dataesr/scientific-paragraphs-categorization huggingface dataset:
4B ). First, we analyzed the monophyly of ophirinids and jakobids, recovering a fully (100%) to highly (≥95%) nonparametric bootstrap (bs) support for this clade until we have only 50% of our data left. We then analyzed the alternative hypothesis suggested by the 18S rDNA phylogeny from  Yabuki et al. (2018) , namely the ophirinids as the sister clade to all Discoba, but never recovered statistical support higher than 14% bs. Thus, this hypothesis was rejected by our analyses. We additionally tested the relationship between ophirinids and the other discobids by using two alternative sequence datasets. First, to avoid possible long branch attraction artifacts (LBA) induced by the very long branches of metamonads, we analyzed a dataset with 34 taxa excluding all metamonads except Trimastix pyriformis, which exhibits the shortest branch among the species of this group.
amphinema and only differing in the absence of rpl19 in O. chinija. The mitogenome of A. voluta contains 94 genes, including 3 ribosomal RNAs (rnl, rns, and rrn5), 1 RNase-P RNA (rnpB), 25 tRNAs, and 65 protein-coding genes. Coding regions account for 91. 6% in O. chinija and 89. 8% in A. voluta. We did not detect introns in the mitogenome of O. chinija. However, we detected two group II introns of 1,320 and 1,105 bp in the mitogenome of A. voluta, the first inserted in the trnK gene and the second in an intergenic region between the rps1 and trnM genes (fig. 5B ). These introns contained the two maturase genes (orf291 GBE and orf192, respectively) mentioned above. The two annotated mitochondrial genome sequences were deposited in GenBank with accession numbers ON980703 (Ophirina chinija) and ON980704 (Agogonia voluta).
Phylogenetic Analysis of Maturases We carried out an ML phylogenetic analysis of a dataset of 452 maturase sequences (1,236 amino acid positions) representing the known diversity of prokaryotic and eukaryotic maturases based on the dataset from  Maciszewski et al. (2022) , using the VT + F + R8 model (fig. 5D ; supplementary fig. S7 , Supplementary Material online). We obtained a phylogenetic tree with fully (100%) to highly (≥95% bs) supported monophyly for the mitochondrial maturase genes of Fungi and Streptophyta (matR), and for the matK, mat7, mat2, mat5 roaA, and ycf13 plastid maturases, which suggests that these sequences still contain some ancient phylogenetic signal. The plastid maturases mat4, mat6, roaA, and matK of Rhodophyta appear to have several independent origins within the diversity of Bacteria and Archaea.
Gene sequence: The SSU rRNA gene sequence has the GenBank accession number ON997588. Type culture: A cryopreserved culture is deposited in the DEEM culture collection, CNRS and Université Paris-Saclay (France) as the type strain FlipomonasEX4. Isolator: Luis Javier Galindo. Type locality: Specimen isolated from a freshwater pond in the town of Orsay, France. Etymology: The name voluta refers to the behavior of the cells to roll while feeding.
Gene sequence: The SSU rRNA gene sequence has the GenBank accession number ON979549. Type culture: A cryopreserved culture is deposited in the DEEM culture collection, CNRS and Université Paris-Saclay (France) as the type strain LJ06. Isolator: Luis Javier Galindo. Type locality: Specimen isolated from an intertidal pool from the island of La Graciosa, Spain. Etymology: The name chinija refers to the Archipelago of Chinijo (Spain), where the samples were collected, and to the small size of the cells (chinijo is a local colloquial adjective from the area used to describe something small). Zoobank registration: urn:lsid:zoobank. org: act:7109A458-9FA3-4B5C-8D79-34A087F7E4EA.
