-identified populations to different tasks addresses the issue of population stratification, and retains the flexibility of identifying population-specific disease loci. Treating all samples together, by contrast with stratified approaches, alleviates the curse of dimensionality. Ensuring sparsity at the level of LD-groups addresses the high correlation between nearby SNPs and also alleviates the curse of dimensionality. Although more time-  Fig. 3. For simulated data, precision and recall of MuGLasso and the stratified approaches on the populations-specific SNPs consuming than a classical GWAS, our implementation is computationally efficient enough to scale to the analysis of entire GWAS data sets of about one million SNPs.
In addition, pathway enrichment analyses could also be very relevant. One could also compare the selected SNPs to those significant in large meta-analyses such as [M + 17,M + 17a] in a more systematic manner to investigate how much power is gained by using MuGLasso on a subset of these GWAS data sets. Finally, we have analyzed jointly all selected SNPs and have not distinguished between those that are specific to one of the two populations and those that are common to both. Table  C1. The 32 potential breast cancer risk genes within 10kb of loci identified by Mu-GLasso and not the adjusted GWAS, together with information as to their biological relevance.
Acknowledgments This work was supported by the  French Agence Nationale de la Recherche  ( ANR-18-CE45-0021-01 ,  ANR-19-CE14-0015  and  ANR-19-P3IA-0001 ). OncoArray genotyping and phenotype data harmonization for the Discovery, Biology, and Risk  of Inherited Variants in Breast Cancer  (DRIVE) breast-cancer case control samples was supported by  X01 HG007491  and  U19 CA148065  and by  Cancer Research UK  ( C1287/A16563 ). We thank  Héctor Climente-González ,  Vivien Goepp  and  Lotfi Slim  for discussions, and acknowledge the contributions of the following reviewers:  Carly Bobak ,  Meghan Muse ,  Lauren McDonnell ,  Amy Farnham ,  Michael Mariani ,  Andrew Hederman  and  Ariana Haidari. Acknowledgments OncoArray genotyping and phenotype data harmonization for the Discovery, Biology, and Risk of Inherited Variants in Breast Cancer (DRIVE) breast-cancer case control samples was supported by  X01 HG007491  and  U19 CA148065  and by  Cancer Research UK  ( C1287/A16563 ).
Simulated data Using GWAsimulator, 23  we simulated GWAS data with realistic LD patterns from two populations (CEU : Utah residents with Northern and Western European ancestry and YRI: Yoruba in Ibadan, Nigeria) of the HapMap 3 data. We induced population structure by varying the case:control ratio within each subpopulation (CEU 1 100:900 and YRI 900:1 100), as well as by simulating population-specific disease loci. We simulated a total of 149 970 disease SNPs, 2 999 (resp 4 999) of which are specific to the CEU (resp. YRI) population (see Appendix A. 1). The data contains 4 000 samples and 1 400 000 SNPs. DRIVE Breast Cancer OncoArrayThe DRIVE OncoArray dataset (dbGap study accession phs001265/GRU) contains 28 281 individuals that were genotyped for 582 620 SNPs. 13 846 samples are cases and 14 435 are controls.
Molecular identification is increasingly used to speed up biodiversity surveys and laboratory experiments. However, many groups of organisms cannot be reliably identified using standard databases such as GenBank or BOLD due to lack of sequenced voucher specimens identified by experts. Sometimes a large number of sequences are available, but with too many errors to allow identification. Here we address this problem for parasitoids of Drosophila by introducing a curated open-access molecular reference database, DROP (Drosophila parasitoids). Identifying Drosophila parasitoids is challenging and poses a major impediment to realize the full potential of this model system in studies ranging from molecular mechanisms to food webs, and in biological control of Drosophila suzukii. In DROP (http://doi.
