dataesr/scientific-paragraphs-categorization huggingface dataset:
Study Population We used data from the prospective "ENDOmiARN" study (ClinicalTrials. gov Identifier: NCT04728152). Data collection and analysis were carried out under the Research Protocol n • ID RCB: 2020-A03297-32. The ENDOmiARN study included 200 plasma samples obtained from patients with chronic pelvic pain suggestive of endometriosis. All had undergone a laparoscopic procedure (either therapeutic or diagnostic laparoscopic) and/or MRI imaging evidencing endometriosis by the presence of endometrioma and/or deep endometriosis  [17] [18] [19] , as stated in the trial registration. The laparoscopy procedures were systematically recorded and the video analyzed by two operators (CT, YD), who were blind to the symptoms and imaging findings, to confirm the presence or absence of endometriosis.
Bioinformatics 3. 1. Raw Data Preprocessing (Raw, Filtered, Aligned Reads) and Quality Control Sequencing reads were processed using the data processing pipeline. FastQ files were trimmed to remove adapter sequences using Cutadapt version v. 1. 18 and were aligned using Bowtie version 1. 1. 1 to the following transcriptome databases: the human reference genome available from NCBI (https://www. ncbi. nlm. nih. gov/genome/guide/human/, accessed on 28 August 2021), and miRBase22 (miRNAs) using the MirDeep2 v0. 1. 0 package. The raw sequencing data quality was assessed using FastQC software v0. 11. 7  [25] [26] [27] [28] [29]. The bioinformatic process used was previously described by Potla et al. [30].
Study of the miRNA Accuracy To evaluate the diagnostic accuracy of each miRNA biomarker, sensitivity, specificity, and ROC analysis was performed, and the ROC AUC was calculated  [34, 35]. Additional statistical analysis was based on the Chi2 test as appropriate for categorical variables. Values of p < 0. 05 were considered to denote significant differences. Data were managed with an Excel database (Microsoft, Redmond, WA, USA) and analyzed using R 2. 15 software, available online (http://cran. r-project. org/, accessed on 28 August 2021).
Methodology and Design: S. B. and E. D. Data collection: S. B. , Y. D. , A. P. , C. T. and E. D. Biologic data collection: Y. D. , L. J. , D. B. Analysis: S. S. , S. B. , E. D. Data Interpretation: S. B. , S. S. , Y. D. , A. P. , C. T. et E. D. Manuscript writing: Y. D. , S. B. , E. D. All authors reviewed the manuscript for critical intellectual content. All authors have read and agreed to the published version of the manuscript. Funding: Part of this work was funded by a grant from the Conseil Régional d'Ile de France (grant number EX024087) and from Ziwig, Inc. Institutional Review Board Statement:The authors state that the data used are from the prospective ENDO-miRNA study (ClinicalTrials. gov Identifier: NCT04728152). Data collection and analysis were carried out under Research Protocol n • ID RCB: 2020-A03297-32.
Author Contributions: Methodology and Design: S. B. and E. D. Data collection: S. B. , Y. D. , A. P. , C. T. and E. D. Biologic data collection: Y. D. , L. J. , D. B. Analysis: S. S. , S. B. , E. D. Data Interpretation: S. B. , S. S. , Y. D. , A. P. , C. T. et E. D. Manuscript writing: Y. D. , S. B. , E. D. All authors reviewed the manuscript for critical intellectual content. All authors have read and agreed to the published version of the manuscript. Funding: Part of this work was funded by a grant from the Conseil Régional d'Ile de France (grant number EX024087) and from Ziwig, Inc. Institutional Review Board Statement:The authors state that the data used are from the prospective ENDO-miRNA study (ClinicalTrials. gov Identifier: NCT04728152). Data collection and analysis were carried out under Research Protocol n • ID RCB: 2020-A03297-32.
