dataesr/scientific-paragraphs-categorization huggingface dataset:
Structural variants (SVs) rearrange large segments of DNA 1 and can have profound consequences in evolution and human disease 2,3. As national biobanks, disease-association studies, and clinical genetic testing have grown increasingly reliant on genome sequencing, population references such as the Genome Aggregation Database (gnomAD) 4 have become integral in the interpretation of single-nucleotide variants (SNVs) 5. However, there are no reference maps of SVs from high-coverage genome sequencing comparable to those for SNVs. Here we present a reference of sequence-resolved SVs constructed from 14,891 genomes across diverse global populations (54% non-European) in gnomAD. We discovered a rich and complex landscape of 433,371 SVs, from which we estimate that SVs are responsible for 25-29% of all rare protein-truncating events per genome.
coverage)  1  , and the substantial technical challenges of SV discovery from WGS  15  has led to non-uniform SV analyses across contemporary studies  [16] [17] [18] [19] [20]. Moreover, short-read WGS is unable to capture a subset of SVs accessible to more expensive niche technologies, such as long-read WGS  21. Owing to the combination of these challenges, SV references are dwarfed by contemporary resources for short variants, such as the Exome Aggregation Consortium (ExAC) and its successor, the Genome Aggregation Database (gnomAD), which have jointly analysed more than 140,000 individuals  4, 6. Publicly available resources such as ExAC and gnomAD have transformed many aspects of human genetics, including defining sets of genes constrained against damaging coding mutations  6  and providing frequency filters for variant interpretation  5.
3 ), which we prototyped in a study of 519 autism quartet families  20. This pipeline integrated four orthogonal evidence types to capture SVs across the size and allele frequency spectra, including six classes of canonical SVs (Fig. 1a ) and 11 subclasses of complex SVs  22  (Fig. 2 ). We augmented this pipeline with new methods to account for the technical heterogeneity of aggregated datasets (Extended Data Fig. 1 , Supplementary Figs. 4, 5), and discovered 433,371 SVs (Fig. 1c ). After excluding low-quality SVs, which were predominantly (61. 6%) composed of incompletely resolved breakpoint junctions (that is, 'breakends') that lack interpretable alternative allele structures for functional annotation and produce high false-discovery rates  20  (Extended Data Fig. 2a ), we retained 335,470 high-quality SVs for subsequent analyses (Supplementary Table  3 ).
In contrast to the well-studied effects of coding variation, the effects of noncoding SVs on regulatory elements are largely unknown. There are a handful of examples of SVs with strong noncoding effects, although they are scarce in humans and model organisms  31, 32. In gnomAD-SV, we explored noncoding dosage sensitivity across 14 regulatory element classes, ranging from high-confidence experimentally validated enhancers to large databases of computationally predicted elements (Supplementary Table  5 ). We found that noncoding CNVs overlapping most element classes had increased proportions of singletons, although none exceeded the APS observed for pLoF SVs (Fig. 5a ). In general, the effects of noncoding deletions appeared stronger than noncoding duplications, and CNVs predicted to delete or duplicate entire elements were under stronger selection than partial element disruption (Fig.
Similarly, although our approach involves extensive methods to resolve complex SV alleles, some variants such as high-copy-state MCNVs often involve complicated haplotype configurations, and we expect that emerging de novo assembly and graph-based genome representations will greatly expand our knowledge of such SVs  21, 23. Nonetheless, 92. 7% of all known autosomal protein-coding nucleotides are not localized to simple-or low-copy repeats, and therefore we expect that the catalogues of SVs accessible to short-read WGS across large populations like gnomAD-SV will capture a majority of the most interpretable gene-disrupting SVs in humans. The scale of short-read WGS datasets currently in production has magnified the need for publicly available SV resources, and gnomAD-SV represents an initial effort to fill this void.
Acknowledgements We thank the many individuals whose sequence data are aggregated in gnomAD for their contributions to research, and the users of gnomAD for their collaborative feedback. We are grateful to the families at the participating Simons Simplex Collection (SSC) sites, as well as the SSC principal investigators. We thank  T. Hefferon  of the  NIH National Center for Biotechnology Information  for his help hosting gnomAD-SV on dbVar. We have complied with all relevant ethical regulations. Research and contributing authors were supported by resources from the  Broad Institute , the  National Institutes of Health (NIH)  ( R01MH115957  to M. E. T. , B. N. and D. G. M. ;  UM1HG008895  to  M. J. D. , B. N. , S. G. , E. S. L. , S. K. , M. E. T. ;  R01HD081256 ,  P01GM061354 ,  R01HD091797 ,  R01HD096326 ,  R01MH111776 ,  R01HD099547  to M.
