dataesr/scientific-paragraphs-categorization huggingface dataset:
Univariate features ranking Given the limited number of samples, it was necessary to reduce the number of features (genes or contigs) in the dataset. We discarded irrelevant features to focus on a subset of 500 top candidates for subsequent feature selection. To rank features, we performed prediction of status (risk/relapse) using a Naïve Bayes classifier on each independent feature, after log transformation of the normalized counts (after adding an offset 1 to avoid numerical problem). To assess the quality of the prediction, we computed the average f 1 score by 5-fold cross validation (f 1 = 2•precision•recall precision+recall , where precision = T P/(T P + F P ) and recall = T P/(T P + F N ) and F P, T P, F N are respectively the False Positive, True Positive and False Negative). In cases where 5-fold cross-validation returned an undefined value, f 1 score was set to 0 (the worst).
Feature selection, model fitting and predictor evaluation To select a subset of non-correlated features (genes or contigs) among the top 500 candidates, we performed penalized logistic regression using the implementation from the glmnet R package  33. We implemented stability selection 34 : only features selected with a frequency of being selected above 0. 5 upon 2000 resamples of the input dataset were retained. To evaluate the performance of the selected features on the discovery (training dataset), we fitted a logistic regression and computed the area under the ROC curve (AUC) using a 10-fold cross validation scheme, repeated 20 times, as implemented in the caret package  35. To assess the performance of the signature on the external validation datasets, we fitted a logistic regression on the whole discovery dataset and applied the predictor to the validation datasets.
In the reference-free approach, some features present in the signature were not found in the validation (see below). In this case, the coefficient of the logistic regression corresponding to missing features were set to zero. Signature contigs were annotated through BLAST alignment vs. Gencode V34 transcripts. HGNC symbols for signature genes were obtained from the Ensembl EnsDb. Hsapiens. v79 R package  36. R scripts to perform the feature selection, model fitting and evaluation on the discovery and validation sets are available at: https://github. com/i2bc/PCa-gene-based_vs_gene-free.
Matching signature contigs in the validation cohort To measure contig expression in the validation cohort we implemented the procedure schematized in Figure  3. The procedure comprises two main steps: (1) all k-mers from signature contigs were extracted and identified in the k-mer count matrix generated from the validation cohort and (2) the resulting sub-matrix was used to estimate each contig's expression in the validation cohort, measured for each sample as the median of extracted k-mer counts. Step 1 is implemented in C++ at: https://github. com/i2bc/PCa-gene-based_vs_ gene-free/tree/master/kmerFilter, step 2 is implemented in R at: https://github. com/ i2bc/PCa-gene-based_vs_gene-free/blob/master/infer_gene-free_risk_signature. R.
Data sharing Data sharing not applicable -no new data generated. Figure  3 : Procedure for inferring signature contig expression in an independent validation dataset. The colored contig from the signature is quantified in the validation cohort by extracting all its constituent k-mers and retrieving the corresponding k-mer counts from validation k-mer count matrix. The count vector of the contig in each sample of the validation dataset is taken as the median of counts for k-mers in this sample.
Interestingly, the 500 top scoring contigs were significantly longer than prior to selection (median 61nt vs. 34nt, Table  4 ), suggesting the procedure tended to eliminate spurious short contigs. Finally, Lasso logistic regression produced a reference-free signature of 26 contigs and a reference-based signature of 14 genes (Table  3 , Figure  4 , Suppl. Figure  S5 ). Ten-fold cross validation performances of both signatures were very high on the discovery dataset (0. 90 and 0. 93 for genes and k-mers, respectively) (Table  5 ), which is an over-estimated performance since features here were tested on the same dataset used to select features  29. Figure  4. A shows the 26 contigs in the reference-free risk signature and their abundance distribution in LR and HR samples. 24/26 contigs mapped Gencode transcripts from 21 unique genes (Supplementary file 1).
Lastly, one contig (ctg_11, EFNA2) was probably misassigned to the EFNA2 gene since it maps to a highly expressed discrete area just 3' of EFNA2 while EFNA2 seems silent. Thus ctg_11 probably comes from an independent lncRNA as well (Supplementary Figure  S3. ). To assess the replicability of risk signatures, we evaluated their performance in the ICGC-PRAD independent dataset. To this aim, we developed a specific procedure to estimate the expression of an arbitrary sequence contig across datasets using matched k-mers (see Methods). The 26 contigs represented 1444 k-mers, of which 97% were present in the ICGC-PRAD validation dataset. Overall 5 contigs (SFRP4, GTSE1, COL3A1, COL1A1. a, COL1A1. c) could not be quantified in the validation set due to lack of supporting k-mers (see Table  3  and Figure  4B ).
