dataesr/scientific-paragraphs-categorization huggingface dataset:
Bowtie2 (Langmead and Salzberg, 2012) https://bowtiebio. sourceforge. net/bowtie2/index. shtml inStrain (Olm et al. , 2021) https://github. com/MrOlm/inStrain scipy. cluster. hierarchy Virtanen et al. , 2020 https://docs. scipy. org/doc/scipy/reference/clust er. hierarchy. html GToTree (version 1. 5. 36) (Lee, 2019) https://github. com/AstrobioMike/GToTree/tree /v1. 5. 36 GTDB (Chaumeil et al. , 2020) https://gtdb. ecogenomic. org/ iTol (Letunic and Bork, 2021) https://itol. embl. de/ CAZy Drula et al. , 2022 http://www. cazy. org/ BlastP (version 2. 3. 0+) Camacho et al. , 2009 https://blast. ncbi. nlm. nih. gov/Blast. cgi?PAGE _TYPE=BlastDocs&DOC_TYPE=Download HMMER (version 3. 1) (Mistry et al. , 2013) http://hmmer. org/download. html Multiqc (version 1. 14) Ewels et al. , 2016 https://multiqc.
info/ Trimmomatic (version 0. 39) Bolger et al. , 2014 http://www. usadellab. org/cms/?page=trimmom atic HiSAT2 (version 2. 2. 0) Kim et al. , 2019 http://daehwankimlab. github. io/hisat2/ SAMtools (version 1. 16. 1) Danecek et al. , 2021 http://www. htslib. org/ StringTie (version 2. 1. 3) (Pertea et al. , 2016) http://ccb. jhu. edu/software/stringtie/index. shtm l?t=manual DESeq2 (version 1. 38. 3) (Love et al. , 2014) https://bioconductor. org/packages/release/bioc/ html/DESeq2. html R (version 4. 2. 2) R Core Team https://www. r-project. org/ tidyverse (version 1. 3. 2) (Wickham et al. , 2019) https://www. tidyverse. org/.
200788 DNeasy PowerLyzer PowerSoil Software and Algorithms Gelatin, bovine Qiagen Sigma Cat#12855-50 G9391 Brilliant III, Ultra Fast SYBR Green QPCR FastQC Agilent Babraham Cat#600883 https://www. bioinformatics. babraham. ac. uk/pr Master Mix Bioinforma ojects/fastqc/ CFX Connect Real-Time PCR Detection Bio-Rad tics Cat#1855201 System BBTools Joint https://jgi. doe. gov/data-and-tools/software- RNeasy PowerMicrobiome Kit RiboMinus™ Transcriptome Isolation Kit, TruSeq® Stranded Total RNA Library Prep Human/Mouse/Rat bacteria SPAdes Qiagen Genome Biotechnol Illumina c Algorithmi Institute Invitrogen Cat#K155004 tools/bbtools/ Cat#26000-50 Cat#20020597 Center for https://cab. spbu. ru/software/spades/ NovaSeq SP Flow Cell Illumina ogy NovaSeq 6000 System Deposited Data RagOUT Kolmogoro https://github.
In this case, each MC step consists of a number  ℎ of local trial displacements involving individual monomers, including both translation and reptation moves  (35). Polymer acceptance rates were computed from Eqs. (  2 ) and (3) based on the Metropolis rule  (92) , and Eq. (  3 ) was also incorporated into the Hamiltonian in Eq. (  1 ) for the determination of HP1 acceptance probabilities in coupled chromatin-HP1 simulations. Simulation and analysis codes are available at https://github. com/physical-biology-ofchromatin/LatticePoly.
ACKNOWLEDGEMENTS We are grateful to members of the  Karpen and Jost labs  for fruitful discussions and  Geoff Fudenberg  and  Aurèle Piazza  for a critical reading of the manuscript. Part of this work was initiated during the  KITP Program: Biological Physics of Chromosomes 2020  (supported by the  National Science Foundation  under Grant No. NSF   PHY-1748958  and  NIH  Grant No. R25GM067110 ). Work in the Jost lab is funded by  Agence Nationale de la Recherche  ( ANR-18-CE12-0006-03 ;  ANR-18-CE45-0022-01 ). The  Karpen lab acknowledges National Institute of Health  ( R35GM139653 ) and  Volkswagen Stiftung  ( 98196 ) for funding. We thank  PSMN (Pôle Scientifique de Modélisation Numérique)  and  CBP (Centre Blaise Pascal)  of the  ENS de Lyon  for computing resources.
