dataesr/scientific-paragraphs-categorization huggingface dataset:
Functional annotation of variants identified by WXS/WGS Variant calls were assigned predicted functional consequences using snpEFF (Single Nucleotide Polymorphism Effect) and dbscSNV (database of splice site consequences of Single Nucleotide Variants). Variants were classified as "loss of function" (LOF) where the sequence change was predicted to encode a premature stop codon, a frameshift causing insertion-deletion, a splice site disrupting SNV. Variants were classified as potentially splice altering if assigned an "ada" or "rf" score >0. 7 by dbscSNV. Splice variants of potential interest were further assessed for putative effects on exon skipping using a secondary algorithm -automated splice site and exon definition server (ASSEDA)  (Mucaki et al. , 2013).
Statistical analyses. Analysis of SNP array genotypes. Standard quality-control procedures were applied to our genotype data using PLINK software package (version 1. 9, PMID: 25722852). We excluded samples that demonstrated: call rates of less than 97. 5%; non-European ancestry; abnormal F inbreeding coefficient; mismatch between phenotypic and genotypic gender; or, cryptic relatedness defined as identity-by-descent proportion of inheritance (pi_hat from PLINK) greater than 0. 125. Samples in common between our study and van Rheenen's study were identified using the checksum program (id_geno_checksum. v2), and were removed from our analyses. We excluded palindromic SNPs, as well as SNPs with: call rates less than 95% in the US and Italian cohorts or less than 99% in the UK, French and Belgium cohorts; minor allele frequency less than 0.
05 in the control cohorts; Hardy-Weinberg equilibrium P less than 10 -7 in the US and Italian control cohorts and less than 10 -5 in the UK, French and Belgium cohorts; missingness by case-control status P less than 10 -5 ; or SNPs associated between the UK and French control cohorts with P less than 5. 0x10 -8. After quality control, 8,229 case and 36,329 control samples were included in the analysis, and 436,746 SNPs were available for imputation in the USA and Italy cohorts, and 420,131 SNPs were available in the UK, French and Belgium cohorts. Estimation of the haplotypes was performed with SHAPEIT (version 2. r790, PMID: 23269371). Imputation was performed for individual batches based on ethnicity using the 1000 Genomes Project dataset (phase 3, version 5a, release 2013-05-02, 1000genomes.
org) as reference and using Minimac3 software (version 1. 0. 11, PMID: 27571263) with default settings. After imputation, principal components were calculated using PLINK software after removing known hypervariable regions and the 1 MB surrounding the C9orf72 region. After analysis of the Scree plots, 2 to 4 principal components were retained per cohort as covariates in the association analyses to compensate for any residual population stratification. Logistic regression was performed per batch using mach2dat software (version 1. 0. 24, PMID: 20517342) incorporating 2 to 4 principal components, age and gender as covariates, with dosage of imputed SNPs selected based on a Minimac3 R 2 value of imputation accuracy greater than 0. 3. SNPs with an absolute beta coefficient value above 5 or with a minor allele frequency less than 0.
01 were excluded from meta-analysis. Meta-analysis was then performed combining the association results of the 13 batches of our individual-level studies with van Rheenen's study summary statistics using METAL software (version 2011-03-25, PMID:20616382) under an inverse-weighted, fixed effect model. A threshold P of 5. 0×10 -8 was set for genome-wide significance after Bonferroni correction for multiple testing  (Pe'er et al. , 2008). The programming code used to analyze these data is freely available on GitHub (link to be supplied at publication), and GWAS summary statistics results for all tested SNPs are available from (link to be supplied at publication). Analysis of WXS/WGS genotypes. For both the discovery and replication phases, samples were excluded from the study in the event of failing to meet standard genotype call rate, heterozygosity, duplication, relatedness or population stratification filters as summarized in Table  S5  and as previously described  (Kenna et al.
, 2016). Each of these filters was performed using a set of autosomal markers meeting all of the following criteria: call rate>0. 95, MAF>0. 01, p>0. 001 for deviation from Hardy Weinberg equilibrium, LD pruning (R 2 <0. 5, window size=50, step=5). Filtering of autosomal markers, sample call rate assessments and sample heterozygosity assessments were performed using PLINK v1. 09. Study duplicates and sample relatedness within the WXS/WGS cohorts was identified using KING. Study duplicates between WXS/WGS cohorts and GWAS datasets were identified using the checksum program (id_geno_checksum. v2, https://personal. broadinstitute. org/sripke/share_links/checksums_download/). Principal components analyses were performed using GCTA (Genome-wide Complex Trait Analysis). RVB analyses were performed by penalized logistic regression of case-control status with respect to number of minor alleles observed per sample per gene as described previously  (Kenna et al.