A total of 13,041,150 transcriptome paired reads and 54,201,638 genome paired reads were generated for O. chinija, and 11,300,930 transcriptome paired reads and 42,101,302 genome paired reads were generated for A. voluta. Raw read sequences have been submitted to GenBank under BioProject accession numbers PRJNA858411 and PRJNA858417 for Ophirina chinija and PRJNA858424 and PRJNA858430 for Agogonia voluta. Paired-end read quality scores were checked with FastQC v0. 11. 8  (Andrews 2010). Illumina adapters were removed with Trimmomatic v0. 32  (Bolger et al. 2014)  in Paired-End mode, with the following parameters: ILLUMINACLIP:adapters. fasta:2:30:10LEADING:28 TRAILING:28 SLIDINGWINDOW:4:30. Each genome/transcriptome was de novo assembled using SPAdes v3. 13. 1  (Bankevich et al. 2012)  with -rna mode in the case of the transcriptomes and default parameters.
The O. chinija assemblies included 26,290 (transcriptome) and 102,719 (genome) contigs. The A. voluta assemblies included 20,605 (transcriptome) and 77,665 (genome) contigs. All assemblies were then translated into peptides using Transdecoder v2 (http:transdecoder. github. io) with default parameters and multiple identical translated sequences GBE were removed with Cd-hit v4. 6  (Li and Godzik 2006)  with 100% identity. We then removed possible cross-species contamination using CroCo v1. 1  (Simion et al. 2018). To assess genome/transcriptome completeness, we used BUSCO v2. 0. 1 on the predicted proteomes with the eukaryo-ta_odb9 dataset of 303 near-universal single-copy orthologs  (Simão et al. 2015). For O. chinija, we obtained a completeness of 86. 9% and 40. 6% for its transcriptome and genome, respectively, and for A.
Phylogenomic Analyses The dataset of 351 conserved proteins from  Lax et al. (2018)  was updated by Blastp  (Altschul et al. 1990 ) search (with an E-value threshold of 1e-05) against the inferred proteomes of representatives of all known eukaryotic lineages, including our two new ophirinid strains, and allowing to keep more than one hit per species. Sequences of each protein marker were aligned with MAFFTv7 (Katoh and Standley 2013) and trimmed with TrimAl with the automated1 option  (Capella-Gutiérrez et al. 2009). Alignments were manually inspected and edited with AliView  (Larsson 2014 ) and Geneious v6. 06  (Kearse et al. 2012). Single-protein trees were reconstructed with IQ-TREE v1. 6. 11 (Nguyen et al. 2015)  with the corresponding best-fitting model. Each single-protein tree was manually inspected to discard contaminants, paralogs, and possible cases of horizontal gene transfer or hidden paralogy.
At the end of this curation process, we kept a final taxon sampling of 41 species, including 19 species representing the diversity of Discoba, and outgroups from Metamonada, Ancyromonadida, Malawimonadida, Diaphoretickes, Hemimastigophora, and CRuMs (supplementary table S1, Supplementary Material online, available on https://doi. org/10. 6084/m9. figshare. 20332842. v3), and a supermatrix of 350 markers that were present in at least one of the three Ophirinina species. For this reduced taxon sampling, all proteins were realigned and trimmed as described above, and then concatenated using Alvert. py from the package Barrel-o-Monkeys (http:// rogerlab. biochemistryandmolecularbiology. dal. ca/Software/ Software. htm), creating a final supermatrix with 112,726 amino acid positions. Bayesian inference (BI) phylogenetic analyses were done with PhyloBayes-MPI v1.
5a  (Lartillot et al. 2009)  with the CAT-GTR model  (Lartillot and Philippe 2004) , with four MCMC chains run for 10,000 generations, saving one every 10 trees. Analyses were stopped once convergence thresholds were reached (i. e. , maximum discrepancy <0. 1 and minimum effective size >100, calculated using bpcomp) and consensus trees constructed after a burn-in of 25%. ML analyses were done with IQ-TREE v1. 6. 11 (Nguyen et al. 2015)  with the best fitting model (LG + C60 + F + R7) as per bayesian information criterion (BIC) using the -TESTNEW option of IQ-TREE and the PMSF approximation  (Wang et al. 2018). We further investigated the phylogenetic position of ophirinids with two alternative datasets. First, a dataset based on the one described above but from which we removed all metamonads with the exception of the shortbranched species Trimastix pyriformis (34 species were kept).