Analysis was performed using whole genome data in 78,142 individuals participants of Genomics England (GeL), a rare disease and cancer cohort that recruited probands and their relatives from clinical centers, and with WES in the 453,455 participants (including a subset of 36,104 with CMR) in the UKB. PTVs in three genes which currently lack sufficient evidence to be definitive causes of Mendelian disease were nominally found to be associated with DCM (MYPN: OR 15. 0, P 0. 03; PRDM16: OR 40. 3, P 0. 008) in GeL, and with HCM (NEXN: OR 24. 1, P 0. 01) in UKB. Given our finding of widespread allelic series for DCM genes, we explored genomic loci that did not harbor genes with assertion for autosomal DCM. Rare PTVs in 3 prioritized genes that are not established causes of cardiomyopathy were found to be associated with binary diseases outcomes (MAP3K7 and NEDD4L with DCM) in at least one cohort (Figure  4B , Table  S10  and S11 ), and with quantitative traits (NEDD4L, MAP3K7, and SSPN) in UKB (Figure  4B , Table  S12 ).
Data and code availability Data from UK Biobank can be requested from the UK Biobank Access Management System. Data from 100,000 Genomes Project can be accessed following application to join the Genomics England Clinical Interpretation Partnership. GWAS summary statistics will be made available on the Cardiovascular Disease Knowledge Portal (https://cvd. hugeamp. org/) upon publication following peer-review. The PGS will be made available on the Polygenic Score Catalog (www. pgscatalog. org) following peer-review. The raw single nuclei gene expression dataset is available from CZI CELLxGENE (cellxgene. cziscience. com). The analyses reported in this article rely on previously published software, as detailed in the Methods. AH and RTL received funding from Pfizer Inc. J. S. W. have acted as a consultant for MyoKardia, Pfizer, Foresite Labs, and Health Lumen, and received institutional support from Bristol-Myers Squibb and Pfizer Inc.
Full details of study-level GWAS methods available in Supplementary Methods. Descriptions of studies and participant characteristics are described in Table  S1. Sensitivity analysis GWAS and metaanalysis of strictly defined DCM (Supplementary Methods) was performed using the same workflow. To assess the effects of ascertainment of DCM using strict criteria, GWAS metaanalysis was performed for the 6 studies that used strict criteria (Supplementary Table  1 ) and genetic correlation was assessed using bivariate linkage disequilibrium score regression with LDSC software  38. GWAS meta-analysis was performed centrally using METAL  39  with an inversevariance weighted fixed-effect model. To boost discovery power, we further conducted a multi-trait analysis of GWAS (MTAG), a method of jointly analyzing summary statistics from multiple overlapping GWAS of genetically correlated traits.
GWAS in the UK Biobank of 10 cardiac MRI-derived LV traits (ejection fraction [LVEF], end-systolic volume [LVESV], enddiastolic volume [LVEDV], stroke volume, global circumferential, longitudinal and radial strains, mass, concentricity, and maximum wall thickness) from 36,083 unrelated participants of White British ancestry and without heart failure, cardiomyopathy, previous myocardial infarction, or structural heart disease  7  were tested for genetic correlation with primary GWAS using ldsc  40, 41. MTAG of the primary GWAS was then performed with CMR traits with high genetic correlation (|r g | >0. 7) using the mtag software  9.
SNP-based heritability estimation The proportion of variance in HF risk explained by common SNPs, i. e. SNP-based heritability (h 2 SNP ), was estimated from GWAS meta-analysis summary statistics using Linkage-Disequilibrium Adjusted Kinships (LDAK) SumHer software with the BLD-LDAK heritability model  6. The h 2 SNP estimates were calculated on a liability scale, which assumes that a binary phenotype has an underlying continuous liability, and above a certain liability threshold an individual becomes affected  42. To model the expected heritability tagged by each SNP, we used pre-computed tagging files derived from 2,000 individuals White British individuals, and used a correction for sample prevalence by calculating the effective sample size assuming an equal number of cases and controls  43.
Locus identification To identify genetic susceptibility loci for DCM, we first identified conditionally independent genetic variants using a chromosome-wide stepwise conditional-joint analysis implemented in the Genome-wide Complex Trait Analysis (GCTA) software  44  at a genome-wide significance threshold of P<5x10 -8 in all GWAS, and additionally at FDR<1% estimated using qvalue) for DCM GWAS. To define a genomic locus, conditionally independent genetic variants across both DCM GWAS and MTAG that are located within 500kb of each other were aggregated, and an additional 500kb were added to flank the variants at the extremes within each set. A genomic locus was considered novel if all conditionally independent variants within the locus are located ≥250kb away and not in LD (R 2 ) with any sentinel variant with a P <5x10 -8 reported in previously published GWAS of DCM  8, 37.