, 2016). Analyses were only performed where the dataset contained >3 variant allele occurrences. Replication analyses of rs113247976 were performed using the same logistic regression protocol as used for RVB analyses. All analyses were conditioned on the first 4 eigenvectors generated by principal components analysis of common variant profiles. Genomic inflation factors were calculated using genome-wide association analysis for quantitative, binary and time-till-event traits (GenABEL). Candidate associations were tested for signs of call-rate or subcohort biases as outlined in Figures S6, S7. Meta-analysis of rs113247976 association results between sequencing and GWAS was performed using METAL. Unless otherwise indicated all statistical analyses were performed using R v3. 2. 0.
). Five previously reported ALS associated genes are labeled in grey and one novel gene, KIF5A, is labeled in black. (B) Regional association plot of the KIF5A locus. Recombination rates are from HapMap phase 2 European ancestry samples. The R 2 pattern is based on most significant SNP per locus using 85 European ancestry samples (CEU) from the November 2010 release of the 1000 genomes project dataset. R 2 of the p. Pro986Leu (rs113247976) with additional SNPs achieving genome-wide significance was 0. 544 (rs117027576), 0. 544 (rs118082508), 0. 741 (rs116900480), and 0. 347 (rs142321490). Figure  2. Discovery and replication for the association of the KIF5A p. Pro986Leu (rs113247976) variant with ALS. Analysis of the p. Pro986Leu (rs113247976) variant within each of the cohorts described is presented.
36 x 10 -6 ). Previously reported (grey) and novel (black) genes exhibiting a significant excess of rare LOF variants in patients are shown. (Crimella et al. , 2011; Jennings et al. , 2017; Liu et al. , 2014; Reid et al. , 2002)  and ALS associated mutation identified within this study are shown. As illustrated, mutations causative for SPG10/CMT2 are predominantly missense changes and located in the N-terminal motor domain. In contrast, ALS mutations are primarily located at the C-terminal motor domain with most penetrant displaying LOF. Figure  S3. Power to detect associate loci for a cohort of 20,806 cases and 59,804 control subjects. Power calculations were performed using the QUANTO software program (version 1. 2. 4, available from: http://hydra. usc. edu/gxe) under an additive model assuming two-tailed P value of 5.
Read level exome sequencing data was obtained for a total of 41,410 non-ALS controls deposited in dbGAP and the EGA by various projects. Utilized dbGaP datasets include the Alzheimer's Disease Sequencing Project (ADSP) phs000572; University of Miami Udall Center of Excellence Identification of Rare Variants in PD through Whole Exome Sequencing phs000908; Myocardial Infarction Genetics Exome Sequencing Consortium: Ottawa Heart Study phs000806; Myocardial Infarction Genetics Exome Sequencing Consortium: Italian Atherosclerosis Thrombosis and Vascular Biology phs000814; Myocardial Infarction Genetics Exome Sequencing Consortium: Pakistan Risk Of Myocardial Infarction Study phs000917; Myocardial Infarction Genetics Exome Sequencing Consortium: U. of Leicester phs001000; Myocardial Infarction Genetics Exome Sequencing Consortium: Malmo Diet and Cancer Study phs001101; Yale Center for Mendelian Genomics (Y CMG) phs000744; Framingham Heart Study Allelic Spectrum Project phs000307; NHLBI GO-ESP Lung Cohorts Exome Sequencing Project: Genetic modifiers of Pseudomonas aeruginosa (Pa) lung infection acquisition in cystic fibrosis phs000254; National Heart Lung and Blood Institute (NHLBI) GO-ESP: Heart Cohorts Component of the Exome Sequencing Project (FHS) phs000401; National Heart Lung and Blood Institute (NHLBI) GO-ESP: Heart Cohorts Component of the Exome Sequencing Project (JHS) phs000402; National Heart Lung and Blood Institute (NHLBI) GO-ESP: Heart Cohorts Component of the Exome Sequencing Project (ARIC) phs000398; NHLBI GO-ESP: Women's Health Initiative Exome Sequencing Project (WHI) -WHISP phs000281; NHLBI GO-ESP: Lung Cohorts Exome Sequencing Project (Pulmonary Arterial Hypertension) phs000290; NHLBI GO-ESP: Lung Cohorts Exome Sequencing Project (Lung Health Study of Chronic Obstructive Pulmonary Disease) phs000291; National Heart Lung and Blood Institute (NHLBI) GO-ESP: Heart Cohorts Component of the Exome Sequencing Project (CHS) phs000400; NHLBI GO-ESP: Lung Cohorts Exome Sequencing Project (COPDGene) phs000296; JHS Allelic