dataesr/scientific-paragraphs-categorization huggingface dataset:
Methods Biological materials, experimental and analytical procedures for ChIP-seq and RNA-seq, as well as the corresponding datasets are all described in Willing et al. (2015)  [18]. Datasets are available at Gene Expression Omnibus (GEO) ([GEO:GSE50636], [GEO:GSE54727], [GEO:GSE54560])  [18]. The following datasets were also used: H3K27me3 data  [8, [42] [43] [44] ; Nucleosome occupancy data  [22] ; Hi-C data  [26, 27]. These reference genome sequences were used: Arabidopsis thaliana (Ath) TAIR10, Arabidopsis lyrata (Aly  [30] ), and Arabis alpina (Aal; sequence  [18]  and gene annotation  [18] ). Numbers of annotated genes and TEs for each genome are indicated in Additional file 1: Table  S11.
Reconstruction of the Brassicaceae ancestral genome and ohnolog detection Ancestral genome reconstruction The set of Aal genes was included in gene families extracted from the Ensembl Genome Plants database 24  [45] , and the phylogeny of each gene family was computed using the EnsemblCompara methodology  [46]. Based on these gene family phylogenies, ancestral gene content was inferred for Brassicaceae. Using the Algorithm for Gene Order Reconstruction in Ancestors (AGORA) method, we automatically reconstructed contiguous ancestral regions (CARs), which describe the order (gene-to-gene adjacencies) and orientation of ancestral genes. The ancestral gene content and order we used is an update of the ancestral Brassicaceae karyotype from  [16]  that was computed with data from Ensembl Genomes Release 24 including four additional species: Aal  [18]  and Tarenaya hassleriana  [19]  for ingroup species and Citrus clementina and Gossypium raimondii as outgroups (both from the Phytozome 10 database  [47] ).
All the annotations and the entire set of protein trees were downloaded from Ensembl Data Release 24. Proteins from the additional species were inserted in the previous families through an assignation based on BlastP results. Phylogenetic gene trees were computed with the EnsemblCompara methodology  [46]  and then used to infer the ancestral gene content, which was ordered using AGORA. The ancestral gene content of Brassicaceae (45,874 protogenes) was extracted from the 10,840 families containing the genes present in extant Brassicaceae species. Only 9583 families contained at least one gene of Ath, Aly, or Aal and 6562 encompassed the set of 13,515 Brassicaceae protogenes with a single gene descendant in all three species. The reconstruction procedure led to 12 ancestral CARs (i. e. , CARs with more than 400 genes) containing 19,534 ordered protogenes (out of 952 scaffolds of at least 2 genes that contain 22,885 ordered protogenes) for the Brassicaceae ancestor.
Ohnolog detection Ohnolog pairs of genes in the three Brassicaceae species were defined as described  [49]. To identify Aal ohnologs, gene locations in Aal were compared with those of gene orthologs in non-duplicated species (Citrus clementine, Theobroma cacao, Carica papaya) using ad hoc scripts in order to identify pairs of regions in the Aal genome that are syntenic with a single region in non-Brassicaceae species. These regions correspond to double conserved synteny (DCS) blocks. Duplicate pairs of Aal genes were defined by their location on two distinct chromosomes belonging to the same DCS block. These genes are most likely duplicates resulting from the αor β-WGDs and are called ohnologs. This set of ohnolog pairs is quite stringent, because only duplicates that are located on different chromosomes in extant species were taken into account, and secondly, because tandemly duplicated genes (that likely occurred after WGD) were removed from this set.
Alignment-based methods We followed a conventional protocol for the estimation of sequence similarity between coding sequences. The coding sequence containing only concatenated exons, starting with a start codon (ATG) and ending with any of the three termination codons (TAA, TAG, TGA), was obtained for each gene transcript. Multiple sequence alignment of these nucleotidic sequences was computed with the TranslatorX software  [31]  using the corresponding amino acid translations as guides and Multiple Sequence Comparison by Log-Expectation (MUSCLE)  [50]  as the alignment algorithm with default parameters. The DNA distance matrix for the three sequences corresponding to each protogene was obtained with the Phylogeny Inference Package (PHYLIP) using the default F84 model  [51, 52]  for nucleotide substitution.
