dataesr/scientific-paragraphs-categorization huggingface dataset:
Pre-processing of genomic alterations data Somatic structural variants data has been used only for the integrative analyses (i) and (iv), while somatic mutations have been used in all analyses. Each gene, altered by somatic splicing or exonic, damaging mutations or structural variants (see Damaging variants and driver detection section), has been 38 integrated in a common data set. Of note, for missense mutations, we used REVEL annotation included in ANNOVAR, for predicting the pathogenicity of these variants and used a 0. 5 cut-off to restrict to the most likely damaging missense events. We also removed genes altered in less than three samples. For consistency, we selected genes in non sex-chromosomes, protein coding or long non-coding RNA genes and with minimum expression of 0. 01 FPKM within the cohort to be sure to include genes expressed in mesothelioma.
Multi-omic integrative analyses To provide an integrative low-dimensional summary of the molecular variation across the samples, we performed continuous latent factors identification using software MOFA (R package MOFA2 v1. 1. 21). Indeed, MOFA is able to integrate different omic data sets by generating independent continuous variables, named latent factors (LF) that transcribe most variation from the joint data sets. In total, we performed four analyses (i) MOFA-MESOMICS (n = 120, Figures  1  and S1A ), (ii) MOFA-Bueno (n = 181, MOFA was performed independently for each analysis, setting the number of latent factors to 10 (function runMOFA from R package MOFA2 v1. 1. 21). The summary of all these runs are given in Figures  1, S1A-G , and S7 and coordinates and proportions of variance explained for (i)-(iv) are given in Table  S2 , while those for MOFA-ITH are given in Table  S4 , and those for the cell lines (model v) are given in Table  S14.
Pareto theory fit The Pareto front model has been fitted to different sets of samples using the ParetoTI R package (https://github. com/vitkl/ParetoTI, release v0. 1. 13), following the above mentioned analyses (i), (ii), (iii) and (iv), and additionally, on two different kinds of molecular maps: using MOFA, restricting to latent factors LF1, LF2, LF3, and LF4 and using expression PCA as technical validation (see RNA Sequencing). In brief, the algorithm tries to find polyhedra by testing successively 1 to n axes adding them one after another in a decreasing order of transcriptomic variance explained. For this technical reason, the MOFA LFs have been ordered as follows by decreasing transcriptomic variance explained: Morphology factor (LF2), Adaptive-response factor (LF3), CIMP factor (LF4), and Ploidy factor (LF1).
For each number n of axes used, ParetoTI identifies the position of the n+1 = k vertices (archetypes) in the molecular map defined. Each polyhedron fit is assessed by the ratio of the volume of the best-fitting polyhedron to the volume of the convex hull of the data (t-ratio). The more the data follows the Pareto optimality theory, the more the t-ratio metric, higher than 1, approaches 1. Finally, the algorithm re-calculates the t-ratio on 1000 shuffles keeping the distribution of loading on each axis but not the associations between them and computes a one-sided p-value to estimate the statistical significance of the fit. Here, we chose to represent the most significant fit with the smallest number k because of the limited number of samples. Using MOFA axes, we found k = 3 archetypes in the LF2-LF3 space and reproduced, for each analysis (i), (ii), (iii), and (iv), the fit using the corresponding expression PCA in the PC1-PC2 space (see Figure  S1G  for the fit for model ii, and iii).
Interpretation of MPM polyhedron To further characterise the phenotype of each archetype we used the proportion of each archetype for each sample estimated by ParetoTI. These proportions have been used as continuous variables to further 41 test the association between each archetype and clinical, epidemiological, morphological variables, as well as molecular data (Table  S3 ). More specifically, we inferred each archetype phenotype by performing integrative gene set enrichment analysis (IGSEA) on the expression data. To do so, we used the ActivePathways r package (https://github. com/reimandlab/ActivePathways, release v1. 02) which is a tool able to integrate different sources of molecular variations to assess the enrichment of GO terms, by combining p-values from different association tests between sources and gene level data.