Colocalization with gene expression To test the hypothesis whether genetic associations with gene expression in a given tissue and with DCM is driven by the same causal variants, we performed a statistical colocalization analysis using R coloc package  46  allowing for multiple causal variants. The colocalization analysis was performed for all genes overlapping the identified DCM genetic loci using summary-level eQTL data from GTEx v8  53  in tissue with a lowest TWAS P-value and the GWAS DCM summary statistics.
The outcomes tested were binary case-control status (DCM [strict and broad definition], heart failure, and HCM) and in the UKB, related cardiac magnetic resonance imaging (CMR) quantitative traits (LVESV, LVEDV, LVEF, LVSV, and maximum LV wall thickness). Firth correction was applied to account for case-control imbalance. Burden tests collapse variants into a single variable that can be tested for association with a phenotype or trait, thereby reducing computational cost and test statistic inflation that is seen with other gene-based rare variant tests (for example, SKAT and SKAT-O). Individuals with missing phenotype data were dropped from analysis. For consistency across UKB and GeL, one rare variant mask of predicted truncating variants (start lost, stop gained, frameshift, splice acceptor or donor lost) with a MAF<1x10 -3 was tested.
Pathway enrichment analysis of effector genes, differentially expressed genes, and intercellular communication in DCM single-nuclei transcriptomics Pathway gene ontology (GO) enrichment of effector genes and DEGs in DCM at cell type and state level, with identification of driver GO terms was performed using a two-stage algorithm implemented by g:Profiler  56. For enrichment of DEGs, only highly expressed genes within a cell type or state were considered in the background. Driver GO terms were highlighted using a two-stage algorithm implemented by g:Profiler to identify pathways that were further examined in the DCM single nuclei dataset. DCM DEGs were required to have normalized log 2 count >0. 0125/nuclei and FDR<0. 05. and differentially expressed genes (DEG) in DCM cell types. To highlight the importance of cardiomyocyte and non-cardiomyocyte cell types in DCM, and the role of candidate genes and effector gene enriched signaling pathways, we explored disease-specific intercellular communication.
The single nuclei transcriptome of DCM and control samples was interrogated using the CellChat package for manually curated ligand-receptor interactions (CellChatDB)  34. In brief, this method identifies overexpressed genes within cell types and states, quantifies receptor-ligand communication probability between cells using the law of mass action, and infers statistically and biologically important cellular communications  34. CellChat was run using default program settings, and analysed at the cell type level. Endocardial cells were separated out from other endothelial cells due to previously reported important biological effects on ligand-receptor signaling  33.
Tissue, cell type and cell state heritability enrichment Tissue-level heritability enrichment analysis was performed using pre-calculated LD scores of gene expression data from GTEx  53  and chromatin data from Roadmap Epigenomics  57  and ENCODE  55  projects, using ldsc  58. For cell type and state heritability enrichment, we used the sc-linker  32  approach to link transcriptome wide gene programs from single nuclei dataset with GWAS summary statistics. Gene programs derived from snRNA-seq were used to investigate heritability enrichment in cardiac cell types and states using the sc-linker framework  32. This approach uses snRNA-seq data to generate gene programs that characterize individual cell types and states. These programs are then linked to genomic regions and the SNPs that regulate them by incorporating Roadmap Enhancer-Gene Linking  57, 59  and ABC models  54, 60.
Polygenic risk score generation and testing Polygenic score (PGS) was generated using a Bayesian framework that models ancestryspecific LD using an external reference set and uses a continuous shrinkage prior, implemented using the PRS-CS package  64. The phi constant was automatically selected by PRS-CS in an unsupervised approach (PRS-CS auto). Whole genome PGS scores for all included UKB individuals were calculated using the PLINK 1. 9 --score function  65. Individual SNP weighted scores were generated from GWAS that excluded the UKB cohort, and a subsequent MTAG, to avoid the substantial inflation that is seen when there is overlap of individuals in the GWAS and testing cohorts  66. The base GWAS and MTAG summary statistics were filtered to exclude rare and uncommon variants (MAF <0. 01), and ambiguous SNPs that were not resolvable by strand flipping.
Phenome-wide association study The pleiotropic effect of genetic risk arising from common variants was tested by performing a phenome-wide association study (pheWAS) of PGS in the UKB. ICD-9 and ICD-10 codes from death records and hospital admission episodes were translated to Phecodes (Phecode Map 1. 2)  67. For binary phenotypes with at least 20 cases, PGS-phenotype association was tested using logistic regression adjusted for age, age 2 , sex and first ten genetic PCs as covariates. Significance threshold was adjusted for the total number of phenotypes tested (P<2. 72x10 -5 ), and data presented with Manhattan plots grouping by body system. PheWAS was performed using PheWAS 68 in R version 4. 0. 3. Outcome for burden testing were DCM and heart failure, with all gene masks reaching nominal significance (P<0.
