dataesr/scientific-paragraphs-categorization huggingface dataset:
3fc) of exons 2-8. Confirmatory testing by an alternate method is suggested, if clinically indicated. RefSeq IDs for the genes with reported variants along with a list of all 400 genes can be found on the last page  highly concordant results with 92. 9% sensitivity and 100% specificity. Discordant calls between SNP array and FACETS2n were attributed to sub-clonal calls made by SNP array and/or tumor fractions below the sensitivity of FACETS2n (less than 20%) as determined by serial dilution of a well characterized tumor sample. (Supplementary Tables  4 5 6 ). Having established saliva and nail tissues as suitable controls for identifying true somatic SNVs and indels, we sought to further leverage paired tumor-normal matched sequencing data to assess the allele specific copy number alterations of this cohort.
This cross-malignancy dataset will support explorations of driver alterations across all blood cancers to support discovery of rare and unanticipated clinically actionable alterations. With continuing growth in the realm of precision therapeutics, this data set will prove a transformative resource for identifying novel biomarkers to inform prognosis and predict response and resistance to therapy. This includes the further definition of putative passengers which may, in fact, have important functional and cooperative roles in driving cancer. In an unmatched model, a very high proportion of private SNP's are classified as passengers or VUS's, necessarily so because they are not yet part of well characterized variants in any of the current publicly available databases. In contrast to solid tumors where the primary focus of genomic profiling has been the selection of targeted therapy for key single genetic drivers, the aims of genomic profiling in hematologic cancers are heavily invested in refining a diagnosis and providing prognostic information, with therapy selection often supported by the former.
The best approach to rapidly achieve these goals is through sharing of these datasets across institutions and establishing broad collaborations. To this end, we have deposited our full data set into the cBioPortal for Cancer Genomics (https://www. cbioportal. org/study?id=heme_msk_ impact_2022). With continued testing and data sharing, it is our belief that broad genomic assessment will support understanding the pathobiology of, identifying novel drug targets for, and improving noninvasive monitoring for response in all patients with blood cancer.
Patient consent and accrual This study complies with all relevant ethical regulations. Informed consent for the molecular profiling of patient tumors was obtained under protocol NCT01775072 "Tumor Genomic Profiling in Patients Evaluated for Targeted Cancer Therapy. " The protocol was approved by the Institutional Review Board at Memorial Sloan Kettering Cancer Center and written consent was obtained from all patients. Following consent, either archival or new tumor samples were obtained. The selection of appropriate matched normal was determined after a review of the patient clinical history and tumor diagnosis. OncoTree (http://www. cbioportal. org/oncotree/), an institutional tumor classification system was used to ensure consistent specimen annotation. Matched saliva was prioritized for lymphoid neoplasms owing to the ease of specimen collection and processing and the known paucity of lymphoid components in the samples.
An automated data management system monitored the sequencers, initiating the analysis pipeline upon completion of the sequencing run. Sequence reads were aligned to the human genome (hg19) using BWA MEM (version 0. 7. 5a). ABRA (version 0. 92) was employed to realign reads around indels to reduce alignment artifacts, and the Genome Analysis Toolkit (version 3. 3-0) was used to recalibrate base quality scores. Duplicate reads were identified and marked for removal, resulting in BAM files that were utilized for variant candidate discovery. We implemented a custom analysis pipeline (see below) to integrate the analysis of any number of normal samples with a given tumor and provide a reliable assessment of somatic alterations, even in posttransplant chimeric patients. Copy number alterations were assessed using FACETS2n, an allele-specific copy number analysis pipeline for next-generation sequencing data, adapted from the FACETS algorithm  21  to allow the incorporation of multiple normal samples for normalization and determination of allelic imbalance in tumor samples, even those from chimeric patients.
All genomic variants called by the analysis pipeline were loaded into MPath, an in-house genomic variant database and user interface that facilitates the manual review of variants and their assessment for therapeutic, diagnostic, and prognostic implications with OncoKB  53. Through the incorporation of variant allele fraction (VAF) in tumor and normal tissues, patient clinical history, and annotated population frequencies  9  , we were able to eliminate variants with low sequencing quality and those of patient and/or donor germline origin.
