dataesr/scientific-paragraphs-categorization huggingface dataset:
Construction of the working dataset and quality control The AdaptMap raw dataset includes 4653 animals representing 169 populations from 35 countries across six continents (see Additional file 1: Table  S1  and Fig. 1 ): 46 populations from Europe, 83 from Africa, 21 from West Asia, seven from North America, eight from South America and four from Oceania (for further details on the geographical distribution of breeds see  [33] ). We used the Illumina GoatSNP50 BeadChip  [30]  that includes 53,347 SNPs to genotype the animals. Original SNP genomic positions were remapped to the new goat reference sequence ARS1  [34]. We performed data quality control (QC) by using Plink 1. 9  [35, 36]. Individuals and SNPs that did not pass the following thresholds were removed: individual genotype call rate higher than 0.
96, SNP call rate higher than 0. 98, and pairwise identity-by-state between genotypes (based on all markers) less than 0. 99. SNPs that were completely monomorphic across the whole dataset were also removed. To identify individuals with high relatedness (e. g. , parent-offspring pairs), we used an in-house script to calculate the number of Mendelian errors (ME) in pairwise comparisons between all individuals  [37]. Pairs of animals with less than 100 ME were considered as related Fig. 1  Geographical distribution of the goat breeds included in the AdaptMap project on the world map  [38]. Animals that occurred most frequently within the pairs of related individuals were removed. Twelve crossbred populations were omitted from the analyses (BOEx, GALxSAA, MTBx, MUBx, OIGx, SAAxANB, SAAxCRE, SEAx, SEAxALP, SEAxGAL, SEAxSAA and SEAxTOG, for the breed code legend see Additional file 1: Table  S1 ), together with five populations with less than three animals sampled (BAG, GSH, MYO, SCL, TET).
Breeds for which a large number of samples was collected from multiple locations (ALP, ANG, BOE, LNR, NBN, SAA) were split according to their origin (see Additional file 1: Table  S1 ). The size of the populations with more than 50 sampled individuals (ALP_CH, ALP_ IT, ANG_AR, BOE_CH, BRK, BUR, CRE, LNR_DK, NBN_EG, OSS, RAN, SAA_FR, SEA, SID, WAD) was reduced to 50 through a representative random sample selection procedure implemented in the BITE R package  [39] , which decreases the sample size to a user-defined number while maintaining the variance structure of the original set. The working version of the AdaptMap dataset (hereafter called "working dataset" or "WD", 3197 animals, 144 populations and 48,827 SNPs) was used for subsequent analyses. Among the SNPs, 46,666 were mapped on autosomes, 1595 on chromosome X and 566 were unmapped.
GenABEL  [40]  and BITE R packages  [39]  were used to estimate minor allele frequencies (MAF), SNP and individual call rates. Phasing and imputing of missing data were carried out on 3197 animals and 46,654 autosomal SNPs with unique positions (hereafter called "phased dataset" or "PD") by using Beagle v4. 0 (r1399)  [41].
Construction of continental datasets To better explore the geographical partitioning of diversity both between and within the most represented continents, separate datasets were created for Africa ("African dataset" or "AD" = 1183 animals, 56 populations, 48,827 SNPs), Europe ("European dataset" or "ED" = 995 animals, 42 populations, 48,827 SNPs), and west Asia ("West Asian dataset" or "WAD" = 555 animals, 23 populations, 48,827 SNPs), and for the three continents together ("3-continent" dataset or "3CD" = 2729 animals, 121 populations, 48,827 SNPs). Furthermore, for the 3CD and African datasets, individuals were grouped according to the results of the population structure analysis carried out on the working dataset in order to mirror gene pools rather than nominal breeds (see Additional file 2).
Historical trends in effective population size (Ne) were estimated with the SNeP software  [47]  with default settings and a correction to adjust linkage disequilibrium (LD) (r 2 ) values for small sample sizes. Since the large differences in population size within the WD set could negatively affect the estimations of SNeP, we fixed the number of analysed individuals per population to 22. Populations with fewer animals (19 populations from Europe, 44 from Africa, 13 from west Asia, three from North America, two from South America and one from Oceania; see complete list in Additional file 2) were discarded, whereas populations with a sample size larger than 22 were sub-sampled using the aforementioned procedure implemented in the BITE R package  [39].
