dataesr/scientific-paragraphs-categorization huggingface dataset:
csv checked Download URL: https://doi. org/10. 5281/zenodo. 7456986 decimalLatitude Geographic latitude (in decimal degrees) of the location Data format: CSV (tab delimited values) decimalLongitude Geographic longitude (in decimal degrees) of the location geodeticDatum coordinateUncertaintyInMeters Uncertainty in coordinates. As the coordinates are usually those of the locality of coordinate system and set of reference points upon which the geographic coordinates are based (i. e. WGS 84) the record, uncertainty is in the range of 5000 m Country Country of capture, in French, as indicated by the label countryCode Two letter country code of the specimen origin stateProvince French departmental administrative division. In the case of non French data, any Data format version: Description: Column label Column description relevant country administrative subdivision occurrenceID locality Individual identification: combination of Museum name, collection identification, Location of capture, usually the locality verbatimLocality box number and specimen number within each box.
Any geographical indication on the label 6 Data package title: Apoidea at the Lyon, Marseille, Aix-en-Provence and Toulon Museums Resource link: https://doi. org/10. 5281/zenodo. 7456986 [NOTE TO REVIEWERS : LINK will be open upon publication of this paper. This file is temporarily submitted as supplementary material on this manuscript. Zenodo allows updates to be uploaded as updates become available] Number of data sets: 1.
6±8. 0% and 66. 2±7. 9% of all quality controlled, paired-end shotgun reads generated from all 942 fecal DNA samples analyzed in the MDCF-2 and RUSF treatment groups, respectively [2. 3±1. 4x10  7  150 nt paired-end reads/sample (mean±SD); Supplementary Table  2a ]. Taxonomy was assigned to MAGs using a consensus approach that included marker gene and kmer-based classification together with the Genome Taxonomy Database (GTDB; Supplementary Table 1a)  14. Abundances were calculated for each MAG in the 707 fecal samples that spanned the beginning of treatment through the 1-month post-intervention time point and for which matching anthropometric measurements from children had been collected. A total of 837 MAGs satisfied our abundance and prevalence thresholds (see Methods and Supplementary Table  2b ).
MDCF-2 effects on WLZ-associated MAG gene expression Microbial RNA-Seq was performed using RNA isolated from fecal samples collected from all study participants just prior to initiation of treatment, and after 1-and 3-months of treatment (n=350 samples). Transcripts were then quantified by mapping reads from each sample to MAGs. The resulting counts tables were filtered based on the abundance and prevalence of MAGs in the full set of all fecal samples. These filtering steps were designed to exclude MAGs with minimal contributions to the metatranscriptome from subsequent differential expression analysis (exclusion criteria were benchmarked against a simulated meta-transcriptomic dataset using the approach described in Supplementary Methods). We employed principal components analysis (PCA) to determine baseline differences in overall (DNA-based) MAG abundance profiles, or the abundance of MAG-derived RNAs in the expressed metatranscriptomes, between the treatment groups, and to subsequently identify microbes that were principal drivers of shifts during treatment.
In contrast, none of the 106 metabolic pathways exhibited statistically significant enrichment in their expression in children who received RUSF. We next investigated which MAGs were responsible for the observed enrichment of expressed pathways. To do so, we turned to 'leading edge transcripts,' a term defined by GSEA as those transcripts responsible for enrichment of a given pathway (Methods). Among positively WLZ-associated MAGs, two belonging to P. copri (MAG Bg0018 and MAG Bg0019) were the source of 11 of the 14 leading-edge transcripts related to aAOS utilization (Supplementary Table  12 ) -a pathway whose expression was significantly elevated in children treated with MDCF-2 compared to RUSF (Fig. 3b ). Of the 11 P. copri MAGs in our dataset, these two were the only MAGs assigned to this species whose abundances were significantly positively correlated with WLZ.
Both MAGs are members of a P. copri clade (Clade 'A') that is broadly distributed geographically  20, 21  (Extended Data Fig. 7a ; see Extended Data Fig. 7b  for the predicted carbohydrate utilization pathways represented in all 51 MAGs assigned to the genus Prevotella that were identified in our 1,000 MAG dataset). Although P. copri MAGs were the greatest source of leading-edge transcripts related to arabinose and aAOS utilization, other MAGs in the microbiome display expression responses consistent with their participation in metabolizing MDCF-2 glycans (or their breakdown products); these include MAGs that are negatively correlated with WLZ. For example, leading-edge transcripts assigned to aAOS, arabinose and fucose utilization also arose from MAGs assigned to Bifidobacterium longum subsp.