Second, a dataset of 14 mitochondrion-encoded markers based on that from  Ettahi et al. (2021). Both datasets were trimmed using trimAl v1. 2  (Capella-Gutiérrez et al. 2009 ) by removing sites with gaps in more than 80% of the sequences (-gt 0. 2) and similarity score below 0. 001 (-st 0. 001), and restricted minimum resulting length of 20% of the original alignment (-cons 20). ML analyses were carried out with IQ-TREE v1. 6. 11 (Nguyen et al. 2015)  with LG + C60 + F + R7, the best fitting model as per BIC using the -TESTNEW option of IQ-TREE, to generate the input tree for PMSF approximation  (Wang et al. 2018). All trees were visualized and edited with FigTree v1. 4. 3  (Rambaut 2016).
Phylogenetic Analysis of Maturases A protein sequence dataset covering the known diversity of maturases was built based on the dataset from  Maciszewski et al. (2022) , in which the authors carried out Blast  (Altschul et al. 1990 ) and PFAM  (Mistry et al. 2020 ) searches of candidate intron maturase genes focusing on euglenid plastid-encoded maturases, but also including mitochondrial and prokaryotic maturases, for a total of a 234 sequences and 832 amino acid-long alignment. We updated this dataset with the A. voluta maturase sequences (orf291 and orf192), which we used as a query in a Blastp (DELTA-BLAST) search against the NCBI nonredundant protein sequence database  (Pruitt et al. 2007 ) to incorporate the first 100 hits, together with additional opisthokont, streptophyte, archaeal, and bacterial maturase sequences from the NCBI protein database  (Clark et al.
2016). MAFFT  (Katoh and Standley 2013)  was used to incorporate these sequences into the original alignment, and redundant sequences were manually removed. The dataset was trimmed using trimAl v1. 2  (Capella-Gutiérrez et al. 2009 ) by removing sites with gaps in more than 70% of the sequences (-gt 0. 3), similarity score below 0. 001 (-st 0. 001), and restricted minimum resulting length of 30% of the original alignment (-cons 30)  (Maciszewski et al. 2022). The final alignment had a total of 452 sequences and 1,236 amino acid positions. An ML tree was reconstructed using IQ-TREE  (Nguyen et al. 2015)  under the VT + F + R8 model (selected using the -m TEST parameter) with 1,000 ultrafast bootstrap replicates and 1,000 GBE replicates of the SH-like approximate likelihood ratio test. The resulting tree was visualized with FigTree  (Rambaut 2016).
Assembly and Annotation of Mitochondrial Genomes To extract the mitochondrial genome sequences of both ophirinid species from their genome assemblies, we used Blastn with the mitochondrial genome of Ophirina amphinema (GenBank LC369600. 1; Yabuki et al. 2018 ) as a query. In the case of A. voluta, we decided to improve the genome assembly by performing an additional run with SPAdes v3. 13. 1 set on default parameters using both transcriptomic and genomic reads to reconstruct the best possible mitogenome. The combined genome + transcriptome assembly included 115,042 contigs and increased the genome completeness from 23. 4% to 77. 7% (according to the BUSCO eukaryo-ta_odb9 dataset). The decision to include transcriptome reads in the assembly came from previous evidence of mitochondrial genomes being obtained from transcriptomes  (Yabuki et al.
g. , orf291 and orf192 of A. voluta) were subjected to DELTA-BLAST  (Boratyn et al. 2012)  to annotate them based on eventual conserved protein domain sequences. To avoid possible bacterial contamination on the assemblies and to confirm the presence of certain protein domains, an additional examination of all ORFs encoding proteins and genes for both mitochondrial genomes was performed with HMM (hmmscan search) protein searches  (Finn et al. 2011 ) and/or with reciprocal BLAST sequence searches across the GenBank non-redundant database  (Pruitt et al. 2007 ). We generated a graphical representation for the annotation of each mitochondrial genome using OGDRAW v1. 3. 1  (Greiner et al. 2019). After annotation, we reconstructed the gain and loss patterns of mitochondrial genes through the evolution of Discoba, based on the analysis made by  Yabuki et al.
by incorporating our ophirinid sequences and several outgroup taxa and excluding the very long-branching Euglenozoa. The ML tree based on these markers also supported the sister relation of ophirinids and jakobids with maximum bootstrap value (100%) (fig. 4C; supplementary fig. S6, Supplementary Material online). All datasets and trees are available at https://doi. org/10. 6084/m9. figshare. 20332842. v3.