Spectrum Project phs000498; NHLBI GO-ESP Family Studies: Idiopathic Bronchiectasis of unknown etiology that is not related to cystic fibrosis or classic primary ciliary dyskinesia or immune deficiency or any other known causes phs000518; Genetic Epidemiology of COPD (COPDGene) Funded by the National Heart, Lung, and Blood Institute phs000179; National Heart Lung and Blood Institute (NHLBI) GO-ESP: Heart Cohorts Component of the Exome Sequencing Project (MESA) phs000403; NHLBI GO-ESP: Family Studies (Thoracic aortic aneurysms leading to acute aortic dissections) phs000347; NHLBI GO-ESP: Lung Cohorts Exome Sequencing Project (Asthma): Genetic variants affecting susceptibility and severity phs000422; NHLBI GO-ESP: Family Studies (Familial Atrial Fibrillation) phs000362; NHLBI GO-ESP Family Studies: Pulmonary Arterial Hypertension phs000354; Building on GWAS: the U.
The advent of the genomic era has made elucidating gene function on a large scale a pressing challenge. ORFeome collections, whereby almost all ORFs of a given species are cloned and can be subsequently leveraged in multiple functional genomic approaches, represent valuable resources toward this endeavor. Here we provide novel, genome-scale tools for the study of Candida albicans, a commensal yeast that is also responsible for frequent superficial and disseminated infections in humans. We have generated an ORFeome collection composed of 5099 ORFs cloned in a Gateway™ donor vector, representing 83% of the currently annotated coding sequences of C. albicans. Sequencing data of the cloned ORFs are available in the CandidaOrfDB database at http: //candidaorfeome. eu. We also engineered 49 expression vectors with a choice of promoters, tags and selection markers and demonstrated their applicability to the study of target ORFs transferred from the C.
INTRODUCTION Over the last decade, there has been an exponential growth in the quantity of available genome sequence data due to the very rapid progress in sequencing technology. In 2004, the genome sequence of the human fungal pathogen Candida albicans was released as Assembly 19  (1). With the challenge of working with a heterozygous diploid organism, new computational methods had to be developed and resulted in the release in 2013 of Assembly 22, an assembly of a completely phased diploid genome sequence for the standard C. albicans reference strain SC5314  (2). This opened new perspectives to understand the genetic basis and functional mechanisms that underlie pathogenesis and evolution in this organism. Although C. albicans gene sequences have been available to the community for more than a decade  (3, 4) , only 1670 out of the 6198 predicted protein-coding genes have been characterized as of 31 January 31, 2018 according to the Candida Genome Database  (5).
Nowadays, the growing availability of whole-genome datasets has encouraged a shift towards the development of functional genomics and systems biology approaches, enabling analysis of highthroughput whole-genome assays to better understand biological networks. In this context, major efforts have been made to generate large-scale deletion  (6) (7) (8) (9) (10)  or overexpression  (11) (12) (13)  mutant collections. Genome-wide ORF libraries or ORFeomes represent useful resources for the implementation of approaches used to elucidate gene function  (14). Besides, the development of collections of overexpression mutants  (12) , ORFeomes facilitate approaches aimed at evaluating protein subcellular localization and identifying protein-protein interactions (yeast two-hybrid) both at steady state and in response to environmental stimuli  (15).
Generation of an entry clone collection Primer design. The DNA sequence of C. albicans was obtained from the Candida Genome Database Release 21 (www. candidagenome. org), before Assembly 22 was released. Gene-specific forward primers were designed by adding the sequence 5 -GGGGACAAGTTTGTACAAA AAAGCAGGCTTG-3 to the 5 end of the first 30 nt of each ORF. Gene-specific reverse primers were designed by adding the sequence 5 -GGGGACCACTTTGTACAAG AAAGCTGGGTC-3 to the 5 end of the last (PCR) fragments amplified from these primers are compatible for C-terminal tagging with destination vectors containing the Gateway™ cassette A. A total of 6205 primer pairs were obtained from Invitrogen in a 96-well format and are listed in Supplementary Table  S1. Gateway™ cloning of the C. albicans ORFeome. The detailed method for the cloning of C.