e. , a given PUL is present in the genomes being compared but component CAZymes or SusC/SusD proteins are missing or fragmented in a way likely to impact their function, or where extra ORFs are present; see Supplementary Methods). The representation of these 10 PULs varied among the other nine P. copri MAGs which span three of the four principal clades of this organism (Fig. 4a ; Supplementary Table  15 ). Strikingly, the degree of genomic conservation of these PULs is significantly associated with the strength of WLZ association for each of the 11 P. copri MAGs in our MAG dataset across both treatment groups [Pearson r between Euclidean distance from Bg0019 PUL profile and β 1 (MAG) = -0. 79 (P = 0. 0035); Fig. 4b , see Supplementary Table  3  for WLZ associations]. Five of the seven highly conserved PULs are related to utilization of mannan and galactan -glycans that are significantly more abundant in MDCF-2 than RUSF.
Collection and handling of biospecimens obtained from participants in the randomized controlled clinical study of the efficacy of MDCF-2 The human study entitled 'Community-based Clinical Trial With Microbiota-Directed Complementary Foods (MDCFs) Made of Locally Available Food Ingredients for the Management of Children With Primary Moderate Acute Malnutrition (MAM)', was approved by the Ethical Review Committee at the icddr,b (Protocol PR-18073; ClinicalTrials. gov identifier: NCT04015999)  4. Informed consent was obtained for all participants. The objective of the study was to determine whether twice daily, controlled administration of a locally produced, microbiota-directed complementary food (MDCF-2  3, 4  ) for 3 months to children with MAM provided superior improvements in weight gain, microbiota repair, and improvements in the levels of key plasma biomarkers/mediators of healthy growth, compared to a commonly used rice-and lentil-based ready-to-use supplementary food (RUSF) formulation.
‫ܼܮܹ‬ ~ ߚ ଵ ‫‪ሻ‬ܩܣܯ‪ሺ‬‬ ߚ ଶ ‫ݕ݀ݑݐݏ‪ሺ‬‬ ‫݇݁݁ݓ‬ሻ ሺ1|ܲ‫ܦܫ‬ሻ Our data normalization strategies prior to linear modeling did not include a consideration of MAG assembly length. Therefore, we analyzed the TPM (reads per kilobase per million) output of kallisto (v0. 43. 0) by applying a filter requiring each MAG's abundance to be >5 TPM in >40% of the 707 fecal samples collected at time points where anthropometry was also measured. This filtering approach yielded 837 MAGs. We then returned to the unfiltered count output from kallisto, performed a variance stabilizing transformation [VST, DESeq2 30 (v1. 34. 0)] to control for heteroskedasticity, and filtered the dataset to the same 837 MAGs. We subsequently fit linear mixed effects models to the transformed abundances of each MAG across all 707 fecal samples (lme4 31 , v1.
Pre-and posttrimmed sequence quality and adapter contamination were assessed using FastQC  35 (v0. 11. 7). Filtered reads were pseudoaligned to the set of 1,000 annotated, dereplicated high quality MAGs to quantify transcripts with kallisto  36. Reads that pseudoaligned to rRNA genes were excluded, leaving an average of 7. 1×10 6 ±3. 9×10 6 bacterial mRNA reads (mean±SD) per sample. Counts tables were further filtered to retain only transcripts that pseudoaligned to the 837 MAGs that passed the abundance and prevalence thresholds described above. To minimize inconsistently quantified counts related to low-abundance MAGs, we assigned a transcript count of zero, on a per-sample basis, to any MAG with a DNA abundance <0. 5 TPM in that sample. Differential expression analysis (edgeR  37  , v3.