Copy number analysis Genome-wide total and allele-specific copy number states were calculated for all tumor samples using the open-source R package FACETS2n (v0. 3. 0). Library-specific coverage biases that stem from differences between tumor (FFPE, blood, and bone marrow) and normal (Nails, Saliva, Blood) tissues may result in log ratios with high levels of noise when calculated with matched normal samples. With FACETS2n, a single unmatched normal is selected from a pool of highquality normal samples previously processed and sequenced with the MSK IMPACT-Heme assay. These normal samples were selected to have a representation of males and females from a variety of tissue types and with different insert size distributions. A single tumor sample was compared to multiple control normal samples to derive distinct log-ratio sets.
Mutational signatures Mutational signatures were assessed for the 261 tumor samples with elevated mutation rates using all synonymous and nonsynonymous SNVs and SigProfiler software  38, 40. In order to limit inter-signature bleeding that stems from difficult to decipher flat signatures  40, 80  and elucidate the mutational processes that contribute to individual cancer genomes, we first performed de novo extraction of single base substitution (SBS) signatures on both individual tumor types and groups of tumors originating from either lymphoid or myeloid lineages. Discovered signatures were decomposed into COSMIC v3. 1 SBS signatures with the inclusion of recently described MMRd signatures  46. We then estimated the contribution of each signature to individual cancer genomes using a nonlinear convex optimization programming solver, SigProfilerAssignment.
Acknowledgements The authors gratefully acknowledge  Nicole Degroat ,  Christine England ,  Sandy Naupari ,  Jake Bakas ,  Yingjuan Xu ,  Hina Patel ,  Srushti Kakadiya ,  Jaclyn Rutter ,  Justina Almodovar ,  Daviana Martinez-Osorio ,  Brandon Gray ,  Hun Jae Jung ,  Nelio Chaves ,  Jada Barbee ,  Kizzia Perez ,  Aqib Abass , and  Shadia Islam  for their important contributions. This research was funded in part through the  NIH/NCI Cancer Center Support  Grant  P30 CA008748.
Data availability The raw sequencing data for the MSK-IMPACT analysis is protected and cannot be broadly available due to privacy laws; patient consent to deposit raw sequencing data was not obtained. All results derived from the analysis of clinical sequencing data (mutations, copy number alterations, and structural variants) are publicly available. Analysis of germline alterations was not performed due to lack of patient consent for dedicated germline analysis and reporting. The minimal clinical and somatic alteration data (including mutations and allele specific copy number calls) necessary to replicate the findings in the article are publicly available on cBioPortal: https://www. cbioportal. org/study?id= heme_msk_impact_2022 and have been deposited to https://github. com/mskcc/MSK_IMPACT_HEME.
One of the main aims of the HBM4EU chromates study was to provide EU relevant data on the current occupational Cr(VI) exposure to support the regulatory risk assessment and decision-making process. In addition, exposure to PFASs was evaluated in a subset of workers performing chrome plating activities. In this article, we have analysed the results of the HBM4EU chromate study from an EU policy perspective, highlighting the main findings that should be considered by regulators working in the field of REACH or occupational safety and health (OSH) regulations. In addition, to obtain further insight on the usability and potential policy uptake of the results, EU and national regulators and stakeholders' opinions on the potential policy implications of the results were obtained using a focused on-line survey.
Although in the HBM4EU chromates study, the main focus was on biomonitoring data, we also collected air monitoring data. We observed that 95% of Cr(VI) personal measurements (inhalable and respirable combined) measured outside RPE being worn, were below a BOELV of 5 μg/m 3 and 75% of the measurements were below 1 μg/m 3 which is the 8-h TWA OEL currently enforced in France, Denmark and the Netherlands for Cr(VI) (Fig. 1 ). The TLV-TWA of 0. 2 μg/m 3 adopted by ACGIH was, however, exceeded in 73% of cases (Fig. 1 ). Thus, our welding data suggest that the BOELV of 5 μg/m 3 , and even lower exposure levels, are technically achievable in these welding activities. In our study, the most common welding processes reported were tungsten inert gas (39. 5%) and shielded metal arc welding (17. 4%) but no differences were observed in U-Cr levels between exposures of workers using these welding techniques  (Viegas et al.