42 Because of the difficulty of inferring the clonality of CNVs, we also assessed the clonality of CNVs using software Facets  (Shen and Seshan, 2016 ; https://github. com/IARCbioinfo/facets-nf; release 2. 0); only CNVs consistently called as clonal or subclonal by PURPLE and Facets are reported in Figure  S5H  (see Table  S7  for all CNV clonalities). Although CNVs are generally called more accurately than small variants in T-only samples, for consistency with the rest of the clonality and evolutionary analyses, we restricted the analyses to tumors with a matched normal (n=43).
Kim et 24. CC-BY-NC-ND 4. 0 International license perpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for this this version posted September 29, 2021. ; https://doi. org/10. 1101/2021. 09. 27. 461908 doi: bioRxiv preprint al. , 2018) using our Nextflow workflow (https://github. com/IARCbioinfo/mutect2-nf release v1. 2a).
Acknowledgements This study is part of the  MESOMICS  project within the  Rare Cancers Genomics initiative  (www. rarecancersgenomics. com). We thank the patients for donating their tumour specimens. The human biological samples and associated data were obtained from the  French MESOBANK. We also thank  Ricard Argelaguet  for his advice in using MOFA;  Hugues Begueret ,  Nathalie Rousseau ,  Didier Bozonnet ,  Eric Wasielewski ,  Gilles Clapisson ,  Christelle Bonnetaud ,  Kevin Washetine ,  Audrey Lupo Mansuet ,  Cyrille Cuenin , and  Estelle Clermont  for their contribution to the biorepository. The authors would like to acknowledge the  American Association for Cancer Research  and its financial and material support in the development of the AACR Project GENIE registry, as well as members of the consortium for their commitment to data sharing.
Interpretations are the responsibility of study authors. The results published here are part based upon data generated by the  TCGA Research Network : https://www. cancer. gov/tcga. We also thank the  National Program for pleural Mesothelioma Surveillance (PNSM)  and  Santé Publique France. This work has been funded by the  French National Cancer Institute (INCa ,  PRT-K 2016-039  to L. F. C. and M. F. ), the  Ligue Nationale contre le Cancer  ( LNCC 2017  and  2020  to L. F. C. and M. F. ). L. M. has a fellowship from the  LNCC. This work also benefited from support of the  France Génomique National  infrastructure, funded as part of the  "Investissements d' Avenir" program  managed by the  Agence Nationale pour la Recherche  (contract  ANR-10-INBS-09 ).
BY-NC-ND 4. 0 International license perpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in bioRxiv preprint doi: ; https://doi. org/10. 1101/2021. 09. 27. 461908 this version posted September 29, 2021. The copyright holder for this European Genome-phenome Archive (EGA) database, which is hosted at the EBI and the CRG, under accession number EGAS00001004812. TCGA whole-exome sequencing, RNA-seq, and methylation array data are available from the GDC portal (TCGA-MESO cohort), the Clinical data Age at diagnosis (in years), sex (male or female), smoking status (no-smoker, ex-smoker, and smoker), asbestos exposure (exposed or non-exposed), previous treatment with chemotherapy drugs (yes or no), Blum et al.
(2019), 2014)d RNA-seq data from the Bueno and colleagues cohort are available from the European Genome-phenome Archive, EGA:EGAS00001001563. Small variants lists, RNA-seq, expression array, and methylation data for the Iorio and colleagues cohort(Iorio et al. , 2016)are available from the GEO (GSE29354), EGA (EGAS00001000828), and SRA (PRJNA523380) websites, and corresponding drug responses are available from the cancerrxgene. orgwebsite(accessedJuly2021). Expression array data for the de Reyniès and colleagues cohort(de Reyniès et al. , 2014)are available from the ArrayExpress platform (E-MTAB-1719), and corresponding drug response data from the supplementary material ofBlum et al. (2019). All the other data supporting the findings of this study are available within the article and its supplementary information files.
