dataesr/scientific-paragraphs-categorization huggingface dataset:
To retrieve and annotate cytochrome P450 monooxygenase genes, 84 hidden Markov models were constructed based on all available P450 amino acid sequences in the NCBI nr and MycoCosm databases (as of August 2018). Sequences were clustered using UCLUST 1. 2. 22q  (59) , and model names were applied following Nelson  (60). To reconstruct origins of lecsl and leggt in Lentinula, the 28 Lentinula core genomes and all available fungal proteomes in the NCBI and MycoCosm  (56)  databases (December, 2020) were searched with BLASTp v2. 5. 0+ (Ye et al. 2006), using known lecsl and leggt genes as queries  (13). Leggt Pfam (61) domains (PF01019. 24) were retrieved from BLAST homologues with at least 40% Pfam query coverage via hmmsearch v3. 3. 2. Phylogenetic analyses of leggt and lecsl genes or Pfam domains were performed using IQ-TREE v2.
05). Phylogenomic Analyses. Phylogenetic datasets of single-copy orthologs were constructed using OrthoFinder  (30)  with the flags -M msa -S blast -T iqtree (unless stated otherwise). Multiple sequence alignments were concatenated using a Biopython-based custom script and trimmed using PhyKIT (65) with default settings. Three datasets were assembled, including 1) a 30-genome/2,258gene "core" dataset containing 28 Lentinula genomes and two Omphalotaceae as outgroups, 2) a 39-genome/294-gene "Agaricales" dataset for molecular clock analyses containing 20 Lentinula genomes and 17 other Agaricales, with Plicaturopsis crispa (Amylocorticiales) and Serpula lacrymans (Boletales) as outgroups, and 3) a 90-genome/196-gene "extended" dataset, with 88 Lentinula genomes and two Omphalotaceae as outgroups.
Unpartitioned phylogenetic analyses were performed using IQTree (all datasets), and partitioned analyses were performed with RAxML (extended dataset only). Species tree analyses were performed using ASTRAL, with 196 gene trees from the extended dataset as input. Molecular clock analyses were performed using BEAST v2. 6. 6, with four fossils used for calibration, including Gondwanagaricites (Agaricales, 117 Mya), Archaeomarasmius (Marasmiaceae, 92 Mya), Nidula (Nidulariaceae, 50 Mya), and Palaeocybe (Psathyrellaceae, 50 Mya). For further details, see SI Appendix. Topology tests were performed using all three datasets. Topologies focused on L. aciculospora placed it as the sister group to 1) all Asian-Australasian lineages (the optimal topology), 2) all other American lineages, 3) L. boryana, 4) L.
raphanica, or 5) all other Lentinula lineages. Topologies focused on L. aff. lateritia placed it as the sister group to (1) all other Asian-Australasian lineages (the optimal topology) or (2) other Australasian lineages (L. aff. lateritia plus L. novae-zelandiae). Topologies were evaluated with the bp-RELL, p-KH, p-SH, and c-ELW tests in IQTree V. 1. 6. 1  (35). As a further sensitivity analysis, the three datasets used in phylogenomic analyses were subsampled according to six metrics: alignment length, degree of violation of the molecular clock (DVMC), median long-branch score, percent parsimony informative sites, saturation, and treeness divided by relative composition variability (RCV). Each statistic is inferred from phylogenetic or alignment properties (e. g. , branch lengths and character composition) and is associated with robust phylogenetic signal  (63, (66) (67) (68).
For each metric, partitioned data matrices were made from the best scoring 50% of genes and analyzed using the concatenated and trimmed alignment as input for IQTree v. 1. 6. 1. A file with the topologies to test was generated manually by modifying a previous IQTree run and provided as input with the option -z. IQTree was run with the options -n 0 -nt AUTO -zb 100. To determine if any taxa are susceptible to potential long-branch artifacts, long-branch scores per taxa were also examined  (36). Metrics were calculated using PhyKIT, v1. 2. 1  (65). Species Delimitation. STACEY v. 1. 2. 5, implemented in BEAST v. 2. 6. 3, was used to assess species limits under the MSC model  (37, 38, 69). SortaDate (70) was used to select the best 200 genes in the core dataset using the ML tree inferred from the core dataset as a topological guide tree.
