dataesr/scientific-paragraphs-categorization huggingface dataset:
Transposable element composition by gene length and LTR family analysis Repetitive sequence composition within genes (grouped by length) was examined by calculating the coverage (in bp) of each class of repetitive element, normalized by gene length. We examined LTR family enrichment in genic regions. All calculations and visualizations are summarized in the jupyter notebook file te_general_analysis. ipynb. All python scripts ran on Python ≥3. 7 and used the package gffutils (v. 0. 10. 1) (https://github. com/daler/gffutils) to operate large gene and repetitive element annotation files from large genomes. Plots were generated using Plotly Python API (https://plot. ly).
Annotation of protein-coding genes Protein-coding genes were predicted by combining transcript and homology-based evidence. For transcript evidence, assembled transcripts (as described in 'Transcriptome assembly') were mapped to the assembly using Gmapl v. 2019-05-12  56  and the gene structure was inferred using the PASA pipeline v. 2. 2. 3  57. Expression of each transcript was measured using the whole RNA-seq dataset (as described in 'Transcriptome assembly') and the pseudoalignment algorithm implemented in Kallisto v. 0. 46. 1  58. For homology evidence, we collected manually curated proteins from UniProtKB/SWISSPROT database (UniProtKB/Swiss-Prot 2020_03)  59  and protein sequences of Callorhinchus milii, L. chalumnae, L. oculatus and X. tropicalis from Ensembl (http://www. ensembl. org) and NCBI (https://www.
ncbi. nlm. nih. gov/genome), and aligned them to the repeat-masked assembly using Exonerate v. 2. 2  60. Transcript and homology-based evidence were then combined by prioritizing the former (homology-based predicted genes were removed when intersecting a gene predicted using the reconstructed transcripts). The combined gene set was then processed by two rounds of 'PASA compare' to add untranslated region (UTR) annotations and models for alternatively spliced isoforms. Low-quality gene models were removed by applying three further quality-filtering steps in an iterative fashion: (1) single-exon genes were retained only when no similarity with exons of multi-exonic genes was found (similarity was identified with the glsearch36 module implemented in the FASTA v. 36. 3. 8g package  61  with e-value cut-offs of 1 × 10 -10 and identity cu-toffs of 80); (2) genes intersecting repeat elements were removed when >50% (single-exonic genes) and >90% (multi-exonic genes) were covered by repeats; and (3) genes with internal stop codon(s) were removed.
Annotation of ncRNA genes ncRNA genes were annotated using tRNAscan-s. e. v. 2. 0. 3 62 and Infernal v. 1. 1. 2  63. The same procedure was applied to the genomes of the nine other focal species. For each of the ten species, the corresponding microRNA sets (obtained from miRBase v. 22  64  database) were used to predict microRNA target sites on 3′ UTRs of canonical mRNAs using miRanda v. 3. 3  65. Further details are provided in Supplementary Information.
Lineage-specific acceleration of CNEs The program phyloP was used to test each CNE for lineage-specific accelerated evolution  69, 70  in the lungfish branch. A likelihood ratio test to compute the P value of acceleration with respect to a neutral model of evolution for each of the conserved elements in the alignment was used. CNEs showing false-discovery-rate (FDR)-adjusted P values < 0. 05 were considered significantly accelerated. The accelerated CNEs were checked for overlap with a set of 1,978 experimentally validated human and mouse noncoding fragments with gene enhancer activity (data from 'VISTA Enhancer Browser' 26 ) (Supplementary Table  19 ).
Orthology assignment Protein sequences of A. carolinensis, C. milii, D. rerio, Gallus gallus, Homo sapiens, L. chalumnae and L. oculatus were downloaded from Ensembl (Lepisosteus_oculatus), and of Xenopus laevis from NCBI (https://www. ncbi. nlm. nih. gov/genome). Sequences of A. mexicanum were taken from ref. 41. In cases of alternative splicing, we kept the longest sequence for the gene. All proteins were pooled together as the query and database for an all-versus-all BLASTP. From the result, we determined an H-score between each two proteins as representative of the distance for sequence similarity  71  , and launched a clustering using Hcluster_sg  72. Finally, for each cluster, a gene tree was built using TreeBeST and orthology between genes was assigned.