https://github. com/hartwigmedic al/hmftools Gistic2 version 2. 20. 23 (Mermel et al. , 2011) SVaba version 1. 1. 0 (Wala et al. , 2018) https://github. com/walaj/svaba Delly version 0. 8. 3 (Rausch et al. , 2012) https://github. com/dellytools/dell y Manta version 1. 6. 0 (Chen et al. , 2016) https://github. com/Illumina/mant a SURVIVOR version 1. 0. 7 (Jeffares et al. , 2017) https://github. com/fritzsedlazeck /SURVIVOR AmpliconArchitect version 1. 2 (Deshpande et al. , 2019) https://github. com/jluebeck/Amp liconArchitect/ CNVkit version 0. 9. 7 https://github. com/etal/cnvkit R version 4. 0. 3 CRAN.
Simulation settings Our simulations are based on different specifications of the model Point 3. ) impacts the construction of interaction scores in the screening step b. ) where a simple linear working model for main effects is assumed. Overall, 32 scenarios can be generated as combinations of the presence or absence of these five factors. For the simulations with non-normal errors, we used the mean centered and standardized FEV1/FVC data from the UK Biobank as the random error to investigate robustness against this error distribution. In addition, we investigated the question if the type 1 error rate is inflated in the case where the SNPs for the analysis were selected based on GWAS results in the same dataset, i. e. , based on their GWAS association p-values (notated as SELECT yes/no). 𝑌 𝑖 = Incorporating this factor, we, therefore, simulated a total of 64 scenarios.
Comparison with linear regression To demonstrate the importance of the robust test statistic in our approach, we also included a standard regression analysis as a comparison. Here, we use the interaction score   , derived by the steps a. ) to d. ), but instead of using the robust statistic ( in  3 and test for interaction based on the coefficient. We denote this approach by REG. We also incorporated REG-robust, where the corresponding standard errors are computed based on a robust sandwich-variance-estimator. Since we only consider type 1 error rate studies and not power results, the fact that REG/REG-robust use  3 data only, whereas RITSS used the whole dataset, is not crucial.
Here, the genotype main effect estimates for the genetic risk scores were estimated based on the 180,000 samples in the main analysis. The results of our validation analysis are described in Table  3. The results show that almost all findings replicate except the  3 based E-S interaction for FEV1/FVC. We also note that the effect directions were consistent between the main analysis and validation analysis. To examine these findings in more detail, we examined the single variant interaction p-values for the genetic variants in the combined set  3 / 2 with the respective environmental factor in the validation dataset. This analysis was also based on a linear regression that fits   ,   , and   , and the respective interaction term. Table  4  lists the number of genome-wide significant single variant interactions, i.
e. ,  < 5 * 10 -8 , as well as the number of significant single variant interactions using a Bonferroni correction adjusting for the number of variants in the combined set  3 / 2 at a significance level of  = 0. 05. Both numbers are either 0 or very small, for all trait/environmental factor combinations. This suggests that the interaction effects at the single variant level are small. However, we make an interesting observation by investigating the empirical distribution of effect directions across the single variant interaction tests. Table  4  provides these empirical distributions of effect direction for a. ) the unweighted interaction terms   *   and b. ) the weighted terms  ̂ *   *   , where  ̂ is the estimated genetic effect based on the main analysis dataset. In addition, we compared the interaction p-value distributions according to the effect direction of the weighted interaction terms.
However, if we test the height interaction separately in males and females in the validation dataset, the signal is strongly diminished. Therefore, we hypothesize that the observed interactions are a result of sex-differential effects, but further analyses are required to disentangle the exact mechanisms. In this context, we also note that, related to recent results regarding so-called participation biases and their impact on sex-related analyses  (Pirastu et al. , 2021) , the genetic variants in  3 / 2 across the three lung function phenotypes and height were not associated with sex. Detailed information about the selected variants in the gene-by-sex interactions for lung function and height and the corresponding closest genes (obtained by the GWAS catalogue data) can be found in Supplementary Tables  1 2 3 4.
