dataesr/scientific-paragraphs-categorization huggingface dataset:
Finally, variants were annotated using snpEff v4. 3T  (Cingolani et al. , 2012). A Python script was used to select protein coding variants specific to CXR clones as compared to wild-type HAP1, with a minimal allele frequency of 0. 9 and a depth greater than 10 reads. Among these variants, we selected variants resulting in frameshifts, mis-and non-sense mutations as compared to the reference human genome hg38. Cytoscape v3. 2. 0  (Shannon et al. , 2003)  was used to identify genes found mutated in several CXR clones. Upon TOP2A identification as a common gene mutated in five CXR clones, IGV v2. 4. 15 was used to scrutinize alignment data and revealed two TOP2A mutations missed by the analysis: for CXR #A2, the point mutation S654N; and, for CXR #A6, a mutation of the first nucleotide of the last intron leading to intron retention.
Clone clustering under Cytoscape, based on shared mutated genes, suggested a common origin for clones CXR #A1, #A3, #A5, and #B4 (multiple common mutations). RNA-seq data from wild-type HAP1 and CXR clones have been deposited on SRA with the project ID PRJNA637883. Targeted sequencing of TOP2A cDNA from HAP1 clones Total RNAs were extracted from wild-type or F14R HAP1 with the RNeasy Plus Mini Kit (Qiagen) according to the manufacturer's instructions. TOP2A cDNA was produced from these RNAs with the Superscript III First-Strand kit (Thermo Fisher Scientific) according to the manufacturer's instructions and using the TOP2A-Rv primer. The resulting TOP2A cDNAs was amplified in four overlapping fragments using the primer pairs [TOP2A-F1, TOP2A-R1], [TOP2A-F2, TOP2A-R2], [TOP2A-F3, TOP2A-R3], and [TOP2A-F4, TOP2A-Rv] and sequenced using the same primers except for the last fragment for which the TOP2A-R4 sequencing primer was also used.
The membrane was successively probed with primary antibody and appropriate goat secondary antibodies coupled to horseradish peroxidase (described in table below). A ChemiDoc CCD imager (Bio-Rad) was used to acquire pictures of the stain-free total protein staining and the chemiluminescence signal after membrane incubation with adequate peroxidase substrate (Clarity ECL, Bio-Rad). Digital data were processed and quantified using ImageJ software. Immunofluorescence RPE1-hTERT cells were seeded, treated, and stained in the same conditions than HeLa cells. HeLa cells and HAP1 cells were seeded in 24-wells plate at respectively 100,000 cells/well and 25,000 cells/well on #1. 5 glass coverslips (VWR, #631-0150). HeLa cells and HAP1 cells were respectively treated 24 hr and 48 hr later, and then fixed with paraformaldehyde 2% in PBS at room temperature (10 min for HeLa cells, 15 min for HAP1 cells), washed with PBS, and permeabilized for 15 min at room temperature with 10 mM Tris-HCl pH 7.
For TOP2Acc and BG4 co-immunodetection, both primary anti-BG4 antibody and appropriate secondary antibody were successively added to cells prior to incubations to primary anti-TOP2A antibody and its appropriate secondary antibody. At last, cells were washed with PBS 0. 1% Tween-20 and stained with 0. 1 mg/mL DAPI for 20 min at room temperature, and coverslips were mounted with Vectashield mounting medium (Vector Laboratories). Nuclear gH2AX foci, 53BP1 foci, BG4 foci, TOP2Acc foci, and EdU-integrated density staining overlapping with DAPI staining were quantified with ImageJ software. Nuclear DAPI-integrated density staining was quantified with ImageJ software and correlated to nuclear EdU-integrated density to determine cell cycle phase for each cell as described in  Roukos et al. , 2015.
Clonogenic assay Clonogenic assay was performed as described by  Bombarde et al. , 2017. Briefly, after transfection with siRNA, HeLa cells was seeded at low density (250 cells/well) the day before treatment, pre-incubated with 100 mM DRB, 0. 2 mM BMH21, or dimethylsulfoxide (DMSO) for 1 hr and treated for 4 hr with PDS or CX-5461 in the presence of transcription inhibitor or DMSO before being replaced by fresh medium. After 10-15 days, cells were stained with crystal violet and the colonies were counted (at least 50 colonies were counted for each condition per experiment). Data were normalized to the NT conditions. The IC 50 was computed in each condition with the GraphPad Prism v8 software using a nonlinear regression to a four-parameter logistic curve.