Introduction Building a knowledge base that encompasses ecology, evolution, genetics, and biological control is contingent on reliable taxonomic identifications. Molecular identification is commonly used in groups of organisms with cryptic species that are difficult to identify morphologically  (Fagan-Jeffries et al. , 2018; Miller et al. , 2016; Novotny & Miller, 2014) , for the molecular detection of species interactions  (Baker et al. , 2016; Condon et al. , 2014; Gariepy et al. , 2019; Hrček & Godfray, 2015; Hrcek et al. , 2011) , and for identification of species from environmental DNA samples  (Shokralla et al. , 2012). The accuracy of molecular identification, however, depends on the accuracy of identifications associated with sequences databased in existing online depositories. The foundations of that accuracy are the voucher specimens which were sequenced and the collaboration of a taxonomic authority in the deposition of the sequence data.
GenBank serves as the most widely used sequence depository; however, deposition of sequences in GenBank, which is required by most peer-reviewed journals, does not require deposition of associated vouchers. The Barcode of Life Data System database (BOLD)  (Ratnasingham & Hebert, 2007)  explicitly aims to provide a framework for identifying specimens using single-locus DNA sequences  (Hebert et al. , 2003; Smith et al. , 2005) , and while these are associated with vouchers and metadata, the curation of these data is not consistently maintained by those submitting material. A recent study by  Pentinsaari et al. (2020)  showed misidentification in both databases caused by missteps in the protocols from query sequences to final determination. Although the BOLD database function "BOLD-IDS" allows considerable database curation (e.
g. , sequences are used for identification and/or flagging of misidentified/contaminated records), it also automatically includes sequences from GenBank, and may perpetuate the shortcomings previously mentioned since these cannot be curated from within BOLD. As such, the quality of sequences and the reliability of identifications obtained from BOLD-IDS can vary, and depends on the curation by systematists focusing on individual taxa  (Meiklejohn et al. , 2019). BOLD-IDS works well for taxa where qualified taxonomists have been involved with assuring data quality; some insect examples include beetles  (Hendrich et al. , 2015) , butterflies  (Escalante et al. , 2010) , geometrid moths  (Hausmann et al. , 2011 (Hausmann et al. , , 2016;; Miller et al. , 2016) , true bugs  (Raupach et al. , 2014) , and microgastrine wasps  (Smith et al.
, 2013). Unfortunately, this is not the case of parasitoids (Insecta: Hymenoptera) of Drosophila flies (Insecta: Drosophilidae). There are vast numbers of Drosophila parasitoid sequences readily available in GenBank and BOLD, as these parasitoids and their hosts are important model organisms in biology. As of this writing, there are 88,666 nucleotide sequences deposited in GenBank for Leptopilina heterotoma (Thomson) and L. boulardi (Barbotin, Carton & Kelner-Pillault) (Hymenoptera: Figitidae) alone. However, less than 1 % of the identifications associated with these sequences have been confirmed by taxonomists or are associated with voucher specimens deposited in museum collections. With sequencing shifting from individual genes to genomes we risk that the identification problems will soon apply to whole genomes.
, 2012)  and other fields. In contrast to this large body of laboratory studies, basic natural history of Drosophila parasitoids, especially their species richness  (Kimura & Mitsui, 2020; Lue et al. , 2018) , is little known. Addressing this knowledge gap is especially pressing given current efforts to use parasitoids in biological control efforts, such as those of the invasive pest spotted wing Drosophila, Drosophila suzukii  (Abram et al. , 2020; Daane et al. , 2016; Giorgini et al. , 2019; Wang et al. , 2020 a&b). Properly executed molecular identification has the potential to be much more efficient for the majority of researchers, and many laboratory strains are commonly identified using DNA sequences alone. While it is practical for researchers to assign species names based on a match to sequence records in genetic databases, this practice often causes a cascade of inaccuracies.
To illustrate the extent of the problem, we note the example of Ganaspis, a genus of parasitoids commonly used in laboratories that includes both superficially indistinguishable species with highly divergent sequences that are often treated as conspecific, as well as specimens with identical sequences identified under different names (Figure  1 ). To address these issues, we introduce a newly curated molecular reference database for Drosophila parasitoids -DROP-in which sequences are either linked to voucher specimens identified by taxonomists or have a traceable provenance (Figure  2 ). The first aim of DROP is to provide a reliable DNA sequence library for molecular identification of Drosophila parasitoids that enables cross-referencing of original taxonomic concepts with those of subsequent studies.