Relapse signatures do not accurately classify independent cohorts As in the risk model, both reference-based and reference-free signatures had excellent crossvalidation performance on the discovery set (AUC of 0. 84 and 0. 93 respectively, Table  6 ). However this should again be considered as an overly optimistic estimation due to the experimental design. Indeed, performances of both relapse signatures on the ICGC-PRAD and Stelloo validation sets were much lower (AUC 0. 51 to 0. 66), bordering randomness and confirming overfitting of the trained signatures. The reference-based model performed slightly better over ICGC-PRAD, and the reference-free model was slightly better over the Stelloo dataset (Table  6 ). Furthermore, several genes and contigs in the discovery signatures had inconsistent expression variations in the validation datasets (Fig  5B, C , Supplementary Figure  S6B, C , Supplementary File 3).
Overall two genes from the reference-based signature (ALB and CTD-2228K2. 7) and 5 contigs from the reference-free signature (KLK2, AC069228. 1, PDLIM5, RTN4, ctg_1) changed logFC sign between the discovery and either validation cohort. This problem, which was not observed in risk models, underlines the poor replicability of the relapse signatures, whether or not reference-free. Low replicability of the relapse model may be caused in part by weaknesses in validation datasets: the ICGC dataset had only 7 samples labelled for non-relapse (Table  1 ) and the Stello dataset had very low coverage (Table  1 ) which caused considerable loss when computing contig expression. Only three of the 14 signature contigs (AC069228. 1, KLK2 and KLK3) could be quantified in the Stelloo dataset (Table  7 , Figure  5.
Our strategy thus identifies RNA variations independently instead of lumping them into a full transcript model. Yet, the mapped genes were highly relevant to PCa etiology and included known cancer drivers LDLRAD4, GMNN, COL1A1, CD38, PT-PRN2, GTSE1 and CAMK2N1 in the risk signature and KLK2, AR, KLK3, SPDEF in the relapse signature. Furthermore the risk signature comprised contigs matching two potential novel lncRNAs, located within LDLRAD4 and immediately downstream of EFNA2. To our knowledge the only other software using a reference-free approach for inferring predictive signatures is Gecko 20. Gecko uses machine learning (genetic algorithm) directly on the k-mer count matrix while we first reduce the matrix by grouping k-mers into contigs, before classification and machine learning. This enabled us to produce a signature composed of sequences larger than k, hence easier to interpret and quantify in an independent dataset.
Transferring a reference-free model to a new dataset is challenging. This requires that important features, such as SNPs, are precisely evaluated in the independent dataset. To this aim, we transferred signatures between datasets based on exact k-mer matches. As k-mer contents vary a lot between library preparation protocols, we expected this strategy to show poor sensitivity when discovery and validation datasets differed substantially. Indeed, transfer of signatures trained on the TCGA-PRAD dataset to the low coverage Stelloo dataset caused the loss of a majority of contigs. However, in this particular case, the remaining contigs were sufficient to maintain a prediction performance at the same level as that of the gene-based signature.
Performances and generalization issues To compare the reference-free and reference-based strategies, a common evaluation framework was adopted. For both risk and relapse predictions, performances of the reference-free classifiers were on a par with that of reference-based classifiers. However while risk signatures showed satisfying reproducibility, relapse signatures performed poorly in independent datasets. On possible reason for the low performance of relapse models is our grouping of patients in discrete relapse and non relapse categories as done in other studies  9, 13, 15, 16. This allowed us to address relapse prediction using the same logistic regression method as for risk, however this meant valuable patient information was left unused. A more accurate prediction of relapse may be achieved using survival models  55, 54, 10, 14, 12.
Conclusion For prediction of PCa risk and relapse, reference-free classifiers did not significantly outperform reference-based classifiers, however they incorporated a distinct set of RNA sequences including unannotated RNAs and novel variants of annotated RNAs. It is likely than with other diseases and datasets, novel biomarkers will be identified with an even greater impact on prediction performance. The reference-free approach will be of particular interest in problems where unknown RNAs are expected to play an important role, such as when studying rare diseases, poorly studied tissue types or when analysing dual human-pathogen RNA-seq samples. Our strategy also permits to infer efficient transcriptome classifiers in species lacking an accurate genome or transcriptome reference.