Statistical analyses All results represent at least three independent experiments. Statistical analyses were performed with the GraphPad Prism software (version 8). For gH2AX and 53BP1 quantifications analyses, multiple unpaired t tests (without corrections for multiple comparisons) were performed between pairs of conditions. For BG4 quantifications analyses, results of at least three independent experiments were pulled together and unpaired Welch's t tests were performed between pairs of conditions. On all figures, significant differences between specified pairs of conditions are shown by asterisks (*p-value<0. 05; **p-value<0. 01; ***p-value<0. 001; ****p-value<0. 0001). NS is for nonsignificant difference.
Acknowledgements This work was funded by grants from  ANR  ( ANR-17-CE18-0002-01  and  ANR-16-CE11-0006-01 ),  Canceropole GSO Emergence funding 'CX-Break ,' and  La Ligue Nationale Contre le Cancer  (Equipe Labellisee 2018). We thank  Julia Coates  and  Steve Coates  for proofreading the manuscript. We are grateful to  Emmanuelle Na ¨ser ,  Antonio Peixoto , and  Elodie Vega  for technical assistance and the  Imaging Core Facility TRI-IPBS , supported by  ITMO Cancer (Alliance Nationale pour les Sciences de la Vie et de la Sante, National Alliance for Life Sciences and Health)  within the  Framework of the Cancer Plan. We thank  P Mailliet  for providing 360A. We also thank  J Coates  and  S Coates  for the careful proofreading and language editing of the manuscript. We thank ' Region Midi-Pyrenees' for supporting the 'Toulouse Reseaux Imagerie ' platform.
Patrick Calsou  is a researcher from  INSERM. This work was performed in collaboration with the  GeT core facility, Toulouse, France  (http://get. genotoul. fr),and was supported by  France Genomique National  infrastructure, funded as part of ' Investissement d'avenir' program  managed by  Agence Nationale pour la Recherche  (contract  ANR-10-INBS-09 ).
For replication analysis, whole-exome sequencing (WES) data were obtained for white LVOTO subjects from the Pediatric Cardiac Genomics Consortium (PCGC). 26, 27 This analysis was conducted using WES data from the Framingham Heart Study (FHS) as an independent control population. 28 While BAV was not curated in the FHS, valve and/or aorta surgeries were recorded, allowing removal of these individuals to minimize the inclusion of BAV subjects. del CNV (nsv4684081). Primers for these PCR reactions are shown in Table  S4 , and PCR reactions were conducted using 60 C annealing temperature. The wild-type PCR product generated is 1,440 bp, and the 13. 6 kb del CNV PCR product is approximately 2,500-3,000 bp. The PCR genotyping protocols were validated with demonstration that the delCNV genotype calls obtained from the WES data available for 166 CHP and 89 Sickkids individuals with LVOTO matched those obtained by PCR analysis performed by independent laboratories in CHP and Sickkids.
PCDHA delCNV genotyping using WES and whole-genome sequencing (WGS) data WES data were analyzed from 166 CHP subjects, 89 Sickkids subjects, Chinese pediatric and PCGC LVOTO subjects (database for Genotype and Phenotype [dbGAP]: phs001194. v2. p2), and FHS subjects (FHS, dbGaP: phs000007. v30. p11). 0] [31]  Plink v. 1. 9  32 was used to identify and remove cryptic relatedness in Framingham and PCGC cohorts using methods previously described. 33 Briefly, second-degree or closer relatives with shared ancestry with PI_HAT > 0. 25 were removed, such that only unrelated individuals were used for downstream analyses. For analysis of the delCNV genotypes, depth information directly extracted from BAM files was used to determine the 16. 8/13. 6 kb delCNV genotype by visually reviewing the depth distribution across the region encompassing the overlapping 16.
8 kb and13. 6 kb deletion intervals in each subject (Figures  S1A-S1E ). The CHP and Sickkids samples were further analyzed by multiplexed PCR, which yielded consistent results, confirming accuracy of the WES-determined genotypes. For analysis of wholegenome sequencing data from the 1000 Genomes Project (1KG), depth information of positions spanning 16. 8 kb and 13. 6 kb deletion intervals were directly extracted from joint-calling VCF files downloaded from the 1KG website. 34 The allele frequency observed for the top CNVs in the PCDHA cluster is shown in Figure  S2C. Similar visual inspection of coverage depth in the whole-genome sequencing data was used to determine the 16. 8/ 13. 6 kb delCNV genotypes in subjects from the IKG (Figures  S1F-S1J ).