These models were used to characterize (i) the effect of treatment group and study week among all participants and (ii) the effect of WLZ quartile and study week among MDCF-2 participants in the upper and lower quartiles of WLZ response using the following model formulae: ‫ݐ݅ݎܿݏ݊ܽݎݐ‬ ~ ߚ ଵ ‫ݐ݊݁݉ݐܽ݁ݎݐ‪ሺ‬‬ ‫‪ሻ‬ݑݎ݃‬ ߚ ଶ ‫ݕ݀ݑݐݏ‪ሺ‬‬ ‫݇݁݁ݓ‬ሻ ߚ ଷ ‫ݐ݊݁݉ݐܽ݁ݎݐ‪ሺ‬‬ ‫ݑݎ݃‬ ‫ݔ‬ ‫ݕ݀ݑݐݏ‬ ‫݇݁݁ݓ‬ሻ ‫ݐ݅ݎܿݏ݊ܽݎݐ‬ ~ ߚ ଵ ሺܹ‫ܼܮ‬ ‫݁ݏ݊ݏ݁ݎ‬ ‫‪݈݅݁ሻ‬ݐݎܽݑݍ‬ ߚ ଶ ‫ݕ݀ݑݐݏ‪ሺ‬‬ ‫݇݁݁ݓ‬ሻ ߚ ଷ ሺܹ‫ܼܮ‬ ‫݁ݏ݊ݏ݁ݎ‬ ‫݈݁݅ݐݎܽݑݍ‬ ‫ݔ‬ ‫ݕ݀ݑݐݏ‬ ‫݇݁݁ݓ‬ሻ From these models, we identified genes that exhibited significant differential expression using the quasilikelihood F-test (edgeR, function glmQLFTest) which accounts for the uncertainty in estimating the dispersion for each gene. For subsequent functional metabolic pathway enrichment analyses, we (i) ordered transcripts assigned to WLZ-associated MAGs based on a ranking metric calculated as the direction of the foldchange × -log 10 (P-value) for a given differential expression analysis, (ii) defined gene sets as groups of these ranked transcripts assigned to the same metabolic pathway, and (iii) performed GSEA (fgsea  38 , v3.
Principal Components Analysis Principal Components Analysis (PCA) was performed on VST-transformed DNA or transcript counts for the 837 MAGs passing the filter described in the section entitled 'Defining the relationship between MAG abundances and WLZ' above. The PCA performed on transcript abundances encompassed 27,518 genes expressed by these MAGs at thresholds for levels and prevalence that are described in the section entitled 'Microbial RNA-Seq analysis of MAG gene expression' above. PCA was performed in R using the 'prcomp' function, with each data type centered but not scaled since the dataset was already VSTnormalized. The functions 'get_eigenvalues,' 'get_pca_ind,' and 'get_pca_var' from the factoextra  39  (v1. 0. 7) package were utilized to extract (i) the variance explained by each principal component, (ii) the coordinates for each sample along principal components, and (iii) the contributions of each variable to principal components 1-3.
Statistical Information Statistical analyses were conducted using the approaches described in Methods and the figure legends. Samples sizes are indicated along with each statistic test. All relevant statistical tests are two-tailed unless otherwise specified. All measurements were collected from distinct samples. Technical replicates were not collected and analyzed unless otherwise noted. 5  and Supplementary Table  6 ); and (ii) a Binary Phenotype Matrix (BPM) reflecting the inferred presence or absence of 106 functional metabolic pathways in each of the 1,000 MAGs shown in Supplementary Table  7a. MAGs/participant. MAG assembly summary statistics were collected from CheckM  54  and quast 57 analyses (v4. 5) and aggregated (Supplementary Table  1 , Extended Data Fig. 12 ). Initial MAG annotations were performed using prokka  58 (v1.
14. 6). Further details about benchmarking the methods we used for assembly are presented in Supplementary Discussion. To quantify the abundance of each MAG in each sample MAGs were processed to create a single kallisto quantification index  59. Reads from each fecal DNA sample were then mapped to this index. MAG taxonomy -Taxonomic assignments were initially made by employing the Genome Taxonomy Database Toolkit 14 (GTDB-Tk) and corresponding database (release 95). We complemented these MAG assignments by using Kraken2 60 (v2. 0. 8) and Bracken 61 (v2. 5) and a Kraken2-compatible version of the GTDB reference.
Extended P. copri has been partitioned into four distinct clades ('A-D') based on marker gene phylogeny  20. To classify Prevotella MAGs in this study, we constructed an unrooted, marker gene-based phylogeny using Phylophlan  62 (v3. 0. 60). This tree encompassed 17 reference isolate genomes and 1006 MAGs from a previous study  20  plus any MAGs from our set classified by GTDB-Tk as belonging to the genera Prevotella (n=51) or Prevotellamassilia (n=13). Putative Prevotella MAGs from the present study that clustered within the four previously identified P. copri clades were assigned to the corresponding clade based on visualization with Graphlan  63 (v1. 1. 4). Certain Bifidobacterium species consist of multiple closely related subspecies (e. g. , B. longum). Therefore, we calculated a pan-genome for 34 Bifidobacterium MAGs in our dataset (Supplementary Table  1 ), plus 14 reference isolate genomes (Extended Data Fig.