In addition, RITSS can be applied to other omics data layers, such as for example, DNA methylation, transcriptomics, or metabolomics. An R package implementing RITSS and providing the framework for extensions, as well as the simulation study code, is available at https://github. com/julianhecker/RITSS. Vansteelandt,S. et al. (2008)  Multiply robust inference for statistical interactions. J. Am. Stat. Assoc. , 103, 1693 -1704. Wang,X. et al. (2020)  Efficient gene-environment interaction tests for large biobank-scale sequencing studies. Genet. Epidemiol. , 44, 908-923. Zhang,M. et al. (2020)  Interaction analysis under misspecification of main effects: Some common mistakes and simple solutions. Stat. Med. , 39, 1675 -1694. Zhang,P. et al. (2016)  Detecting Gene-Environment Interactions for a Quantitative Trait in a Genome-Wide Association  Study.
Genet. Epidemiol. , 40, 394-403. Zhao,G. et al. (2015)  Assessing gene-environment interactions for common and rare variants with binary traits using gene-trait similarity regression. Genetics, 199, 695-710. we downloaded all reported genetic associations from the GWAS catalog  (MacArthur et al. , 2017 ) (February-August, 2021). We extracted the corresponding genetic variants with a minor allele frequency above 1% (estimated in the analysis dataset) and performed LD pruning (indep-pairwise command with parameters 500, 50, and 0. 2) using PLINK2 (Version v2. 00a2. 3LM) (Chang et al. , 2015)  to avoid reported genetic associations that are in high LD. We also excluded multi-allelic variants. Our analyses are based on expected minor allele count information, as computed by PLINK2. The final number of variants for the analysis of the respective phenotypes is described in Table  1.
Ghorbani et al. [56]  used video images as network input to simultaneously analyze the data characteristics that contain spatiotemporal information and created the final output by averaging the generated output from cine frames. By analyzing local cardiac structures, enlarged left atrium, left ventricular hypertrophy, and the presence of a pacemaker lead were determined, and the positions of the area important to the determination were marked to present explanations on the output. The study also tried to estimate age, sex, weight, and height from representative views, such as the apical four-chamber view. A chronic kidney disease (CKD) scoring system  [65]  using ultrasonographic parameters such as kidney length, parenchymal thickness, and echogenicity is widely used. Issues still exist, however, regarding the user's subjective evaluation.
Built in a CNN framework, the method utilized the eye movement data of a sonographer. The movement data were collected from experienced sonographers to generate visual heat maps (visual fixation information) of each frame and used the data for identifying the correct planes. Using Sononet  [84] , a real-time detection technology of fetal standard scan planes in freehand ultrasound, the heatmaps and image feature maps were integrated to enhance the accuracy of AC plane detection.
SAVAGE is also employed in VG-flow  14  , a de novo approach which enables fulllength haplotype reconstruction from pre-assembled contigs of complex mixed samples. All the above methods are not applicable when the input consists of long reads only, and the last two are limited to small genomes (approximately up to 200 Kbp). Related works have analyzed strains without performing separation, but rather by mapping to existing databases of genomes. StrainPhlAn  15  performs phylogenetic analysis of a population and strain-level variation profiling, through read mapping on reference species-specific markers. StrainEst  16  is a referencebased method exploiting the single-nucleotide variant profiles of available genomes of selected species to determine the number and identity of coexisting strains and their relative abundances in mixed metagenomic samples.
All previous works on strain-level assembly/characterization are based on short-read sequencing data. With the advent of long-read metagenomics sequencing, they cannot be re-used due to fundamental differences in the nature of the data. Many of them (e. g. , LSA  11  and references therein) rely on the reliable detection of error-free k-mers with high k values (>20), which cannot be performed on long reads due to the high error rate. Some methods  8, 10, 15  have been specifically designed to work with multiple related samples (e. g. , time series), to reconstruct core genes, or to simply provide a strain-level profile of a metagenomic dataset (with no actual assembly). It would be desirable to have a method that, using long reads, produces a strain-separated de novo assembly from only a single sample.