Motif finding in promoters of H3K27me3-marked gene orthologs We searched for sequence motifs in promoters using the Multiple EM for Motif Elicitation (MEME) algorithm, which implements a probabilistic model to discover recurring ungapped fixed-length patterns from a set of unaligned sequences  [56, 57]. The algorithm was run with default parameters with the following modifications: sequences may contain any number of nonoverlapping occurrences of each motif; search for 5 different motifs up to 20 bp per sequence; use a Markov model of order 5 calculated over the promoters of all H3K27me3-marked gene orthologs as the background model. To assign a potential function to the predicted motifs, we compared them to a database of known Arabidopsis motifs (DNA affinity purification (DAP) motifs  [58]  using the Tomtom tool v4.
H3K27me3 marking of TF-encoding gene families TF-encoding gene families of Ath were retrieved from the Arabidopsis cis-regulatory element database (AtcisDB) of the AGRIS database. To allow meaningful comparisons, we considered only the 20 TF-encoding gene families with more than 10 members and for which at least 40% of members are among the 13,515 unambiguous gene orthologs defined initially. Additionally, promoter sequence similarity and tissue specificity of expression were estimated for genes in each TFencoding gene family as indicated above.
Identification and analysis of synteny blocks Synteny blocks were determined using the PhylDiag software  [29]  starting from two genomes and the corresponding gene homologies. PhylDiag allows for gene deletions to be considered as events that may break the synteny and also accounts for gene orientations, tandem duplications, and lineage-specific de novo gene births. PhylDiag was run to compare the ancestral Brassicaceae genome  (27, 343  Brassicaceae protogenes) to each of the modern genomes (Ath, Aly, and Aal) with the maximum gap parameter set to 1. Using this stringent setup, we obtained 537, 497, and 867 synteny blocks for Ath, Aly, and Aal, which contained in total 17,442, 14,569, and 13,439 genes. For all synteny blocks resulting from each pairwise comparison, we calculated the proportion of genes with plastic or constrained H3K27me3 marking and tested for the enrichment in each case.
Counting was done separately for all marked genes, genes with plastic H3K27me3 marking, and genes with constrained H3K27me3 marking. Proportions were calculated by dividing these numbers by the total number of marked genes, of genes with plastic H3K27me3 marking, and of genes with constrained H3K27me3 marking, respectively. We assessed the relation between distance and strength of genomic interactions with high density of gene orthologs with constrained H3K27me3 marking using Ath Hi-C-seq data obtained for the WT Col-0 and the clf swn mutant  [26]. The distance and the strength of every intra-chromosomal interaction between 20-kb windows was inferred from the numerical matrix representation of the 2D interaction map (Fig. 1  and supplemental file S2 of Feng et al. 2014  [26]  for WT and clf swn, respectively) and each interaction was defined as being weak, moderate, or strong.
Funding This work was supported by the  European Union   Seventh Framework Program EpiGeneSys Network of Excellence  ( Award 257082 ) and the  Investissements d'Avenir  from the  Agence Nationale de la Recherche (ANR)  ( ANR-10-LABX-54MEMOLIFE ,  ANR-11-IDEX-0001-02 PSL* Research University ,  ANR-12-ADAP-0020-01  and  Ancestrome  Project  ANR-10-BINF-01-03 ).
Limitations In addition to the inter-model differences described above, ice shelf/ocean models in general are still subject to several limitations. Most of them are specific to our model setup as the large uncertainties in geometry and forcing data, and critical gaps in our knowledge of dynamics at the ice/ocean interface. For many ice shelves, there are only indirect observations of ice draft, based on satellite surface elevation data, while the sub-ice bathymetry is often based on nothing more than extrapolation from surrounding regions of grounded ice and open ocean. More data are needed for effective modelling  (Fretwell, et al. , 2013) , because cavity geometry has a major impact on the simulated melting by controlling the water mass structure and circulation within the cavity  (Rydt, et al.
Acknowledgements The authors acknowledge financial support from the  National Environmental Research Council  and the  UK Met Office. Computational resources were provided by the supercomputing facilities of the  British Antarctic Survey  and the  ARCHER UK National Supercomputing Service. The DFS5. 2 forcing fields and the open boundary conditions from the ORCA025-GRD100 simulation were provided by the  DRAKKAR coordination (CNRS GDRI  No  810 ).
Code availability The model code for NEMO 3. 6 is available from the NEMO website (www. nemo-ocean. eu). On registering, individuals can access the FORTRAN code using the open source subversion software (http://subversion. apache. org/). The branch used for both configurations used in this study is the 2015 development branch named dev_r5151_UKMO_ISF at revision 5200. The ice shelf module is now included in the public NEMO distribution.