Whole-genome-alignment-based phylogeny The ten-species whole genome alignment was processed by MafFilter v. 1. 3. 0  77  to keep only alignment blocks >300 bp that were present in all species. Filtered noncoding blocks were then concatenated and exported in. phylip format. Poorly aligned regions were removed using trimAl v. 1. 2 with option '-automated1'. The final dataset (99,601 aligned nucleotides) was used to reconstruct the phylogeny with RAxML v. 8. 2. 4 under the GTRGAMMA model and 1,000 bootstrap replicates.
Genome size evolution Genome size evolution was modelled by maximum likelihood using the 'fastAnc' function in the phytools R package  78. We used a time-calibrated tree representing all major jawed vertebrate lineages obtained from the phylotranscriptomic tree of ref. 5  ; ages are a genome-wide estimates across 100 time-calibrated trees inferred from 100 independent gene jack knife replicates inferred in PhyloBayes v. 4. 1  79  under a log-normal autocorrelated clock model with 16 cross-validated fossils as uniform calibrations with soft bounds, the CAT-GTR substitution model and a birth-death tree prior. Genome size data (haploid DNA content or c-value) were obtained from ref. 80. Genome size estimates were averaged per species (if several were available) and, in six species, genome size was approximated as the average of closely related species within the same genera.
Dynamics of gene family size CAFE  83  was used to infer gene birth and death rates (lambda) and retrieve gene families under significant dynamics. As input, we took the species tree with divergence time from the output of MCMCTree and the results of gene clusters from Hcluster_sg. Each gene cluster was deemed to be a gene family. We ran CAFE under a model in which a global lambda was set across the whole tree. To symbolize each gene family, we took the longest member as representative and BLAST-searched with diamond  84  against SWISSPROT and NR databases. The best hit from both was retained. To compare the repertoire of olfactory receptors, taste receptors and pulmonary surfactant proteins across all studied species, we followed the same procedure for each species. First, we collected sequences of olfactory receptors, taste receptors and pulmonary surfactant proteins from Swiss-Prot and NR database as query.
For sequences from NR database, we only kept those with identifiers starting with 'NP_', which are supported by the RefSeq eukaryotic curation group. Second, we mapped the query set to each genome using Exonerate in server model (maxintron set to six million for lungfish and axolotl). The alignment was extended to start and stop codon when possible. Third, we BLAST-searched all retrieved sequences to NR database and removed those with a best hit that was not an olfactory receptor, taste receptor or pulmonary surfactant. The final result sequences had alignment coverage ranging from 32% to 100% (first quartile 95%), and percentage of identity from 17% to 100 (first quartile 62%) to its query. Following a previous study  85  , we separated the final sequences into three categories on the basis of their alignment to their query: (1) pseudogene, sequences with premature stop codon or frameshift; (2) truncated gene, sequences without premature stop codon and frameshift but broken open reading frame (ORF) (start or stop codon missing); and (3) intact gene, sequences with intact ORF.
To identify orthologous proteins, all protein sequences were compared to lungfish using Inparanoid  86  (default settings). To match protein and cDNA, sequences were searched by TBLASTN and only 100% hits were kept. Codon alignments for the protein and cDNA sequence pairs were constructed using pal2nal v. 14  87. Resulting sequences were aligned by MUSCLE  88  (option: -fastaout) and poorly aligned positions and divergent regions of cDNA were eliminated by Gblocks v. 0. 91b  89  (options: -b4 10 -b5 n --b3 5 --t = c). An in-house script was used to convert the Gblocks output to PAML format. As a phylogenetic tree, we took the species tree with divergence times from MCMCTree as input for detection of positive selection with C. milii as outgroup. For the phylogenetic analyses by maximum likelihood, the 'Environment for Tree Exploration' (ETE3) toolkit  90  -which automates CodeML and Slr analyses by using preconfigured evolutionary models-was used.
a, Maximum likelihood phylogeny from noncoding conserved alignment blocks totalling 99,601 informative sites (using RAxML; GTRGAMMA model). All branches were supported by 100% bootstrap value; scale bar is in expected nucleotide replacements per site. Branch lengths of the trees obtained by the CNE method or from the protein sequences show a high correlation (R 2 = 0. 84, P < 0. 05). b, Relaxed clock time-calibrated phylogeny (MCMCTree). Plots at nodes correspond to full posterior distribution of inferred ages. Scale is in Ma, and main geological periods are highlighted. Plot generated with MCMCTreeR (https://github. com/PuttickMacroevolution/ MCMCtreeR). c, Evolution of genome size in jawed vertebrates. Maximum likelihood reconstruction of ancestral genome sizes using a time-calibrated phylotranscriptomic tree  8  and genome size values obtained from ref.
