dataesr/scientific-paragraphs-categorization huggingface dataset:
15) onto mm10 and the TLA-derived configuration that assumed a 63. 2 kb integration, comprising one entire copy of region CS38-40 followed by the partial CS38 segment, after position chr10:97019221 (mm10) (see Fig  2D, top). Reads mapping on the integration site (mm10, chr10:97018026-97020425) or transgene region (chr2:75122684-75160161 of mm10) sequence components of the TgN  (38) (39) (40)  mutant construction were converted from fastq to fasta in order to produce the dot plots displayed in Fig 2E. This was achieved using a Perl script as in  [50] , with the following modification: 20 bp of the MinION reads were tested against the reference for 5 bp-sliding windows and only 20-mers completely identical to unique 20-mers in the reference were kept. The output was then processed in R (www.
Hi-C Hi-C was performed as in  [5]  and  [58]. For each genotype (i. e. wild-type or TgN  (38) (39) (40) /TgN  (38) (39) (40) ; del(CS38-40) -/-), one sample corresponding to four E12. 5 whole limbs was used as starting material. Hi-C libraries were multiplexed and sequenced so as to obtain 75/75 bp paired-end reads with an Illumina NextSeq (first run, 80 million reads per sample) or HiSeq 4000 (second run, idem). Hi-C data analysis was as in  [58] , with some modifications. HiCUP (version 0. 7. 3)  [59]  was applied providing either the mm10 reference genome, or the custom genome of TgN  (38) (39) (40). For the TgN  (38) (39) (40)  mapping, the pipeline was adapted in order to prevent removal of reads aligning to the duplicated sequences of this line. All valid hybrid pairs were kept since no MAPQ filter was applied.
Sequencing data analysis and display Chromatin immunoprecipitation (ChIP-seq and ChIPmentation) data were analyzed on our Galaxy platform  [65]. Chromosome conformation capture (4C-seq and Hi-C), MinION and TLA data, as well as total input DNA data for Control-FREEC were analyzed through the Scientific IT and Application Support Center of the Ecole Polytechnique Federale de Lausanne (EPFL). Data were plotted using the pyGenomeTracks visualization tool (https://github. com/ deeptools/pyGenomeTracks)  [61, 66]. Gene annotations shown in Figs 1, 3 and 4 were retrieved from GENCODE (GRCm38-mm10, VM23 protein-coding). The scripts used to generate all TLA, MinION, 4C-seq and Hi-C outputs as well as those used to produce NGS data figures were deposited in GitHub (https://github. com/lldelisle/scriptsForWilleminEtAl2021).
All figures were processed with Adobe Illustrator. 3 ) the 602 bp duplicated region (mm10, chr10:97019222-97019824) inside chromosome 10 at position chr10:97019824 using the SeqinR package  [67]. The sequence of the TgN  (38) (39) (40)  mutant chromosome 10 is available at (https://zenodo. org/record/4292337). The TgN  (38) (39) (40)  mutant genome was completed by adding wild-type chromosomes of mm10 (retrieved from UCSC), as well as the del(CS38-40) mutant chromosome 2 available at (https://zenodo. org/record/3826913) [18].
homozygous E12. 5 limbs (Fig 4A-4C) and observed a split of the host TAD into two sub-domains, right at the level of the transgene insertion (Fig 4B, arrow). To better compare the wild-type and mutant Hi-C maps, we adapted the signal of the former to account for the potential effect of increasing the genomic distance. We thus calculated the value-of-alpha of the function relating contact frequencies to distance along the Btg1 TAD (S5A Fig, pink curve). Direct comparison of wild-type and mutant datasets showed a clear loss of interactions (-39%, p-value = 2e-29) between the two new sub-domains in a differential heatmap (Fig 4D, dashed box). The TAD partitioning was reminiscent of a sub-TAD boundary formation, for it was only detected by the hicFindTADs algorithm at a window size of 240 kb (Fig 4B and 4C, light red bars and track, S1 Table) and a substantial amount of interactions were scored across the new border (Fig 4B; asterisk), similar contact changes beyond the integration site are shown.
Acknowledgments We would like to thank  Thi Hanh Nguyen Huynh ,  Sandra Gitto  and  Benedicte Mascrez  for help with mice breeding and genotyping;  Mylène Docquier ,  Brice Petit ,  Didier Chollet  and  Christelle Barraclough  from the  Geneva Genomics Platform , as well as  Bastien Mangeat ,  Elisa Cora  and  Lionel Sylvain Ponsonnet  from the  EPFL Gene Expression Core Facility. We are grateful to all members of the  Duboule laboratories  for comments and discussions.