(10)  and earlier studies  (11) , except the sequence of L. platinedodes, which was downloaded directly from the GenBank. Maximum likelihood trees were constructed using RAxML  (73)  hosted at vital-it. ch with default settings and 1,000 bootstrapping replicates; bayesian trees were computed using Mr. Bayes version 3. 2. 7  (74) , and pairwise distances among sequences were calculated. Topology tests were conducted in IQ-TREE using an unedited ITS alignment (1,182 nt) or an alignment that was trimmed using ClipKIT (945 nt)  (63). Population Structure Analyses. PacBio sequenced L. edodes isolate HRB7683 was utilized as the reference genome for variant calling. The repeat masked assembly (Lenedo1_AssemblyScaffolds_Repeatmasked. fasta. gz from JGI) was indexed with bwa index (BWA), and paired-end Illumina reads (from QC) were mapped with bwa mem (BWA) using parameters "-M -t16" and samtools view using "-buS" (SAMtools).
BAM files were sorted with SAMtools sort and read groups added with bamaddrg (2020 Erik Garrison). Sorted BAM files were again indexed with samtools index, and the reference genome indexed with samtools faidx. FreeBayes-parallel was run on 10,000-bp subsets of the indexed reference genome using parameter "-C 10" to set a minimum of 10 observations per variant. Linkage disequilibrium analysis was conducted using LDkit v1. 0 (75), and SNPs were filtered using VCFtools including "--thin 5,000 --remove-filtered-all --maxmissing-count 6 --maf 0. 03 --recode --recode-INFO-all" to eliminate low-occurrence SNPs and subsample at a spacing of 5,000 bp to avoid linkage. SNP arrays were converted to multifasta alignments with VCF kit and analyzed with SplitsTree using the NeighborNet algorithm.
best-supported phylogenetic SNaQ network inferred from pseudolikelihood inference with incomplete lineage sorting based on 196 gene trees from an 86-genome dataset and enforcing M1 and M2 clades as hybrids, implemented in PhyloNet. The number of genomes per group is given in parentheses. Both M1 and M2 were resolved in a clade with L. edodes W with different amounts of introgression from L. edodes C + W. M1 as sister to the M2, and the W clade shows more introgression with the L. edodes C + W clade. This pattern may be indicative of cultivated strains being reintroduced to hybridize with natural populations of the W clade. Inset: Comparison of model likelihoods for different maximum number of putative hybridizations (hmax) shows the best model at hmax = 3.
best-supported phylogenetic SNaQ network inferred from pseudolikelihood inference with incomplete lineage sorting based on 196 gene trees from an 86-genome dataset and enforcing M1 and M2 clades as hybrids, implemented in PhyloNet. The number of genomes per group is given in parentheses. Both M1 and M2 were resolved in a clade with L. edodes W with different amounts of introgression from L. edodes C + W. M1 as sister to the M2, and the W clade shows more introgression with the L. edodes C + W clade. This pattern may be indicative of cultivated strains being reintroduced to hybridize with natural populations of the W clade. Inset: Comparison of model likelihoods for different maximum number of putative hybridizations (hmax) shows the best model at hmax = 3. Fig. 6. Rarefaction curve showing pangenome (blue line) and core genome (red line) in L.
Replication cohort: TrypAFORA TrypAFORA is a post hoc study of AFORA (Serum Concentration of Adalimumab as a Predictive Factor of clinical Outcomes in Rheumatoid Arthritis) that was conducted within the HUGO (Hôpitaux Universitaires du Grand Ouest -Western France University Hospitals) network and whose main objective was to estimate the pharmacodynamic parameters of the concentration-effect relationship of adalimumab in RA ( ClinicalTrials. gov NCT01382160). A total of 69 patients (28% male; mean age ~54 years) were included between January 2011 and January 2013 with a 26-week follow-up. Blood samples were taken at baseline, W4, W12 and W26. Serum was aliquoted, labelled and stored in each investigating centre at -20°C before being sent centrally on dry ice to the Tours biobank where all samples were stored at -80°C until use.
Statistical analysis Statistical analysis of human data was performed in the R statistical environment (R V. 3. 6. 2). Plotting was performed with ggplot2 (V. 3. 3. 2), Prism V. 9 (Graphpad Software, San Diego, California, USA) and Morpheus (https://software. broadinstitute. org/morpheus/). In all statistical analyses, differences with p<0. 05 were considered significant. The p values were corrected using the Benjamini and Hochberg (BH) procedure to control for the false discovery rate. The murine data were analysed using Prism V. 9 (Graphpad Software). Values are expressed as mean±SEM.