Analysis of genetic structure Multi-dimensional scaling (MDS) plots based on kinship matrices were constructed with GenABEL  [40]  and BITE R packages  [39]  at the level of both single individuals and populations to investigate the relationships within and between breeds. Analysis of MOlecular VAriance (AMOVA)  [48]  was carried out with the Arlequin software on the single continent datasets, and on the WD and 3CD sets to estimate the partitioning of variation within and between continents, and to understand how the presence/absence of admixed populations and gene-pool based grouping impacted the variance distribution. In all analyses, three hierarchical levels were considered, "within individuals", "among individuals within populations", and "among populations". For the 3CD set, we added the "among groups" level corresponding to the subdivision of the populations into continental groups.
To assess possible differences in the LD structure between continents, we used the phased data to calculate the pairwise LD between SNPs with PLINK 1. 9  [35, 36]  by considering a distance of up to 10 Mbp. For each population, SNP pairs with an r 2 higher than 0. 3 were selected. Then, we calculated the number of populations per continent for which a specific pair of SNPs was in LD. To assess reticulate relationships between populations, we used Arlequin to calculate the Reynolds unweighted distances, D R , between populations  [49] , which were subsequently visualized via a Neighbournet graph with the software SplitsTree ver. 4. 14. 2  [50]. Population structure was assessed by the maximum-likelihood based approach implemented in the Admixture software v1. 3. 0  [51]. Analyses were run for K values ranging from 2 to 100 under the assumptions of Hardy-Weinberg equilibrium, complete linkage equilibrium and under the 'unsupervised' mode (i.
e. , no prior information on the population of origin of the individuals). To identify the best fitting number of hypothetical populations, for each K value, both fivefold cross-validation error values and the number of iterations needed to reach convergence were considered. To investigate fine-scale genetic differentiation, we ran Chromopainter and FineStructure software version 2. 0. 4  [52]  on phased data. Chromopainter was run on unlinked mode to calculate the chromosomal chunk donor frequencies using a uniform recombination map, and the coancestry matrix was then computed (using greedy optimization and default settings). FineStructure clustering for each analysis was repeated until independent runs broadly agreed, i. e. , until the difference in cluster number (K) reached 5. Convergence was assessed by inspection of the coincidence matrix of the final clustering of the runs.
Migration events The occurrence of migration events was evaluated with the Treemix software  [53]  on the unphased version of the WD set with the Bezoar (BEZ) population set as a root. Windows with 200 consecutive SNPs were used to account for the non-independence of SNPs located in close vicinity. Migrations from m0 to m11 were tested, with three replicates per m to assess consistency. To further assess the consistency of the migration edges found over the WD set, the Treemix software was also run on the 3CD set with the same settings as described above for a number of migrations between m0 and m15, and with three replicates per m.
3), ARR_ IE from Ireland, MEN_MG and SOF_MG from Madagascar, PAL_ES from the Canary islands, except for BAB_PK, KAC_PK, KAM_PK and PAT_PK from Pakistan. Most low F ST values were observed within specific geographical areas in Africa, e. g. , Morocco and North Africa in general, Uganda-Burundi-Kenya, Tanzania-Ethiopia, or Mali-Burkina Faso-Nigeria-Cameroon, etc. (see Additional file 4: Table  S2  and Fig. 2 ). Estimates of gene flow obtained with the Jaatha software were visualized via a heatmap (Fig. 2 ) and the numerical values are in Table  S2  (see Additional file 4: Table  S2 ). They show extensive gene flow within welldefined geographical areas, as in the case of Southern Europe, Morocco, Ethiopia-Cameroon, Mali-Burkina Faso-Nigeria and Pakistan. Extensive gene flow among geographical areas involve mostly the Turkish breeds and a few breeds native to Zimbabwe, and also between the populations from South Europe and North Africa.
09, 8. 93 and 3. 18% of the total variance, respectively; cumulated percentage 25. 20%) of the multi-dimensional Scaling analysis. As shown in Fig. 4a , dimension 1 differentiated European breeds from the other ones, while dimension 2 separated the Pakistani populations (lower left corner) from the African populations and from another cluster including the Boer, Angora, Turkish and Iranian breeds (IRA_IR, ANK_TR, KLS_TR and KIL_ TR). These subdivisions were consistent with the major splits in the structure of the Neighbour-network based on between-breed D R distances calculated from the WD set (see Additional file 9: Table  S4  and Fig. 5 ), and the clustering obtained with Admixture software at K = 2 and 3 (Fig. 6 ). A more precise estimate of the amount of existing variation corresponding to different hierarchical levels was obtained by analysis of molecular variance (Table  1 ); 8.