Code availability The gnomAD-SV discovery pipeline is publicly available via a series of methods configured for the FireCloud/Terra platform (https:// portal. firecloud. org/#methods) under the methods namespace 'Talkowski-SV'. The svtk software package used extensively in the gnomAD-SV discovery pipeline is publicly available via GitHub (https:// github. com/talkowski-lab/svtk). Most custom scripts used in the production and/or analysis of the gnomAD-SV dataset are publicly available via GitHub (https://github. com/talkowski-lab/gnomad-sv-pipeline). All code is made available under the MIT license, unless stated otherwise.
Hymenoptera Gene and Protein Domain Repertoires Display a Reductive Mode of Evolution A previous study reported that more genes were gained than lost in the evolution of protein-coding gene families in Hymenoptera  (Rappoport and Linial 2015). Here, we analyzed the evolution of gene families inferred from OrthoDB  (Zdobnov et al. 2017 ) using the CAFE software  (Han et al. 2013 ) and exploiting recently published divergence time estimates of Hymenoptera  (Peters et al. 2017). We additionally identified protein domains as well as protein domain arrangements and inferred their respective losses and gains across the Hymenoptera tree applying the Fitch parsimony optimality criterion. In contrast to the study of  Rappoport and Linial (2015) , we found a pronounced pattern of reduction of genes, gene families, and protein domains during the evolution of this insect order, with more losses than gains at most nodes (fig.
Genome Assembly Genome sizes and individual library coverage were estimated with jellyfish (version 2. 0)  (Marc¸ais and Kingsford 2011) , using 17-mers of the 180-bp genome sequencing reads. Prior to assembly, we removed all adapters from the reads with the software SeqPrep (https://github. com/jstjohn/SeqPrep). The genomes were separately assembled using ALLPATHS-LG (version 35218)  (Gnerre et al. 2011 ) and applying the program's default parameters and the haploidy option. Contigs were scaffolded and scaffold gaps were filled with the BCM tools Atlas-Link (version 1. 0) and Atlas gap-fill (version 2. 2). The gene space coverage of the assemblies was assessed using BUSCO (version 1. 1b1, Arthropod gene set)  (Simão et al. 2015)  and CEGMA (version 2. 4)  (Parra et al. 2007 ).
Automated Protein-Coding Gene Annotation Protein-coding genes were annotated using the Maker 2. 0 annotation pipeline  (Cantarel et al. 2007) , tuned specifically for annotating the genomes of arthropods. Specifically, the genome assembly was first subjected to de novo repeat prediction and CEGMA gene space coverage analysis; the latter for generating gene models for initial training of the ab initio gene predictors. Three rounds of training of the gene prediction programs Augustus (version 2. 5. 5) (Stanke et al. 2008)  and SNAP (version 1. 0b6)  (Korf 2004 ) within Maker were used to infer a high-quality training set with a bootstrap method. Input protein data included 1 million peptides from a nonredundant reduction (if proteins shared > 90% amino acid sequence identity, only the first in the protein list was retained) of all Uniprot Ecdysozoa entries (1.
2013; Crassostrea gigas, Zhang et al. 2012; Lottia gigantean, Simakov et al. 2013; Schistosoma mansoni, Berriman et al. 2009 ) leading to a final nonredundant peptide evidence set of 1. 03 million peptides. We additionally provided MAKER RNAseq transcription data derived from two males and two females (At. rosae) and a single male and single female (O. abietinus) to identify exon-intron boundaries. We also ran a heuristic script (included as supplementary file S42, Supplementary Material online) to identify and split erroneously joined gene models.
Manual Annotations Gene models were manually annotated with the aid of Web Apollo  (Lee et al. 2013 ) and the i5k interface  (Poelchau et al. 2015). The manual annotation process was guided by multiple intrinsic and extrinsic evidence tracks: 1) cleaned RNAseq raw read data mapped onto the genome assembly using TopHat2 (version 2. 0. 12) (Kim et al. 2013 ) with its default settings; 2) transcripts of the respective species assembled with Cufflinks (version 2. 2. 1)  (Trapnell et al. 2010) ; 3) transcripts of the respective species assembled with Trinity (version trinityrnaseq_r20140413p1)  (Grabherr et al. 2011 ) and mapped onto the genome assembly using the Exonerate (version 2. 20)  (Slater and Birney 2005)  fork Exonerate-gff3 (https://github. com/hotdogee/exonerate-gff3) with the est2genome model (selected options were: -model est2genome -showtargetgff yes -gff3 yes-showalignment no -showvulgar no -geneseed 250 -bestn 2 -percent 50 -minintron 20maxintron 10,000) and marking transcripts mapping to two locations with a custom Perl script.