Extended Data Extended Data Fig. 4  | CLG, gene and repeat density along lungfish chromosomes. a, CLG content profiled within windows of 20 genes with available orthology and CLG identity, and using a 10-gene sliding window. If genes were more than 10 Mb or 100 Mb apart in gar or lungfish, respectively (breaking the 20-gene window), the area is highlighted as grey, indicating areas that lack a sufficient amount of orthologous CLG markers. Blue bar indicates gene density (as measured by the 6,337 marker genes used in the CLG analysis) along 10-Mb windows. White or grey indicates gene desert; blue indicates generich areas. Top row, previously reconstructed CLGs and their colour labels, followed by lungfish, spotted gar and chicken from top to bottom. b, Gene and repeat density along 10-Mb windows on lungfish chromosomes.
f, Significance of the association (homology) between CLGs and lungfish chromosomes. Fisher's test was run on the number of orthologous gene families shared between any given pair of chromosomes in CLGs and lungfish, compared to the overall distribution of orthologous gene families on all other chromosomes. Silhouette of the lungfish is from ref. 36. Extended Data Fig. 6  | Correlation between expression of transposable element families and copy number in the genome. a, Expression was estimated for each transposable element family using poly (A)-enriched RNAseq data from gonad, brain and liver. For all tissues and transposable element classes, a positive correlation is observed between expression level and copy number. When a transposable element family is highly expressed, this family tend to have more copies.
k-mer frequency analysis and transcript coverage by genomic sequences. a, The Illumina dataset was used to generate the spectra of k-mer abundances using seven k-mer sizes. b-e, Transcript coverage by genomic sequences. b, Histogram of the proportion of all transcript lengths covered by the alignment to contigs. c, Histogram of the proportion of all transcript lengths covered by the alignment to scaffolds. d, e, Histogram of the proportion of the transcript lengths covered by the alignment to contigs (d) or to scaffolds (e) of those transcripts with alignments that were improved after scaffolding.
Acknowledgements We thank the late  J. Clack  and  R. L. Carroll  for their contribution to our understanding of the water-land transition of vertebrates. This work was supported by the  German Science Foundation (DFG)  through a grant to A. M. , T. B. and M. S. ( Me1725/24-1 ,  Bu956/23-1 ,  Scha408/16-1 ) and to J. M. W. ( Wo2165/2-1 ), and core funding from the  IMP  to E. M. T. J. -N. V. and M. S. were supported by a joint grant of the  French Research Agency (ANR Evobooster)  and  DFG  ( SCHA408/13-1 ). I. I. was supported by the  Spanish Ministry of Economy and Competitiveness (MINECO)  ( Juan de la Cierva-Incorporacioń  fellowship  IJCI-2016-29566 ) and the  European Research Council  (grant agreement no. 852725 ;  ERC-StG 'TerreStriAL'  to  J. de Vries  ( University of Göttingen )).
The biomonitoring methods implemented by water authorities are mostly developed for perennial rivers, and do not apply to temporary rivers (TRs). We propose a new classification for TRs to better assess their ecological status. It arises from the LIFE+ TRivers project, which was conducted in the Catalan and the Júcar Mediterranean river basin districts (RBD). The European Water Framework Directive (WFD) provided two systems to set river types (systems A or B from Annex II), which have been officially used by water authorities across Europe to set "national river types" (NRTs). However, essential hydrological variables for TRs are largely omitted. NRTs established according to the WFD were compared with TR categories obtained by using a rainfallrunoff model, "natural flows prescribed regimes" (NFPRs), and with "aquatic phases regimes" (APRs) calculated by using TREHS software.