Funding: DD received support under Grant No. 310030B_138662  from the  Swiss National Research Foundation  (http://www. snf. ch),Grant SystemHox No. 232790  from the  European Research Council  (https://erc. europa. eu),Grant RegulHox No. 588029  from the  European Research Council  (https://erc. europa. eu). L. L-D. was supported by the  ERC  grant  RegulHox  ( No 588029 , to D. D. ) C. C. B. by a fellowship of the  Eunice Kennedy Shriver National Institute of Child Health & Human Development of the National Institutes of Health  (No  F32HD093555 , to C. C. B. ) https://www. nichd. nih. gov. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
We used metagenomic analysis rather than the widely used metabarcoding approach because it enables the sequencing of DNA fragments from entire genomes without taxon-specific amplifications, therefore improving the specificity and sensitivity of taxonomic identification, as well as facilitating the authentication of endogenous ancient DNA from modern contaminants  9. However, metagenomic analysis requires genome-scale reference data, which are limited for most regions of the world, including the Arctic. Thus, a key component of our study is the generation of a substantial corpus of plant reference sequences. From the 535 samples, we generated 10. 2 billion sequencing reads that passed the filtering criteria and were used for analysis (Methods). We created a comprehensive reference database for taxonomic identification by merging the NCBI-nt and NCBI-RefSeq databases, and supplemented the limited genomic-scale public reference data for Arctic species with 12 Arctic animals and an extensive sequencing effort of 1,541 modern Holarctic plant genome skims (PhyloNorway; Methods).
These new sequences comprise 311. 3 million whole-genome contigs and provide a broader and more reliable plant reference database than previously available. The merged reference database contains a total of 380. 4 million entries and covers about 1. 47 million organisms. We developed a k-mer-based method to evaluate the availability and coverage of our combined reference database for different taxa (Methods) and found that it covers a wide range of both Arctic and non-Arctic species (Supplementary Information 9. 2. 3). Accordingly, the addition of our new reference genomes did not cause bias towards Arctic taxa, providing confidence in our identifications. We used robust approaches to identify taxa from individual reads and collated the resulting taxonomic composition at the generic or familial level (Methods).
We applied several methods to authenticate the plant and animal taxonomic profiles; the identifications were reliably classified despite the short DNA sequences that were preserved in these samples (Methods). Moreover, 131 samples in this dataset were processed for metabarcoding, targeting the short DNA barcodes of plants  12  , enabling a comparison between the two approaches (Methods). The results showed that the metagenomic analysis captured greater floristic and faunal diversity and achieved better taxonomic resolution (Supplementary Information 11. 2). We also found that only about 1. 26% of the plant DNA reads are of ribosomal and chloroplast origin (Supplementary Information 9. 2. 5), suggesting that the metabarcoding approachwhich relies on organelle DNA-makes use of only a small fraction of preserved DNA.
Population diversity of megafauna Megafaunal eDNA from across the Arctic also enables us to resolve population-level patterns, which is crucial for uncovering species-specific demographic and evolutionary responses to past climatic and environmental changes. We applied a method for phylogenetically assigning the identified eDNA to mitochondrial haplogroups of mammoth and horse, the two most abundant species detected in our dataset (Methods). A mammoth phylogeny composed of four previously described major mitochondrial clades (clade 1, including 1C and 1DE, and clades 2 and 3)  29  was reconstructed from 78 mammoth mitochondrial genomes. The recovered mammoth eDNA was then assigned to a best-fit node on the tree based on single-nucleotide polymorphism (SNP) support/ conflict, enabling clade assignment for 79 eDNA samples (Extended Data Fig.
Instead, our results suggest that their extinction came when the last pockets of the steppe-tundra vegetation finally disappeared, when the Arctic-wide paludification was brought on by warmer and wetter climates  7, 20. What we have mined from this substantial dataset does not exploit its full potential. For example, we detected DNA of Camelidae (most probably the Arctic camel  34  ) and Panthera (possibly the steppe lion). However, due to a lack of reference genomes for these species, we could not confirm these identifications. This constraint also applies to other species because our reference database-large as it is-is far from complete, despite our extensive sequencing efforts. With more species sequenced and new bioinformatics methods developed, this dataset can be reanalysed to explore more questions of Arctic biotic history.