All manually edited gene models were submitted to an automated quality control and visual inspection before being merged with the MAKER annotations into the official gene sets (OGS). The automated QC procedure (supplementary section II. 3. 2, Supplementary Material online) detects $50 types of formatting errors caused by manual curation. Some errors are automatically fixed, whereas other error types need to be manually reviewed by curators or administrators. Curators were provided with a list of errors to correct in Web Apollo. After a correction period, QC reports were regenerated and the procedure repeated until no errors remained. An in-depth description of the QC procedure is available on github (https:// github. com/NAL-i5K/I5KNAL_OGS/wiki).
TE Annotation Species-specific repeat libraries were generated using RepeatModeler (version open-1. 0. 8)  (Smit and Hubley 2015)  with the program's default settings. The identified TEs were classified using a reference-based similarity search against RepBase (version update 20140131)  (Jurka et al. 2005). Identified TEs were verified and annotation artifacts were removed by querying the identified sequences against the NCBI nr database (downloaded February 4, 2017) with BlastX of the BLASTþ (version 2. 6. 0) software suite  (Camacho et al. 2009 ) using the software's default settings, discarding candidates without hits against known TE proteins and domains. The filtered library was finally combined with the TE sequences of RepBase (version 20140131) referring to Metazoa and used to annotate TEs with RepeatMasker (version open-4.
0. 5)  (Smit et al. 2015)  applying the software's default settings. Genomic TE coverage was calculated using the software "One code to find them all" (Bailly-Bechet et al. 2014) and intrafamily Kimura distances, used as a proxy for TE age distribution, were calculated using scripts available from the RepeatMasker (version open-4. 0. 5) software package. The full TE annotation pipeline was implemented in a custom shell script that is available on GitHub (github. com/mptrsen/mobilome). Testing for a correlation between genome size and TE content was done by applying a linear regression, Spearman rank sum method, and Kendall's Tau within R (R Core Team 2017). We also applied the phylogenetic independent contrast (PIC) method  (Felsenstein 1985)  as implemented in the ape package  (Paradis et al.
Comparative Analysis of Gene Structure The structural properties of the MAKER-inferred protein-coding gene set of the two sawflies were compared with those of the selected apocritan Hymenoptera and the red flour beetle Tribolium castaneum  (Richards et al. 2008)  using COGNATE (version 1. 01)  (Wilbrandt et al. 2017)  with the software's default settings. The N. vitripennis assembly version 2. 1 was used instead of version 1. 0 and the NCBI release 102 annotations of the N. vitripennis and B. terrestris genomes were used instead of the eviogene and Gnomon 1. 0 annotations, respectively.
Orthology Prediction and Microsynteny The predicted sawfly genes were clustered along with those of other Hymenoptera in OrthoDB (version 9. 1)  (Zdobnov et al. 2017)  and orthology assessed at the systematic level Holometabola, with T. castaneum as outgroup. To investigate Hymenoptera genome evolution on a microsyntenic level, we utilized the identified SCOs and the recently published Hymenoptera divergence estimates  (Peters et al. 2017). SCOs represent conserved genes that likely evolve under similar constraints  (Ciccarelli 2005)  and have consequently been exploited as markers to quantify genome shuffling in insects  (Zdobnov and Bork 2007). Using a custom Perl script (included as supplementary file S39, Supplementary Material online), the conservation of microsynteny was inferred as the fraction of shared SCOs that retain the same neighboring SCO between two species relative to their divergences time (supplementary section II.
4. 5, Supplementary Material online). Positional information of the SCO was extracted from the respective OGS. GO terms were assigned to all groups of SCOs (SCOG) using the Argot2. 5 web server  (Lavezzo et al. 2016;  http://www. medcomp. medicina. unipd. it/Argot2-5/, last accessed February 6, 2019) with the default settings, retaining only GO terms with a score of 200 or more, and InterPro2GO  (Mitchell et al. 2019 ) (https://www. ebi. ac. uk/ GOA/, last accessed February 6, 2019), using InterProScan with the default settings (version 5. 33. 72)  (Mitchell et al. 2019). GO terms were assigned to each SCOG when shared by ten or more species in the group. Testing for GO term enrichment in the SCOGs which remained in synteny across all pairwise comparisons (754) against the background of all SCOGs (3,983) was performed using topGO's weighted Fisher test (weight01) (R package version 2.
