dataesr/scientific-paragraphs-categorization huggingface dataset:
Moreover, PathQuant provides an efficient and quantitative solution to extract information from metabolic pathways and enables non-experts to efficiently leverage their combined genomics and metabolomics data based on a straightforward metric. This fast and automated solution has the potential to become a new standard metric within the mGWAS toolkit, and could help to systematically assess the proportion of false negatives in these studies. Furthermore, this metric could be added within already available user-friendly resources such as mGWAS Explorer  14  or the recently developed Paired Omics Data Platform. 56  SRD values could also play a role in designing targeted studies, in a context where extensive mGWAS cannot be done or is not relevant, for example if the research question is about finding the genes or proteins that regulate the levels of a specific metabolite, or conversely, finding which metabolites are regulated by a specific enzyme.
Conclusions We consider this work as a proof of concept of the benefits of using shortest reactional distance metrics for annotating mGWAS results based on a model representation of the human metabolism provided by the KEGG database. These metrics can also be used as a tool for metabolic databases in order to more easily identify gaps within current metabolic pathway graphs by using new information provided by mGWAS. In this multi-omics era, we anticipate that large scale studies looking for associations between genomics, proteomics and metabolomics signals will soon become a new standard, as illustrated by recent studies in mice and humans,  [57] [58] [59]  resulting in additional protein-metabolite pairs, for which the computation of SRD values may add great value.
d KEY RESOURCES In the SCD study, an mGWAS was performed here using genetics and metabolomics data published in different studies  22, 23  on a total of 651 SCD patients (plasma samples) including 401 individuals of African ancestry in the Genetic Modifier (GEN-MOD) cohort and 250 African-American individuals from Southwest USA in the Duke University Outcome Modifying Genes (OMG) cohort. Metabolomics profiling was performed at the Broad Institute, and appropriate statistical modelling was used to account for residual batch effects. 61  Briefly, for association testing, 128 metabolites were profiled using a targeted approach in 651 plasma samples from SCD patients. Metabolites were inverse normal transformed, adjusting for age and sex. We then generated summary statistics for each cohort individually in a linear regression model, accounting for relatedness using a kinship matrix as implemented in rvtest (v.
20171009)  62  in GEN-MOD. The software SNPTEST  63  was employed in OMG to generate cohort-specific summary statistics. We then meta-analyzed the effect size estimates and standard errors from GEN-MOD and OMG using METAL. 60  After the pre-processing step, 6 million SNPs were kept. For the TK study, we downloaded the file named ''NIHMS58114-supplement-2. xlsx'' from the supplementary section of the publication. This first file contains only stringently associated pairs. We make a distinction between the TK and the ''TK None'' datasets as they do not use the same files from the original publication. The TK None is referring to the GWAS summary stats, without any p value cut-off, directly downloaded from here: https://metabolomics. helmholtz-muenchen. de/gwas/index. php?task=download. We downloaded and merge the files named: shin_et_al.
Mapping shortest reactional distances (SRD) onto KEGG To compute the SRD metric, the R package PathQuant was developed [available at: www. github. com/HussinLab/PathQuant]. PathQuant converts a metabolic pathway map into a graph of biochemical reactions with metabolites as nodes and genes as edges (Figure  1 ). Briefly, PathQuant takes as input a list of gene-metabolite pairs as pairs of KEGG identifiers (IDs) and a list of metabolic pathways (e. g. , ''hsa01100'', referred herein as KEGG overview graph, a global concatenation of multiple distinct pathways). PathQuant then uses a KEGG XML file format (KEGG Markup Language, KGML), downloaded using the KEGG API [www. kegg. jp/kegg/rest/keggapi. html] to build the most up-to-date KEGG undirected graphs. Next, PathQuant computes the SRD path between a gene and a metabolite from a given pair.
Extracting and annotating gene-metabolite pairs from mGWAS summary statistics Using bedtools version v2. 30. 0, 65 we annotated each SNP to genes with KEGG IDs, using a custom bed file: we downloaded gene coordinates for human genome build GRCh37. p13 from NCBI (NCBI Homo sapiens Updated Annotation Release 105. 2020/10/22, gff format) and modified it to add 10kb upstream and downstream for each of the 26,105 genes with KEGG IDs, as well as broader intervals of 50kb, 500kb and 1Mb. We also mapped additional gene-metabolite pairs using eQTL annotations from the eQTL Gen consortium with a False Discovery Rate (FDR) threshold %5%. Because we focused on pairs involving genes encoding enzymes, which are the most represented genes in the KEGG database, we only annotated SNPs with KEGG IDs classified as enzymes within the KEGG Brite database by using the Brite enzyme code ''BR:hsa01000'', leading to a subset of 4,049 enzymes/26,105 genes.