Quality control We performed separate quality control assessments for the genotype data generated in batch 1 and batch 2 (see supplemental ma-terial and methods for more details). For both datasets, we first restricted the variants to a list of recommended SNPs provided by Affymetrix. Next, we used Plink v. 1. 9  53 to remove variants with duplicate marker names, structural variants, variants with no physical position in the NCBI Build GRCh37 human reference, and the genotyping controls. Lastly, we flipped all SNPs to the forward strand using snpflip (see web resources). After QC, the batch 1 dataset included 713,667 bi-allelic SNPs and 480 individuals, and the batch 2 dataset included 777,946 bi-allelic SNPs and 467 individuals. We next intersected batch 1 and 2 datasets at overlapping sites and did another round of quality control on the merged dataset.
We removed SNPs with genotype missing rate >5%, minor allele frequency (MAF) <0. 5%, and failing Hardy-Weinberg equilibrium at 10EÀ10. We also filtered for individuals with call rate <90%, duplicate individuals, and cryptic relatedness. In total, 31 individuals were removed, and 56 family pedigrees were updated after this second round of QC on the merged dataset (see Table  S4  for list of individuals assigned as unrelated after pedigree revision). We found that for some affected and control families, offspring sex was missing, or some individuals' sex was misassigned in the medical records at the time of data collection (e. g. , mothers noted as male, fathers noted as female). Therefore, chromosomal sex was estimated using Plink v. 1. 9 for 176 individuals whose biological sex was either not previously recorded (n ¼ 124, mostly offspring) or was incorrectly recorded (n ¼ 52) during sample collection.
After QC, the merged dataset (batch 1 þ 2) included 504,475 genome-wide distributed SNPs and 914 individuals (Figure  S1 ). Finally, we tested for batch effects on the merged dataset by calculating principal components analysis (PCA) in Plink after filtering for linkage disequilibrium (LD) and removing related offspring. We initially identified a strong batch effect with the top principal components statistically significantly associated with batch (p < 0. 05) (Figure  S2 ). To correct this effect, we conducted an additional round of site and sample-specific filtering, again filtering for excess heterozygosity and cryptic relatedness. This resulted in a further 31 individuals being removed from the dataset. We then repeated the PCA as detailed above. The final dataset after batch effect correction included 439,314 SNPs and 883 individuals.
Population structure We intersected our dataset with reference panels including five populations from 1000 Genomes Phase 3 (1KG): Yoruba from Ibadan, Nigeria (YRI), Utah residents with Northern and Western European ancestry (CEU), Han Chinese from Beijing, China (CHB), Mexican Americans from Los Angeles, USA (MXL), and Peruvians from Lima, Peru (PEL). After merging, we removed offspring and related individuals, restricted to autosomal markers and re-applied quality filters. The filtered, merged dataset consisted of 422,224 variants and 1,057 individuals. The unsupervised clustering algorithm ADMIXTURE  54  was run on this dataset to explore global patterns of population structure. After LD pruning, 45,496 variants remained for analysis. Ten ancestral clusters (K ¼ 2 through K ¼ 10) were tested and the best fit model was selected after examining cross-validation errors.
To account for possible convergence variation, we performed 10 additional runs using different random seeds per run and estimated parameter standard errors using 200 bootstrap replicates per run. ADMIXTURE results were plotted with the R pophelper package. 55 PCA was applied to the LD pruned dataset using EIGENSOFT v. 7. 2. 1  56  and plots were generated using the ggplot2 package in R v. 4. 0. 3. 57, 58.
Phasing and local ancestry estimation We used RFMix v. 1. 5. 4  59  to infer genome-wide local ancestry calls for the Puno cohort founders, assuming a model of K ¼ 3 ancestral populations. Given that local ancestry methods have decreased accuracy when attempting to distinguish between closely related ancestries,  59, 60  in this analysis we focused on the three major continental ancestries identified in the Puno cohort through the ADMIXTURE analysis. The reference panel included 108 YRI and 94 CEU individuals from 1KG, and 94 native individuals from Mexico (30 Mixe, 15 Zapotec, 49 Nahua) genotyped as part of the GALA II study. 61 These reference samples were used as proxies for African, European, and Native American ancestral source populations, respectively. After merging, the working dataset consisted of 420,105 overlapping variants and 899 individuals.