Destination plasmids All destination plasmids constructed in this study were derived from CIp-P TET -GTW  (12)  and were confirmed by Sanger sequencing. Plasmid sequences have been submitted to GenBank. Accession numbers are given in Table  2. All oligonucleotides used for PCR and/or sequencing are listed in Supplementary Table  S2. SP cloning. First, a spacer sequence (SP) was amplified from the E. coli kanR gene borne on pCR-topo-blunt plasmid (Invitrogen) with primers SP3 and SP4. The PCR product was cloned in the SacII site of CIp-P TET -GTW, yielding CIp-P TET -GTW-SP, also referred to as pCA-Dest1100. This 390 bp-long sequence is flanked by the SP1 and SP2 Illumina paired-end sequencing primers 1 and 2 and can be used to insert molecular barcodes if expression plasmids are to be used in signature-tagged mutagenesis approaches.
Among the 6179 remaining ORFs, 5099 (83%) were successfully cloned and full-length sequence-validated. Sequences were validated by comparing the entire sequence of each cloned ORF against the reference sequences for haplotype A and B of the C. albicans SC5314 genome available at the Candida Genome Database (CGD; version A22-s07-m01-r18; (48)). We defined 4 groups: (i) 4061 sequences that are 100% identical with a reference sequence (No SNP, no DIP; where SNP stands for Single Nucleotide Polymorphism and DIP stands for Deletion/Insertion Polymorphism); (ii) 108 sequences that do not carry any SNP but carry DIPs (No SNP, DIPs); (iii) 843 sequences that carry SNPs but no DIP (SNPs, no DIP); and (iv) 87 sequences that carry both SNPs and DIPs (SNPs and DIPs) (Figure  1A  and Supplementary Table  S1 ).
CandidaOrfDB: a database for the C. albicans ORFeome CandidaOrfDB was created to integrate the data of the C. albicans ORFeome project and is available at http:// candidaorfeome. eu. CandidaOrfDB enables the scientific community to search for availability and quality of the clones. All data pertinent to the cloning process are stored in the database. This information includes the name and sequence of the primers used to amplify the ORFs from C. albicans SC5314 genomic DNA, the coordinates of the primers in their 96-well storage plates and any sequencing information available on the clones. SNPs and DIPs, their location in the ORF and their influence on the corresponding amino acid sequences are shown (Figure  2 ). Nucleotide sequence of the cloned ORF and the corresponding amino acid sequence are displayed with highlights of SNPs or DIPs relative to the closest haplotype sequence (Figure  2 ).
DISCUSSION A partial C. albicans Gateway™-adapted ORFeome (OR-FeomeV1; 644 ORFs) and the corresponding C. albicans overexpression strains have already been successfully used to investigate morphogenesis, biofilm formation and genome dynamics in C. albicans  (12, 32, 57). In this study, we have now generated the C. albicans ORFeomeV2 within the Gateway™ recombination cloning system, as well as a collection of destination vectors suitable for expression in C. albicans. This new C. albicans ORFeome resource encompasses 5099 ORFs (83% of the annotated ORFs) and will be made available to the community upon request. The resource is supported by the CandidaOrfDB database, providing information on the individual plasmids and their sequences. The C. albicans ORFeomeV2 is of unprecedented quality, as all ORFs in the collection are fully sequenced, which remains an exception in large-scale ORFeome projects where ORFs are rarely sequenced in their entirety and only the 5 -and 3 -ends of the cloned ORFs are sequenced to confirm identity and the absence of frameshift mutations in the primers  (16, 17, 19, 22, 24, 30).
This size bias could be attributed to an increased difficulty to amplify the ORF by PCR, a reduced efficiency of the Gateway™ BP Clonase™ reactions with long PCR products, and the error rate of the PCR polymerase, which increases with longer products. In addition, we also corrected the sequences of 110 ORFs with sequence ambiguities (N-tracts and IUPAC code nucleotides) according to the Candida Genome Database and identified 116 ORFs that display a recombinant haplotype. Recombinant haplotypes could be explained by template switching during PCR cycles or by incorrectly phased SNPs in reference sequences. This could be addressed by performing long-read sequencing of the C. albicans SC5314 genome. However, many challenges remain to be addressed. For heterozygous genes, only one allele is present in the OR-Feome.