Data availability The ISOMIP configuration is distributed in NEMO version 3. 6 as an unsupported configuration. No file is required to run ISOMIP configuration. For the Circum-Antarctic configuration, the input files (cpp keys, namelist, bathymetry, ice shelf draft, iceberg runoff, initial condition, river runoff, tidal mixing, and weights for the surface forcings) could be requested from the authors. The surface forcing and the open boundary were provided by the DRAKKAR consortium (http://www. drakkar-ocean. eu).
The Root-Tip Nuclear and poly(A) + RNA Transcriptomes Differ in Complexity and Transcript Enrichment To compare the nuclear and poly(A) + transcriptomes we determined the number of genes detected (complexity) and evaluated differences in transcript abundance using averaged data from the four biological replicate samples for each RNA population. First, we evaluated the portion of the transcript mapping to introns, coding regions and untranslated regions (UTRs; Supplemental Fig. S5 ). The proportion of intronic to exonic reads (coding regions, 59 UTR, 39 UTR) was 2-fold higher in the nuclear RNA in all of the biological replicates. To estimate complexity (i. e. the total number of gene transcripts detected), we applied a threshold of. 5 reads per kilobase per million reads (rpkM) on exonic regions (Supplemental Table  S2B ).
Sequencing and Data Analysis for T-DNA Insertion Mapping The Illumina-compatible library was sequenced on MiSeq (Illumina) using 500 cycles of MiSeq reagent kit v2 at the Institute for Integrative Genome Biology at the University of California Riverside. The resulting 2 3 250 bp sequence data were analyzed using R statistical software. Paired-end reads containing overlapping sequences were merged to obtain longer reads using the FLASH (fast length adjustment of short reads; http://ccb. jhu. edu/ software/FLASH/) module. The GREP (globally search a regular expression and print; http://www. gnu. org/software/grep/) module was used to filter out reads containing the left and right T-DNA border sequences, and the BOWTIE2 (http://bowtie-bio. sourceforge. net/bowtie2/index. shtml) module was used to map the border sequences against the rice genome (MSU Rice Genome Annotation Project Release 7).
This result has been widely reported with Lokiarchaeota being presented as the "missing link" that bridges the gap between prokaryotes (simple life) and eukaryotes (complex life) and as an almost definitive argument supporting the eocyte hypothesis  [6, 13, 19]. Indeed, several recent studies have already mined the Lokiarchaeota genomes to reconstruct the critical pathways of eukaryogenesis  [20] [21] [22] [23] [24]. The proposed Lokiarchaeota-Eukarya affiliation was only challenged by Caetano-Anolles and colleagues who noticed that the lokiarchaeal proteomes added only 10 new members (0. 1%) to the archaeal protein fold superfamilies  [25]. More recently, these authors showed that the imbalanced number of species in the dataset studied by Spang et al. (10 Bacteria, 10  Eukarya and 87 archaea) could have impacted the topology of the Tree  [26].
To position the Lokiarchaota within the Tree of Life, Spang and co-workers concatenated 36 universal markers  [14]. They reported that most individual protein trees were not resolved, and suggested that it was due to the small amount of information contained in single-gene alignments  [14]. Besides a lack of phylogenetic signal, we think that another possible explanation could be the inclusion of many sequences from taxa known to be fast-evolving in their dataset. In their main phylogenetic tree (thereafter called the Loki ancestor tree, Fig 2b in  [14] ), Archaea were rooted in the branch leading to Methanopyrus kandleri, a notorious fast-evolving archaeon that normally branches close to Methanobacteriales in the Euryarchaeota phylum  [27]. It is well known that inclusion of fast-evolving species (FES) in datasets can lead to longbranch attraction (LBA) artefacts  [28, 29].
Removal of FES from the initial dataset revealed different stories for the lokiarchaeal proteins, as well as for different universal proteins. We identified a subset of proteins (hereafter called the Woese's proteins) that support the three-domains ToL  [7]  in which Archaea are monophyletic, and another subset of proteins (hereafter called the eocyte proteins) that support trees in which Eukarya are sister group to various archaeal lineages (hereafter called the eocyte trees). Remarkably, exclusion of a single protein, Loki 3 Elongation Factor 2 (EF2; likely contaminated by eukaryotic sequences) was sufficient to break the Lokiarchaeota/Eukarya affiliation with the Spang et al. dataset, and to recover the Woesian ToL with the FES-curated dataset. Finally, we performed a robust phylogenetic analysis of the two largest RNA polymerase subunits using a new dataset containing an equal number of species from each domain, Archaea, Bacteria and Eukarya.