5 ; K = 15 and 20 in Fig. 6 ), such as the insular breeds from Iceland (ICL_IS), Ireland (ARR_IR, BLB_IR, OIG_IR), the Canary Islands (PAL_ES), and Madagascar  [54]. The populations from Pakistan, Brazil, Malawi and Mozambique were also highlighted as reproductively isolated, thus confirming the Jaatha software results (Fig. 2  and see Additional file 4: Table  S2 ). In the case of the wild goats from Iran, BEZ_IR, the long branch in the Neighbour-network probably reflects the combined effects of ascertainment bias, reproductive isolation, bottleneck and distinctiveness. Remarkable admixture was also highlighted, particularly a strong introgression of African ancestry into the breeds of South America, Spain and Southern Italy (Fig. 5  and K = 2 in Fig. 6 ), and also into the Palmera goats from the Canary Islands (Fig.
In the MDS plot of individual genotypes (see Additional file 11: Figure  S5 ), the degree of scattering confirmed the variable genetic background of some populations of recent admixed origin, e. g. , CRE_AR, MOX_ BR, RAN_AU. As expected, the coancestry matrix obtained with the fineSTRU CTU RE software represented as a heat map (see Additional file 12: Figure  S6  and Additional file 13: Figure  S7 ) showed that the highest values of coancestry concentrated along the diagonal and corresponded to within-breed relationships. Larger blocks of high coancestry were also revealed, which involved the breeds from Pakistan and the European populations as a whole, and, within Europe, those from the Alpine region, the Pyrénées and some breeds from Ireland (see Additional file 12: Figure  S6  and Additional file 13: Figure  S7 ).
Population clusters identified by the fineSTRU CTU RE software agreed closely with breed labels, particularly in the case of European populations, and geographical areas of origin (see Additional file 13: Figure  S7  and Additional file 14). In the worldwide analysis, the consecutive levels of differentiation (see Additional file 14) are consistent with the MDS, Admixture and Neighbour-net patterns and support the strong geographical partitioning and admixture described above. In particular, the first and second subdivisions (K = 2 and 3) separated European, African and West Asian breeds, whereas successive hierarchical levels split many South European populations from the other European populations (K = 5), Pakistani goats (K = 6), and West African goats from the Southeast African ones (K = 7).
The Palmera goats from the Canary Islands clustered with populations that were sampled around the Gulf of Guinea (Cameroon, Nigeria, Mali, Burkina-Faso). Within Southeast Africa, subclusters corresponded to the breeds from (i) Malawi, Zimbabwe and Mozambique, (ii) Madagascar, (iii) Uganda and Burundi, and (iv) Kenya, Tanzania and Ethiopia. The variance explained by the Treemix software analysis increased from about 0. 85 at m0 to 0. 95 at m11 (see Additional file 15: Figure  S8 ). However, this increase fluctuated between repeated runs for the same m value due to the complexity of the dataset and the high degree of admixture between the populations from specific areas (see Additional file 15: Figure  S8 ). Thus, as m increased, alternative but recurrent migration edges were highlighted, which often involved admixed or imported breeds.
In the case of our dataset, low H O were scored in breeds that are isolated by geographical barriers (e. g. islands) or by strict management procedures. In addition, moderate differences in heterozygosity values between breeds from different continents showed that any potential bias did not appear to have a strong impact on the major  outcomes of our analyses. However, we cannot exclude an effect over the slightly lower H O that was scored for populations from specific geographical areas (Fig. 8a ), especially for the wild Bezoar (H O = 0. 267). In addition, the inclusion of the Boer breed in the discovery panel may have emphasized its distinctiveness, as suggested by the Admixture results (K = 4; Fig. 6 ), and the third MDS dimension (Fig. 4b ).