Online content Any methods, additional references, Nature Research reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at https://doi. org/10. 1038/s41586-021-04016-x. N 80° N 120° W 60° W 0°6 0° E 120° E 180° W 60° N 80° N 120° W 60° W 0°6 0° E 120° E 180° W 60° N 80° N 120° W 60° W 0°6 0° E 120° E 180° W 60° N 80° N 120° W 60° W 0°6 0° E 120° E 180° W 60° N 80° N 120° W 60° W 0°6 0° E 120° E 180° W 60° N 80° N 120° W 60° W 0°6 0° E 120° E 180° W 3.
Sampling, chronology and eDNA taphonomy Sampling and subsampling methods are described in Supplementary Information 1. Sample ages were determined through conventional or accelerator mass spectrometer radiocarbon ( 14 C) as well as optically stimulated luminescence. In total, 631 radiocarbon ages and 81 optically stimulated luminescence dates were used. For sedimentary sections with multiple contiguous dates without stratigraphic inversions, age-depth models were built to calculate sedimentation rates and estimate the ages of undated samples within these sections. All radiocarbon ages are in calibrated years before present, calibrated using IntCal20 (ref. 35 ). Chronological information is provided in Supplementary Information 2 and Supplementary Data 1 and 2. To determine whether DNA was in situ, control samples were obtained from modern surfaces, from water in adjacent rivers and lakes, and from stratigraphic layers bracketing the samples.
PhyloNorway plant genome database construction The PhyloNorway plant genome database was constructed by sequencing 1,541 Arctic and boreal plant specimens collected from herbaria. DNA was extracted from the selected specimens using a modified Macherey-Nagel Nucleospin 96 Plant II protocol. Two different library preparation protocols were applied depending on DNA yields. All of the libraries were then sequenced. Nuclear ribosomal DNA and chloroplast genome from each plant were assembled to evaluate the data quality. Whole-genome contigs for each plant were assembled and annotated as the final reference database. A list of plant species, herbarium information, DNA extraction, sequencing and database statistics are supplied in Supplementary Data 3. Data for three standard barcodes skimmed from this database were also used in ref.
Taxonomic identification, authentication and quantification We performed taxonomic classification by mapping reads against a comprehensive genomic database that was annotated with taxonomic information according to the principle of the Holi pipeline  36. Details of the composition of the reference database are provided in Supplementary Information 9. 2. 1. All reads were first quality-controlled, and each read was then offered an equal chance to be aligned against all entries in the database after duplicate removal (Supplementary Information 9. 1 and 9. 2). No limitation to specific taxonomic group, geography or environment was applied for the alignment. The lowest common ancestor of all of the hits with 100% similarity was assigned to each read that had been aligned to multiple taxa. The taxonomic coverage of different database compositions and their effects on taxa identification were evaluated using a k-mer-based method (Supplementary Information 9.
2. 2). We found that using a proper reference database is important for eDNA metagenomics-based taxa identification, particularly for ancient datasets in which the DNA is highly fragmented. Even reference genome availability across taxa can improve the sensitivity and specificity of the identification by increasing the identified reads and correcting the misidentifications (Supplementary Information 9. 2. 5). Taxa that were detected in the laboratory controls were combined into a list, and all of the listed taxa were subtracted from samples (Supplementary Information 9. 3). The resulting plant and animal taxonomic profiles were thereafter parsed for additional authentication using a series of conservative thresholds (Supplementary Information 9. 4 and 9. 6), on the basis of an Arctic flora and faunal checklist (Supplementary Information 8).
Vegetation diversity and dissimilarity The Shannon diversity index was calculated according to the method in ref. 42. Plant morphological forms were assigned at the genus level on the basis of the plant trait database of eFloras (http://www. efloras. org). Beta-diversity (dissimilarity) between every two plant assemblages was calculated according to the method in ref. 43. For the pan-Arctic vegetation turnover (Fig. 2a ), plant genera identified in all samples in each 2,000-year interval were combined as an assemblage; beta-diversity between each two consecutive intervals was calculated. Regional vegetation turnover (Fig. 2c ) was calculated at 5,000-year intervals. NMDS (k = 3; Extended Data Fig. 1c ) was performed using the R package vegan  44  , allowing 100,000 iterations of random starting to find the best convergent solution.
Comparison of eDNA shotgun metagenomics and metabarcoding We applied two modules for comparing the metabarcoding and shotgun metagenomics in taxa identifications. (1) We conducted the two sequencing techniques in parallel on 14 DNA extracts to directly compare the retrieved taxonomic profiles. (2) We compared the floristic profiles reconstructed by this study and a previous metabarcoding study  12  on 131 overlapping samples of the two datasets. The results show that metagenomics performed better on our samples in both captured floristic and faunal diversity. Details are provided in Supplementary Information 11.