8 ), using Roary 64 (v3. 12. 0) and a 60% minimum sequence identity threshold for blastp  65. The reference isolate genomes included 10 Bifidobacterium species and three subspecies of B. longum (subsp. longum, infantis, and suis). Concatenated nucleotide sequences of 142 identified core genes were aligned using MAFFT  66 (v7. 313 ). The resulting alignment was trimmed [microseq  67  R package (v2. 1. 4)] and then used to construct a maximum likelihood phylogenetic tree [IQ-TREE  68 (v1. 6. 12) ]. The B. gallicum DSM 20093 genome was selected as an outgroup. Putative Bifidobacterium MAGs from this study that clustered together with reference genome clades were assigned to the corresponding clade. Using this method, we were able to confirm or update our initial GTDB-Tk-based classifications of all Bifidobacterium MAGs and resolve nearly all closely related subspecies (Supplementary Table  1 , Extended Data Fig.
Subsystems-based annotation and prediction of functional capabilities ('inferred metabolic phenotypes') of MAGs and cultured P. copri strains MAG and isolate genes were assigned functions, and metabolic pathways were reconstructed using a combination of (i) public domain tools for sequence alignment and clustering, (ii) custom scripts to process the results of sequence alignments (e. g. , for domain annotation in multifunctional proteins), and (iii) a reference collection of 2,856 human gut bacterial genomes for which we have reconstructed and manually-curated metabolic pathways related to 98 distinct metabolites and 106 metabolic phenotypes  17. These annotations are captured in the mcSEED database, a microbial community-centered adaptation of the SEED genomic platform  15, 16  , featuring subsystems-based annotation and pathway reconstruction applied to representative human gut bacterial genomes that were initially automatically annotated by RAST or downloaded from the PATRIC (recently renamed Bacterial and Viral Bioinformatic Resource Center, BV-BRC) database  71.
Each mcSEED subsystem includes a set of functional roles (e. g. , enzymes, transporters, transcriptional regulators) that contribute to the prediction of functional metabolic pathways and pathway variants  72  involved in utilization and catabolism carbohydrates and amino acids, biosynthesis of vitamins/cofactors and amino acids, and generation of fermentation end-products such as short-chain fatty acids. A complete list of MAG genes comprising these pathways, their abbreviations and functions is provided in Supplementary Table  5  (annotations based on the January 2021 version of the mcSEED database). Our annotation workflow is illustrated in Extended Data Fig. 3. Briefly, we constructed a reference database containing 995,591 functionally annotated proteins comprising the entire set of curated metabolic subsystems from the 2,856 reference genomes, plus an additional 2,988,751 proteins ('outgroup' not included in these metabolic subsystems), clustered at 90% amino acid identity ('cluster' command, MMSeqs 73 ; v1-c7a89).
We aligned the predicted protein sequences from the set of 1,000 highquality MAGs against this reference protein database (DIAMOND  74  , v2. 0. 0). To account for any influence of MAG fragmentation on metabolic reconstruction, we also identified gene fragments using prodigal  75  (v2. 6. 3) and annotated them in parallel. We implemented the following method to account for instances of multidomain structure that require multiple annotations. For each MAG query protein, we used the top 50 hits based on the bitscore, clustered the start and end position coordinates of the corresponding alignments using DBSCAN (Scikit-learn  76  ), used the center of each clustered start and end position as potential domain boundary coordinates, and split query proteins into domains with database hits attributed to the corresponding domains.
Next, for each domain ≥35 amino acids we used gaussian kernel density modeling (KernelDensity function, neighbors module, Scikit-learn 77 , v0. 22. 1) of the sequence identity distribution of each set of hits to that domain. A highest local minimum (argrelextrema function, signal module, Scikit-learn) was employed as a threshold to remove low confidence hits. Finally, functional annotations were applied from the reference database to each query protein or domain by majority rule within each set of high-scoring, domain-specific reference hits. High-identity hits to proteins from the outgroup of the reference database were used as criteria to vote "against" applying annotation to each query. This procedure yielded a set of 199,334 annotated MAG proteins, representing 1,308 unique protein products across a set of 80 mcSEED subsystems (Supplementary Table  5 ).
Phenotype prediction strategies (Extended Data Fig. 3 ) -We integrated the results of gene-level functional annotation into in silico predictions of the presence or absence (denoted as binary: "1" for presence or "0" for absence) of 106 functional metabolic pathways (Supplementary Table  7 ) using a semi-automated process based on a combination of the following three approaches: Pathway Rules (PR)-based phenotype predictions -This approach uses explicit logic-based "pathway rules" to assign binary phenotypes. These rules combine (i) expert curators' knowledge regarding the gene composition of various metabolic pathway variants contained in the mcSEED database with (ii) a decision tree method to identify patterns of gene representation in reference genomes corresponding to an intact functional pathway variant (and a respective binary phenotype value denoted as "1").