This approach was implemented in the open-access TREHS software  [37]. Here, we present experience from two Mediterranean river basin districts (RBDs), in which the above-mentioned methods for TR classification were applied, and compared with the traditional WFD classifications. The LIFE + TRivers project was conducted in these two Mediterranean RBDs to test the TR classification methods, and their relationship to biological quality indices. Additionally, a tailored hydromorphological quality (HYMO) index for TRs was applied to analyze the results in ephemeral watercourses. LIFE + TRivers outputs were crucial for establishing a suitable management tool focused on TRs and, therefore, for translating research findings into management practices. Considering that the frequency and extension of flow intermittence is expected to increase in the near future because of climate change  [38]  and the increasing human water demand  [3] , the development of methods for managers that enable adequate assessment and biomonitoring is timely.
The TREHS software allows the classification of TRs taking into account three main axes (aquatic phases): flow permanence (Mf), disconnected pools permanence (Mp), and dry river permanence (Md); in a FPD (flow-pools-dry) plot (Figure  2 ), which aims at describing the main hydrological regime controls on biological communities  [37]. TREHS proposes a classification of the TR regime that takes into account these three axes for a total of nine different "aquatic phases regimes" (APRs) (Figure  2 ): perennial (P), permanently flowing; quasi-perennial (Qp), usually flowing; fluent-stagnant (FS), occasionally drying with disconnected pools; alternate-fluent (AF), usually flowing and stagnant at infrequent intervals; stagnant (St), usually with disconnected pools but never dry; alternate-stagnant (AF), usually with disconnected pools that occasionally dry; alternate (A), rotates between disconnected pools and dry aquatic phases; occasional (Oc), usually dry; and episodic (Ep), with long dry periods (almost never flowing).
The APR classification was designed to: (i) be fully applicable from obtainable information, (ii) take into account the statistics of the three aquatic phases, (iii) be represented in a single graph, (iv) be conflict-free from the most usual terminologies, and (v) be defined from hydrological features assumed to have biological implications. Data for this classification can be collected from several sources: gauging stations, model simulations, interviews or surveys, in situ observations, and aerial or ground-level photographs for a significant period  [37]. TREHS was tested in the Catalan RBD in order to properly classify the hydrological regime of TRs after visiting field sites, analyzing historical aerial photographs, and doing interviews with locals for historical and current river regimes, together with information provided from the Sacramento model for simulated natural flow regimes.
Effects of Non-Perennial River Classification When Applying Biological Quality Indices As stated above, the main target for setting river typologies is to accurately assess biological quality according to reference conditions. Therefore, river types that better explain biological community patterns need to be used to properly classify ecological status. In order to analyze the different procedures for classifying TRs presented above, we compared the response of three biological quality indices used in the Catalan RBD: the IBMWP  [46]  and the IMMi-T  [32]  indices for macroinvertebrates, and the IPS index  [47]  for diatoms. Biological quality data were obtained from the monitoring program carried out from 2013 to 2018 by the Catalan Water Agency. Samples were taken annually for all river water bodies (in the spring period), and values were assessed as EQR (ecological quality ratio) by dividing the biological quality values by the reference value according to each national river type (NRT).
Thus, a total of six EQR values for 248 river water bodies were obtained for each biological quality index (data are available on the ACA webpage: http://aca. gencat. cat (accessed on 9 March 2021)), and the average of these six values for each site was calculated to get final biological quality values (IBMWP, IMMI-T, and IPS). These values were assessed along a stressor gradient considering chemical parameters (ammonia, nitrate, and phosphate) taken monthly from all river water bodies from 2013 to 2018. Additionally, human pressures for all river water bodies were taken into account according to the pressures and impact analysis (IMPRESS) carried out in the Catalan RBD  [48]. Thus, 10 pressures were used to set the general degradation value for each river water body: density of weirs and dams; % of river straightened, and/or channelization; % of water withdrawal, flow regime alteration; % of human land uses in the floodplain area; % of urban wastewater discharge (purified and not purified); % of industrial wastewater discharge; % of agricultural use in the drainage area; % of mine activity in the drainage area.
