dataesr/scientific-paragraphs-categorization huggingface dataset:
INTRODUCTION The identification of orthologs--pairs of genes in different species which have evolved from a common gene in the last ancestor of the species (1)--is essential for a wide range of analyses in phylogenetics and comparative genomics (reviewed in 2). However, because ortholog inference requires reconstructing evolutionary events which have taken place in single genes potentially hundreds of millions of years in the past, inference can be a challenging task (reviewed in 3). For over 10 years, the Quest for Orthologs consortium has brought together orthology method developers, orthol-Nucleic Acids Research, 2020, Vol. 48, Web Server issue W539 ogy and model organism database providers, and orthology users to improve inference, provision and interpretation of ortholog data  (4) (5) (6) (7) (8).
Among the main achievements of the consortium are agreements on standard data file format for orthology  (9) , an orthology ontology  (10, 11) , curation of a reference set of proteomes  (5) , curation of a reference species tree  (12)  and community-led benchmarking  (13). However, the available genomes and methods change over time, requiring both reference proteomes and benchmarking to be continuously updated and re-interpreted. Furthermore, since benchmarking requires gathering and comparing ortholog calls obtained from various methods, there is an opportunity to provide the community with consensus calls. Here, we provide updates of the Quest for Orthologs Reference Proteomes and benchmark service, and introduce a new ortholog consensus service, which is integrated to the website of the Alliance for Genome Resources--the joint portal of NIH-funded model organism databases (14).
REFERENCE PROTEOMES Orthology benchmarking needs a consensus dataset of proteomes and common file formats to be used by the different orthology prediction methods for standardized benchmarks and result interpretation. Since 2011, the Quest for Orthologs (QfO) makes available a Reference Proteomes dataset, providing a representative protein for each gene in the genome of selected species. These datasets have been generated annually from the UniProt Knowledgebase (UniProtKB) (15) using an automatic gene-centric pipeline which identifies all protein isoforms for a gene and selects the canonical protein sequence as representative of the set. The canonical sequences are typically the longest isoform which best describes the sequence annotations e. g. domains, isoforms, polymorphisms, posttranslational modifications (https://www.
uniprot. org/help/ canonical and isoforms). The QfO community has been working with UniProt over the years to identify species for the reference data set, and now comprises well-annotated model organisms, organisms of interest for biomedical research and species broadly covering the tree of life for phylogenetic interpretation. The QfO Reference Proteomes are a manually compiled subset of the UniProt Reference Proteomes and their generation is now an integral part of the UniProt release pipelines. The QfO Reference Proteomes dataset is updated every year and the latest version (2019) comprises 78 species (48 Eukaryotes, 23 Bacteria and 7 Archaea) that are based on the UniProtKB 2019 04 release of 8 May 2019. In total, this represents 991,290 protein sequences. The set of included species has remained stable over the years, but the protein sequences fluctuate more rapidly due to improvements in UniProtKB annotations.
The number of species included is a compromise between keeping the dataset manageable for the benchmarking efforts, representing the available protein sequence space, and including the model species of highest interest to the research community. For the species included, a phylogenetic tree reflecting the consensus in the literature is maintained alongside the Reference Proteomes  (12). The Quest for Orthologs Reference Proteomes are available for download in different formats: the protein sequences as FASTA and SeqXML files, CDS sequences for most proteins as FASTA files, and, for an increasing number of species, the genomic locus coordinates are available in XML format. The benchmark service aims to follow the yearly release cycle of the Reference Proteome datasets, with a lag of a few months.
Improving the generalized species tree discordance test The most substantial change to the benchmarks themselves relate to the generalized species tree discordance benchmark. This benchmark assesses the quality of orthology predictions by comparing gene trees built from predicted orthologs with the underlying species phylogeny. The benchmark requires an undisputed, bifurcating species phylogeny. However, the set of species in the QfO proteome dataset includes some unresolved nodes--either due to inference uncertainty or genuine biological complications (such as incomplete lineage sorting, which causes bona fide local variation in the gene trees). When we introduced the benchmark (13), we showed how we can sample undisputed bifurcating species trees from the multifurcating QfO tree by randomly pruning the multifurcating nodes.