We pay special attention to live parasitoid strains which are available for future experiments. The second aim is to standardize and expedite the linkage between specimens and available sequence data; we place a premium on museum vouchers as they allow for repeatable scientific research. In DROP, this goal is facilitated through a consolidated digital infrastructure of data associated with laboratory strains, offering the opportunity for researchers to re-examine past experimental results in a permanent context. The third aim is to provide an up-to-date catalogue of the diversity of Drosophila parasitoids as a foundation for advancing the understanding of their taxonomy. Finally, the fourth aim of DROP is for our collaborative effort to serve as an inspiration to communities of researchers studying other groups of organisms who are experiencing difficulties with the reliability of molecular reference databases.
Data sources To assemble the DROP database, we targeted 20 genera that potentially parasitize frugivorous Drosophila species. We compiled DNA sequence and voucher data from four sources: 1) museum collections, 2) publications, 3) molecular biodiversity inventories publicly available in BOLD and GenBank, for which we managed to secure inspection of the vouchers by taxonomists, and 4) conducted a sequencing and taxonomic inventory of laboratory strains. We first gathered species information into a catalogue of Drosophila parasitoid species (Table  1 ) from 212 references (see DROP database reference table  )  and 36  institutes (Table  S2 ). To ensure reliable names for nominal species (sequences identified by a species name) in our database, we confirmed their taxonomic validity using the Ichneumonoidea 2015 digital catalogue  (Yu et al.
, 2016;  https://web. archive. org/web/20161022093945/http:/ichneumonoidea. name/global. ph p) and Hymenoptera Online (HOL; http://hol. osu. edu/), both of which are curated by taxonomic experts. To obtain reliable molecular identification data, we harvested 8,298 DNA sequences from GenBank and BOLD (all compiled into BOLD system: DS-DROPAR dataset). These sequences represent 443 Barcode Index Numbers (BINs -a form of provisionary taxa in BOLD) and 520 taxa, for a total of 963 operational taxonomic units (OTUs). We use the term "OTU" as a general and neutral designation encompassing described species, provisional species, undescribed species, cryptic species, and misidentified species. The majority of the harvested sequences were Braconidae (6690), Diapriidae (967), Figitidae (622), and Pteromalidae (19).
Because of the concerns with generic databases (noted above and in Figure  1  and Table  S1 ), we assembled a list of sequences with valid species names that could either be traceably linked to vouchers examined by taxonomists or referred to directly in publications authored by a recognized expert in the relevant taxon group. We then cross-checked species names with their corresponding BINs in BOLD and flagged potential conflicts between species names and BINs (Table  S1 ). A core goal of DROP besides that of a tool for biodiversity research is to function as a platform that accommodates Drosophila parasitoids kept in culture (for experimental work) or in quarantine (for biological control applications). So far, there has been a lack of a coherent and reliable means of verifying species kept in laboratory settings, which can be a serious problem.
Both DNA extraction and vouchers were entered into the database and uploaded to BOLD (DROP project: DS-LABS) with an associated GenBank ID [NOTE: the BOLD records will be pushed to GenBank at revision stage; these data are not embargoed]. Where possible, we identified OTU strains using a combination of morphological and sequence data, and characterized provisional species or species clusters using neighborjoining trees (Figure  S1 ) based on the COI gene sequences (Supplemental material). For establishing BIN limits in the context of DROP, we have adopted an initial percent cutoff at 2%. As  Ratnasingham & Hebert (2013)  pointed out, this is a good starting point for many taxa, but it also may be adjusted as more samples are acquired and compared.