Secondly, none of these studies had a specific design for establishing diagnostic or classification criteria. Thirdly, none of these criteria has been validated in an independent cohort. This summarizes all the problems of a rare disease with publications that are essentially monocentric. It is necessary that the teams that support the relapsing polychondritis put together their database in order to have a high number of patients and therefore evolve towards new criteria. These new criteria should consider: 1) diagnosis and classification; 2) early diagnosis; 3) integration of the progressive nature of clinical presentation; 4) to determine the place, also surrogate diagnostic biological markers and imaging findings; and 5) to determine whether clinical response to corticosteroids still have a place in the diagnosis and/or classification of relapsing polychondritis, which appears much more fragile.
Due to the time-consuming interpretative method used (see below), it was not possible to include entire monographs. The process of selecting these texts involved multiple stages. I first identified a series of influential, that is, frequently cited, articles on consent in data protection journals by compiling references from the authoritative legal commentary on the Germany Federal Data Protection Act (BDSG)  (Simitis 2011, 432). In addition, I compiled all of the references from the literature review chapters of recent dissertations on consent, assuming that they include the most important and recent work on the topic  (Rogosch 2013; Hermstrüwer 2016). In a second step, I systematically searched a major German database 9  as well as Google scholar for German-speaking articles on consent in data protection.
In my case, I reached this point after interpreting 27 texts. To organize the interpretation process, I used the qualitative data analysis software MAXQDA, which makes it possible to visualize and perform the procedure of assigning attributes to text segments, referred to as "coding" in qualitative and interpretative research  (Saldaña 2009, 3). As findings from interpretative research and discourse analysis take the form of narratives and rationales, they cannot be presented as graphs and numbers. They are usually presented first as a comprehensive, evidence-rich analysis, allowing the reader to clearly understand the interpretation derived from the material (see  Schwartz-Shea and Yanow 2012, 113). As I will discuss in the following, I was able to reconstruct two different discourses on consent from the material: the world of data protection rules and the data market world  15.
Genotyping and quality control. Genome wide genotyping of the case cohort was undertaken in two batches using the Illumina Human Omni Express Exome 8v1. 2 (2567 cases) and Illumina Infimium Omni Express Exome 8v1. 3 (1961 cases). Genotype calling was performed using the Genome Studio Software package (Illumina). Control genotypes were obtained from the English Longitudinal Study of Aging (ELSA, genotyped on the Illumina Human Omni 2. 5) and the Understanding Society Project (USP, genotyped on the Illumina Human Core Exome v12. 0). Both control cohorts are unselected population control cohorts. Quality control was performed in two batches, one containing genotypes from 2567 cases and 7452 controls from the ELSA and the second with 1961 cases and 9500 controls from the USP. Variants were excluded if they were only genotyped in either cases or controls within each batch, had a call rate <0.
3  ), performed with METAL (release 2011-03-25)  26. Variants with evidence of heterogeneity between the two studies (P-het < 0. 05) or with a MAF < 0. 005 were excluded from further analysis, resulting in a total of 7,441,713 variants utilised in downstream studies. LD score regression. Linkage disequilibrium score regression was performed using LDSC v1. 0. 0 software using summary statistics on variants that had been directly genotyped or imputed with INFO > 0. 95  27. LD score regression was also used to estimate the genetic correlation between severe acne and 175 different phenotypes for which GWAS have been performed in European populations. This analysis was performed through the Ldhub interface (http://ldsc. broadinstitute. org/ ldhub/)  28, 29. Chromosome 8 inversion genotyping. The orientation of the 3.
8 Mb segment on chromosome 8 was inferred using a total of 736 variants that lie within the boundaries of the inversion. The posterior probability of each the three possible inversion genotypes (N/N, N/I and I/I; N = not inverted, I = inverted) for each individual was calculated from the first principal component calculated across these 736 variants (KING  24  ) using a Gaussian mixture model fitted with an EM algorithm (R package mixtools  30  ). Transcription factor motif analysis. To evaluate the effect of the single variant substitution on TP63-binding capacity, the sum occupancy score  31  for both alleles was calculated with PWMtools 32 from the TP63 nucleotide position weight matrix (PWM) from the JASPAR database  33. Locus definition. An LD window was calculated for every variant with a metaanalysis P value of < 5 × 10 -8 , defined by the most proximal and distal variants with an r 2 of > 0.