In brief, we recursively traverse the multifurcating species tree and sample two random subtrees whenever a visited node has more than two subtrees. This results in a fully bifurcating subtree. Since the benchmark was introduced, we noticed some biases in the resulting trees. Many gene trees covered only a few species, especially on the Last Universal Common Ancestor (LUCA) dataset. The reason for this skew is that, in addition to the uneven species sampling across the tree, the reference species tree is uneven in terms of resolved internal nodes--some parts are well resolved (i. e. , contain binary splits only) while others are not, with some of the deep nodes placed under successive multifurcations. Due to the sampling procedure, such nodes were rarely sampled, leading to a bias in the species selection.
Moving to OpenEBench, the ELIXIR platform to support community-led scientific benchmark efforts Despite the clear value of the benchmark service to the community, maintaining individual infrastructures for supporting community-led benchmark efforts is costly and labour-intensive. For this reason, ELIXIR--the pan-European infrastructure for life sciences--contributes to the development and maintenance of a platform to support community-led scientific benchmark activities  (18). OpenEBench provides a platform where communities with different needs of support for their benchmark activities can benefit from a modular architecture. OpenEBench implements the FAIR data principles  (19)  and has adopted the use of software containers for an easy deployment of benchmark workflows. Indeed, the QfO benchmark service now uses software containers within Nextflow workflows  (20).
Benchmark results on the 2018 proteome dataset Because it can take up to a year for the main orthology resources to submit their predictions on a new proteome set, the latest results were obtained on the 2018 QfO Reference Proteomes set. The benchmark service provides public results for 10 different resources or algorithms, some of which A top performing method is expected to lie on the 'Pareto frontier', i. e. the curve defined by data points which are not outperformed by any other ones in both precision and recall. For some of the benchmarks, many methods lie on or near the Pareto frontier, and thus mainly differ in terms of the tradeoff between precision and recall (Figure  3 ). This is because poorly performing methods and/or erroneous submissions are typically not released publicly by the submitter.
ensembl. org/info/genome/ compara/homology method. html) and OMA GETHOGs (29) result in a large increase in the number of reported orthologs (2. 5 fold increase for OMA GETHOGs, 15. 9% increase for Ensembl compara, both on the 2011 dataset), which is apparent in the 2018 dataset results (Figure  3B ). This increase does however not translate into a larger number of completely sampled trees in the species tree discordance test (Figure  3A ). The reason for this discrepancy between the two recall measures is not clear at this stage, and resolving it will require more investigation. The source of the very large number of orthologs appears to be deep many-to-many relationships, which may be hard to sample complete trees from. Meanwhile, the discrepancy illustrates the benefit of reporting more than one kind of recall measure in the benchmark.
W542Figure 3. 3 Figure 3. Excerpts from the public benchmark results on the 2018 QfO reference dataset. (A, B) The choice of recall measure (x-axis) can have a big impact on the generalized species tree discordance test. (C) In the Gene Ontology conservation benchmark, nearly all methods lie on the 'Pareto frontier' (dotted line). (D) In the reference gene tree benchmark based on SwissTree, most methods have a similar precision (y-axis) but vary considerably in recall (x-axis). Error bars indicate 95% confidence intervals. Note that the axis ranges have been chosen to optimise the separation of the data points. As such they do not show proportional changes in accuracy measures and so careful interpretation is required. The full results, with interactive viewing options (selection of methods included, choice of precision and recall measures, display of full axis range etc.
The variation cannot be explained by environmental factors alone. There is also a genetic component  (Strohl, 2003). In two different mouse strains In acute hypoxic states, recording commenced at the onset of hypoxia. Data from prolonged hypoxic states were recorded following 6 h of acclimatisation. BP, barometric (ambient) pressure; F IO 2 , fraction of inspired O 2 ; HH, prolonged hypobaric hypoxia (real/terrestrial altitude); HH a , acute hypobaric hypoxia (real/terrestrial altitude); NH, prolonged normobaric hypoxia (simulated altitude); NH a , acute normobaric hypoxia (simulated altitude); NN, controls (normobaric normoxia); P iO 2 , partial pressure of inspired O 2. exposed to the exact same conditions, only one exhibited post-hypoxic PB. It is now accepted that genetics plays an important role in the hypoxic ventilatory response (HVR), demonstrated in humans by family and population studies  (Weil, 2003; Teppema & Dahan, 2010; Hennis et al.