Early migration waves out of the domestication centre Goat domestication took place ca. 10,500-9900 YBP in the region that covers present-day East Anatolia and Northwest Iran  [2, 62]. In general, breeds from the areas close to the domestication centre are expected to have retained partial ancestral diversity, and indeed the goat populations from Turkey and Iran included in our dataset actually display a high level of diversity and a genomic composition similar to that of the wild ancestor, the Bezoar (Fig. 6 ). Furthermore, at ca. 1000 generations ago (see Additional file 8), these populations had larger Ne than all the other breeds, which likely accounts for the greater genomic variation retained by the populations at that time, and derives from the original variability of the wild ancestors sampled at the time of domestication.
Subsequently, after the Neolithic spread of goats, a geographical within-continent structuring of diversity emerged, as illustrated by goat breeds in Africa and Europe (Figs. 5, 6, and 8b)  where the persistence of regional gene pools was further promoted by the high levels of gene flow that characterizes the populations in large areas within both continents (Fig. 2 ). In the case of Europe, goat populations are partitioned locally into regions corresponding to the east Mediterranean, the central Mediterranean, the east Alps together with continental France, and Ireland and North Europe. The clusters revealed by Chromopainter and fineStructure software (Fig. 8b ) differed slightly, which may be due to imperfect phasing of the 52 K genotypes. Within Africa, breed clusters correspond to West, Northeast, East, Southeast Africa and Madagascar.
Formation of breeds, importations, and selection Since the 18th century, formation of goat breeds started in most of Europe and in several other countries with an organized agricultural infrastructure (e. g. , South Africa, North America), via morphological standardization and systematic selection to improve production traits. At the genomic level, these practices caused a remarkably higher degree of LD in European and West Asian breeds than in the African populations, and the emergence of several single-breed clusters in the two high K reconstructions obtained with the Admixture software (Fig. 6 ). They also resulted in reduced effective population sizes during the last 50 generations (ca. 200 years) which halved in a 22-generation interval (see the 23 and 45 generations panels in Additional file 8).
Funding MM was supported by grant  2016/05787-7 ,  São Paulo Research Foundation (FAPESP). AT was supported by the  Ph. D. fellowship of the University of Milan. AC, GC and RS were supported by the project " Improvement of Italian livestock  through the use of innovative biotechnologies: functional genomics, transcriptomics and proteomics (GENZOOT)" funded by the  Italian Ministry of Agriculture. Financial support for FB and MFR was provided by  Ensminger, Hatch and State of Iowa Funds. Financial support to the publication of this manuscript was provided by the Linea D. 3. 1 (year 2017) of the  Università Cattolica del S. Cuore  within its activities of promotion and dissemination of scientific research.
The paucity of available genetic resources is currently an impediment to any future effort for genetic improvement in the species. However, through next generation sequencing (NGS) technologies, and in particular RNA-Sequencing (RNA-Seq), one can collect sequence information for thousands of genes in a single experiment  (WANG et al. 2009). Transcriptome characterization is one of the main applications of NGS as it lays the groundwork for future studies on physiology, genetics, immunology, etc. , creates inventories and gives access to thousand of single nucleotide polymorphisms (SNP) and short tandem repeats (STR) markers. Up to now, it has been widely used for numerous fish species leading to a tremendous pool of genetic knowledge (e. g. see database FISHIT [http://www. fish-it. org/hcmr/] for 20 transcriptomes).
Genetic marker discovery To detect single nucleotide polymorphisms (SNPs) we used GATK pipeline (MCKENNA et al. Following the filtering steps conducted within GATK, variants without a "PASS" filter tag were excluded, ii. the variants that included insertions and deletions (indels), and SNPs with more than two alleles. Further, to avoid sampling the same SNP locus twice due to alternative splicing, we kept only those identified in the longest transcript of each locus. Finally, to check whether they belong to non-coding (3'UTR and 5'UTR) or coding regions (first, second or third codon positions), we excluded those that were identified in transcripts without ORF and analyzed the rest with a custom python script taking into account the SNP position in the longest predicted ORF and the ORF coordinates in each transcript.
To detect short tandem repeats (STRs) we scanned the longest transcript of each assembled locus using the software Phobos (http://www. ruhr-unibochum. de/ecoevo/cm/cm_phobos. htm). In particular, we detected non-exact STRs with 2-10 repeat unit length and a minimum length of 20 nucleotides. A custom Perl script was used to parse the output. Once again, for markers included on transcripts with ORFs, STRs were categorized in coding, 3'UTR or 5'UTR according to the position in relation to the longest ORF within the longest transcripts per locus using python scripts.