For metabolites, if available, we used the provided IDs, either KEGG IDs directly or HMDB IDs. For metabolites with HMDB ID the list was queried to Metaboanalyst 66 [www. metaboanalyst. ca/faces/upload/ConvertView. xhtml] to get the corresponding KEGG IDs. For metabolites without KEGG or HMDB IDs provided, KEGG IDs retrieval was achieved automatically by parsing the common names into the most standard KEGG name format, and then queried the metabolite names with new format to MetaboAnalyst. Additionally, specific metabolites were manually treated: acylcarnitines without KEGG IDs were swapped to their acyl-CoA counterparts when available in KEGG. For each gene-metabolite pair within a dataset (TK, HMC and SCD study), only one mGWAS p value was kept, which is the minimum p value obtained for the association between any SNP annotated to the gene and that specific metabolite.
Running time of PathQuant package The overview's null SRD distribution is the largest distribution of tested gene-metabolite pairs in this study, involving millions of pairs (Figure  S7 ). In order to compute the SRDs for the null distribution, as we recommend, we parallelized the query by generating different queries of 20,000 pairs. Here to show the running time of PathQuant, influenced by the query size, we selected randomly: 1, 10, 100, 1000, 10000, and 20000 gene-metabolite pairs from the null distribution and ran PathQuant using the get. srd() function within the largest graph of the KEGG database (''overview'', hsa01100). In all following experiments, to address the variability of the different running times we reproduced the experiments 10 times, we used PathQuant on the Compute Canada servers, with a sbatch job allocation of 64G memory, and 1 CPU per task.
ACKNOWLEDGMENTS We thank all SCD participants for their contribution to this project. We thank members of the  Hussin group  for helpful discussions and advice throughout this project, specifically  Jean-Christophe Grenier. We thank  Fabien Jourdan ,  Clement Frainy  and the  iGenoMed Consortium  for initial discussions on this project. This work was completed thanks to computational resources provided by  Calcul Quebec clusters Narval , Beluga and Cedar, maintained by the  Digital Research Alliance of Canada. C. B. was supported by a PhD Scholarship from the  NSERC-CREATE Metabolomics Advanced Training and International Exchange (MATRIX) program. M. R. is an  FRQS Junior  1 research scholar, J. G. H. is FRQS Junior 2 research scholar, G. L. holds the Canada Research Chair (tier 1) in the genetics of heart and blood diseases, J.
D. R. holds a Canada Research Chair in Genetics and Genomic Medicine. This study was supported by funding from the  Montreal Heart Institute Foundation  and  IVADO PRF  Grant ( PRF-2019-3378524797 ) to J. G. H. and M. R. , and a  National Sciences and Engineering Research Council (NSERC)   Discovery Grant  ( RGPIN-2022-04262 ) to J. G. H. , financial support of  Genome Quebec ,  Genome Canada , the  Government of Canada , and the  Ministere de l'enseignement superieur, de la recherche, de la science et de la technologie  du Quebec, the  Canadian Institutes of Health Research  (with contributions from the  Institute of Infection and Immunity , the  Institute of Genetics , and the  Institute of Nutrition, Metabolism and Diabetes) ,  Genome BC and Crohn's Colitis Canada  (via  Genome Canada  grant number  GPH-129341 ) to C.
D. R. and J. D. R. ,  Agilent Technologies Research  Grant (3893 and  4075 ) to C. D. R. The work in the  GEN-MOD cohort  is funded by the  Canadian Institutes of Health Research (CIHR , PJT # 156248 ) and the  Doris Duke Charitable Foundation. GEN-MOD sample and data collection were supported by  NIH  grant  HL-68922. The  OMG-SCD  study has been supported by grant awards  2015131  and  2012126  from the  Doris Duke Charitable Foundation ,  R01HL68959  and  R01HL079915  from the  National Heart, Lung, and Blood Institute , and  DK110104  and  DK124836  from the  National Institute of Diabetes and Digestive and Kidney Diseases.
Data and code availability Data: Source data statement. This paper analyzed existing, publicly available summary mGWAS data for the TK and HMC studies. The SCD study data comes from genomic and metabolomics of two previously published studies. Raw data can be made available upon request to GL. Summary statistics of SCD study will be be shared by the lead contact upon reasonable request. All additional files necessary to reproduce the reported results of this paper are available at: www. github. com/HussinLab/PathQuant/Publication/ References. Code: PathQuant source code is available at www. github. com/HussinLab/PathQuant/. Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.