The universal lokiarchaeal proteins have different origins We performed individual phylogenetic analyses of the 36 universal proteins used by Spang and co-workers, using the same methodology (Maximum likelihood, ML). The 36 phylogenetic trees obtained are presented in S1 Fig. We noticed, as stated by the authors, that most individual phylogenies provided trees without support at most nodes (summarized values for this initial dataset in Table  1 , detailed values in S1 Table ). Notably, the monophyly of at least one major archaeal phylum (Euryarchaeota, Crenarchaea or Thaumarchaeota) was never recovered with significant support, with very few exceptions (Table  1 ); one protein, the ribosomal protein L1, supported the Woese's tree with 100% bootstrap (BS) value, whereas all the others supported an eocyte tree (S1 Table ).
kandleri and N. equitans are fast-evolving species whose correct position in the archaeal tree can be only recovered by very careful analyses  [32, 33]. M. kandleri turned out to be sister group of Methanobacteriales (they also are the only archaeal species containing pseudomurein) and N. equitans an early branching Euryarchaeon, possibly sister group of Thermococcales. These positions were not recovered in most of the individual trees obtained here. In contrast, M. kandleri and N. equitans were often grouped with environmental sequences and "Ca. K. cryptophylum", and all these sequences were frequently located at the base of the archaeal tree, suggesting an attraction effect induced by the long bacterial branch. To test if the observed variability of lokiarchaeal proteins positions within most of the 36 universal trees could be explained by the presence of all these FES in the original dataset, we decided to reanalyze the data of Spang and co-workers after removing all species known to be fast-evolving (such as M.
kandleri, N. equitans), or "Ca. K. cryptophylum" that was already mentioned as possible source of artefact (Supplementary data in  [14] ). We also removed all environmental and genomic sequences obtained by metagenomics reconstruction for which the presence of FES cannot be excluded (S1 Fig). Indeed, many of them, such as Parvarchaeota, correspond to nanosized archaea with small genomes and limited metabolic capacity, suggesting that they are evolving by genomic reduction  [36]. This interpretation is supported by the fact that many of them branch with Nanoarchaea in archaeal trees and share with them instability regions in universal protein sequences that are not present in other archaea (see methods section for more details). Inspection of individual ML phylogenies obtained without presumptive and bona fide FES (thereafter simply called FES) revealed a clear-cut improvement in trees resolution (summarized values for the curated dataset in    The monophyly of at least one major archaeal phyla was now recovered more frequently, especially in trees obtained with large proteins (S1 Table ).
Furthermore, with few exceptions, the BS values at the monophyletic nodes previously detected with the initial dataset were higher. However, the positions of the different lokiarchaeal proteins remained variable from one tree to another, even when major archaeal phyla were monophyletic, indicating that this odd behavior was not due to FES. We calculated that 71 of the 90 lokiarchaeal proteins branched within Archaea, whereas 19 branched between Archaea and Eukarya or as sister group to Eukarya, suggesting at least two different origins for lokiarchaeal proteins. Loki 1, Loki 2 or Loki 3 proteins were rather equally present in proteins with or without specific eukaryotic affinity, indicating that all three lokiarchaeal genomes included proteins from different sources. The extreme variability in the positions of Lokiarchaeal proteins in individual phylogenies should probably have prevented their use as concatenated markers to determine the position of Lokiarchaeota in the universal tree, since concatenation of protein sequences (i.
e. the supermatrix method) can only increase statistical support as long as most of the genes have a congruent evolutionary history  [37]. Indeed, sophisticated methods that have been developed these last decades for the phylogenetic analysis of concatenated datasets assume that most proteins have a congruent evolutionary history and hence were designed to deal with the few proteins included that could have been transferred and display a conflictual history. However, this does not seem to be the case here, with at least two opposite evolutionary histories embedded in lokiarchaeal proteins.