This novel procedure should be rolled out in coming RBMPs third cycle (2022-2027) by Spanish water authorities. Therefore, additional information will be available in the coming years to thoroughly analyze this classification. Table  3. Criteria to set the four "management temporary river categories" (MTRCs): perennial or quasi-perennial, intermittent-fluent, intermittent-stagnant, and ephemeral, proposed in the Mediterranean Spanish basins  [54]  according to the three main axis (aquatic phases): flow permanence (Mf), disconnected pools permanence (Mp), and dry river permanence (Md) provided by TREHS software.
These, and other similar results  [57]  suggest that additional hydrological parameters should be taken into account when river types are defined, especially for TRs. One of the more relevant features of the water regime that affect biological communities are no-flow periods and rewetting  [58, 59] , since the occurrence or disappearance of aquatic habitats depends on the water presence and flow. Thus, flow intermittence should be considered as a key variable when river types are set for management purposes, especially in RBDs where TRs are, or can become, abundant (e. g. , Mediterranean RBDs). As demonstrated above, TREHS software provides a useful and easy procedure to properly classify TRs, clearly differentiating intermittent-stagnant and ephemeral MTRCs (stagnant, alternate-stagnant, occasional, and episodic).
The three analyzed indices used in this paper were intercalibrated successfully  [68]. The European Commission set common water body types for the intercalibration exercise, named IC-types, in order to compare thresholds between quality classes calculated by using indices from member states, and applying a common biological quality index. A total of five different river types were set for Mediterranean rivers in a specific working group settled in the Mediterranean area (Mediterranean Geographic Intercalibration Group-MedGIG): RM1, RM2, RM4, and RM5. From all these IC Mediterranean river types, RM5 was defined as "temporary" among IC participants (France, Spain, Italy, Portugal, Cyprus, and Greece): high flow variability and even dry for some periods. However, little data were collected among Med-GIG member states from RM5 since most water bodies had been found dry or with scarce aquatic habitats when monitoring this IC type; neither biological nor physicochemical data.
Most of the collected data for RM5 were obtained from seasonal quasi-perennial or fluent-stagnant watercourses; but extremely seasonal, intermittent with disconnected pools, or occasional water bodies were not properly represented or simply absent. Therefore, quality thresholds considering biological quality indices applied in highly temporary water courses were not possible to properly intercalibrate due to the lack of enough data. Thus, the RM5 river type used in IC would be equivalent to quasi-perennial or alternate-fluent APRs, according to the TREHS categories, or even fluent-stagnant, in which biological quality indices currently used by MS can be used properly by taking into account suitable monitoring periods. Nevertheless, new approaches are required to be applied when stagnant (St), alternate-stagnant (AS), occasional (Oc), or episodic (Ep) APRs are evaluated.
Rainfall-runoff models are widely used by water authorities for water allocation in planning scenarios, but a wider perspective must be taken into account when assessing biological quality in TRs. Therefore, we propose using the TREHS flow-pools-dry plot  [37] , grouped into management temporary river categories (MTRCs) to better classify TRs, and then to use suitable protocols for each to assess ecological status. This proposal has some weaknesses, as TRs have a wide range of singularities, which change over time and along the river courses, so trying to classify them into a few management types can lead to underestimate singularities, and getting misleading results. In addition, the lack of data in small and temporary rivers can be identified as a threat to applying TREHS software properly and classifying TRs successfully.
Acknowledgments: We appreciate the help provided by the TRivers LIFE + Project team setting up the protocols provided and collecting data. We also appreciate civil servers and river keepers from Catalan and  Júcar RBDs  who gathered field data in TRs. Special thanks to  Gorka Muñoa  for providing some figures and GIS work. We also appreciate discussions and advice from several researchers and water managers carried out in SMIRES Cost meetings.
Data Availability Statement: Biological and chemical data from the Catalan Water Agency are available in the ACA webpage: http://aca. gencat. cat, accessed on 9 March 2021. Hydro-morphological results applied in ephemeral streams in the Júcar RBD can be found in www. chj. es, accessed on 9 March 2021. TREHS software and outcomes from TRivers LIFE+ Project are available in http://www. lifetrivers. eu, accessed on 9 March 2021. The MIRAGE FP7 Project (2009-2011) can be consulted in https://cordis. europa. eu/project/id/211732, accessed on 9 March 2021. COST Action Project and related papers can be found in https://www. smires. eu, accessed on 9 March 2021.