Acknowledgments We thank  Dr. Sergey A. Karpov  ( Zoological Institute RAS ) and  Dr. Denis V. Tikhonenkov  ( IBIW RAS ) for their helpful discussions on the ultrastructural observations, and  Dr. Hwan Su Yoon  ( Sungkyunkwan University ) for the mitochondrial marker alignments. This work was supported by the  European Research Council (ERC) Advanced Grants "Protistworld" and "Plast-Evol " (322669 and  787904 , respectively) and the  Horizon 2020 research and innovation program  under the  Marie Skłodowska-Curie ITN  project  SINGEK H2020-MSCA-ITN-2015-675752  (http://www. singek. eu/). G. T. was supported by the 2019  BP 00208 Beatriu de Pinos-3 Postdoctoral Program  ( BP3 ;  801370 ).
Data Availability Genome and transcriptome raw read sequences are available in GenBank under accession numbers PRJNA858411 and PRJNA858417 (Ophirina chinija) and PRJNA858424 and PRJNA858430 (Agogonia voluta). All assemblies, inferred protein sequences, alignments, and phylogenetic datasets and trees can be found at https://doi. org/10. 6084/ m9. figshare. 20332842. v3.
Clinical genetic datasets We used 4 'genetically-informed' CNV datasets, which were recruited based on the presence of a CNV associated with neurodevelopmental and psychiatric disorders, regardless of symptomatology (detailed in eMethod in Supplement). Of note, the term oligogenic refers to CNVs containing: 1 gene <CNV ≤ 5 genes, while multigenic CNVs contain more than 5 genes. These categories are descriptive, and cutoffs are descriptive. None of the analyses rely on these categories. These four datasets included the Simons Variation in Individuals Project (SVIP for 16p11. 2 and 1q21. 1 CNVs)  (25)  and the University of California, Los Angeles 22q11. 2 CNV project (UCLA). fMRI data have not yet been published for the Montreal rare genomic disorder family project (MRG, Canada)  (8) , and the Define Neuropsychiatric-CNVs Project (Cardiff, UK).
Idiopathic psychiatric conditions cohorts We used the ABIDE1 (29), ABIDE2  (30) , , the Consortium for Neuropsychiatric Phenomics (CNP)  (32) , and an aggregate dataset of 10 SZ studies  (14, 33) ; collectively these datasets include individuals with idiopathic autism, ADHD, SZ, and BIP, as well as their respective controls. Psychiatric assessments are detailed in eMethods in Supplement.
Computing connectomes We segmented the brain into 64 functional seed-based regions and 12 networks defined by the multiresolution MIST brain parcellation  (38). FC was computed as the temporal pairwise Pearson's correlation between the average time series of the 64 seed-based regions, and then Fisher-z transformed. The connectome of each individual encompassed 2,080 connectivity values: (63x64)/2 = 2016 region-to-region connectivity + 64 within seed-based region connectivity. We chose the 64 parcel atlas of the multi-resolution MIST parcellation (https://simexp. github. io/multiscale_dashboard/index. html) as it falls within the range of network resolution previously identified to be maximally sensitive to FC alterations in neurodevelopmental and psychiatric disorders such as autism  (39).
Main findings In this large rsfMRI dataset, we demonstrated that most rare and common genetic risks for neurodevelopmental and psychiatric disorders affect FC but effect sizes vary over an order of magnitude across variants. We showed that the effect sizes of CNVs on FC were correlated with their previously reported effects on cognitive ability and risk for autism and SZ connectome-wide. This relationship was observed across all brain networks which is consistent with the fact that fluid intelligence is thought to be subserved by networks widely distributed across the brain  (54). Whether these associations across all networks are causal of IQ decrease in CNV carriers remains an open question. Multigenicity had a profound impact on FC signals: As CNVs increased in size and number of genes, effect size of CNVs adjusted for gene content (number of genes and severity score), rapidly decreased.