Bioinformatic analysis of RNA sequencing data from human fetal heart Single-cell RNA-sequencing (RNA-seq) data of human embryos spanning 5 to 20 weeks of gestation from a previously published study were used to examine expression of genes in the PCDHA gene cluster. 41 The gene-cell UMI (unique molecular identifier) count sparse matrix of single-cell RNA-seq data was directly downloaded from GEO database under GEO: GSE106118. Cell type annotation was obtained from the original publication. 41 The sum of UMI counts for each cell was normalized to 10,000 and then log-transformed. A total of 2,500 cardiac cells were used for these analyses.
The animal experiments were not randomized; however, the investigators were blinded to the identity of the samples. In the mouse studies, comparison was conducted with unpaired Student's t test. p values for t tests are reported in the respective figures, and p < 0. 05 was considered statistically significant. All values are shown either as mean 5 SEM (standard error of the mean) or as mean 5 SD (standard deviation). Statistical analysis was performed using SPSS, SAS, or R package.
004), and BAV/CoA with OR 2. 94 (95% CI, 1. 06-8. 12) (p ¼ 0. 038) (Figure  3A ; Table  S7 ). Further analysis with genotype comparisons showed significant association of the 13   3C ; Table  S7 ). Analysis for D/þ genotype showed significant association only for the 13. 6 kb delCNV. This was observed for LVOTO with OR at 2. 94 (95% CI, 1. 44-6. 04) (p ¼ 0. 0032), HLHS with OR at 4. 05 (95% CI, 1. 56-10. 50) (p ¼ 0. 0041), and BAV/CoA with OR at 2. 94 (95% CI, 1. 06-8. 12) (p ¼ 0. 038) (Figure  3C ; Table  S7 ). Overall, these findings support the association of the PCDHA delCNV with LVOTO seen in the white discovery cohort. PCDHA delCNV in Black LVOTO cohort PCA analysis yielded 24 Black subjects from PCGC and 2 from the Pittsburgh cohort (Figure  S2 ). PCDHA delCNVs in these 26 Black LVOTO subjects were examined using 661 African individuals from the 1KG database as control subjects, given the FHS control subjects had few Black individuals.
Acknowledgments We are indebted to all family members for their research participation and contribution to this project. We acknowledge  Tara Paton  from the  Centre for Applied Genomics at the Hospital for Sick Children  for assistance with PCDHA9 genotyping of Canadian samples. This work was supported by  NIH  grants  HL132024 ,  HL142788  (C. W. L), HL109132 (T. G. G. ),  F30HD097967  (G. C. G. ), and  R01 HL109759-A1  and  Nationwide Children's Hospital Foundation  (K. L. M. ),  Ted Rogers Centre for Heart Research (S. M. ) , and  Heart and Stroke Foundation of Canada / Robert M. Freedom Chair  (S. M. ). Additional support was provided by start-up funds to P. T. from the  Chinese University of Hong Kong.
Data and code availability Sequencing coverage for PCDHA9 in the Pittsburgh cohort and in the region of the PCDHA delCNV for the Anzhen Hospital cohort are available under SRA accession numbers BioProject: PRJNA632119 and GSA: PRJCA002663, respectively. WES data from the PCGC and the FHS are accessible in NCBI dbGAP database under accession numbers dbGAP: phs001194. v2. p2 and phs000007. v30. p11, respectively. Single-cell RNA-seq data of human embryonic hearts are available from GEO database under accession number GEO: GSE106118.
Sequence read alignment For each species, sequences were aligned to a reference assembly (Supplementary Table  2 ) using the BWA-MEM algorithm  59  as implemented in BWA v. 0. 7. 17-r1188, with options '-T 30 -Y -p -t 8'. The aligned reads were sorted using the bamsort tool from the biobambam2 package (v. 2. 0. 86; gitlab. com/german. tischler/biobambam2), with options 'fixmates=1 level=1 calmdnm=1 calmdnmrecompindetonly=1 calmdnmreference=<reference_fasta> outputthreads=7 sortthreads=7'. Duplicate reads were marked using the bammarkduplicates2 tool from biobambam2, with option 'level = 0'.
3. 3. 0), using a second sample from the same individual as a matched control. The indel calls produced by Pindel were subsequently re-genotyped using the vafCorrect tool (https://github. com/cancerit/vafCorrect), which performs a local sequence assembly to address alignment errors for indels located at the end of sequence reads, and produces corrected counts of sequence reads supporting the indel and corrected estimates of variant allele fraction (VAF; the fraction of reads supporting the alternate allele at the variant site).