Statistical analysis of clinical phenotypes We assessed batch bias of clinical phenotypes measured in affected mothers and correlation with each other by statistical analysis in R v. 3. 4. 0. The following dichotomous phenotypes were tested for batch association with a chi-squared test: severity of diagnosis (mild or severe), proteinuria (þ/þþ or þþþ), parity (nulliparous or more than one birth), newborn sex, and delivery mode (vaginal or C-section). The following continuous phenotypes were tested for batch association by t test: gestational time measured by the mother (date of last menstrual period [LMP]) and by the fetus (Capurro test), neonate weight, systolic and diastolic blood pressure measurements, and maternal age. We provide summaries of relevant phenotypic data for all case pregnancies in Table  1.
49  note that these additional analyses do not constitute independent replication, as the CANDELA cohort does not include preeclamptic individuals, so a CANDELA-only replication analysis could not be performed. Yet, incorporating external controls can serve as a valid strategy to increase statistical power, especially when affected individuals are rare or difficult to sample. The SNP array-based CANDELA dataset includes multiple populations of diverse origin across Latin America. Thus, to mitigate the effects of population structure and include control subjects with ancestry related to the Puno cohort, we selected Peruvian and Chilean individuals with >70% Native American ancestry and only Andean Native American ancestry (i. e. , related to Andean native populations such as Aymara, Quechua, or Uros and <1% lowland Mapuche Native American ancestry) from the CANDELA dataset.
70 We next merged these two datasets at overlapping autosomal sites and performed an additional round of quality control after which we excluded a single PUN control individual and retained a total of 381,675 autosomal SNPs (supplemental material and methods). Visual inspection of the scree plots and PCA scatterplots resulting from the merging of the Puno cohort and CANDELA datasets did not show any apparent batch effect (Figures  S4-S6 ), only some variation due to varying levels of European and regional Native American ancestry. Finally, reproducing the earlier analyses, we conducted the two separate case-control GWAS analyses of mothers and offspring as affected individuals with this additional set of 551 Peruvian and Chileans from CANDELA as control subjects (i. e. , including PRE, PUN, and CANDELA individuals), using logistic regression in Plink with the first 3 PCs as covariates.
GWAS data visualization All genome-wide analyses were filtered by MAF R 0. 05 within the analyzed cohorts and visualized by Manhattan plots using the qqman R package v. 0. 1. 4. 71 QQ plots were generated with the same package to confirm no effects from population structure or other confounders. Top suggestive SNPs and their genomic regions were plotted using LocusZoom. 72 Maps displaying the geographic distribution of candidate associated variants were produced using the Geography of Genetic Variants (GGV) browser (web resources). 73.
Functional annotation data Functional annotation of candidate associated variants was reviewed using the Open Targets Genetics Platform. 74 We queried for significant associations linking these variants to changes in gene expression (eQTLs) or protein abundance (pQTLs) in blood based on the eQTLGen Consortium  75  and Sun et al. ,  76  respectively. We note that unlike the eQTLGen Consortium data, the study by Sun et al. 76  contained blood level measurements of approximately 3,000 proteins. To prioritize candidate causal genes, we assessed whether the expression of the genes within the region was controlled by the associated SNP (i. e. , is an eGene  75  ) or whether the gene had the highest overall Variant-to-Gene (V2G) scores from the Open Targets Genetic Platform. The overall V2G score aggregates differentially weighted evidence of variant-gene association from several sources, including cis-QTL data, chromatin interaction experiments, in silico function predictions (e.
Sequencing capture We conducted fine mapping of potential causal variants in a subset of families genotyped in batch 1 prior to batch 2 genotyping. Preliminary data obtained from batch 1 genotypes were analyzed using standard family-based TDT on Plink for preeclampsia associations (as above), and regression analysis on secondary phenotypes was conducted using linear mixed models in GTCA  77  (flag: -mlma-loco). Based on these preliminary results, we designed a targeted capture assay including windows around top hits for preeclampsia and secondary phenotypes, as well as several genes previously suggested to be associated with preeclampsia in the GWAS catalog (release 2. 0. 5). 78 The total capture size was approximately 10 Mb (see supplemental material and methods). We next selected families from batch 1 with the strongest associations on the preliminary TDT analysis (n ¼ 86 individuals, Table  S2 ).