, 2019b). Often exposure models were used to provide these estimates, even when measurement data was available. In the cases where estimates were obtained through actual measurement data, further adjustments were made to account for the use of RPE and for the frequency of the tasks being considered. Our HBM4EU chromates air measurement and biomonitoring data from the chrome plating activities, and other surface treatment activities (including machining and spray applications) show that exposure levels were not always below 2 μg/m3. Although median air Cr(VI) levels in plating and machining tasks, and  Values of 2 and 7 μg/g creatinine correspond to 8 h TWA inhalation exposure to 1 and 5 μg/m 3 in plating activities with exposure to soluble chromates. Value of 3. 4 μg/g creatinine corresponds exposure to 5 μg/m3 in welding activities with exposure to poorly soluble Cr(VI) compounds.
Policy observation 4: biological guidance values for Cr(VI) in welders need to be based on the welder specific data One of the challenges related to the use of Cr biomonitoring data, both in the OSH field and in REACH authorisations, has been the uncertainty related to the interpretation of the biomonitoring data. Although there are existing correlations between air Cr(VI) and U-Cr levels available  (Chen et al. , 2002; Lindberg and Vesterberg 1983) , those have shown considerable variability. As discussed in  (Viegas et al. , 2022) , our regression analysis made for bath plating workers supports the analysis performed by  (Chen et al. , 2002). Using the regression equation based on the dataset of  (Chen et al. , 2002) , an exposure level of 2 μg/m 3 (used as a reasonable exposure estimate in some REACH applications for authorisation for chrome plating) corresponds to a U-Cr level of 3.
Acknowledgements This project has received funding from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement No  733032  and received co-funding from the author's organizations and/or Ministries. The project team would like to thank all the companies and workers who participated in the HBM4EU chromates study and all the experts who have contributed to the conduct of the study. Participants of the HBM4EU chromates study workshop and policy questionnaires are also acknowledged. Mr. Jouko Remes  and  Dr. Kia Gluschkoff  ( Finnish Institute of Occupational Health ) are acknowledged for their assistance with the statistical analyses and figures.
(See section below for details on cohort inclusion and errors). A total of 45 cohorts participated in our study (Table  S1 ). Table  S2  provides an overview of cohort-specific details, including an adjusted pooled analysis of women and men in the case of family data (see below). Cohorts who agreed to participate followed an Analysis Plan posted on the Open Science Framework preregistration site https://osf. io/b4r4b/ on February 08, 2017. For autosomal chromosomes and NEB the total number of individuals in the pooled metaanalysis was 785,604, with a somewhat larger sample for women (39 cohorts) than men (29  cohorts). Table  S3  provides detailed information about the cohorts with X Chromosome data. For the NEB meta-analysis this included 671,349 individuals (12 cohorts) for the pooled cohorts, 384,976 for women (12 cohorts) and 286,373 for men (10 cohorts).
Analysis of X chromosome Analysis of X chromosome variants was performed using one of two complementary approaches, XWAS or SNPtest, the results of which could be combined by meta-analysis. In XWAS software (http://keinanlab. cb. bscb. cornell. edu/content/xwas) we used the --var-hetweight command. In SNPtest, we used the -method newml; while this assumes complete Xinactivation (i. e. a male with one allele is considered the same as a homozygous female) the effect estimates and standard errors approximate ½ of those produced by XWAS.
d) Data harmonization. We matched the cohort based summary statistics with a 1000 Genome reference panel phase 1 version 3 reference panel provided by Winkler et al  38. EasyQC drops mismatched variants which cannot be solved straight away such as duplicates, allele mismatches or missing or invalid alleles. Based on graphical inspections (see below), we applied cohort specific filters to drop variants with obvious deviations between expected allele frequency based on the reference panel and observed allele frequency.
Replication Replication was performed using the FinnGen study -a public-private partnership project combining genotype data from Finnish biobanks and digital health record data from Finnish health registries (https://www. finngen. fi/en). Six regional and three country-wide Finnish biobanks participate in FinnGen. Additionally, data from previously established populations and disease-based cohorts are utilized. Release 4 of Finngen includes 176,899 participants. In this analysis we included women that participated to Finngen release 4 and were at least 45 years of age at 31 st December 2017. This was the last date we had information from national registries. We also excluded women younger than 16 in 1969 (the start of the registries). Using these inclusion criteria, we included women born between 1953 and 1973 and children born between 1969 and 2017.