Methods Pillar 2 SARS-CoV-2 testing data Publicly available daily SARS-CoV-2 test result data from testing for the wider population outside the National Health Service (Pillar 2 newCasesBySpecimenDate) was downloaded from https://coronavirus. data. gov. uk/ spanning the date range from 2020-09-01 to 2021-06-30 for 315 English lower tier local authorities (downloaded on 2021-07-20). These data are mostly positive PCR tests, with about 4% of results from lateral flow tests without PCR confirmation. In this dataset, the City of London is merged with Hackney, and Isles of Scilly are merged with Cornwall due to their small number of inhabitants, thereby reducing the number of English LTLAs from 317 to 315. Population data for each LTLA was downloaded from the Office of National Statistics, https://www.
ons. gov. uk/peoplepopulationandcommunity/populationandmigration/populatione stimates/datasets/populationestimatesforukenglandandwalesscotlandandnorthernireland. SARS-CoV-2 surveillance sequencing 281,178 (Sep-June) were collected as part of random surveillance of positive tests of residents of England from four Pillar 2 Lighthouse Labs. The samples were collected between 2020-09-01 and 2021-06-26. A random selection of samples was taken, after excluding those known to be taken during quarantine of recent travellers, and samples from targeted and local surge testing efforts. The available metadata made this selection imperfect, but these samples should be an approximately random selection of infections in England during this time period, and the large sample size makes our subsequent inferences robust.
We amplified RNA extracts from these tests with C t < 30 using the ARTIC amplicon protocol, https://www. protocols. io/workspaces/coguk/publications. We sequenced 384-sample pools on Illumina NovaSeq, and produced consensus fasta sequences according to the ARTIC nextflow processing pipeline https://github. com/connor-lab/ncov2019-artic-nf. Lineage assignments were made using Pangolin 5 , according to the latest lineage definitions at the time, except for B. 1. 617, which we re-analysed after the designation of sub-lineages B. 1. 617. 1,. 2 and. 3. Lineage prevalence was computed from 281,178 genome sequences. The genomes were mapped to the same 315 English LTLAs for testing data described above. Mapping was performed from outer postcodes to LTLA, which can introduce some misassignment to neighbouring LTLAs.
This allows us to infer plausible ranges for numbers of introduction events for large datasets and to quickly update our analyses as new sequences become available. The other advantage of this approach is that it allows us to easily customize the analysis and to focus on inferred UK introductions that result in at least one UK surveillance sample, while still making use of non-surveillance UK samples to inform the inferred phylogenetic tree and migration history. Note that possible biases due to uneven sequencing rates across the world (e. g. 54 ) apply to our approach as well as other popular phylogeographic methods. Our approach works by traversing the maximum likelihood tree starting from the terminal nodes and ending at the root (postorder traversal). Here, we define a "UK clade" as a maximal subtree of the total phylogeny for which all terminal nodes are from the UK, all internal nodes are inferred to be from the UK, and at least one terminal node is a UK surveillance sample; the size of a UK clade is defined as the number of UK surveillance samples in it.
ONS infection survey analysis Data from the cross sectional infection survey was downloaded from https://www. ons. gov. uk/peoplepopulationandcommunity/healthandsocialcare/conditionsanddi seases/bulletins/coronaviruscovid19infectionsurveypilot/30april2021. Comparison of ONS incidence estimates with hospitalisation, case and death rates was conducted by estimating infection trajectories separately from observed cases, hospitalisations and deaths  58, 59  , convolving them with estimated PCR detection curves  60  , and dividing the resulting PCR prevalence estimates by the estimated prevalence from the ONS Community Infection Survey at the midpoints of the 2-week intervals over which prevalence was reported in the survey.
