dataesr/scientific-paragraphs-categorization huggingface dataset:
Reads were aligned with BWA-MEM v. 0. 7. 12-r1039 51. FreeBayes v. 1. 1. 0-50-g61527c5-dirty  52 was used to call SNPs and short indels with the parameters -C 2 -0 -O -q 20 -z 0. 10 -E 0 -X -u -p 2 -F 0. 6. Any SNP and short indels showing heterozygosity (for example, 0/1 genotype) were excluded. The quality value was estimated at 34. 75 using the PHRED formula with SNPs as the numerator (597,798) and number of bases with at least threefold coverage as the denominator, including alternate alleles (1,782,885,792). Identification of mitochondrial contigs. During the submission process for this genome, two contigs were identified as mitochondrial in origin and were removed from the genomic assembly, manually circularized, and submitted separately. The mitochondrial genome is available as GenBank accession number MF194022.
1, RefSeq accession number NC_035159. 1. Bionano optical mapping. HMW DNA extraction. HMW DNA extraction was performed using the Bionano Animal Tissue DNA Isolation Kit (RE-013-10), with a few protocol modifications. A single-cell suspension was made as follows. First, 47 mg of frozen male pupae was fixed in 2% v/v formaldehyde in Homogenization Buffer from the kit (Bionano 20278), for 2 min on ice. Then, the pupae were roughly homogenized by blending for 2 s, using a rotor-stator tissue homogenizer (TissueRuptor, Qiagen 9001271). After another 2 min fixation, the tissue was finely homogenized by running the rotor-stator for 10 s. Subsequently, the homogenate was filtered with a 100-μm nylon filter, fixed with ethanol for 30 min on ice, spun down, and washed with more Homogenization Buffer (to remove residual formaldehyde).
Specifically, 300 ng of purified genomic DNA was nicked with 7 U nicking endonuclease Nt. BspQI (New England BioLabs, NEB) at 37 °C for 2 h in NEBuffer3. The nicked DNA was labelled with a fluorescent-dUTP nucleotide analogue using Taq polymerase (NEB) for 1 h at 72 °C. After labelling, the nicks were ligated with Taq ligase (NEB) in the presence of dNTPs. The backbone of fluorescently labelled DNA was counterstained with YOYO-1 (Invitrogen). Data collection. The DNA was loaded onto the nanochannel array of Bionano Genomics IrysChip by electrophoresis of DNA. Linearized DNA molecules were then imaged automatically followed by repeated cycles of DNA loading using the Bionano Genomics Irys system. The DNA-molecule backbones (YOYO-1 stained) and locations of fluorescent labels along each molecule were detected using the in-house-generated software package, IrysView.
The set of label locations of each DNA molecule defines an individual single-molecule map. After filtering data using normal parameters (molecule reads with length greater than 150 kb, a minimum of 8 labels and standard filters for label and backbone signals), a total of 299 Gb and 259 Gb of data were collected from Nt. BspQI and Nb. BssSI samples, respectively. De novo genome map assembly. De novo assembly was performed with nonhaplotype aware settings (optArguments_nonhaplotype_noES_irys. xml) and pre-release version of Bionano Solve3. 1 (Pipeline version 6703 and RefAligner version 6851). On the basis of the overlap-layout-Consensus paradigm, pairwise comparisons of all DNA molecules were performed to create an overlap graph, which was then used to create the initial consensus genome maps.
Transposable element identification. Identification of known transposon elements. We first identified known transposable elements using RepeatMasker (version 3. 2. 6)  53 against the mosquito TEfam (https://tefam. biochem. vt. edu/tefam/, data downloaded July 2017), a manually curated mosquito transposable-elements database. We then ran RepeatMasker using the TEfam database and Repbase transposable-elements library (version 10. 05). RepeatMasker was set to default parameters with the -s (slow search) flag and NCBI/RMblast program (v. 2. 2. 28). De novo repeat family identification. We searched for repeat families and consensus sequences using the de novo repeat prediction tool RepeatModeler (version 1. 0. 8)  54 using default parameters with RECON (version 1. 07) and RepeatScout (1. 0. 5) as core programs.
Consensus sequences were generated and classified for each repeat family. Then RepeatMasker was run on the genome sequences, using the RepeatModeler consensus sequence as the library. Tandem repeats. We also predicted tandem repeats in the whole genome and in the repeatmasked genome using Tandem Repeat Finder  55. Long tandem copies were identified using the 'Match=2, Mismatch=7, Delta=7, PM=80, PI=10, Minscore=50 MaxPeriod=500' parameters. Simple repeats, satellites and low complexity repeats were found using 'Match=2, Mismatch=7, Delta=7, PM=80, PI=10, Minscore=50, and MaxPeriod=12' parameters. A file representing the coordinates of all identified repeat and transposableelement structures in AaegL5 can be found at https://github. com/VosshallLab/ AGWG-AaegL5. Generation of RefSeq gene set annotation.