Overview of the Metabolite genome-wide association studies datasets In this study we used three different mGWAS datasets. The TK study refers to an mGWAS  11  previously performed on 7,824 participants (plasma or serum samples) from the TwinsUK cohort  19  and the KORA study. 20  The mGWAS was carried out on 2. 1 million genotyped SNPs and 529 metabolites (assessed by targeted and untargeted metabolomics). The authors reported 299 SNP-metabolite (or ratio of metabolites) significant associations (at cut-off of p % 1. 03 3 10 À10 for metabolite concentrations and p % 5. 08 3 10 À13 for pairwise metabolite ratios) involving 187 unique metabolites and 145 loci, annotated as 132 causal genes. 11  The TK study also made available mGWAS output files from METAL software  60  involving 486 metabolites without any p value cut-off [www.
metabolomics. helmholtz-muenchen. de]. REAGENT or RESOURCE SOURCE IDENTIFIER Deposited data TK https://doi. org/10. 1038/ng. 2982 https://static-content. springer. com/esm/art%3A10. 1038%2Fng. 2982/MediaObjects/ 41588_2014_BFng2982_MOESM50_ESM. xlsx TK None https://doi. org/10. 1038/ng. 2982 Summary stats without p value cutoff: https:// metabolomics. helmholtz-muenchen. de/gwas/ HMC https://doi. org/10. 1038/s41467-017-01972-9 Supplementary 7, https://static-content. springer. com/ esm/art%3A10. 1038%2Fs41467-017-01972-9/ MediaObjects/41467_2017_1972_MOESM9_ESM. xlsx SCD https://doi. org/10. 3324/haematol. 2022. 281180 and https://doi. org/10. 1016/j. bcmd. 2020. 102504 https://github. com/HussinLab/PathQuant/blob/main/ Publication/SCD_study_download_link. txt eQTLGen Consortium https://doi.
Line 1 : 1 Dataset label for TK, HMC, and SCD studies and the TK dataset without p values cut-off (TK-None). Line 2: Maximum p value cut-off. Line 3: Maximum number of samples available for the mGWAS study. Line 4: Number of SNP-metabolites pairs. Line 5: Number of gene coding for enzyme-metabolite pairs for which KEGG IDs have been obtained. Line 6: Number of SRD annotation within map hsa01100 (KEGG overview graph) categorized in three categories. Lines 4, 5, and 6 have the count of unique genes and metabolites involved in pairs between parenthesis. *: the proportion of genes and metabolites mapped from the previous line is indicated between brackets. **: the number of unique genes and metabolites are ignoring NA values. Abbreviations: ID = Identifier; SNP = Single-nucleotide polymorphism; SRD = Shortest Reactional Distance; Inf = Infinite value; NA = Not available, a missing annotation.
In the ongoing debates about eukaryogenesis-the series of evolutionary events leading to the emergence of the eukaryotic cell from prokaryotic ancestorsmembers of the Asgard archaea play a key part as the closest archaeal relatives of eukaryotes 1. However, the nature and phylogenetic identity of the last common ancestor of Asgard archaea and eukaryotes remain unresolved 2-4. Here we analyse distinct phylogenetic marker datasets of an expanded genomic sampling of Asgard archaea and evaluate competing evolutionary scenarios using state-of-the-art phylogenomic approaches. We find that eukaryotes are placed, with high confidence, as a well-nested clade within Asgard archaea and as a sister lineage to Hodarchaeales, a newly proposed order within Heimdallarchaeia. Using sophisticated gene tree and species tree reconciliation approaches, we show that analogous to the evolution of eukaryotic genomes, genome evolution in Asgard archaea involved significantly more gene duplication and fewer gene loss events compared with other archaea.
been unclear whether eukaryotes evolved from within Asgard archaea or whether they represented a sister lineage  3. Furthermore, two studies questioned this view of the tree of life altogether, suggesting that Asgard archaea represent a deep-branching Euryarchaea-related clade  11, 12. These studies suggested that, in accordance with the three-domain tree, eukaryotes represent a sister group to all Archaea; however, this view has been challenged  13, 14. More recently, a study that included an expanded taxonomic sampling of Asgard archaeal genome data failed to resolve the phylogenetic position of eukaryotes in the tree of life  4. Here we expand the genomic diversity of Asgard archaea by generating 63 new Asgard archaeal metagenome-assembled genomes (MAGs) from samples obtained from 11 locations around the world.