Acknowledgements COG-UK is supported by funding from the  Medical Research Council (MRC) part of UK Research & Innovation (UKRI) , the  National Institute of Health Research (NIHR)  and  Genome Research Limited , operating as the  Wellcome Sanger Institute. We would like to thank our colleagues at  EMBL-EBI , the  Wellcome Sanger Institute  and from  COG-UK  for stimulating discussions and helpful comments on this manuscript. HSV and MG are supported by a grant from the  Department of Health and Social Care. AWJ, EB and MG are beneficiaries from grant  NNF17OC0027594  from the  Novo Nordisk Foundation. TS is supported by grant  210918/Z/18/Z , and JH and SF by grant  210758/Z/18/Z  from the  Wellcome Trust. HSV, NDM, AWJ, NG, EB and MG are supported by  EMBL. We would like to thank  Elias Allara  (Cambridge) and  Georgia Whitton (Sanger)  for providing outer postcode to LTLA mappings,  Rupert Beale  for comments and  John McCrone  for setting up Thorney Beast analysis.
Code availability Code for spatio-temporal modeling of different viral lineage is available at https://github. com/gerstung-lab/genomicsurveillance and as a PyPI package (genomicsurveillance). This phylogeographic model has been implemented in python scripts, and the code is available from https://github. com/NicolaDM/phylogeographySARS-CoV-2. Code for ONS infection survey analysis is available at https://github. com/jhellewell14/ons_severity_estimates.
Characterization of POLYCERATE Mutations in Sheep and Goats To identify the genetic determinants of polyceraty, we reanalyzed the Illumina OvineHD Beadchip genotyping data (600 k SNPs) of 111 case and 87 control sheeps generated by two Evolutionary Co-option of HOXD1 for Horn Patterning in Bovidae. doi:10. 1093/molbev/msab021 MBE previous studies  (Greyvenstein et al. 2016; Kijas et al. 2016 ) (supplementary tables 1 and 2, Supplementary Material online). Assuming autosomal dominant inheritance and genetic homogeneity in the three breeds investigated, we finemapped the ovine POLYCERATE locus between positions 132,717,593 and 133,151,166 bp on Chr2 (Oar_v4. 0 assembly; supplementary fig. 2 , Supplementary Material online). By comparing whole-genome sequences of 11 polycerate specimens and 1,179 controls representing the world-wide sheep diversity, we identified a single candidate variant in this interval: a four-nucleotide deletion located at position þ4 to þ7 bp after exon 1 of the HOXD1 gene (g.
Then, for sliding windows of n markers (n set to 10 in goat and 50 in sheep, considering differences in marker density) we scored the numbers of 4H4H pairs and 4H2H pairs for which "no" versus "at least one" Mendelian inconsistency has been recorded. Finally, we compared the contingency tables produced using Fisher's exact test. SNP Array Genotypes, Sample, and Variant Pruning Illumina GoatSNP50 BeadChip genotypes specifically generated for this research and Illumina OvineHD Beadchip genotyping data generated by two previous studies  (Greyvenstein et al. 2016; Kijas et al. 2016)  were considered in the analyses. Polled (i. e. , hornless) animals were removed from the sheep data set. Markers with a minor allele frequency below 5% or which were called in less than 95% of the samples were eliminated.
Definition of the Boundaries of the 503-kb Deletion-137-kb Insertion in Goat The boundaries of variant g. 115,652,290_116,155, 699delins137kb were reconstructed manually using split read and paired-end read information obtained from IGV. Sequences of reads affected by the mutation were extracted from the. bam file using linux command lines and aligned manually to reconstruct the nucleotide sequence at each fusion point. For verification, amplicons encompassing these fusion points were PCR amplified in a Mastercycler pro thermocycler (Eppendorf) using Go-Taq Flexi DNA Polymerase (Promega), according to the manufacturer's instructions and primers listed in supplementary table 15, Supplementary Material online. Amplicons were purified and bidirectionally sequenced by Eurofins MWG (Hilden, Germany) using conventional Sanger sequencing.
Genotyping of DNA Sequence Variants SNP and small Indels were genotyped using PCR and Sanger sequencing as described above. PCR primers were designed with Primer3 software  (Rozen and Skaletsky 2000)  and variants were detected using NovoSNP software  (Weckx et al. 2005). Transgene insertions and large indels were genotyped by PCR and electrophoresis on a 2% agarose gel. Ovine variant g. 132,832,249_132,832,252del was genotyped with primers TTTGGGGCCACACTAGAATC and CCTAGAGGGGGCCTA CGAG, whereas caprine and murine variants were genotyped with the primers listed in supplementary tables 7 and 14, Supplementary Material online, respectively. Analysis of Nucleotide Sequence Conservation at the HOXD1 Exon 1-Intron 1 Junction Nucleotide sequences of the HOXD1 gene in 103 sarcopterygian and tetrapod species were obtained from the Ensembl (http://www.