[14] [15] [16] [17]  The HMDP enables high-resolution genome-wide association studies (GWASs) and assessment of gene-by-environment interactions (e. g. , physical activity). The HMDP has been particularly powerful when integrated with multi-omics analyses. In the current study, 12 tissues were harvested from each animal of the exercise (Exc) HMDP providing quantification of 50 distinct wholebody and tissue-specific physiological traits. Furthermore, we integrated our ExcHMDP data with the human Skeletal Muscle, Myokines and Glucose Metabolism (MyoGlu) study, including longitudinal collection of clinical traits and multiple biopsies of tissues following acute (one session of exercise training) and long-term (12 weeks) exercise intervention. 18, 19  MyoGlu and ExcHMDP are complementary datasets where both human and mouse tissue samples were subjected to multi-omics analyses, and these data were integrated with a variety of phenotypic traits.
Relationships between exercise workload and several cardiometabolic traits, including but not limited to tissue weights, plasma lipid levels, and insulin sensitivity, were examined. We performed global transcriptomic analyses of key metabolic tissues including skeletal muscle, white adipose, brown adipose, brain, liver, and heart, and integrated these data with phenotypic trait outcomes following exercise intervention. We identified mouse genetic factors underlying voluntary exercise behavior and the molecular and physiologic responses to exercise training. These findings have been made publicly available in a user-friendly and simple Web-based application designed to allow researchers the opportunity to visualize, compare, and interrogate data between mouse and human. Our study highlights the value of cross-species, multi-tissue genetic analyses and underscores the need for personalized prediction modeling to improve individual exercise prescription and adherence outcomes to mitigate disease risk.
As would be predicted, these data suggest that running distance is a polygenic trait. Polygenic traits can be assessed by examining the cumulative effect of separate single-nucleotide polymorphisms (SNPs). 29  A genome-wide polygenic score was computed by summing the effect of each SNP from the GWAS of running distance (Figures 1B-1D and S2). Regardless of the computational method, as few as 100 SNPs were significantly associated with running distance and genomewide polygenic score. Although it is well documented that VO 2 max, an index of exercise capacity,  30  is largely regulated by the cardiorespiratory system, 31-34 and volitional activity is arguably controlled by the CNS,  35, 36  we questioned whether prolonged voluntary wheel running could also be regulated by exercise capacity of the periphery.
Mean running distance per day from the ExcHMDP was correlated with heart phenotypes from a prior sex-and age-matched HMDP. 37  Of the nearly 30 heart phenotypes, none significantly correlated with running distance (p > 0. 05; Figure  S3A ). We also conducted candidate gene identification analysis and found no candidate genes in cardiac tissue central in the regulation of daily running distance (false discovery rate [FDR] >0. 05). These data suggest that voluntary wheel running distance is predominantly regulated by cardiac-independent factors in Mus musculus. Because published findings suggest that ambulatory movement is driven by the CNS,  38  we performed candidate gene identification analysis on three brain regions from prior age-matched mouse panels. 39, 40  Several potential candidate genes were Resource identified in the three brain regions (FDR < 0.
sedentary) was observed (p < 0. 001; Figure  S13E ). These findings reflect the existence of a conserved exercise program masked by exercise unresponsive strainand tissue-dependent transcripts. The overarching goal of the ExcHMDP was to develop an expandable, user-friendly, and open-access resource for the scientific community. All transcriptomic data from mice and visual representation of tissue transcripts from humans are publicly available at https://exchmdpmg. medsch. ucla. edu/app/. This Web site application enables side-by-side comparisons of expression and DEGs between tissues, species, exercise groups, and exercise time points (Figures  7A  and 7B ) and was designed for ease of data mining to advance hypothesis generation by the research community.
Finally, data have been made publicly available in a Webbased application allowing for hypothesis development and exploration (https://exchmdpmg. medsch. ucla. edu; Figures  7A  and 7B ). This Web application provides opportunity for users to compare different datasets between species, tissues, exercise groups, and exercise time points. This Web application is expandable and will be utilized as a study repository as additional HMDP data become available. The major goal of this research was to improve understanding of the effects of genetic architecture on metabolic tissue adaptation to long-term physical activity. Our findings in mouse and humans substantiate known physiological outcomes of exercise (e. g. , reductions in adipose mass and circulating lipids, and increases in lean mass and insulin sensitivity).
1x10 À5 ). d KEY RESOURCES Heritability SNP-heritability was calculated for mice of the ExcHMDP. 15  QUANTIFICATION AND STATISTICAL ANALYSES Statistical analysis of the biological data Phenotype-phenotype correlations  15  and principal component analyses were performed using the FactoMineR v2. 3 and factoextra v1. 0. 7 packages in R. 90  Groups differences were determined using the Vegan v2. 5-6 package in R. Key driver analyses were conducted  45, 46  and the final resulting networks were visualized using Cytoscape 3. 8. 0. 91  For the ExcHMDP and MyoGlu, raw RNAseq reads were inspected for quality using FastQC v0. 11. 9 (Barbraham Institute, Barbraham, England). Reads were aligned and counted using kallisto v0. 45  92  against the Ensembl mouse transcriptome (v97) to obtain counts and transcripts per million (TPM).