Expanded Asgard archaea genome diversity To increase the genomic diversity of Asgard archaea, we sampled aquatic sediments and hydrothermal deposits from 11 geographically distinct sites (Supplementary Table  1  and Supplementary Fig. 1 ). After extraction and sequencing of total environmental DNA, we assembled and binned metagenomic contigs into MAGs. Of these MAGs, 63 belonged to the Asgard archaea superphylum, with estimated median completeness and redundancy values of 83% and 4. 2%, respectively (Supplementary Table  1 ). To assess the genomic diversity in this dataset, we reconstructed a phylogeny of ribosomal proteins encoded in a conserved 15 ribosomal protein (RP15) gene cluster from these MAGs and in all publicly available Asgard archaea assemblies (retrieved 29 June 2021; Fig. 1 ).
Identification of phylogenetic conflict Inferring deep evolutionary relationships in the tree of life is considered one of the hardest problems in phylogenetics. To interrogate the evolutionary relationships within the current set of Asgard archaeal phyla, and between Asgard archaea and eukaryotes, we performed an exhaustive range of phylogenomic analyses. We analysed a pre-existing marker dataset comprising 56 concatenated ribosomal protein sequences (RP56)  2, 3  for a phylogenetically diverse set of 331 archaeal (175 Asgard archaea, 41 DPANN archaea, 43 Euryarchaea and 72 TACK archaea representatives) and 14 eukaryotic taxa (Supplementary Table  2 ). Of note, the inclusion of an expanded diversity of 12 new Korarchaeota MAGs among these TACK archaea considerably affected phylogenomic analyses (see below).
Initial maximum-likelihood (ML) phylogenetic inference based on this RP56 dataset confirmed the existence of 12 major Asgard archaeal clades of high taxonomic rank (Supplementary Fig. 2 ). These included the previously described Lokiarchaeia, Odinarchaeia, Heimdallarchaeia and Thorarchaeia  2, 3  , for which we present 36 new genomes here. The clades also included the recently proposed Sifarchaeia  16  , Hermodarchaeia  15  , Jordarchaeia  19  , Wukongarchaeia  4  and Baldrarchaeia  4  , for most of which we also identified new near-complete MAGs. Finally, we identified 15 MAGs that represented the recently described Njordarchaeales  20  (which we show below is a divergent candidate o rd er within Heimdallarchaeia, see below) and a s i n gle M AG t h a t r e p r e s e nted a n e w c a n di date c l a ss, A sgardarchaeia (which will be described elsewhere) (Fig.
Alternative phylogenomic markers Despite often being used in phylogenomic analyses, ribosomal proteins have been suggested to contribute to phylogenetic artefacts owing to inherent compositional sequence biases  21, 22. Our results revealed a placement of eukaryotes inconsistent with previous analyses, the previously mentioned incoherent placement of Njordarchaeales and the presence of long branches at the base of both of these clades in the RP56 tree. Therefore, we sought to use an alternative phylogenetic marker set to obtain a stable Asgard archaeal species tree and to further investigate the phylogenetic position of eukaryotes. We constructed an independent new marker dataset comprising 57 proteins of archaeal origin in eukaryotes (NM57 dataset; Methods). The NM57 proteins are mostly involved in diverse informational, metabolic and cellular processes, but do not include ribosomal proteins (Supplementary Table  2 ).
These proteins are longer and therefore putatively more phylogenetically informative compared with the RP56 markers. Moreover, the broader functional distribution of NM57 markers is less likely to cause phylogenetic reconstruction artefacts induced by strong co-evolution between proteins-something that is to be expected for functionally and structurally cohesive ribosomal proteins  23. If co-evolving protein sequences are compositionally biased, then they would violate evolutionary model assumptions of fixed composition across species. Consequently, their concatenation is expected to strengthen the artefactual, non-phylogenetic signal and the statistical support for incorrect relationships  24. We therefore decided to independently evaluate the concatenated NM57 and RP56 marker datasets for downstream phylogenomic analyses.