Palaeoclimate panels and human distribution niche modelling For the ice-core data from Greenland (Fig. 2a ), we rescaled the available δ  18  O ratios (20-year slices) retrieved from NGRIP1 (ref. 45 ), NGRIP2 (ref. 46 ) and GISP2 (ref. 47 ) to the range of the corresponding ratio of GRIP  48  , for which there are valid values for all age slices, using the rescale function in the R package scales. The mean of the available ratios for each time slice from the four datasets was calculated and used. Calcium concentrations were calculated from refs. 49, 50 using the same method as for δ  18  O. Snow-accumulation rates were based on GISP2 (ref. 51 ). We also modelled monthly palaeoclimate anomalies at 1,000-year time steps using an emulator  52  and downscaled them onto a modern baseline climatology (CHELSA)  53  at a spatial resolution of 1°.
From these data, we calculated four environmental variables-annual mean temperature, temperature seasonality, annual precipitation and precipitation seasonality-that were used to represent the climate for each of our eDNA sites. Details are provided in Supplementary Information 12. 1. We developed distribution models to map environmentally suitable conditions for Palaeolithic human occurrence in steps of 1,000 years from 5 ka to 31 ka and steps of 2,000 years from 32 ka to 47 ka. First, geo-references for human remains in the Arctic were collected and dates from  14  C calibrations inferred from two databases CARD2. 0 (ref. 54 ) and the Palaeolithic of Europe 55. These data were filtered for quality, resulting in a final set of 6,497 occurrences. From 32 ka to 47 ka, we calculated 2,000-year averages of the four environmental variables.
Acknowledgements We thank  D. H. Mann  for his detailed and constructive comments; and  T. Ager ,  J. Austin ,  T. B. Brand ,  A. Cooper ,  S. Funder ,  M. T. P. Gilbert ,  T. Jørgensen ,  N. J. Korsgaard ,  S. Liu ,  M. Meldgaard ,  P. V. S. Olsen ,  M. L. Siggaard-Andersen ,  J. Stenderup ,  S. A. Woodroffe  and staff at the  GeoGenetics Sequencing Core and National Park Service-Western Arctic National Parklands  for help and support. E. W. and D. J. M. thank the staff at  St. John's College, Cambridge , for providing a stimulating environment for scientific discussion of the project. E. W. thanks Illumina for collaboration. The  Lundbeck Foundation GeoGenetics Centre  is supported by the  Carlsberg Foundation  ( CF18-0024 ), the  Lundbeck Foundation  ( R302-2018-2155 ), the  Novo Nordisk Foundation  ( NNF18SA0035006 ), the  Wellcome Trust  ( UNS69906 ) and  GRF EXC CRS Chair (44113220)-Cluster of Excellence.
70184209)   and  The Arctic University Museum of Norway. Metabarcoding sequencing was funded by the  Central Public-Interest Scientific Institution Basal Research Fund ,  CAFS  ( 2017B001  and  2020A001 ). B. D. S. is supported by the  Wellcome Trust  programme in Mathematical Genomics and Medicine (WT220023); F. R. by a  Villum Fonden Young Investigator  award (no. 00025300 ); D. J. M. by the  Quest Archaeological Research Fund ; P. M. by the  Swedish Research Council  (VR); R. D. by the  Wellcome Trust  ( WT207492 ); and A. R. by a  Marie Skłodowska-Curie Actions Individual Fellowship  ( MSCA-IF, 703542 ) and the  Research Council of Norway  (KLIMAFORSK, 294929). L. O. has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (no.
681605 ); I. G. A. and Y. L. from the  ERC  under the European Union's  Horizon 2020 research and innovation programme  (no. 819192 ). J. I. S. and J. M. are supported by the  Research Council of Norway. P. B. H. and  N. R. E. acknowledge NERC  funding (grant  NE/ P015093/1 ). D. W. B. was supported by a  Marie Skłodowska-Curie Actions Incoming International Fellowship  ( MCIIF-40974 ). T. S. K. is funded by a  Carlsberg Foundation Young Researcher Fellowship  ( CF19-0712 ).