Alignment of RNA-seq data to AaegL5 and quantification of gene expression. Published RNA-seq reads  13, 57  and unpublished RNA-seq reads from tissue-specific libraries produced by Verily Life Sciences were mapped to the RefSeq assembly GCF_002204515. 2_AaegL5. 0 with STAR aligner (v. 2. 5. 3a)  58  using the two-pass approach. Reads were first aligned in the absence of gene annotations using the following parameters: --outFilterType BySJout; --alignIntronMax 1000000; --alignMatesGapMax 1000000; --outFilterMismatchNmax 999; --outFilterMis-matchNoverReadLmax 0. 04; --clip3pNbases 1; --outSAMstrandField intronMotif; --outSAMattrIHstart 0; --outFilterMultimapNmax 20; --outSAMattributes NH HI AS NM MD; --outSAMattrRGline; --outSAMtype BAM SortedByCoordinate. Splice junctions identified during the first pass mapping of individual libraries were combined and supplied to STAR using the --sjdbFileChrStartEnd option for the second pass.
Reads mapping to gene models defined by the NCBI annotation pipeline (GCF_002204515. 2_AaegL5. 0_genomic. gff) were quantified using fea-tureCounts  59  with default parameters. Count data were transformed to transcripts per million values using a custom Perl script. Details on libraries, alignment statistics and gene expression estimates (expressed in transcripts per million) are provided as Supplementary Data 4-8. Identification of 'collapsed' and 'merged' gene models from AaegL3. 5 to AaegL5. 0. VectorBase annotation AaegL3. 5 was compared to NCBI Ae. aegypti annotation release 101 on AaegL5. 0 using custom code developed at NCBI as part of NCBI's eukaryotic genome annotation pipeline. First, assembly-assembly alignments were generated for AaegL3 (GCA_000004015. 3) × AaegL5. 0 (GCF_002204515.
5 genes represent the 5′ and 3′ ends of a single gene on AaegL5. 0, such as sex peptide receptor. Detailed lists of merged genes are in Supplementary Data 10, 11. Comparison of alignment to AaegL3. 4 and AaegL5. 0. The sequences comprising transcripts from the AaegL5. 0 gene set annotation were extracted from coordinates provided in GCF_002204515. 2_AaegL5. 0_genomic. gtf. Sequences corresponding to AaegL3. 4 gene set annotations were downloaded from Vectorbase (https:// www. vectorbase. org/download/aedes-aegypti-liverpooltranscriptsaaegl34fagz). Salmon (v. 0. 8. 2)  60  indices were generated with default parameters, and all libraries described in Supplementary Data 4 were mapped to both AaegL3. 4 and AaegL5 sequences using 'quant' mode with default parameters. Mapping results are presented as Supplementary Data 9 and Fig.
bioinformatics. babraham. ac. uk/projects/ fastqc) and mapped to the AaegL5 and AaegL3 versions of the Ae. aegypti genome using Bowtie v. 2. 2. 9  62. Aligned reads were processed using Samtools 1. 3. 1  63  and Picard 2. 6. 0 (http://broadinstitute. github. io/picard/index. html) and only uniquely mapped and non-redundant reads were used for downstream analyses. To compare the annotation and assembly of the sex peptide receptor gene in AaegL3 and AaegL5, we used NCBI BLAST  64  to identify AAEL007405 and AAEL010313 as gene fragments in AaegL3. 4 annotation that map to sex peptide receptor in the AaegL5. 0 genome (BLAST E values for both queries mapping to sex peptide receptor were 0. 0). Next, we used GMAP  65  to align AAEL007405 and AAEL010313 fasta sequences to AaegL5. The resulting GFF3 annotation file was used by Gviz  66  to plot RNA-seq reads and sashimi plots as well as ATAC-seq reads in the region containing sex peptide receptor.
Transcription start site analysis was performed using HOMER v. 4. 9  67. In brief, databases containing 2-kb windows flanking transcription start sites genome-wide were generated using the 'parseGTF. pl' HOMER script from AaegL3. 4 and AaegL5. 0 GFF3 annotation files. Duplicate transcription start sites and transcription start sites that were within 20 bp from each other were merged using the 'mergePeaks' HOMER script. Coverage of ATAC-seq fragments in predicted transcription start site regions was calculated with the 'annotatePeaks. pl' script. Fold change in predicted transcription site regions was calculated by dividing the ATAC fragments per base pair per predicted transcription start site in the AaegL5. 0 genome by ATAC fragments per base pair per predicted transcription start site in the AaegL3.