We observed that ML phylogenomic analyses of the NM57 dataset recovered Njordarchaeales as bona fide Asgard archaea and placed them as the closest relatives of eukaryotes (bootstrap support, BS = 98%; Supplementary Fig. 3 ), as was proposed in a recent analysis  20. We set out to investigate the underlying causes for the contradictory results between the NM57 and RP56 datasets. To that end, we first assessed the effect of taxon sampling on phylogenetic reconstructions by removing eukaryotic Article and/or DPANN and/or Korarchaeota sequences from the alignments. This was done for two main reasons: (1) eukaryotes and DPANN archaea represent long-branching clades that potentially induce long-branch attraction artefacts; and (2) we wanted to investigate the effects of removing eukaryotes and Korarchaeota, which were the sister lineages of Njordarchaeales in the NM57 and RP56 phylogenetic analyses, respectively.
Following this, we recoded the alignments into four states (using SR4 recoding  25  ) to ameliorate potential phylogenetic artefacts arising from model misspecification at mutationally saturated or compositionally biased sites  14, [26] [27] [28]. Furthermore, with a similar goal, we applied a fast-evolving site removal (FSR) procedure to the concatenated datasets, as fast-evolving sites are often mutationally saturated. We performed phylogenetic analyses of the abovementioned datasets in both ML and Bayesian inference (BI) frameworks under sophisticated evolutionary models that account for sequence heterogeneity in the substitution process across sites (mixture models; Supplementary Table  2 ). Phylogenomic analyses of the abovementioned combinations of taxon sampling, data treatments and phylogenetic frameworks revealed that Njordarchaeales are artefactually attracted to Korarchaeota in RP56 datasets (Supplementary Information).
This attraction is likely to be caused by the high compositional similarity of njordarchaeal and korarchaeal RP56 ribosomal protein sequences, which is probably linked to their shared hyperthermophilic lifestyle (Supplementary Figs. 4-6). Analyses of RP56 datasets from which Korarchaeota were removed recovered Njordarchaeales as an order at the base of or within Heimdallarchaeia (Supplementary Fig. 7 ). This result was consistent with phylogenomic analyses of the NM57 dataset that included Korarchaeota (Supplementary Fig. 3 ). Next, in our efforts to resolve the phylogenetic placement of eukaryotes, we initially performed phylogenomic analyses on variations of the RP56 and NM57 datasets (Supplementary Table  2  and Discussion ). However, compared with the RP56 dataset, the NM57 dataset is larger and less compositionally biased and is therefore expected to have retained a stronger historical phylogenetic signal.
Eukarya emerged within Heimdallarchaeia Subsequent phylogenetic analyses of untreated NM57 datasets with diverse taxon sampling variations recovered eukaryotes as a sister clade to Njordarchaeales in ML analyses (Supplementary Fig. 3 , Supplementary Table  2  and Supplementary Information). However, ML analyses of the SR4-recoded datasets retrieved a complex phylogenetic signal. In some cases, eukaryotes were placed at the base of all Heimdallarchaeia (including Njordarchaeales) and Wukongarchaeia. This result strongly suggested that the previously observed phylogenetic affiliation between Njordarchaeales and eukaryotes could represent an artefact. Furthermore, when both SR4-recoding and FSR treatments were combined, eukaryotes were nested within Heimdallarchaeia as a sister group to the order Hodarchaeales (Fig.
2  and Supplementary Fig. 8 ). This position was supported by ML analyses of NM57 datasets across all taxon selection variations (removing DPANN archaea and/or Korarchaeota and/or Njordarchaeales). Congruently, the monophyly of eukaryotes and Hodarchaeales was systematically recovered by BI of recoded datasets (both with and without FSR; Fig. 2  and Supplementary Table  2 ). In addition, the position of Njordarchaeales shifted during these analyses, moving from a deep position at the base of Heimdallarchaeia and Wukongarchaeia to a more nested position, forming a clade with Gerdarchaeales, Kariarchaeaceae, and Heimdallarchaeaceae (Supplementary Discussion). This shift was observed in analyses of both the NM57 and the RP56 datasets when SR4 recoding and FSR was combined (Supplementary Figs.
Discussion Beyond genomic exploration, several studies have started to uncover important physiological, cytological and ecological aspects of Asgard archaea  38, 39, [49] [50] [51]. Yet, although such insights are relevant, the cellular and physiological characteristics of present-day Asgard archaea will probably not resemble those of the LAECA. Therefore, inferences about the identity and nature of the LAECA and the process of eukaryogenesis should be made within an evolutionary context. We used an evolutionary framework to analyse an expanded Asgard archaeal genomic diversity comprising 11 clades of high taxonomic rank. We also performed comprehensive phylogenomic analyses involving the evaluation of distinct marker protein datasets and systematic assessments of suspected phylogenetic artefacts and state-of-the-art models of evolution.