Data availability Adapter-removed plant or animal eDNA data were deposited at EMBL-ENA under project accession ERP127790. The raw data of Phy-loNorway plant genome database are available at EMBL-ENA under project accession PRJEB43865. Assembled plant genome contigs of the PhyloNorway database are available at DataverseNO 61. NCBI databases are available at the NCBI ftp server (https://ftp. ncbi. nlm. nih. gov). The Canadian Archaeological Radiocarbon Database (CARD2. 0) is available online (https://www. canadianarchaeology. ca). The Radiocarbon Palaeolithic Europe Database is available online (https://ees. kuleuven. be/geography/projects/14c-palaeolithic). All other data are provided in the Supplementary Information and Supplementary Data 1-9.
Data Policy information about availability of data All manuscripts must include a data availability statement. This statement should provide the following information, where applicable: -Accession codes, unique identifiers, or web links for publicly available datasets -A list of figures that have associated raw data -A description of any restrictions on data availability Adapter-removed plant or animal eDNA data is deposited at EMBL-ENA with project accession PRJEB43822. The raw data of PhyloNorway plant genome database is available at EMBL-ENA with project accession PRJEB43865 Assembled plant genome contigs of the PhyloNorway database are available at https:// doi. org/10. 18710/3CVQAG. NCBI databases are available at the NCBI ftp server https://ftp. ncbi. nlm. nih. gov. Canadian Archaeological Radiocarbon Database.
RNA-Seq analysis of L. tarentolae aaRS transcripts It is established that, with a few exceptions, the RNA of trypanosomatids do not contain introns and, therefore, the genomic coding sequence reflects that of the mRNA  (35). Nonetheless, mRNAs undergo two mechanisms of maturation: the addition of a spliced leader sequence (of 39 nt in L. tarentolae) by trans-splicing at the 5 0 -end and the polyadenylation of the 3 0 -end  (16, 36). However, the dataset used to generate L. tarentolae aaRS sequences in the TriTryp database is based on genome sequencing from which gene identification was performed by homology with other Leishmania organisms (37). To confirm these predicted transcript sequences and to validate the presence of the above-described trypanosomatidspecific insertions and extensions within aaRS sequences, we performed direct nanopore cDNA sequencing on polyAenriched RNA extracted from L.
tarentolae. From the total 5. 7 million reads, only reads containing the spliced leader sequence were selected and aligned to the genome. As shown in Table  1 , not all aaRSs were sequenced with the same coverage, with numbers of reads ranking from 2 (for the IleRS) to 393 reads (for the mt-TrpRS). Nevertheless, enough reads were obtained for each aaRS to confirm that the sequences coding for the previously described trypanosomatid-specific extensions and insertions are indeed present in the mature mRNAs (selected examples are shown in Fig. 5A  and Fig. S4 ). Sequencing mature trans-spliced mRNAs also provided information concerning splice-site location and allowed us to determine the 5 0 -UTR lengths (shown in Table  1 ), which had been completely absent from the TriTryp database. The size of the 5 0 -UTRs is heterogeneous among the aaRS genes ranging from 21 bp for the GluRS-encoding mRNA to 526 bp for the ThrRS-encoding mRNA.
Furthermore, precise annotation of trans-splicing sites enabled us to correct the misannotated sequences of some aaRSs in the TriTryp database. Thus, the cytosolic L. tarentolae TrpRS (LtaP29. 0060) does not start at the predicted methionine but rather at the third methionine of the TriTryp proposed protein sequence, which removes 71 aa from the predicted sequences (Fig. 5B ). Looking at the TrpRS MSA, we can assume that the Leishmania braziliensis cytosolic sequence (LbrM29. 0070; the only other Leishmania sequence that display a similar N-terminal extension) is similarly misannotated, and the 78 N-terminal amino acids should be removed. Regarding the mt-AspRS (LtaP21. 1070), it appears to start at the second methionine of the predicted sequence (Fig. 5B ). This removes the first 72 amino acids from the predicted sequence.
Regarding the ProRSs, two isoforms are encoded by distinct genes in Leishmania species  (14). Fulllength L. tarentolae ProRS sequences are however missing from the TriTryp database. Our RNA-Seq data allowed us to identify a sequence coding for the mt isoform (Fig. S5 ). When inserted into the ProRS MSA, it appears that the corresponding protein sequence displays an N-terminal extension of 86 amino acids, similar to the 88-aa N-terminal extension found in the L. major ProRS. This strongly suggests that a similar N-terminal extension (likely the MTS) is present in orthologs from other Leishmania species. Regarding the cytosolic isoform of the ProRS, only a fragment of 486 aa is found in the TriTrypDB (LtaP29. 2400). Its insertion into the ProRS MSA showed that it corresponds to the C-terminal part of the protein but could not be a functional enzyme because catalytic residues are missing.