Phenolyzer takes free text input and interprets these as disease names by using a word cloud to identify synonyms. It then queries precompiled databases for the disease names to find and score relevant seed genes. The seed genes are subsequently expanded to include related (predicted) genes based on several types of relationships, e. g. protein-protein interactions, transcriptional regulation and biological pathways. Phenolyzer uses machine learning techniques on seed genes and predicted gene rankings to produce an integrated score for each gene. We used search terms capturing three broad areas, i. e. (in)fertility, congenital neurological disorders and psychological traits, based on results from pathway, tissue and cell type enrichment analyses. Phenolyzer identified 16 and 8 candidate genes for NEB and CLN, respectively for gene scores >0.
Acknowledgements This research was conducted using the  UK Biobank Resource  under application  32696  and  9797. This work was supported by the  Medical Research Council  [Unit Programme number  MC_UU_12015/2 ],  ERC  grants  615603  and  835079 ,  NIH  grant  R35GM133708  and  The Leverhulme Trust, Leverhulme Centre for Demographic Science. Full study-specific and individual acknowledgements can be found in the supplementary information. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or other funders.
Age and 10 PCs and genotyping batch were used as covariates. Each genotyping batch was included as a covariate to avoid convergence issues. We considered common (MAF >= 0. 05) high quality variants: not in chromosome X, imputation INFO>0. 95, genotype imputed posterior probability>0. 95 and missingess<0. 01. LD-pruned (r 2 <0. 1) common variants were used for computing PCA with Plink 1. 92. SAIGE mixed model logistic regression (https://github. com/weizhouUMICH/SAIGE/releases/tag/0. 35. 8. 8 ) was used for association analysis.
users to switch from one mix of technologies to another. In order to help the community, several benchmarks are already available. The type of repeated sequences present in the genome strongly influences the ability to establish a complete genome assembly. There was therefore a great interest in having another type of dataset next to the already available benchmarks such as the human dataset proposed by GIAB. The SeqOccIn project aims to explore the use of new sequencing technologies in agronomic research. We created reference datasets for genome assembly, haplotyping, and variability discovery for species of agronomic interest. In this article, we describe the datasets corresponding to the sequencing of a Charolais heifer. In order to infer her haplotypes, we also sequenced her parents. We produced 13 datasets using 3 sequencing technologies and 6 library preparation methods (see Fig.
1 ). These datasets include high-depth paired-end short read whole genome sequencing (WGS), Hi-C, linked reads WGS, long read WGS, as well as metadata. For each dataset, we describe the library preparation and sequencing methods, the currently available data records, and technical validation. Thanks to these datasets, we were able to provide a high quality assembly of a bovine Charolais heifer. A haplotype-resolved as well as a consensus assembly were produced. The size of our best assembly, using Pacific Biosciences HiFi, is 3. 2 Gb, compared to 2. 7 Gb for the reference assembly, ARS-UCD1. 2; our assembly contains 1444 contigs and has an N50 of 87 Mb, compared to 2596 contigs and an N50 of 25 Mb for the reference assembly (see Table  3 ). After scaffolding using Hi-C data, our assembly reaches an N50 of 88 Mb, with 1391 scaffolds (see Table  4 ).
Methods Genome information. Domestic cattle genome follows the Bovidae family chromosomal organization with 29 autosomes and 2 sex chromosomes. Autosomes are acrocentric, meaning that their centromeres are terminal. Liu, Y. et al. 1  estimated Bos taurus genome size to be 2. 87 Gb based on measurement from ESTs and previous genome size. Other studies referenced in Animal Genome Size Database 2 based on flow cytometry, ultraviolet microscopy or biochemical analyzes have found genome sizes ranging from 3. 15 Gb to 3. 93 Gb. We estimated the genome size using k-mers with Jellyfish 3 and Genomescope2. 0  4  on PacBio Sequel II HiFi data. Genomescope2. 0 provided an estimate of 2. 975 Mb with an heterozygosity of 0. 281% and 36. 9% of repeated sequences. The current ARS-UCD1. 2 reference assembly metrics are: 2.