ADDITIONAL RESOURCES A publicly available interactive web browser for tissue gene expression exploration of MyoGlu and ExcHMDP datasets analyzed in this study was created for hypothesis generation. Description: https://exchmdpmg. medsch. ucla. edu/app/. MyoGlu was a controlled clinical trial (clinicaltrials. gov: NCT01803568) and the ethical committee statement can be found at https://link. springer. com/article/ 10. 1007/s00125-020-05296-0.
ACKNOWLEDGMENTS We sincerely thank the  UCLA Division of Laboratory Animal Medicine  staff for all animal husbandry work and the  UCLA TCGB core  for the extensive transcriptomics analyses. We also thank  Katherine E. Moore  for help with tissue harvest preparations. T. M. M. was supported by the  UCLA Intercampus Medical Genetics Training Program  ( T32GM008243 ),  NRSA   predoctoral fellowship  ( F31DK108657 ) from  NIDDK ,  Carl V. Gisolfi Memorial Research  grant from the  American College of Sports Medicine , and a predoctoral graduate student award from the  Dornsife College  at the  University of Southern California. T. O. was supported by internal funding at the  Institute of Basic Medical Sciences  and by the  Research Council of Norway  ( 310475 ). M. M. was supported by a  QCB   Collaboratory Postdoctoral Fellowship  and the  QCB Collaboratory  community.
A. J. L. (Stanford) was supported by the  NIH   T32 Muscle Cell Biology, Pathogenesis, and   Therapeutics Training Program  ( T32AR065972 ) and the  NIH   T32 Laboratory of Neuroendocrinology Training Program  ( T32HD007228 ). Z. Z. was supported by  UCLA Claude Pepper Older Americans Independence Center  funded by the  National Institute on Aging  ( 5P30AG028748 ),  NIH/NCATS UCLA CTSI  grant ( UL1TR000124 ), and  Wellstone NIH NIAMS Center of Excellence  grant ( U54 AR052646 ). M. M. S. was supported by  NIH  grants  DK097771  and  HL138193. G. D. was supported by a postdoctoral fellowship through the  American Diabetes Association   1-19-PDF-007-R. X. Y. was supported by an  NIH   NINDS grant  ( NS117148 ). L. P. T. was supported by  Women in Science and Engineering (WiSE)   program  at the  University of Southern California.
F. N. was supported by  Norwegian Research Council   240405/F20. MyoGlu studies were supported by grants from the  Institute of Basic Medical Sciences ,  UiO ,  Johan Throne-Holst Foundation for Nutrition Research ,  Freia Medical Research Foundation , the  Functional Genomics and Infrastructure programs  of the  Research Council of Norway , and the  Southeastern Regional Health Authorities  and EU-financed  FP7  project (NutriTech grant agreement no:  289511 ) to C. D. A. J. L. was supported by  NIH  grants  HL28481  and  HL30568. A. J. L. and A. L. H. are collaboratively supported by an  NIH Specialized Center of Research Excellence (SCORE)   on Sex Differences and Women's Health  ( U54DK120342 ). A. L. H. is also supported by the  NIH   DK109724 ,  DK063491 , and  AG070959 , as well as the  UCLA Department of Medicine ,  Veterans Administration Greater Los Angeles Healthcare System, Geriatric Research Education and Clinical Center (GRECC) , and the  Iris Cantor-UCLA Women's Health Center.
Prism v9 GraphPad Software https:www. graphpad. com R Studio Posit Rv4. 0. 0 R Studio Desktop Complex Heatmap package https://posit. com WCGNA This paper https:cran. r-project. org DESeq2 This paper https://bioconductor. org/packages/ release. bioc/html/DESeq2. html Gene Enrichment Analysis This paper http://pantherdb. org Graphics This paper Adobe Illustrator v24. 3 http://www. adobe. com Graphics This paper Biorender www. biorender. com Other Resource Website This paper https://exchmdpmg. medsch. ucla. edu/app Code This paper https://github. com/tho-ols/exchmdpmg https://doi. org/10. 5281/zenodo. 7729799 Clinical Trial Registry -MyoGlu Langleite et al. 18 NCT01803568 HMDP RNAsequencing (hypothalamic studies) Dryad Digital Repository https://doi. org/10. 5061/dryad. vm525.