Drosophila parasitoid database-DROP To compile the above information, we built a simple Structured Query Language (SQL) database in sqlite3 format using SQLiteStudio. There are eight linked tables in the database-species, strain, voucher, sequence, genome, transcriptome, proteome and reference-along with additional tables for linking these to reference table (Figure  S2 ). The database incorporates all sample fields used by BOLD for compatibility and includes a number of new fields to accommodate a catalogue of Drosophila parasitoid species, laboratory strain information, and links from the DROP database to BOLD and GenBank records. DROP is available on Zenodo (http://doi. org/10. 5281/zenodo. 4519656) for permanent deposition and version control. In addition to the main database, the Zenodo repository includes additional files to facilitate easy use of the database.
Species, provisional species, and OTU designations In addition to the inherent value of a formal taxonomic name, a reliable provisional taxon label can also be used for exchanging scientific information and conveying experimental results among researchers  (Schindel & Miller, 2010). Based on the amount of sequence divergence between described species, we observed what appears to be a significant number of provisional OTUs in the initial dataset we compiled. Furthermore, among the data linked to a valid species name, some of these provisional OTUs are actively being used in research and have sequences available to the public. We therefore provide a list of potential new species with their molecular vouchers. We use the following designation format for OTUs that refer to a provisional species: "Drop_strainX_sp.
Overview of DROP We catalogued 182 OTUs in the DROP database with 113 described species of Drosophila parasitoids and 69 provisional species (Table  1 ). In total, we documented 154 laboratory strains (Table  S3 ), 853 vouchers from 36 institutions (Table  S2 ). Among the described species, 98 have voucher information, of which 61 are traceable to type specimens, including 45 to holotypes (i. e. , specimen used to root a name to the taxonomic author's concept of the species). Leptopilina is represented by the highest number of species with 45 OTUs, followed by Asobara with 26 OTUs. Within the 154 catalogued lab strains, 86 were actively being used in ongoing research (i. e. , a live strain being cultivated). These strains represent 39 OTUs: 11 described species and 28 provisional species (Table  S3 , Figure  S1 ).
Molecular Vouchers So far, DROP includes 545 DNA sequences and links to 16 genomes (Table  2. 1), 11 transcriptomes (Table  2. 2), and 6 proteomes (Table  2. 3). From the total of 8298 DNA sequences (dataset: DS-DROPAR) collected from public databases, only 322 sequences (less than 4% of available sequences) satisfied the criteria for validity we imposed for molecular vouchers (see material and methods) included in DROP. The DS-DROPAR dataset initially referred to 520 taxa names, but only 52 names were valid, linked to vouchers, or linked to a publication with evidence that the specimens had been identified by taxonomists. The remaining 223 of 545 DROP DNA sequences were generated by this project (dataset: DS-LABS, DS-AUSPTOID) and came from 121 OTUs (12 provisional species and 101 lab strains).
The DROP database is largely made up of standard barcode COI sequences (340 sequences), which includes 77 OTUs: 43 described species and 33 provisional species. We aimed to supplement COI with secondary markers (28SD2, 18S, ITS2) when possible, resulting in an additional 120 sequences from 26 OTUs: 15 described species and 11 provisional species. There are currently 19 OTUs that have sequences from more than one genetic marker.
Discussion In this paper, we introduce and describe a free and open-access database for the reliable molecular identification of Drosophila parasitoids. The guiding principle of DROP is data credibility, based on the prerequisite that genetic data be associated with explicit criteria linking voucher specimens with taxonomic concepts of the original authors  (Troudet et al. , 2018). When incorporating information from public genetic databases, we include only sequences that have passed our filtering protocol. This protocol ensures each entry is associated with a valid scientific name, provisional name, or consistently applied OTU designation that can be used to integrate genetic and organismal data from independent studies. The following discussion expands on the utility of DROP and how we hope it will benefit molecular species identification, connect research from various disciplines, support biological control applications, and serve as a long-term molecular voucher repository and clearinghouse for vetted data.