The contact map contigs organisation shown by juicebox is considered correct if the contact density is maximal on the diagonal. Chromosomes should also be clearly separated in the map by areas with a very low contact density. Both criteria were visualy inspected. When these criteria were not fullfilled contigs were moved, reversed or split in order to maximize contact signal on the diagonal and chromosome separation. Finally, we loaded a contact map generated from 10X linked reads presenting very local contact information in Juicebox in order to visually check the scaffold organization (See Code availability).
Structural variation. To evaluate the contribution of the different technologies for structural variant (SV) detection, a structural variant analysis was performed for each of the available datasets. By structural variation we mean the differences between the Charolais heifer genome and the ARS-UCD 1. 2 bovine reference assembly. The general approach used was to map the reads from all technologies to the ARS-UCD 1. 2 bovine reference assembly, followed by an SV detection using state-of-the-art detection software for each technology. More specifically, for the ONT data set, the reads were aligned using minimap2  8  with sequencing technology specific parameters (see below), and SV detection was performed using SVIM  15. For PacBio CLR and HiFi, reads were aligned using pbmm2  16  and SV detection was performed using pbsv  17.
For the Illumina dataset, the reads were aligned using bwa mem  18  and SVs were detected using the manta software  19. We also took advantage of the assembly produced for the heifer using PacBio Sequel II HiFi reads (see Assembly production). For the phased assembly, the two corresponding haplotypes were aligned to the ARS-UCD1. 2 bovine assembly using minimap2  8  and SVs were  Table  4. Summary of heifer produced chromosomes assemblies. As CLR assembly is better than ONT assembly, we can expect at least similar result after polishing and final steps. For details about pipeline used in this study, refer to Fig. 2. detected using SVIM-ASM  20. The SV sets of the different technologies were merged using Jasmine 21 which enabled the identification of the shared SVs and the technology-specific SVs.
Data Records All read files have been uploaded to ENA European Nucleotide Archive  22  and can be accessed on 23. Table  2  contains the run accession numbers per sample for the runs used to produce our seqoccin. Bt. char. v1. 0 assembly and both our Trio phased assemblies. Our seqoccin. Bt. char. v1. 0 assembly is also available on  24. The others assembly files in fasta format can be accessed on  25  and  26  for the phased assemblies. All the structural variation files in vcf format can be accessed on  27.
Raw read quality. A random selection of 100,000 reads for each set was used to produce a per read mean nucleotide quality and read length distribution plot with seqkit 28 (See Fig. 3 ). All the values are in agreement with the libraries produced, thus showing the good quality of the data used. Mapping the reads to the ARS-UCD1. 2 reference assembly resulted in 119X coverage and more than 99% of genome covered by at least 50 reads for Illumina, 54X and more than 99% of genome covered by at least 10 reads for ONT, 42X and more than 99% of genome covered by at least 10 reads for HiFi, and 34X and more than 99% of genome covered by at least 10 reads for CLR. In addition, Juicer  12  was used to align Hi-C reads on the ONT wtdbg2 polished assembly. From around 250 million read pairs sequenced, more than 94% are considered as alignable (Normal paired + Chimeric paired).
Assembly completeness. To check the potential of each data type to produce an accurate and complete assembly, we evaluated the completeness of our bovine assemblies (10X supernova, ONT wtdbg2, CLR wtdbg2, HiFi hifiasm) and compared their metrics with Bos taurus reference ARS-UCD1. 2 (See Figs. 4, 5  and Table  2  for detailed information). The assemblies sizes were close to the 3. 0 Gb Genomescope estimation, ranging from 2. 6 Gb for 10X chromium, 2. 7 Gb for ONT and CLR wtdbg2 assemblies to 3. 2 Gb for HiFi hifiasm assembly. These values represent between 86. 6% and 106. 6% of the expected genome size. The HiFi hifiasm assembly is slightly longer than other assemblies, since the high read quality makes it possible to distinguish different copies of repeated regions, found, for instance, in centromeres and telomeres (see Fig.