Ethical approval The study protocol was registered at ClinicalTrials. gov (NCT02780908) and approved by the National Committee for Medical Ethics of the Republic of Slovenia (0120-101/2016-2). In addition to this ethical approval, which covered the whole experiment (all laboratories in Slovenia and France), local approval was obtained through the Ifremmont (Institut de formation et de recherche en médecine de montagne) for the experiments conducted in Chamonix (France). All experimental procedures were conducted in line with the standards set by the latest version of the Declaration of Helsinki. The participants were given detailed verbal and written instructions regarding all experimental procedures and any possible risks involved prior to the experimental protocol. Written informed consent was obtained from all participants before the start of the first measurements.
, Goleta, CA, USA). The recorded signal is proportional to the expanding and contracting volume of the thorax as generated by breathing. Blood flow signals were measured using laser Doppler flowmetry (LDF) (DRT4, Moor Instruments, Axminster, UK). Before data analysis, LDF signals were inspected, and movement artefacts were removed using linear interpolation. ECG and respiration signals were recorded at a sampling frequency of 1200 Hz, and blood flow signals at a sampling frequency of 40 Hz. Not all participants attended all measurements; sample sizes are shown in Table  1. All datasets with simultaneously recorded signals for each participant and each condition (normoxic and hypoxic), including the genetic information collected from each participant (for details see below), are available at the Publications and Research (Pure) portal on Lancaster University's research information management system at https://doi.
Step-by-step algorithm The functions used during the analysis are available to download as part of the Multiscale Oscillatory Dynamics Analysis (MODA) toolbox from https://github. com/luphysics/MODA. Heart rates. All ECG time series, having been downsampled from 1200 Hz to 40 Hz, were input into the ridge extraction function, choosing a frequency range of 0. 5-2 Hz in which to extract the IHR. This provided an output of time series of the variability of the heart rate over time of the same length as the original signal, one for each subject in each condition (i. e. NN, NH, etc. ). For each time series, the mean, standard deviation and coefficient of variation values were calculated, and these values were then compared for significant differences between experimental conditions using the Kruskal-Wallis and rank-sum tests, which compare the medians of the groups (see Fig.
The whiskers extend to the most extreme data points not considered outliers, and the outliers are plotted individually using the '+' symbol. A, mean heart rate (P = 0. 0000). B, mean respiration rate (P = 0. 0001). C, standard deviation of the heart rates (P = 0. 0151). D, standard deviations of the respiration rate (P = 0. 0002). E, coefficient of variation of heart rate (P = 0. 0453). F, coefficient of variation of the respiration rate (P = 0. 2931). P-values are from the Kruskal-Wallis test. NN, control conditions (normobaric normoxia); n = 22; NH a , acute normobaric hypoxia (simulated altitude); n = 22; NH, normobaric hypoxia; n = 18; HH a , acute hypobaric hypoxia (real/terrestrial altitude); n = 16; HH, hypobaric hypoxia; n = 13. The same subjects were included on all 5 occasions, although some dropped out because they could not travel from Ljubljana, Slovenia either to Planica, Slovenia or to Aiguille du Midi, France, or felt unwell because of prolonged hypobaric hypoxia at Aiguille du Midi.
Methodological considerations The data presented here were recorded from individuals who were all born pre-term. Whilst this is not usually a factor that is considered during recruitment of healthy control participants for physiological research studies, it should be noted that pre-term individuals that were exposed to intermittent hypoxia have an enhanced ventilatory response to hypoxia  (Nock et al. 2004) , and a more recent study showed a blunted hypoxic ventilatory response in the same group of participants presented in this paper  (Debevec et al. 2019 ). Thus, it cannot be ruled out that the responses to hypoxia presented here are influenced by these effects. Analyses of the relationship between PB coherence and the results of genetic testing were performed blind to experimental conditions.
Acknowledgements We would like to thank all of our volunteers, without whom this study would not have been possible. We would like to acknowledge  Miro Vrhovec  for his indispensable technical assistance throughout the study,  Bojan Musizza  for his help with the instrumentation,  Benediktas Valys  and  Sam McCormack  for their help with some of the figures, and  Julian Newman  and  Peter McClintock  for their most useful detailed comments on the manuscript. We would also like to acknowledge the  Iffremont institute (Chamonix, France ) for excellent assistance and for generously enabling us to use the Aiguille du Midi high-altitude laboratory. In particular, we are grateful to the  late Emmanuel Cauchy , 'docteur vertical' , who made possible the access to the laboratory and who died tragically in an avalanche.