Molecular (mis-)identification We observe that 17% of the described Drosophila parasitoid OTUs in BOLD and GenBank (dataset: DS-DROPAR) are associated with more than one BIN; these are examples of BIN-ID conflict. Roughly half of these OTUs are used as lab strains. This latter observation is disturbing, because it demonstrates that the criteria used to differentiate and reference species in active research programs are clouded. For example, BIN-ID conflicts were observed in the Drosophila parasitoids Ganaspis brasiliensis (Ihering) and Asobara japonica Belokobylskij (Table  S1 ), both of which are in active use in numerous research programs (e. g. Moreau et al. , 2009; Nomano et al. , 2017; Reumer et al. , 2012; Wang et al. , 2020a Wang et al. , & 2021) )  as well as in biological control efforts against the invasive D.
suzukii (e. g. Abram et al. , 2020; Daane et al. , 2016; Giorgini et al. , 2019). All the BINs from G. brasiliensis carry the name G. xanthopoda (Figure  1 ). In such instances, assigning an identification by matching specimens to barcode records in the genetic database is problematic, as two names are applied to the same BIN. If sequences comprising the BIN are not linked to a voucher that can be examined, teasing apart the two names and how they are applied is impossible. Applying explicit, consistent criteria for species determination ensures that experimental results can be reliably repeated, and that any potentially novel observations will not be explained away as artifacts of identification. DROP addresses these concerns by linking reliable reference sequences and vouchers for G. brasiliensis (Figure  1 ) and from different studies: one with reference to the morphological description  (Buffington & Forshage, 2016)  and the other with reference to the genome (using voucher specimens from the morphological study;  Blaimer et al.
, 2002; Goldstein & DeSalle, 2011). Therefore, in taxonomic treatments, it is critical to clarify the range of applicability of a given BIN and it overlap with a taxonomic name (see example in Figure  1 ). Public genetic databases have adopted a longstanding convention in treating undetermined OTUs and sequences, referring to provisional species with numbers, as for example "sp. 1", and these are rarely linked to vouchers. For OTUs designated as provisional species, DROP enables cross-indexing of specimens, sequences and references with studies and provides researchers with valuable tools for taxonomic revisions, including the means of discovery, corroboration, and description of new species. For example, "drop_Gan1_sp. 1" refers to voucher USNMENT01557320 deposited in the USNM, Washington DC, COI sequence (DROP sequence_id 2), BOLD process ID: DROP143-21, BIN number: XXXXXX (will update in the revision), 28D1 sequences (DROP sequence_id 289), and 28D2 sequences (DROP sequence_id 303).
g. Thierry et al. , 2021). Molecular vouchers of the lab strains were then submitted to DROP as a reference database (DS-LABS in BOLD) to ensure that criteria for species determination were applied consistently-and will continue to be applied consistently-between the natural community studies and the laboratory experiments. The presence of a foundational DNA reference library and species catalogue in DROP will enable the process of exploring parasitoid biodiversity to become more efficient. For example, DROP includes molecular vouchers from Drosophila parasitoids that were collected across seasons and along latitudinal gradients in the eastern Unites States  (Lue et al. , 2016 (Lue et al. , , 2018)). These data proved to be extremely useful for identifying species in a more recent exploration of native parasitoid biodiversity across North America (e.
Long-term molecular voucher preservation During the curation of DROP, we found that holotype specimens were missing from museums for several iconic Drosophila parasitoid species: Asobara tabida (Nees von Esenbeck), Leptopilina clavipes (Hartig), and Leptopilina longipes (Hartig). This is not uncommon and impedes future taxonomic revisions regardless of whether molecular data are used. To avoid contributing to this problem, DROP uses museums as depositories for ensuring that sequenced vouchers of both described species and provisional species are permanently stored. In order to stabilize nomenclature, we further advocate the designation of neotypes (a replacement specimen for a missing holotype) that have museum-vouchered DNA barcodes and additional genomic extractions in storage. Natural history museums are designed to maintain vouchers (including types) for long-term preservation, and increasingly they implement institutionalized workflows that link DNA sequences to specimens and specimen metadata  (Prendini et al.