19% GC content. To evaluate to completeness of the assembly, we ran BUSCOv2 and gVolante to find that out of 233 queried genes, 208 (89. 27) were complete, 15 were partial (summing up to 95. 71% complete and partial genes) and only 10 genes (4. 29%) were missing. The results revealed a satisfying assembled transcriptome covering the great majority of meagre genes. However, future sequencing of more tissue types would lead to a more complete transcriptome in the species. To annotate the assembly, we conducted a blastx similarity search against the highly curated SWISSPROT database. The results revealed that 33,638 out of 95,945 transcripts were significantly homologous to a known SWISSPROT sequence. Finally, targeted BLASTN search of meagre transcripts against tilapia cDNA retrieved 15,589 unique tilapia genes as top hits, once again confirming the thorough representation of the expected geneset in the transcriptome.
Following similarity search through blast, GO mapping resulted in 31,986 annotated sequences. The most important GO terms in the 'biological process' ontology at the level 2 are shown in Figure  1. Search for InterPro domains resulted in 46,647 sequences annotated with protein domains and raised the number of GO annotated transcripts to 34,252. Then, EC number mapping through GO terms resulted in 1,016 potential enzymes in 8,682 total transcripts with EC:6. 3. 2. 19 'ubiquitin-protein ligase' as the top enzyme group in meagre transcriptome, followed by EC:3. 6. 1. 3 (adenosine triphosphatase) and EC:2. 3. 1. 48 (histone acetyltransferase). Based on EC mapping, we identified the corresponding KEGG pathways to find that the EC-annotated 8,682 sequences are involved in 382 total KEGG pathways.
Conclusions Our study has built the first transcriptome assembly and at the same time the first nextgeneration based genomics resource for meagre. Following the annotated transcriptome, we launched a genetic marker discovery pipeline that led to the construction of a valuable dataset of SNPs and STRs transcriptome-wide coming out from a pool of 16 individuals. The low rate of polymorphism discovered imply that the transcriptome of meagre has been possibly shaped by inbreeding, a factor that raises even more the risk for further inbreeding through aquaculture. The provided assembly and genetic markers dataset will lay the groundwork for further studies of meagre biology and genetics and will set the basis for future applications of genetic breeding and marker-assisted selection for the species.
2010) according to the GATK best practices (DANECEK et al. 2011 ; VAN DER AUWERA et al. 2013). The implemented steps included mapping of the filtered reads to the assembled transcriptome using the highly accurate and fast aligner STAR (DOBIN et al. 2013) , duplicate marking and sorting with Picard (https://github. com/broadinstitute/picard) and finally variant calling and filtering (options -window 35 -cluster 3 -filterName FS -filter "FS > 30. 0" -filterName QD -filter "QD < 2. 0"). The filtering options chosen filtered out SNPs that form clusters (more than 3 SNPs in a window of 35 bases), and variants with QualByDepth (QD) < 2. 0 and FisherStrand (FS) > 30 accounting for variant quality and strand bias. Finally only SNPs with at least 15 reads coverage were kept.
with unit length of 3, and then 6 and 9) fell into the first category (coding). The distribution of the 3-mer STRs are significantly higher than expected in the coding regions and significantly lower in the UTRs (chi-square p-value < 0. 00) as expected due to the nondisturbance of the open reading frame by those repeats (Figure4). The rest were mostly in the UTRs or in transcripts without ORFs. The distribution of both marker types in the different regions of the transcriptome is consistent with the expected distribution of genetic variants as discovered in other studies as well (e. g. MANOUSAKI et al. 2014 ) and provide a high quality dataset for future genetic analysis in this new but important to aquaculture species.
Acknowledgements This study was funded by the  European Union 's  Seventh Framework Programme  for research, technological development and demonstration (KBBE-2013-07 single stage, GA 603121, DIVERSIFY). The sequencing service was provided by the  Norwegian Sequencing Centre  (www. sequencing. uio. no), a national technology platform hosted by the  University of Oslo (UiO)  and supported by the  'Functional Genomics' and 'Infrastructure' programs of the Research Council of Norway  and the  South-Eastern Regional Health Authorities.