These included one known virus, Ae. japonicus narnavirus 1 (AejapNV1), and three new virus species that we named Ae. japonicus totivirus 1 (AejapTV1), Ae. japonicus anphevirus 1 (AejapAV1) and Ae. japonicus bunyavirus 1 (AejapBV1). We also discovered sequences that were presumably derived from two additional novel viruses: Ae. japonicus bunyavirus 2 (AejapBV2) and Ae. japonicus rhabdovirus 1 (AejapRV1). All six viruses induced strong RNA interference responses, including the production of 21 nucleotide sized small interfering RNAs, a signature of active replication in the host. Notably, AejapBV1 and AejapBV2 belong to different viral families, however, no RNA-dependent RNA polymerase sequence has been found for AejapBV2. Intriguingly, our small RNA-based approach identified a ~1 kb long ambigrammatic RNA that is associated with AejapNV1 as a secondary segment but showed no similarity to any sequence in public databases.
S2 did not align to any known sequence in public databases. Still, it could be associated with the same virus on the basis of similar (di)nucleotide composition in S1 and S2, and by applying unsupervised clustering techniques using small RNA features generated by our small RNA metagenomic approach. Additionally, we found sequences derived from two putative, novel virus species, and together with the other four discovered viruses, we characterized their genome organisation and small RNA profiles. Finally, reverse transcriptase PCR (RT-PCR) was used to study the prevalence of these viruses in Ae. japonicus adults, larvae and eggs.
RNAs were resuspended in RNAse-free water (Ambion) and stored at -80˚C until further notice. Total RNA was used as input for library preparation utilizing the kit NEBnext Multiplex Small RNA Library Prep Set for Illumina following the recommended protocol, except for one minor modification: the 5' adapter was replaced by an analogue that contains 6 extra nucleotides at the 3' extremity to improve barcoding precision, which is sequenced along with the cloned small RNA. Libraries were sequenced at the GenomEast sequencing platform at the Institut de Génétique et de Biologie Moléculaire et Cellulaire in Strasbourg, France, using Illumina HiSeq 4000 equipment. The Ae. japonicus small RNA libraries from Lelystad, the Netherlands, reanalyzed in this study, were previously deposited in the NCBI sequence read archive (SRA) under BioProject PRJNA545039  [12].
Small RNA-based metagenomics for virus identification Raw reads from small RNA libraries were submitted to adapter trimming using Cutadapt v1. 12  [29] , and Illumina libraries from France had the 6 inserted nucleotides trimmed with the adapters. Sequences with an average Phred quality below 20, ambiguous nucleotides, and/or a length shorter than 15 nt were discarded. The remaining sequences were mapped to genome sequences of Ae. aegypti (AaeL5)  [30] , Ae. albopictus  [31] , and bacterial reference genomes using Bowtie v1. 3  [32]. Unmapped reads from the previous step were assembled in contigs combining metaSPAdes  [33]  and Velvet v1. 0. 13  [34]. More details about k-mers and read sizes combinations for assemblies using small RNAs in the current work can be found in  [28]. Contigs larger than 200 nt were characterized based on sequence similarity against the NCBI nt database using BLAST+  [35]  and against nr using DIAMOND (mode --very-sensitive)  [36] , considering significant hits with e-values lower than 1e -5 for nucleotide comparison or 1e -3 for amino acid comparison.
Phylogenetic analysis We selected the contig containing the largest RdRp sequence for the identified viruses. For bunyaviruses, we also selected the contigs containing the largest sequences of nucleocapsid and glycoprotein. The presence of conserved protein domains for RdRp, nucleocapsid, and glycoprotein was confirmed with NCBI Conserved Domain Search (https://www. ncbi. nlm. nih. gov/Structure/cdd/wrpsb. cgi). Searches for potential homologous viral sequences were performed using Blastx against the nr database. Coding sequences were translated to amino acids, and multiple sequence alignments were performed using MAFFT  [44]. The best-fit protein evolution model was selected using MEGA-X  [45]  under the Akaike Information Criterion. Phylogenetic inference was executed with MEGA-X using Maximum Likelihood method.