Mitochondrial variant calling and filtering For six species whose reference genome assemblies did not include the mitochondrial sequence, mitochondrial reference sequences were obtained from the GenBank database (Supplementary Table  5 ). For each species, alignment to the reference genome was performed using BWA (v. 0. 7. 17-r1188), as described above (see 'Sequence read alignment'). Pileup files were generated for mtDNA genomes using the 'bam2R' function in the deepSNV (v. 1. 32. 0) R package  62, 63. The mapping quality cut-off was set to 0, taking advantage of the fact that the mitochondrial genome coverage for most samples was more than 100-fold higher than the nuclear genome coverage, and hence most reads with poor mapping scores should be of mitochondrial origin. Mitochondrial variants were called using the Shearwater algorithm  62  (deepSNV package v.
Life-history data Obtaining accurate lifespan estimates is challenging; although point estimates of maximum lifespan are available for many species, their veracity is often difficult to assess and estimates can vary widely for the same species (Supplementary Table  6 ). There can be many causes for this variation, including errors in recording and real variation in longevity between populations (that is, captive versus wild). As we were interested in whether the somatic mutation burden has an association with lifespan in the absence of extrinsic mortality, we sought to obtain estimates of longevity from individuals under human care, to minimize the effect of external factors such as predation or infection. Mortality records for 14 species were obtained from the Species360 database, authorized by Species360 research data use agreement no.
60633 (Species360 Zoological Information Management System (ZIMS) (2020), https://zims. species360. org). This database contains lifespan data of zoo animals from international zoo records. Using records from 1980 to the present, we excluded animals for which the date of birth or death was unknown or uncertain. To avoid infant mortality influencing the longevity estimates for each species, we removed animals that died before the age of female sexual maturity, as defined by the AnAge database  65. This resulted in a mean of 2,681 animal lifespan records per species for the species in the study (minimum 309, maximum 8,403; Supplementary Table  6 ). For the domestic dog, we combined records for domestic dogs (Canis lupus familiaris) and wolves (Canis lupus), because of the paucity of records for domestic dogs in Species360.
Although the data are curated, they are still vulnerable to the presence of inaccurate records, which can bias the lifespan estimates. To reduce the effect of these outliers, for each species lifespan was estimated as the age at which 80% of the adults from that species had died 66 (Supplementary Table  6 ). Human longevity estimates were obtained using census birth and death record data from Denmark, (1900-2020), Finland (1900-2019) and France (1900-2018), retrieved from the Human Mortality Database (University of California, Berkeley (USA), and Max Planck Institute for Demographic Research (Germany); https://www. mortality. org, https:// www. humanmortality. de). We selected these countries because they had census records going back at least 100 years. To remove the effect of infant mortality, we excluded individuals who died before the age of 13.
For each country, we selected the cohort born in 1900 and calculated the age at which 80% of the individuals had died (Denmark, 87 years; Finland, 83 years; France, 81 years). We then used the mean of the three countries as our estimate of the human 80% lifespan (83. 7 years) (Supplementary Table  6 ). To test the effects of different estimates of lifespan on our results, we also obtained maximum longevity estimates for each species from a range of databases  67  and a survey of the literature (Supplementary Table  6 ). Other life-history metrics were obtained from the AnAge database  65  (Supplementary Table  6 ).
Mutational signature analysis Mutational signatures of substitutions on a trinucleotide sequence context were inferred from sets of somatic mutation counts using the sigfit (v. 2. 1. 0) R package  31. Initially, signature extraction was performed de novo for a range of numbers of signatures (N = 2,. ,10), using counts of mutations grouped per sample, per individual and per species. To account for differences in sequence composition across samples, and especially across species, mutational opportunities per sample, per individual and per species were calculated from the reference trinucleotide frequencies across the analysable genome of each sample (see 'Calculation of analysable genome size'), and supplied to the 'extract_signatures' function in sigfit. The 'convert_signatures' function in sigfit was subsequently used to transform the extracted signatures to a human-relative representation (Fig.
999, 0. 98 and 0. 89). This de novo signature extraction approach, however, failed to deconvolute signatures SBSA and SBSB entirely from each other, resulting in a general overestimation of the exposure to SBSA (Extended Data Fig. 15 ). To obtain more accurate estimates of signature exposure, the deconvolution was repeated using an alternative approach that combines signature fitting and extraction in a single inference process  31. More specifically, the 'fit_extract_signatures' function in sigfit was used to fit COSMIC signature SBS1 (retrieved from the COSMIC v,3. 0 signature catalogue; https://cancer. sanger. ac. uk/signatures/) to the mutation counts grouped by species (with species-specific mutational opportunities), while simultaneously extracting two additional signatures de novo (SBSB and SBSC).