Normalization was not necessary for these datasets because the mean and median CQs of the autosomes (chromosomes 2 and 3) are all near 1. A CQ value lower than the 0. 05 indicates that the sequences within the corresponding 1,000-bp window had at least 20-fold more hits to the male Illumina data than to the female Illumina data. Not every 1,000-bp window produces a CQ value because many were completely masked by RepeatMasker  53. To ensure that each CQ value represents a meaningful data point obtained with sufficient alignments, only sequences with more than 20 male hits were included in the calculation. The CQ values were then plotted against the chromosome location of the 1,000-bp window (Fig. 3d ). Under these conditions, there is not a single 1,000-bp fragment on chromosomes 2 and 3 that showed CQ = 0.
'featureCounts' , part of the 'Subread' v. 1. 5. 0-p2 package  74  , was used to assign read pairs or reads ('tags') aligned to either DNA strand ('-s 0') and overlapping the coding regions of a gene by at least 100 bp ('-t CDS-minOverlap 100') to genes as an estimate of representation in the genome. Gene-wise tag counts were normalized by calculating the fragments per kilobase of gene length per million mapped reads (FPKM), using the following equation: (tag count/gene length in kb)/ (sum of tag counts for all genes in genome/1,000,000). Median FPKM for all genes in the genome was calculated (48. 22), allowing FPKM of GSTe genes to be expressed relative to this. To examine strain differences in coverage at this cluster, we repeated this analysis for the four laboratory colonies analysed in Extended Data Fig.
Complete viral genome sequences were previously deposited into GenBank (accession numbers HG316481, HG316582, HG316483, and HG316484)  35. Phylogenetic analysis assigned the viruses to known viral lineages that were circulating in southeast Asia in the previous years  35. Each isolate was amplified twice in C6/36 (Aedes albopictus) cell lines (maintained at AFRIMS in Bangkok, Thailand; used only to grow virus, not explicitly authenticated or checked for mycoplasma contamination) before vector competence assays. Four-to seven-day-old F 2 females were starved for 24 h and offered an infectious blood meal for 30 min. Viral titres in the blood meals ranged from 2. 0 × 10 4 to 2. 5 × 10 5 plaque-forming units per ml across all isolates. Fully engorged females were incubated under the conditions described above.
2. 6. 0. Markers with >1 blast hit on chromosomes over their 140-bp length and 100% identity were excluded from linkage analysis. Reported RAD markers were distributed as follows: chromosome 1, n = 76; chromosome 2, n = 80; chromosome 3, n = 99. QTL mapping. The newly developed linkage map was used to detect and locate QTLs that underlie the DENV vector competence indices described above. Midgut infection was analysed in all F 2 females whereas viral dissemination was analysed only in midgut-infected females. The four different DENV isolates were included as a covariate to detect QTL-isolate interactions. Single QTL detection was performed in the R/qtl environment  85  using the expectation-maximization algorithm of the scanone function using a binary trait model. Genome-wide statistical significance was determined by empirical permutation tests, with 1,000 genotype-phenotype permutations of the entire dataset.
Reporting summary. Further information on research design is available in the Nature Research Reporting Summary linked to this paper. Code availability. The overview of the Hi-C workflow, as well as modifications to 3D-DNA associated with AaegL5, is shared on GitHub at https://github. com/ theaidenlab/AGWG-merge. The source code and executable version of Juicebox Assembly Tools are available at http://aidenlab. org/assembly. Data files and scripts used for the final polishing of scaffolded, gap-filled assembly are available at https:// github. com/skingan/AaegL5_FinalPolish. Data availability. All raw data have been deposited at NCBI under the following BioProject accession numbers: PRJNA318737 (primary Pacific Biosciences data, Hi-C sequencing primary data and processed contact maps, whole-genome sequencing data from a single male (Fig.