(F) Genetic heterogeneity within a CNV Genes encompassed in the 16p11. 2 CNV color-coded based on their LOEUF score. Two scenarios are represented: Right, genes converge on shared brain patterns: the effect size of the CNV increases linearly with the number of intolerant genes and is large. Left, genes within a CNV are associated with distinct patterns: the resulting effect size is weaker. GP UKBB ✓ ✓ ✓ ✓ Idiopathic psychiatric conditions ABIDE1-2 ✓ ✓ ADHD200 ✓ ✓ CNP ✓ ✓ Cobre ✓ ✓ Clinical genetic datasets SVIP ✓ ✓ ✓ ✓ MRG ✓ ✓ ✓ ✓ UCLA ✓ ✓ ✓ ✓ Cardiff ✓ ✓ ✓ ✓ Datasets Aim1.
Acknowledgments This paper has been posted on medRxiv (65). supported in part by the  Courtois foundation  ( Dr Bellec ). This work was supported by  Simons Foundation  Grant Nos. SFARI219193  and  SFARI274424. We thank all of the families at the participating Simons Variation in Individuals Project (VIP) sites, as well as the  Simons VIP Consortium. We appreciate obtaining access to imaging and phenotypic data on SFARI Base. Approved researchers can obtain the Simons VIP population dataset described in this study by applying at https://base. sfari. org. We are grateful to all families who participated in the 16p11. 2European  Consortium. Dr. P. Thompson  was funded in part by the  U. S. NIH  grants  R01MH116147 ,  P41EB015922 ,  R01MH111671 , and  U01 AG068057. Ms. Petra Tamer  received the  Canadian Institute of Health Research (CIHR) Scholarship.
Funding This research was supported by  Compute Canada  ( ID 3037  and gsf-624), the  Brain Canada Multi investigator research initiative (MIRI) ,  Canada First Research Excellence Fund ,  Institute of Data Valorization, Healthy Brain Healthy Lives  ( Dr. Jacquemont ). Dr. Jacquemont is a recipient of a Canada Research Chair in neurodevelopmental disorders, and a chair from the  Jeanne et Jean Louis Levesque Foundation. This work was supported by a grant from the  Brain Canada Multi-Investigator initiative  ( Dr. Jacquemont ) and a grant from The  Canadian Institutes of Health Research  ( CIHR 400528 ,  Dr. Jacquemont). Clara Moreau  and  Thomas Bourgeron  are supported by AIMS-2-TRIALS -which received support from the  Innovative Medicines Initiative 2 Joint Undertaking  under grant agreement No  777394.
The Cardiff CNV cohort was supported by the  Wellcome Trust   Strategic Award "DEFINE"  and the  National Centre for Mental Health  with funds from  Health and Care Research Wales  (code  100202/Z/12/Z ). Data from the  UCLA  cohort provided by  Dr. Bearden  (participants with 22q11. 2deletions or duplications and controls) was supported through grants from the  NIH  ( U54EB020403 ),  NIMH  ( R01MH085953 ,  R01MH100900 ,  1U01MH119736 ,  R21MH116473  ), and the  Simons Foundation  ( SFARI Explorer Award ). Finally, data from another study were obtained through the  OpenFMRI  project (http://openfmri. org)from the  Consortium for Neuropsychiatric Phenomics (CNP) , which was supported by  NIH   Roadmap for Medical Research grants   UL1-DE019580 ,  RL1MH083268 ,  RL1MH083269 ,  RL1DA024853 ,  RL1MH083270 ,  RL1LM009833 ,  PL1MH083271 , and  PL1NS062410.