Funding The work was supported by the  Slovene Research Agency  (Grant No. J3-7536  and Program No. P20232 ), the  Engineering and Physical Sciences Research Council (UK)  (Grant Number  EP/M006298/1 ) and the  Innovative Training Network COSMOS program  (funded by the  EU   Horizon 2020 research and innovation program  under the  Marie Sklodowska-Curie Grant  Agreement No. 642563 ).
Samples XG human myeloma cell lines were obtained as previously described  [16]. AMO-1, OPM2 and SKMM2 were purchased from DSMZ (Braunschweig, Germany) and RPMI8226 from ATCC (Manassas, USA). JJN3 was kindly provided by Dr. Van Riet (Brussels, Belgium), and KMS-12-BM by Dr Otsuki (Okayama, Japan). HMCLs characteristics are available in Table  1. Bone marrow samples were collected after patient's written informed consent in accordance with the Declaration of Helsinki and institutional research board approval from Montpellier University Hospital. Bone marrow was collected from 69 patients at diagnosis and 28 patients at relapse, this cohort was called Montpellier cohort. MM cells of patients were purified using anti-CD138 MACS microbeads (Miltenyi Biotec, Bergisch Gladbach, Germany). We also used RNA sequencing data of 674 newly diagnosed MM patients with longitudinal follow-up from the Multiple Myeloma Research Foundation CoMMpass trial (NCT01454297; version IA11a), termed in the following CoMMpass cohort.
HMCLs response to drug treatment HMCLs were cultured in RPMI-1640 medium (Gibco, Thermo Fisher Scientific, Waltham, USA) supplemented with 10% fetal bovine serum (Eurobio, Les Ulis, France) and IL-6 (Peprotech, Rocky Hill, USA) for XG cell lines. We evaluated the sensitivity of HMCLs to GSK525762, Chaetocin (Sigma-Aldrich, Saint-Louis, USA), EPZ-6438, lenalidomide and romidepsin (Selleckchem, Houston, USA). The IC50 of GSK525762, Chaetocine, lenalidomide and romidepsin was determined at day 4 using the CellTiter-Glo assay (Promega, Madison, USA), as previously described  [11, 19]. HMCLs were cultured 8 days with or without EPZ-6438. Cell concentration and viability were assessed using trypan blue dye exclusion test, as previously described  [12]. The data represent the mean ± standard deviation of three independent experiments that were carried out on sextuplet culture wells (Figure  6A  and Supplementary Figure  16A ).
Gene expression profiling and statistical analysis HMCLs, patients MM cells and normal plasma cells RNA sequencing was done as previously described  [12]. The RNA sequencing (RNA-seq) library preparation was done with 150 ng of input RNA using the Illumina TruSeq Stranded mRNA Library Prep Kit. Paired-end RNA-seq were performed with Illumina NextSeq sequencing instrument (Helixio, Clermont-Ferrand, France). RNA-seq read pairs were mapped to the reference human GRCh37 genome using the STAR aligner  [20]. All statistical analyses were performed with the statistics software R (version 3. 6. 2; https://www. rproject. org), and R packages developed by Bioconductor project (https://www. bioconductor. org)  [21]. The expression level of each gene was summarized and normalized using the DESseq R/Bioconductor package  [22].
For the Montpellier cohort and HMCLs, Affymetrix U133P chips were also used, as previously described  [23, 24]  (ArrayExpress public database under accession numbers E-TABM-937 and E-TABM-1088), to calculate previously published risk scores including RS score  [25] , HRS score  [26] , IFM score  [27] , EZ score  [12]  and HA score  [11].
After 5 min washes, eluates were recovered and reverse cross-linked for 4h at 65°C. Samples were treated for 1h with RNase at 37°C, prior to DNA purification with the Auto IPure kit v2 (Diagenode, C03010010). Libraries were performed using NEBNext Ultra II DNA Library Prep Kit for Illumina (New England Biolabs). ChIP-seq were performed with Illumina NextSeq500 technology (Helixio, Clermont-Ferrand, France) using the following parameters: single-ended, 50bp, 40 million reads. ChIP-seq for H3K4me1, H3K4me3, H3K9me3, H3K27ac, H3K27me3 and H3K36me3 histone marks of 4 MM patients were performed using purified MM cells (MMC) of 4 MM patients as previously described  [33]. LiftOver function from the rtracklayer R package (version 1. 52. 1) was used to change the genomic coordinate of MMC peaks, from hg38 to hg19.