Protein structure analysis Primary structure physical-chemical properties were calculated with ProtParam software  [52]. Global sequence alignments were performed with Clustal Omega  [53]  and colored with ESPcript web server  [54]. PSIPRED Workbench  [55]  was used for structural assessments, with secondary structures predicted using PSIPRED 4. 0  [56] , DISOPRED3  [57]  for disordered regions and MEMSAT-SVM  [58]  for membrane interaction. AlphaFold  [59]  tertiary structure modelling was performed locally with the complete database downloaded and using default parameters and "06/01/2022" as maximum template date.
Virus identification To further characterize putative viruses represented by the contigs from the clusters highlighted in Fig. 2 , we focused on sequences encoding viral polymerases. It was possible to identify contigs encoding viral polymerases in four clusters that were compared to the most similar sequences in GenBank (Table  2 ). One cluster contained AejapNV1 (genus Narnavirus, family Narnaviridae) that had been previously identified as a possible ISV  [12]. According to phylogenetic analysis (Fig. 3 ), this virus clustered with other mosquitoassociated narnaviruses containing long reverse ORFs (rORFs) from the Alphanarnavirus clade  [25]. The other contigs containing viral polymerase sequences showed significant but low sequence similarity to known references of totiviruses, anpheviruses and bunyaviruses in GenBank only at the amino acid level (e-value < 1e -3 ) (Table  2 ).
1B ). The phylogenetic analysis of amino acid sequences shows that AejapBV2 segments coding for the glycoprotein and nucleocapsid are distant from AejapBV1 equivalent sequences (Fig. 4 ). Indeed, sequence similarity and phylogenetic analysis of nucleocapsid and glycoprotein of AejapBV2 indicate that this virus belongs to the family Phasmaviridae in contrast to AejapBV1 that belongs to the Phenuiviridae family (Fig. 4 ; Table  2 ). In an attempt to find sequences encoding an RdRp belonging to AejapBV2 in our libraries, we mapped reads from each library to the segment L sequence of the virus whose segments M and S were most similar to AejapBV2 (Table  2 ), Wuhan mosquito virus 2 (GenBank: NC_031312. 1) (Suppl. Fig. 2 ). Even allowing multiple mismatches, we did not observe an expressive amount of reads nor a continuous coverage of the segment L of this virus.
To further characterize the identified viruses (Table  2 ), the (di)nucleotide usage of their genomes was analyzed, as in particular CpG dinucleotide usage and GC content markedly differ between the genomes of distinct virus families  [61] [62] [63]. The largest representative sequence of each virus or viral segment identified was analyzed (Table  2 ). The 22 contigs of unknown origin larger than 500 nt were also included to evaluate possible associations with the viruses found in our datasets. The L, M and S segments of AejapBV1 (blue) and the M and S segments of AejapBV2 (purple) showed CpG underrepresentation and a relatively low GC content (Fig. 5 ). AejapTV1 (yellow) greatly differed from these bunyaviruses in its CpG usage and GC content (Fig. 5 ), adding yet another piece of evidence at sequence level that, despite clustering together based on co-occurrence (Fig.
Small RNA profiles of AejapBV1, AejapBV2, AejapAV1 and AejapRV1 For the two bunyaviruses in our datasets, AejapBV1 and AejapBV2, a peak consisting of 21 nt small RNAs (characteristic of siRNA pathway activation) was observed for all three (Fig. 8A ) or two (Fig. 8B ) segments, respectively, and siRNAs aligned across entire segments. Also, RNAs with characteristics of piRNAs (~24-29 nt in length) were detected for both viruses (Fig. 8A, 8B ). Those RNAs showed U enrichment at the 1st nt of antisense reads and A enrichment at the 10th nt of sense reads, further suggesting their piRNA biogenesis (Fig. 8A, 8B ). Moreover, reads that mapped to the segments M and S from both viruses presented a significant 10 nt overlap between small RNAs from sense and antisense strands, which is in accordance with the piRNA signature of the ping-pong amplification mechanism  [74].
Detection of DNA viruses using RNA-seq approaches is not supposed to be a limitation since DNA viruses produce different RNA molecules during their replication cycle  [75]. Indeed, DNA viruses have been successfully identified in mosquitoes via metatranscriptomics approaches based on long and small RNA sequencing  [76, 77]. From a broader perspective, DNA viruses seem to be underrepresented in mosquito viromes  [15]. Therefore, DNA viruses may indeed be absent in the Ae. japonicus virome rather than undetected due to methodological limitations. Regarding our results on the identification of Ae. japonicus-associated viruses, AejapNV1 is a good example of the power of our strategy. De novo assembly of small RNAs did not only yield the ~3 kb long primary genome segment for AejapNV1, but interestingly also revealed the presence of a ~1 kb long secondary genome segment (also ambigrammatic, but containing ORFs with no hits in the databases), which we could confidently associate to the same virus.