Data and materials availability Data from UK Biobank was downloaded under the application 40980, and can be accessed via their standard data access procedure (see http://www. ukbiobank. ac. uk/register-apply). UK Biobank CNVs were called using the pipeline developed in Jacquemont Lab, and described in: https://github. com/labjacquemont/MIND-GENESPARALLELCNV. The final CNV calls are available from UK-Biobank returned datasets (Return ID: 3104, https://biobank. ndph. ox. ac. uk/ukb/dset. cgi?id=3104 ). ABIDE1, ABIDE2, COBRE, ADHD200, CNP, 16p11. 2 SVIP data are publicly available: http://fcon_1000. projects. nitrc. org/indi/abide/abide_I. html, http://fcon_1000. projects. nitrc. org/indi/abide/abide_II. html, http://schizconnect. org/queries/new, http://fcon_1000. projects. nitrc. org/indi/adhd200/, https://www.
openfmri. org/dataset/ds000030/, https://www. sfari. org/funded-project/simons-variation-in-individuals-project-simons-vip/. The 22q11. 2 UCLA raw data are currently available by request from the PI. Raw imaging data for the The Cardiff raw data is not publicly available yet, contact the PI for further information. All processed connectomes are available through a request to the corresponding authors. Code for all analyses and visualizations, beta values, and p-values for the 29 FC profiles are available online through the GitHub platform with Jupyter notebook: https://github. com/claramoreau9/NeuropsychiatricCNVs_Connectivity.
Expression of macromolecular N degrading genes identified in de novo assembled metatranscriptomes Raw reads were quality-trimmed (Q20) and rRNA and tRNA reads were removed. Library size, evenness, richness and Shannon diversity were comparable between experimental groups with a mean library size of 43 M paired end reads after filtering. In contrast to the single approach used previously  [28] , we also de novo assembled quality-controlled metatranscriptomic reads into contigs within each sample. Contigs smaller than 200 bp were discarded and the remaining contigs from all samples were clustered at 99% identity with cd-hit-est keeping the longest sequence as the cluster representative  [47]. Open reading frames (ORFs) were predicted using Prodigal  [48]. Extracellular protease ORFs were identified by reciprocal BLAST to extracellular peptidases from the MEROPS database  [19, 49].
We acknowledge that this method may generate a conservative estimate of the number of proteases detected. ORFs were assigned a peptidase group (serine-, metallo-, cysteine-peptidase and others) by their best reciprocal BLAST hit. Taxonomy was determined by best BLAST hit to the NCBI non-redundant database (NR, access date July 29th 2019). Chitinase ORFs were identified using six chitinase hidden Markov models (HMMs) from the Kyoto Encyclopedia of Genes and Genomes (KEGG): K01183, K03791, K13381, K17523, K17524, and K17525. Only the first three were detected in our dataset. Lysozyme ORFs were identified using the HMM for KEGG orthology K07273, extracellular nuclease with K01150 (codes for Dns gene, undetected) and K07004, and urease subunit A, B and C with K01428, K01429, and K01430 respectively.
Reads were then mapped back to ORFs requiring minimum identity 95% and 75% breadth using bbmap  [50]. Read counts were normalized using DESeq2  [51]. Heat maps of normalized counts were generated in R using the heatmap2 function in gplots  [52]. Normalized transcript counts per gene per time point were compared between groups (rhizosphere, root litter and root litteramended rhizosphere) using ANOVA and Tukey post-hoc test. Boxplots were generated in ggplot2  [53].
Expression of extracellular protease genes from a curated collection of Hopland-soil genomes The quality-controlled reads were mapped using BBsplit  [50]  requiring 80% identity against a dereplicated reference database of 282 HREC soil genomes including isolates  [11] , single amplified genomes (SAGs)  [44] , metagenomic assembled genomes (MAGs) (NCBI PRJNA517182) and stable isotope probing MAGs (SIP-MAGs)  [54]. On average, 12. 3% (range 6. 2-31. 9%) of the reads per library mapped unambiguously to genomes from the reference database. This additional approach was taken to investigate gene expression within the context of a genome and to search for overlap in guild membership between extracellular protease defined guilds and previously defined CAZy guilds  [44]. Three MAGs that were classified as unknown or domain bacteria in previous studies were assigned taxonomy using GTDB-Tk version 1.