4d ) and pools of male and females (Fig. 3d ), Bionano optical mapping data (Figs. 3c, 4c ) and 10X linked-read sequences (Extended Data Fig. 8a  and Supplementary Data 21)); PRJNA236239 (RNA-seq reads and de novo transcriptome assembly  13  (Extended Data Fig. 2c  and Supplementary Data 4, 5, 7, 9)); PRJNA209388 (RNA-seq reads for developmental time points  57  (Fig. 1h  and Supplementary Data 4-6, 9)); PRJNA419241 (RNA-seq reads from adult reproductive tissues and developmental time points, Verily Life Sciences (Fig. 1h  and Supplementary Data 4, 5, 8, 9)); PRJNA393466 (full-length Pacific Biosciences Iso-Seq transcript sequencing); PRJNA418406 (ATAC-seq data from adult female brains at three points in the gonotrophic cycle (Extended Data Fig. 2c, d  and data not shown)); PRJNA419379 (whole-genome sequencing data from four colonies (Fig.
4d  and Extended Data Fig. 9a, b )); PRJNA399617 (restriction-site-associated DNA-sequencing data (Fig. 5a-d )); PRJNA393171 (exome-sequencing data (Fig. 5e-g )). Intermediate results related to the AaegL5 assembly are also available via GitHub (http://github. com/theaidenlab/AGWG-merge) and have been uploaded to GEO (GSE113256). The Hi-C maps are available via http://aidenlab. org/juicebox. The complete mitochondrial genome is available as Genbank accession MF194022. 1, RefSeq accession NC_035159. 1. The final genome assembly and annotation are available from the NCBI Assembly Resource under accession GCF_002204515. 2. Extended Data Fig. 1  | Project flowchart, measured genome size and assembly process. a, Flowchart of LVP_AGWG strain inbreeding, data collection and experimental design of the AaegL5 assembly process.
b, Estimated average 1C genome size for each strain for five Ae. aegypti strains and Ae. mascarensis, the sister taxon of Ae. aegypti, for which the genome size has not previously been measured. There were no significant differences between the sexes within and between the species and strains analysed (P > 0. 2). Significant differences between strains were determined using Proc GLM in SAS with both a Tukey and a Scheffé option with the same outcome. Data labelled with different letters are significantly different (P < 0. 01). c, Combining Hi-C maps with 2D annotations enabled efficient review of sequences identified as alternative haplotypes by sequence alignment. The figure depicts a roughly 24 Mb × 24 Mb fragment of a contact map generated by aligning a Hi-C dataset to an intermediate genome assembly generated during the process of creating AaegL5.
Acknowledgements We thank  R. Andino ;  S. Emrich  and  D. Lawson  (Vectorbase);  A. A. James ,  M. Kunitomi ,  C. Nusbaum ,  D. Severson ,  N. Whiteman ;  T. Dickinson ,  M. Hartley  and  B. Rice  ( Dovetail Genomics ) for early participation in the AGWG;  C. Bargmann ,  D. Botstein ,  E. Jarvis  and  E. Lander  for encouragement and facilitation. N. Keivanfar ,  D. Jaffe  and  D. M. Church  ( 10X  Genomics) prepared DNA for structural-variant analysis. We thank  A. Harmon  of the  New York Times  and acknowledge generous pro bono data and analysis from our corporate collaborators. This research was supported in part by federal funds from the  National Institute of Allergy and Infectious Diseases, National Institutes of Health, Department of Health and Human Services , under grant number  U19AI110818  to the  Broad Institute  (S.
S. A. ). Other support was provided by  Jane Coffin Childs Memorial Fund  (B. J. M. ),  Center for Theoretical Biological Physics postdoctoral fellowship  (O. D. ),  Robertson Foundation  (L. Z. ), and  McNair & Welch  ( Q-1866 ) Foundations (E. L. A. ),  French Government's Investissement d'Avenir program ,  Laboratoire d'Excellence Integrative Biology of Emerging Infectious Diseases  (grant  ANR-10-LABX-62-IBEID  to L. L. ),  Agence Nationale de la Recherche  grant  ANR-17-ERC2-0016-01  (L. L. ),  European Union 's  Horizon 2020 research and innovation program  under ZikaPLAN grant agreement no. 734584  (L. L. ),  Pew and Searle Scholars Programs  (C. S. M. ), Klingenstein-Simons Fellowship in the Neurosciences (C. S. M. ). A. M. W. , B. J. W. , J. E. C. and S. N. M. were supported by  Verily Life Sciences.
METHODS Cohort The NIHR BioResource-Rare Diseases (NIHRBR-RD) study was established in the United Kingdom to further the clinical management of patients with rare diseases by providing a national resource of whole-genome sequence data. All participants provided written informed consent, and the study was approved by the East of England-Cambridge South national institutional review board (13/EE/0325). At the time of our analysis, the NIHRBR-RD study included whole-genome sequence data from 8066 subjects, of whom 1299 were part of the PID cohort. These were predominantly singleton cases, but additional affected and/or unaffected family members of some of the patients were also sequenced; in total, there were 846 unrelated index cases. Patients with PIDs were recruited by specialists in clinical immunology (either trained in pediatrics or internal medicine) from 26 hospitals in the United Kingdom and a smaller number came from The Netherlands, France, and Italy.