A total of 106 functional pathway-specific decision trees were generated (Rpart  78 , v4. 1. 15) , where the presence or absence of a particular phenotype was the response variable, and the presence or absence of functional roles (encoded by genes) in each reference pathway were predictor variables. The resulting pathway rules were formally encoded into a custom R script that allowed us to process MAG gene data and assign values (1 or 0) for each of the 106 functional metabolic pathways. Machine Learning (ML)-based phenotype predictions -We compared >30 ML methods (Caret 79 , v6. 0. 86), using a 'leave one out' cross-validation approach in which we removed a single reference genome from the set of 2,856 reference genomes, trained ML models on the remaining genomes, then applied the models to the "test" genome to predict phenotypes.
This procedure was then repeated for each genome and each metabolic phenotype. The results of this analysis identified Random Forest as the bestperforming method (i. e. , it produced the greatest number of correctly predicted phenotypes in our reference training dataset). We then built Random Forest models for each phenotype based on the reference dataset, optimized model parameters using a grid search, and used these models to predict binary (1/0) values for the same set of 106 phenotypes for all MAGs. Neighbor Group (NG)-based phenotype predictions -This approach identifies reference bacteria that are closely related to the MAGs in this study and uses these high-quality reference genomes for phenotype predictions that are robust to variation in MAG quality. Examination of groups of closely related reference organisms suggested that close phylogenetic neighbor genomes tend to either possess or lack an entire pathway variant, whereas more distant neighbors (e.
Supplementary Tables Next, we employed the same simulated dataset to test the accuracy of kallisto-based MAG quantitation. We mapped the short-read data for each of the 64 fecal samples to the set of 791 reference genomes using kallisto and bowtie2. We then correlated the abundance profiles generated by each quantitation method to the 'true' abundance profile for each sample. The correlations between true genome abundances and kallisto genome abundances were stronger than those calculated using bowtie (mean Pearson's r 2 = 0. 99 for kallisto versus r 2 = 0. 97 for bowtie; P<2. 2×10  -16  , Wilcoxon test comparing each distribution of correlation coefficients). We determined the false positive and false negative rate of MAG detection across all samples. Notably, kallisto quantitation resulted in more false positive abundances across the 64 mock fecal samples [300.
2±50. 1 versus 69. 3±28. 4 for bowtie2, respectively (mean±SD); P< 2. 2x10 -16 , Wilcoxon test] while bowtie2 quantitation resulted in more false negative abundances [0. 09±0. 42 versus 17. 2±26. 1 (mean±SD), respectively; P<2. 2×10 -16 , Wilcoxon test]. Importantly, analysis of the average values of false positive abundance generated using kallisto suggested that a low abundance filter would significantly reduce the false positive rate. For example, applying a filter to this dataset that required >5 TPM for a MAG to be designated as 'detected' resulted in a false positive rate significantly lower than that of bowtie2 (P=0. 02, Wilcoxon test). The greater number of high quality (less contaminated and more complete) MAGs assembled using kallisto quantitation, plus the increased accuracy of MAG quantitation using this method, led us to employ kallisto for all quantitation tasks in the MAG analysis workflow described in the current study.
Acknowledgements: We thank  Martin Meier ,  Justin Serugo ,  Su Deng ,  Kazi Ahsan ,  Jessica Hoisington-Lopez , and  MariaLynn Crosby  for superb technical assistance, members of the  Genome Technology Access Core at Washington University School of Medicine  for Illumina NovaSeq-based generation of fecal DNA shotgun sequencing and microbial RNA-Seq datasets, plus  Eric Martin  and  Brian Koebbe  for high-performance computing support. This work was supported by the  Bill & Melinda Gates Foundation , the  National Institutes of Health  ( DK30292 ) and the  Washington University-Centene Personalized Medicine Initiative.
1b  for statistical analyses). Comparing MAG assembly accuracy and quantitation using a pseudoalignment and expectation maximization approach MAG assembly algorithms that synthesize both contig sequence characteristics and contig abundance to assemble MAGs (e. g. , MaxBin2 49 , MetaBAT2 50 ) require accurate contig quantitation. Alignment-free quantitation approaches (e. g. , kallisto) have demonstrated superior speed and accuracy compared to readmapping-based quantitation in the context of metagenomic analyses where read-mapping ambiguity is common  59. We investigated the utility of kallisto-based quantitation for (i) contigs, prior to MAG assembly, and (ii) MAGs themselves after assembly and curation. For this analysis, we employed a 'mouse gut metagenome toy dataset' from CAMI II that included 64 'mock fecal samples'; these mock samples were produced using sequencing data from 791 publicly available bacterial genomes (representing 549 species) and genomic abundances that mirrored bacterial 16S rRNA gene profiles of 64 actual mouse fecal biospecimens  88.