It is based on the overlap-layout-consensus paradigm and during its development it introduced new overlapping and assembly algorithms in order to better handle the advances and challenges offered by long-read sequencing technologies. As opposed to Flye, Canu was not specifically designed for metagenomic data although it is often considered an alternative to Flye in this context  [19] [20] [21]. Lathe 22 is a recently developed workflow for Nanopore reads which attempts to provide complete closed bacterial genomes from microbiomes. It combines existing methods for basecalling, longread assembly (either Flye or Canu), extensive polishing steps to detect and correct misassemblies, and performs genome circularization. Throughout the manuscript, we will use the term strainoblivious assembly to refer to a metagenome assembly generated by an assembler which did not attempt any strain separation.
Results A software pipeline for automated strain separation using long-read sequencing data from low-complexity metagenomes. The Strainberry pipeline is depicted in Fig. 1  and consists of three main steps: (i) haplotype phasing and read separation, (ii) haplotype assembly, and (iii) strain-aware scaffolding. It requires two inputs: a strain-oblivious metagenome assembly, and a set of long reads aligned to the assembly. The input assembly is either one generated from the input reads, or one made from a combination of sequencing technologies. In our evaluation of Strainberry, we often used a single long-read dataset assembled using the (meta) Flye assembler which is, to the best of our knowledge, the only long-read assembler explicitly supporting metagenomic data. We additionally tested the performance of the Canu assembler.
The three main steps of the pipeline are iterated n À 1 times, where n is the maximal number of conspecific strains detected by Strainberry. The output of Strainberry is a multi-FASTA file containing contigs shared between multiple strains, and strain-specific contigs. Components of the pipeline are presented in more details in the Methods section. Overview of evaluation data. Datasets of increasing complexity were considered in order to assess to which extent our method is able to reconstruct complete strain genomes. Two low-complexity mock communities (Mock3 and Mock9) containing respectively three and nine precisely-known bacterial strains (Supplementary Table  1 ) enable an accurate assessment of the quality of results returned by our pipeline. 24 simulated communities were additionally created (or downsampled from Mock3) in order to evaluate Strainberry's ability to assemble conspecific strains characterized by different levels of coverage, divergence, number of strains, and recombination rate.
Two metagenomic samples of real sequencing data, one with four dominant bacterial strains and another one with a higher number of species, enabled us to reconstruct novel sequences of strains. They were selected from two different studies. The first one is a low-complexity microbiome from natural whey starter cultures for which two strains of Lactobacillus helveticus were identified and assembled  23. Reads were sequenced with both PacBio and Nanopore technologies which allowed us to assess the performance of our approach with different technologies and validate it with available reference genomes. The second study contains a more complex dataset that is based on Nanopore sequencing data from a human stool microbiome  22. Previous analyses hinted at the presence of multiple uncharacterized strains: species-level binning yielded nearcomplete genome reconstructions characterized by a significant percentage of SNVs with respect to the assembled reads.
Specific to synthetic data, the NG50 variant (i. e. , the length for which all contigs of that length or longer cover at least half of the total genome length) is reported instead of the N50 value. We will also report the percentage of unaligned bases in both references and assemblies, which is a key metric for evaluating whether complete strain-level genomes were reconstructed. In addition, the average sequence identity, the duplication ratio (i. e. , the total number of aligned bases in the assembly divided by the total number of aligned bases in the reference), and the number of misassemblies, are also reported with respect to known reference genomes. Specific to real datasets, for which a comprehensive set of reference genomes was not available, we used CheckM  24  to quality-control assemblies.
We checked that the locations of these putative misassemblies are at the same chromosomal locations in both strains, except in E. coli W where Flye generated two short insertions and Strainberry failed to reconstruct two short repetitive sequences (Supplementary Note 1). Nevertheless, all identified misassemblies do not correspond to major rearrangements and, even though the amount of detected misassemblies is numerically higher, this is a natural consequence of having assembled both E. coli strains and a significantly larger number of bases. Separation of five close strains in a nine-strain mock community. The more challenging Mock9 dataset is composed of 9 genomes (Supplementary Table  1 ) with two strains of E. coli and two strains of S. aureus; the other genomes are of different species.