001 in the Exome Aggregation Consortium data set, and having a minor allele frequency of less than 0. 01 in the NIHRBR-RD cohort. Large deletions called by both Canvas and Manta algorithms, passing standard Illumina quality filters, overlapping at least 1 exon absent from control data sets,  32  and having a frequency of less than 0. 01 in the NIHRBR-RD genomes were included in the analysis. All variants reported as disease causing in this study were confirmed by using Sanger sequencing with standard protocols. Large deletions were inspected in the Integrative Genomics Viewer plot (see Fig E1 in this article's Online Repository at www. jacionline. org), and breakpoints were confirmed by sequencing the PCR products spanning each deletion.
Statistical analysis of lymphocyte data Differences between groups with 1 variable were calculated with a nonpaired Student t test or 1-way ANOVA with the Bonferroni post hoc test, differences between groups with 2 or more variables were calculated with 2-way ANOVA with the Bonferroni post hoc test by using GraphPad Prism 6 software (GraphPad Software, La Jolla, Calif). A P value of less than. 05 was considered significant.
jacionline. org). Importantly, based on the gnomAD data set of 135,000 predominantly healthy subjects, none of the NFKB1 variants reported here are observed in a single gnomAD subject, even though 90% of our CVID cohort and all of the NFKB1-positive cases had European ancestry. Therefore our results suggest that LOF variants in NFKB1 are the most commonly identified monogenic cause of CVID in the European population, with 16 of 390 patients with CVID explaining up to 4. 1% of our cohort. None of the variants nonsense, frameshift, splice and missense variants; Exome Aggregation Consortium (ExAC) allele frequencies; and conditional probability of variant pathogenicity inferred by using BeviMed. Only variants labeled as being of moderate or high effect relative to the canonical transcript ENST00000226574 are shown.
The initial inference that formed part of the genome-wide analysis included variant chr4:103423325G>A, which was observed in 1 control sample. This variant is intronic (low effect) relative to ENST00000226574 but is a splice variant (high effect) relative to the minor transcript ENST00000505458. Because variants were filtered based on the highest-effect variant annotation against any Ensembl transcript, this variant was originally included in the inference. For this plot, the inference was rerun, including only missense, truncating, and gene deletion variants relative to the canonical transcript.
43 Consequently, within the relatively pathogen-free environment of developed countries, the relevant pathogen for triggering disease might be scarce, and reports documenting partial penetrance of the clinical phenotype will increase. This makes the traditional approaches to genetics for determining causality difficult. The BeviMed algorithm used in this study prioritized both the gene NFKB1 and individual variants within NFKB1 for contribution to causality; the power of methods like this will increase with greater data availability. Identification of a number of rare NFKB1 variants with high Combined Annotation Dependent Depletion scores in both the PID and control data sets highlights the potential for false attribution of disease causality when the genetics of an individual case are considered outside the context of relevant control data.
RESULTS Analyzing the evolutionary relationship among Leishmania strains. Ten Leishmania strains belonging to the L. tropica, L. major, or L. donovani complexes were obtained from different sources and regions (see Materials and Methods and see Table  S1  at GitLab [https://gitlab. pasteur. fr/gbussott/Leishmania_genome_dynamics _during_environmental_adaptation_reveals_strain_specific_differences/]), and parasites from early passage (passage 2) and later culture passages (passage 5 [designated EP and EP ϩ 3, respectively]) were subjected to sequencing analysis (see Fig. S1  in the supplemental material and Table  S2  at GitLab). We first used the EP sequence information to confirm species determination and to characterize strain-specific genetic variations that may inform on mechanisms of adaptation.
1C ). The massive amount of SNVs identified in L. tropica confirmed the large evolutionary distance to L. major strains observed by PCA and the clustering analyses (Fig. S2 ). Differences in the evolutionary relationship were further supported by the absence of inversions or translocations in the L. major and L. infantum strains compared to the corresponding reference genomes and the presence of translocations in the Cypriot Ldo_CH33 strain and the Sudanese L. donovani strain Ldo_LTB (Fig. 1D ; see Table  S6  at GitLab [https://gitlab. pasteur. fr/ gbussott/Leishmania_genome_dynamics_during_environmental_adaptation_reveals _strain_specific_differences/]), revealing a potential role of these structural genome variations in L. donovani adaptation. Strain-specific gene copy number variations.