14 GSE64770 Parks et al. 17 HMDP RNA expression profiling Norheim et al. 15 GSE16780 (multitissue studies, Chella Krishnan et al. 45 GSE121098 raw and processed data) HMDP RNAsequencing (hypothalamic studies) Hasin-Brumshtein et al. 40 GSE79551 RNAsequencing MyoGlu Human Exercise Studies Langleite et al. 18 GSE227419 RNAsequencing Exc HMDP This paper GSE230102 Experimental models: Organisms/strains 100 strain mouse panel The Jackson Laboratory Strain IDs in Table S1A d RESOURCE AVAILABILITY B Lead contact B Materials availability B Data and code availability d EXPERIMENTAL MODEL AND SUBJECT DETAILS B Human subjects MyoGlu B Mouse strains d METHOD DETAILS B Plasma hormone and metabolite analyses B Euglycemic-hyperinsulinemic clamp studies B Tissue trait analyses B Heart phenotype correlations B Cumulative exercise effect: Metabolic traits B.
At each sampling date, a vertical profile of environmental variables was obtained using a handheld SAIV 204 STD/CTD probe. However, in this paper we present only the data for 25 m depth where the samples were taken from (see  10  for complete profile). Photosynthetically active radiation (PAR), size-fractionated chlorophyll a and nutrient concentrations (nitrate/nitrite, phosphate, silicate), were obtained as described in  10. mRNA extraction and amplification. Total RNA was extracted with the RNAqueous Total RNA Isolation Kit (Invitrogen, Thermo Fisher Scientific) according to manufacturer's recommendation. Samples were thawed on ice, vortexed and kept on ice during RNA extraction. The thawed lysate was added to a tube with 200 μm molecular biology-grade zirconium beads from pre-filled tubes.
Data processing. Generated sequences were processed in four main steps: pre-processing, metatranscriptome co-assembly, mapping of reads from individual metatranscriptomes onto the assembly and finally annotation of assembled transcript isoforms (Supplementary Figure  S1 ). The quality of the data was assessed with FastQC v. 0. 11. 5  31. The pre-processing step aimed to remove unwanted sequences from the metatranscriptomes. First, Illumina adapters were removed using BBDuk v. 37. 36  32. Overrepresented sequences in each metatranscriptome reported by FastQC, consisting predominantly of poly(A) and poly(T) fragments, were removed with BBDuk. The same software was used to remove PhiX control reads. Although we used poly(A) selection to capture only eukaryotic mRNA during sample preparation, rRNA may remain in the samples  33.
The initial assembly step of Trinity -Inchworm, ran on 199 million of all read pairs with no further normalisation. The assembled output ran through the remaining part of the co-assembly, first constructing de Bruijn graphs (Chrysalis step) and then resolving them (Butterfly step). Expression levels were estimated by mapping clean reads against the gene catalogue in RSEM 1. 3. 0  37. Due to varying numbers of reads in each of the metatranscriptomes (Supplementary Table  S1 ) and to ensure between-sample comparison  38  we used a relative measure of transcripts per million reads (TPM).
Annotations. De novo assembly produced unique 12,245,433 transcript isoforms, with clean reads mapping at least once to 11,010,859 isoforms. Most transcripts were characterized by low sum of relative abundance across samples (8 transcripts with > 10,000 TPM, 154 with > 1000 TPM, 3483 with > 100 TPM, 68,166 with > 10 TPM and 2,390,862 with > 1 TPM; Supplementary Figure  S3 ). To increase the robustness of analyses and avoid stochasticity due to low abundance transcripts, further analyses were carried out on a core dataset that contained 68,166 of the most abundant transcript isoforms for which the sum of TPM in all the samples was greater than 10 (Supplementary Figure  S1 ; from now on we will refer to the transcript isoforms as "transcripts"). Coding regions were predicted using TransDecoder 5.
1. 0 (https:// github. com/ Trans Decod er/ Trans Decod er/). To avoid false positive predictions, which are reported to drastically increase for shorter ORFs, only sequences longer than 100 amino acids were used for functional annotation. The core dataset was functionally annotated using Trinotate 3. 3. 1 with default parameters  39. Similarities between the de novo assembled transcripts/predicted coding regions and proteins in the UniProt database  40  were assessed using blastx/blastp, with max_target_seq = 1 and e-value = 1e-3 (BLAST +)  41. Protein domains were identified with HMMER3  42  against the Pfam database (31. 0 release)  43. Functional annotations were retrieved with Trinotate based on blast results against Pfam and protein domains identified using eggNOG 3. 0  44  , The Gene Ontology (GO)  45  and Kyoto Encyclopedia of Genes and Genomes (KEGG)  46, 47.