Removing fast-evolving sequences revealed conflicting histories within universal protein markers The removal of FES from the initial dataset dramatically increased the number of trees in which Archaea were monophyletic (curated dataset in Table  1 , detailed in S1 Table ). We obtained 11 protein trees in which the monophyly of Archaea was supported with 100% BS, as compared to one in the dataset with FES, and 25 proteins displaying an eocyte tree (35 in the dataset with FES)(S2 Fig,  S1  Table ). This clearly indicates that the addition of FES generally favors the eocyte versus the Woese's trees in the analysis of universal proteins. Based on this analysis, we divided the 36 markers in two protein subsets: the 11 proteins that support a Woese's ToL (thereafter called Woese's proteins), and the 25 proteins that apparently favor an eocyte ToL (thereafter called eocyte proteins).
The 11 Woese's proteins were larger than the average universal proteins and represent a total of 3,499 positions after trimming, compared to the 4,869 positions for the 25 eocytes proteins (Fig  1A ). In particular, they included the two largest universal proteins (the A and B DNA-dependent RNA polymerase subunits) and the four largest ribosomal proteins of the dataset. The consensus, internal archaeal phylogeny obtained using conserved archaeal ribosomal or core archaeal proteins  [34, 38]  was recovered in several Woese trees (especially in the case of large proteins). In contrast with the phylogenies obtained with Woese's proteins, most of those obtained with the 25 individual eocyte proteins were still poorly resolved and often did not recover the monophyly of the major archaeal phyla (S2 Fig).
For example, the monophyly of Euryarchaea was recovered in 9 of the 11 Woese's trees but only in 8 of the 25 eocyte trees. Notably, Eukarya still branched at very different positions from one eocyte tree to another. The sisterhood between Eukarya and one or several Lokiarchaea was observed in 11 cases instead of 10 in the initial dataset, but again never strongly supported, except for EF2 (100% BS value with Loki 3), and for Kae1/YgjD (74% BS value with Loki 2). Interestingly, we even observed a significant decrease in BS support for the node grouping Loki 1 and 3 with Eukarya in the SecY tree (from 84 to 62%). The ML tree produced by the concatenation of the 11 Woese's proteins (Fig 1B, S3 Fig) not only recovered the monopyly of Archaea but also the monophyly of the three major archaeal phyla with 100% BS value.
However, we did not recover the TACK (or proteoarchaeal) superphylum  [18]. Instead, the archaeal tree was rooted between Thaumarchaeota and all other Archaea with strong support. This rooting was previously obtained in a tree based on conserved archaeal ribosomal proteins rooted with eukaryal sequences  [39]. Importantly, in our analysis, we obtained this rooting when bacterial and eukaryal sequences were present together in the same dataset. When we removed the two large RNA polymerase A and B subunits (corresponding to the three proteins A', A" and B) from our dataset and concatenated the remaining 8 Woese's proteins, the lokiarchaeal sequences were attracted at the root of Archaea, but we also obtained the monophyly of Archaea and of the three major archaeal phyla (S5 Fig). This indicates that signal supporting the Woese tree is not limited to RNA polymerase proteins.
Considering that all those sets are included in Loki 1 genome, these results suggest that the Lokiarchaeum (Loki 1) genome is a chimera of related strains and contaminated sequences (see Methods part for more details). The genome quality classification scheme, proposed by CheckM authors  [41] , establishes a threshold at 15% above which a genome has to be considered as "very highly" contaminated. As a comparison, the contamination of Thorarchaea genomes, evaluated between 4. 7 and 6. 7%, would be considered as medium  [30]. The quality of the Loki 2 and Loki 3 genome reconstructions could not be verified, because for these two lineages only 21 and 34 coding sequences (CDS) were available on the NCBI database (S4 Table ), and analysis of the available reads coverage by reads mapping and BLASTn search against the SRA database were not conclusive (see Methods for more information).
Another alignment software, PRANK, confirmed the presence of these regions matching with eukaryotes, even if obviously aligned differently. Three different insertions were located at the same position in the N-terminal regions of the three Loki (A1, A2, A3), and three others (B3, C3 and D3) were located in the central and Cterminal regions of Loki 3 EF2 protein (Fig 2B, S13-S16 Figs). Interestingly, these insertions are missing in EF2 sequences from Thorarchaeota, a putative archaeal phylum (also obtained from metagenomic data) sister group to lokiarchaeon 1 in a tree based on the concatenation of 16 ribosomal proteins  [30]. All these lokiarchaeal-specific insertions were large (between 5 and 31 amino-acids long) and were surrounded by regions highly conserved within all Archaea, including FES.