We then compared the numbers of colibactin-and APOBEC-positive crypts in the human and non-human sets using two separate Fisher's exact tests ('fisher. test' function in R). This revealed the difference in colibactin exposure to be highly significant (P = 7 × 10 -14 ), unlike the difference in APOBEC exposure (P = 0. 30). Mutational spectra of somatic indels identified in each species were generated using the 'indel. spectrum' function in the Indelwald tool for R (24/09/2021 version; https://github. com/MaximilianStammnitz/ Indelwald).
Selection analysis Evidence of selection was assessed using the ratio of nonsynonymous to synonymous substitution rates (dN/dS) in the somatic mutations called in each species. The dNdScv (v. 0. 0. 1. 0) R package  38  was used to estimate dN/dS ratios for missense and truncating substitutions in each species separately. Reference CDS databases for the dNdScv package were built for those species with available genome annotation in Ensembl (https://www. ensembl. org; Supplementary Table  2 ), using the 'buildref' function in dNdScv. For each species, the 'dndscv' function was applied to the list of somatic substitutions called in samples of that species, after de-duplicating any substitutions that were shared between samples from the same individual to avoid counting shared somatic mutations multiple times.
For the first set, estimates of total and allele-specific CN were inferred by maximizing the joint likelihood of a beta-binomial model for BAF and a negative binomial model for relative coverage, as previously described  19. The most likely combination of allele CN values was obtained for each bin by conducting an exhaustive search of CN values between 0 and 4, and selecting the combination maximizing the joint likelihood (calculated on the basis of expected BAF and relative coverage values). A penalty matrix was used to penalize more complex solutions over simpler ones, as previously described  19. For the second set of bins (bins without SNPs), only estimates of total CN were inferred, by maximizing the likelihood of a negative binomial model for relative coverage. The most substantial differences between these methods and the one previously published are: (i) SNPs were obtained from the variant calling output, instead of from a public database; (ii) relative coverage was calculated per 100-kb bin, rather than per SNP; (iii) SNPs were not phased within each gene, but within each bin; (iv) no reference bias was assumed (that is, the underlying BAF of heterozygous SNPs was assumed to be 0.
For each variable, an LME model was implemented for the regression of somatic mutation rates per sample on the variable of interest, using the 'lme' function in the nlme R package (v. 3. 1-137; https://cran. r-project. org/web/packages/nlme). To account for non-independence of the samples, both at the individual level and at the species level, the model included fixed effects (intercept and slope parameters) for the variable of interest, and random effects (slope parameters) at the individual and species levels. In addition, to account for the heteroscedasticity of mutation rate estimates across species, the usual assumption of constant response variance was replaced with explicit species-specific variances, to be estimated within the model. To determine the fraction of inter-species variance in mutation rate explained by each life-history variable individually, the LME model described above was used to produce predictions of the mean mutation rate per species; only fixed effects were used when obtaining these predictions, random effects being ignored.
Point estimates of the regression coefficients for each model are provided. c, Distributions of regression FVE under individual-and specieslevel bootstrapping. For the LME models regressing somatic mutation rates on inverse lifespan (zero intercept; left) and log-transformed mass (free intercept), the curves present distributions of FVE from 10,000 bootstrap replicates, obtained through random resampling of either individuals (blue) or species (orange) (Methods). Vertical lines indicate the FVE values obtained using the entire dataset.
Acknowledgements We thank the staff of the  Wellcome Sanger Institute  for help in this project, including the  Scientific Operations, Informatics  and  CASM  support teams. This research was made possible by the worldwide information network of zoos and aquariums that are members of  Species360 , and is authorized by Species360 research data use and grant agreement  60633. We thank  D. Conde  and  J. Staerk  for help accessing Species360 data; and  J. P. de Magalhaes ,  J. DeGregori ,  J. Vijg  and  M. Lynch  for their comments on the manuscript. This research was funded by  Wellcome  (grant number  206194 ), the  Dunhill Medical Trust  ( RPGF2002\188 ) and the  Deutsche José Carreras Leukämie-Stiftung. I. M. is funded by  Cancer Research UK  ( C57387/A21777 ) and the  Wellcome Trust. P. J.