ensembl. org/index. html, last accessed November 4, 2020; release 98) and UCSC (http://genome. ucsc. edu/, last accessed November 4, 2020) genome browser databases. The localization of the nucleotide sequence (between MTX2 and HOXD3) was verified in each genome assembly to avoid possible confusion with paralogs. In addition, only one sequence was arbitrarily retained when genome assemblies for distinct individuals of the same species were available. Then sequences were put in the same orientation and trimmed to get 40 nucleotides before and 20 nucleotides after the splice donor site of HOXD1 exon 1. A multispecies alignment was generated with ClustalW software  (Thompson et al. 1994) , version 2. 1 (https://www. genome. jp/tools-bin/ clustalw, last accessed November 4, 2020) and a sequence logo was generated using WebLogo  (Crooks 2004 ) (http:// weblogo.
Acknowledgments We thank  L. Orlando  ( Universities of Toulouse III, France and Copenhagen, Denmark ) for tentatively extracting DNA from museum skull specimens as well as  C. Hoz  e,  F. Lejuste , and  A Michenet  (ALLICE),  R. McCulloch  ( CSIRO ),  S. Chahory , and  C. Degueurce  (ENVA),  F. Andreoletti ,  M. Boussaha ,  J. Kergosien ,  D. Mauchand ,  M. Femenia ,  N. Perrot , and  M. Vilotte  ( INRAE ),  B. Camus-Allanic  ( LABOGENA DNA ),  J. Peters  ( LMU ),  C. Bens ,  A. Delapr  e, and  A. Verguin  (MNHN),  L. Ludes-Fraulob  (MZS), andB. Mascrez ( University of Geneva ) for their assistance. We also thank the staff of the  INRAE experimental unit UE 1298 SAAJ  for animal husbandry and management, as well as breeders and zoological parcs for making animals available for sampling and for providing pictures.
Contributors include in particular the Capgenes breeding company (France),  L. Pachot  ( Mouton Village, Vasles, France ),  A. Archiloque  (France),  L. Fiorenzi  ( Az. Agr. Madonna delle Alpi, Italy ), the  Schafzuchtverein Jakobschaf Schweiz (Switzerland) ,  Tierpark Hamm (Germany) ,  A. Schumann  (Germany), and  Dr A. Ennaifer  ( Zoological Park of Tunis, Tunisia ). Finally, the authors thank the  VarGoats Consortium  for allowing variant filtering against their data set. The Vargoats project was supported by  France G enomique  (Grant No. ANR-10-INBS-0009 ). This work was supported by  APIS-GENE  (Grant AKELOS) and the  Swiss National Research Foundation  (Grant No. 310030B_138662  to D. D. ). A. Hi. was supported by a  PhD fellowship  from the  University of Geneva.
Data Availability Raw sequencing data reported in this study were deposited to the European Variation Archive (EVA, https://www. ebi. ac. uk/eva/) under accession number PRJEB39341. Sequences from previous studies can be found at the following URL (www. goatgenome. org/vargoats_data_access. htm) or in the NCBI BioProject and EVA databases under accession numbers PRJEB6025, PRJEB6495, PRJEB9911, PRJEB14098, PRJEB14418, PRJEB15642, PRJEB23437, PRJEB31241, PRJEB31930, PRJEB32110, PRJEB35553, PRJEB35682, PRJEB37460, PRJEB39341, PRJEB39341, and PRJNA624020. Illumina GoatSNP50 Beadchip genotyping data generated for this study have been deposited in the Dryad Digital Repository (doi: 10. 5061/dryad. rxwdbrv6n). Illumina OvineHD Beadchip genotyping data from previous studies can be found in the same repository (doi: 10.
Methods: We searched two bibliographic databases, four web-based search engines with search-by-key-words capacity, 5 specialist websites, and conducted a grey literature call through two project stakeholders. We screened articles by title, abstract, and full text using pre-defined inclusion criteria all the while assessing the consistency of the reviewers. All relevant translocations were coded from retained publications. Key variables of interest were extracted and coded for each translocation event. The quantity and characteristics of the available evidence and knowledge gaps/clusters are summarised. The distribution and frequency of translocations are presented in heat-and geographical maps. Review findings: A total of 613 articles were considered eligible for coding bibliometric data. Metapopulation management and review articles were not coded for quantitative and qualitative variables.