Gene Family and Domain Evolution Gene family and domain evolution was analyzed with CAFE (version 4. 1)  (Han et al. 2013) , with coupled birth and death rates, using the orthology predictions (see above) and an ultrametric tree derived from a recently published Hymenoptera phylogeny  (Peters et al. 2017)  as input. Following the suggestions of the authors of CAFE, the birth and death rate was determined considering only gene families with fewer than 100 copies in each species before reanalyzing the full data set with the inferred rate. Protein domains were annotated in a subset of the selected genomes with Pfam (version 29)  (Finn et al. 2016) , using the provided "pfam_scan. pl" script with the default settings. The number of unique domains and domain arrangements (the linear sequence of domains present in a protein without repeats) occurring in each species were Presence and absence of domains among species were inferred using a custom python script (pyDomrates; https://github.
Immune Genes A set of immune genes was selected based on the Insect Innate Immunity Database (IIID)  (Brucker et al. 2012 ) and modified according to previous studies on Hymenoptera  (Evans et al. 2006; Gadau et al. 2012; Barribeau et al. 2015). Immune genes were identified with the aid of profile hidden Markov models (HMM), utilizing reference immune response-related amino acid sequences obtained from OrthoDB (Version 9) and the NCBI protein database (including RefSeq;  Pruitt et al. 2012). All amino acid sequences were aligned with MAFFT with the default settings (version 7. 130)  (Katoh and Standley 2013)  and the HMM profiles were inferred with the software HMMER (version 3. 1b1) (http://hmmer. org/) with the default settings. The HMM profiles were searched against the predicted proteins with the HMM search tool hmmsearch with the default settings.
Vision Genes Opsin-coding genes were identified using amino acid reference sequences of the corresponding proteins in Ap. mellifera, Drosophila melanogaster, and T. castaneum obtained from UniProt. Reference sequences were searched against the genome assemblies with the TBlastN software of the BLASTþ software suite (version 2. 6. 0) with the default settings. Candidate orthologs were reciprocally searched with the aid of the BLASTþ software suite, with the default settings, against the Ap. mellifera, Dr. melanogaster, and T. castaneum genome assemblies to sort out false positives. Finally, all verified opsin genes were manually curated within Web Apollo. Opsin amino acid sequences of At. rosae and O. abietinus were aligned to those of Ap. mellifera, Dr. melanogaster, and N. vitripennis  (Pultz and Leaf 2003)  using ClustalW (v2.
1)  (Larkin et al. 2007 ) with the default settings. Ambiguous alignment regions were excluded using the software TrimAl (version 1. 3)  (Capella-Gutierrez et al. 2009) , implemented on the Phylemon 2. 0 server  (Sanchez et al. 2011 ) and applying the "Automated 1" settings. A maximum-likelihood tree was estimated with the MEGA software (version 6. 0) and applying the JTTþG amino acid substitution model. Branch support values were estimated from 500 nonparametric bootstrap replicates.
Metabolism We functionally annotated all predicted proteins of At. rosae, O. abietinus, and N. vitripennis with the CycADS pipeline (version 1. 32)  (Vellozo et al. 2011 ) (supplementary section II. 5. 10, Supplementary Material online) with the default settings. CycADS is an annotation database system that collects functional annotations predicted by multiple computational methods including BLAST2Go (version 2. 5)  (Go ¨tz et al. 2008) , InterProScan (version 5. 0)  (Mitchell et al. 2019) , Kaas-Kegg server (version 2. 0)  (Moriya et al. 2007) , and Priam (March 13. release)  (Claudel-Renard et al. 2003). Predicted EC numbers and Gene Ontology terms (GO) collected by CycADS were then processed with the Pathway Tools software  (Karp et al. 2016)  to infer enzymatic reactions and metabolic pathways that were finally manually curated and compared.