Peaks identified in MMC were compared to HMCL peaks for each histone marks using the findOverlaps function from the regioneR R package (version 1. 24. 0). Reads were mapped to the human reference genome (hg19) using Bowtie2 (version 2. 3. 2)  [29]. Peak-calling was performed using MACS2 (version 2. 1. 2)  [34]. Peak annotation and differential binding analyses of ChIP-seq peak data were performed using ChIPseeker  [35]  and DiffBind  [36]  R/bioconductor packages, respectively. Heatmaps and average profiles were generated by deeptools (version 3. 5. 0) [37]. Super-enhancers (SE) were identified using the ROSE (Rank Ordering of Super-Enhancers) algorithm  [38, 39]  based on H3K27ac and H3K4me1 ChIP-seq signals. TSS exclusion zone size was adjusted to 2500 bp to exclude promoter regions. Transcriptionally active genes were assigned to super-enhancers using a simple proximity rule (50 kb window).
To evaluate the link between the score and the response of MM cells to bromodomain and extra-terminal (BET) inhibitors, we calculated the SE-associated gene score in HMCLs. Interestingly, we found a significant anti-correlation between the score and the response to a BET inhibitor in clinical development in hematological cancers (GSK525762) in HMCLs (R 2 = 0. 4081; P-value < 0. 05) (Figure  4C ). The same tendency was obtained using JQ1 and OTX015 BET inhibitors (Data not shown). Using other scores based on gene expression profiling (EZ  [12] , HA  [11]  and HRS  [26]  scores) calculated in 8 HMCLs, we did not find a significant correlation between these scores and the BET inhibitor sensitivity (Supplementary Figure  10 ). Nevertheless, a correlation was found between HRS score and the response to GSK525762 (R 2 = 0.
489; P-value < 0. 05). High SE-associated gene score allows the identification of high-risk MM patients that could benefit from BET inhibitors. Conversely, high HRS score is associated with resistance to GSK525762 BET inhibitor. Interestingly, 21 genes (HK2, HNRNPC, HSPA9, ILF3, LDHB, MDH1, MYBPC2, NCL, NUDC, PARP1, PDIA6, PRPS1, RPL13A, RPL27A, SF3B2, SLC25A39, SMARCA4, STC2, TNPO2, TPR and YWHAQ) were significantly overexpressed in HMCLs compared to plasma cells of MM patients (Supplementary Figure  5 ), suggesting that genes may play a role in disease progression and independence of MM cells from micro-environment. Moreover, DEMETER2  [50]  and CERES  [51]  dependency scores calculated from public datasets of RNAi  [52, 53]  and CRISPR/Cas9-based  [51]  screening (Dependency Map data, Broad Institute, www.
The x axis shows the genomic position and the y axis shows signal coverage of ChIP-seq occupancy in units of reads per bin mapped reads. (D) Molecular signature of differentially bound sites localized on gene promoters enriched in lenalidomide-sensitive and -resistant HMCLs was investigated using GSEA database (FDR ≤ 0. 05). No overlap was found for gene promoters enriched in lenalidomide-sensitive HMCLs. Of major interest, we identified 5903 significantly differentially H3K4me3-enriched sites between lenalidomide-resistant (AMO1, JJN3, KMS-12-BM, SKMM2, XG1, XG5, XG7, XG12, XG20 and XG21 HMCLs) and lenalidomide-sensitive cell lines (OPM2, RPMI8226, XG2, XG6, XG13 and XG19 HMCLs) (Figure  6A ) (FDR ≤ 0. 05). Regarding gene promoters, 203 H3K4me3 differentially bound sites were significantly enriched in lenalidomide-sensitive HMCLs compared to lenalidomide-resistant HMCLs (FDR ≤ 0.
Romidepsin is a class I histone deacetylase inhibitor currently evaluated in combination with lenalidomide in phase I/II (NCT017755975) in patients with relapse or refractory multiple myeloma. Among the 250 H3K4me3 bound sites located on gene promoters, 180 were significantly enriched in romidepsin-sensitive HMCLs associated with an HDAC3 and PRDM6 target gene signature (Supplementary Figure  16D ). We found 70 differentially bound sites enriched in the romidepsin-resistant group compared to the romidepsin-sensitive group. Gene set enrichment analysis revealed a significant association with H3K27me3 and polycomb complex subunit (SUZ12 and EED) target genes. That indicates that ChIP-seq strategies could be an interesting tool to identify biomarkers associated with drug response or resistance in MM.