Acknowledgments This genome sequencing project contributes to the i5K initiative, whose target is to sequence the genomes of 5,000 arthropods (Robinson et al. 2011;Evans et al. 2013). We thank the staff at the  Baylor College of Medicine Human Genome Sequencing Center  for their contributions. E. B. B. , Al. D. , S. K. , C. M. , B. M. , O. N. , J. P. O. , R. S. P. , Ma. P. , L. P. , J. W. , and T. Z. acknowledge the  Leibniz Graduate School on Genomic Biodiversity research. B. M. v. R. thanks the  Fraunhofer IME (Gießen)  for infrastructure. B. M. and O. N. thank  Claudia Etzbauer  and  Sandra Kukowka  ( ZFMK, Bonn ) for technical support. O. N. and J. P. O. thank  Caroline Mu ¨ller  ( University of Bielefeld ) for providing samples of At. rosae to take photographs of this species. O. N. furthermore thanks  Stefan Tischendorf  ( Darmstadt ) for sending detailed information where and when to collect O.
abietinus in the field. Mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the  U. S. Department of Agriculture. USDA  is an equal opportunity provider and employer. Genome sequencing, assembly, and annotation were funded by a Grant No. U54 HG003273  from the  National Human Genome Research Institute (NHGRI  to R. A. G. ). B. M. , V. C. M. , O. N. , Ma. P. , B. M. v. R. , and J. W. additionally acknowledge the  German Research Foundation (DFG)  for financial support (MI 649/x-1;  NI 1387/2-1 ;  NI 1387/3-1 ;  RE 3454/2-1 ;  RE 3454/6-1 ). J. H. W. thanks the  US National Science Foundation  ( DEB 1257053  and  IOS 1456233 ). R. M. W. acknowledges support from the  Swiss National Science Foundation  ( PP00P3_170664 ).
V. C. M. and O. N. acknowledge the support of the  Freiburg Galaxy Team  and  Prof. Rolf Backofen ,  Bioinformatics, University of Freiburg, Germany , funded by  Collaborative Research Centre 992 Medical Epigenetics (DFG  Grant No. SFB 992/1 2012 ) and  German Federal Ministry of Education and Research (BMBF  Grant No. 031 A538A RBC [de. NBI] ). This project was also supported by  Det Frie Forskningsra ˚d  (Grant No. 7014-0008B  to C. J. P. G. ).
Abstract. The systematic bioturbation of single particles (such as foraminifera) within deep-sea sediment archives leads to the apparent smoothing of any temporal signal as recorded by the downcore, discrete-depth mean signal. This smoothing is the result of the systematic mixing of particles from a wide range of depositional ages into the same discrete-depth interval. Previous sediment models that simulate bioturbation have specifically produced an output in the form of a downcore, discrete-depth mean signal. However, palaeoceanographers analysing the distribution of single foraminifera specimens from sediment core intervals would be assisted by a model that specifically evaluates the effect of bioturbation upon single specimens. Taking advantage of advances in computer memory, the singlespecimen SEdiment AccuMUlation Simulator (SEAMUS) was created for MATLAB and Octave, allowing for the simulation of large arrays of single specimens.
Bioturbation understanding and previous models The most commonly used mathematical model of bioturbation in deep-sea sediments is the so-called Berger-Heath bioturbation model, which assumes a uniform, instantaneous (on geological timescales) mixing of the bioturbation depth (BD), the uppermost portion of a sediment archive where oxygen availability allows for the active bioturbation of sediments  (Berger and Heath, 1968; Berger and Johnson, 1978; Berger and Killingley, 1982). Observations of uniform mean age in the uppermost intervals of sediment archives do indeed support this mixing model  (Peng et al. , 1979; Boudreau, 1998) , and the BD itself has been shown to be related to the organic carbon flux at the seafloor  (Trauth et al. , 1997). Researchers wishing to carry out transient bioturbation simulations with dynamic input parameters have incorporated the Berger-Heath mathematical model into their computer models, most notably the FORTRAN77 model TURBO  (Trauth, 1998) , its updated MATLAB version TURBO2  (Trauth, 2013)  and the more recent R model Sedproxy  (Dolman and Laepple, 2018).
From these virtual subsamples, mean carrier signal values and species abundances can be calculated, allowing users to evaluate their downcore reconstructions for the possible presence of artefacts. Furthermore, these virtual subsamples can be used to calculate virtual laboratory 14 C dates, which are subsequently calibrated within SEAMUS using the MatCal  (Lougheed and Obrochta, 2016)  14 C calibration software. Calibrated age distributions for a discrete depth can be compared to their associated simulated true age distribution, thus evaluating the accuracy of the 14 C dating and calibration process. The SEAMUS simulation is broken down into two main functions that the user can call. The first function seamus_run, carries out the actual single-specimen sedimentation simulation based on the input parameters designated by the user.