Genomic DNA from 86 individuals (Figure  S7 ) was fragmented by mechanical shearing (Covaris) and prepared using the KAPA Hyperprep library preparation kit (Kapa Biosystems). DNA capture was performed on the libraries using the Agilent (USA) SureSelect platform following manufacturer's instructions. Paired-end sequencing of captured libraries was performed on Illu-mina's NextSeq platform. Sequence data were analyzed through a standard FASTQC-BWA-GATK pipeline following published guidelines. 79 We then performed the same GWAS analyses listed above (TDT test for the preeclampsia phenotype and linear regressions for continuous phenotypes) in the captured regions for a limited set of individuals: 25 trios, 4 duos (3 mother-offspring, 1 father-offspring), and 3 singletons (1 offspring and 2 mothers).
83 Mothers from control families (labeled PUN) were experiencing a pregnancy without complications at time of hospital recruitment. 88 PREaffected families and two PUN control families were collected as complete trios-including both biological parents and offspring; the rest are duos (one parent and offspring) or single individuals (mothers) (Table  2 ). Overall, the Puno cohort collected for this study includes 815 individuals from the hospital case group (PRE), 204 from the hospital control group (PUN), and 110 from the university (UNA) as population controls. We extracted DNA from blood and genotyped PRE, PUN, and UNA individuals in two batches on the Affymetrix Axiom LATarray. Our final dataset after quality filtering included 439,314 SNPs genome wide and 883 individuals (see Tables  2  and S2  for breakdown of PRE, PUN, and UNA groups).
Puno individuals have high proportions of native American ancestry We sought to understand the ancestral background of our test population by characterizing patterns of genetic diversity and population structure in the Puno cohort. To this end we intersected the entirety of the Puno cohort dataset (n ¼ 883) with a reference panel including five continental populations from the 1KG panel: YRI, CEU, MXL, CHB, and PEL. Using PCA, we find that individuals from Puno (either PRE, PUN, or UNA) cluster together in PC space and are distributed in a clinal pattern alongside Peruvians from Lima (PEL) who have high proportions of Native American ancestry (Figures  1  and S8 ). We next investigated admixture patterns in the Puno population with the goal of estimating proportions of Native versus non-Native genomic ancestry.
8EÀ56) and rs553316 (eQTL p ¼ 3. 4EÀ93) (Tables  S9  and S10 ). The global distribution of allele frequencies for rs5960 and rs553316 in 1KG populations are shown in Figure  S13  and noted in Table  S11. Given the importance of clotting genes in pregnancy, we sought to complement the genotype analysis by performing deep sequencing of targeted genomic regions surrounding rs5960 in a subset of cohort participants (Tables  S12  and S13 , Figure  S7 ). To fine-map potential causal variants, we repeated the same TDT analysis described above in the fine-mapped individuals and cross-referenced with the GTEx database for expression phenotypes. This analysis found an association of preeclampsia with several eQTLs for PROZ (Table  S14 ). Other top hits from the SNP array-based TDT that were recapitulated in this sequencing-based analysis include variants in SLC46A3 (MIM: 616764) and CUL4A (MIM: 603137), also located on chromosome 13 (Table  S14 ).
Both genes have been previously associated with preeclampsia risk in clinical studies. 9, 90 These data suggest that clotting factors on chromosome 13 may play an important role in preeclamptic pregnancies. Next, we asked whether this PROZ eQTL resulted in differential PROZ protein expression between PRE-affected individuals and PUN control subjects. Since the TDT identifies associated variants in the offspring, we analyzed the umbilical cord plasma of 8 PUN control subjects and 16 PREaffected individuals by ELISA. In this limited sample, we detected no difference of PROZ protein levels in umbilical cord plasma collected after delivery (difference in means ¼ 41. 550 mg/mL, 95% CI À342. 758 to 425. 858, p ¼ 0. 85; Table  S15 , Figure  S14 ). We note that this result may reflect our previous finding in which the PROZ locus reached a genome-wide suggestive, but not significant, association with preeclampsia, likely due to sample size limitations.
All analyses were corrected for batch, plus the first three PCs and maternal age were included as continuous covariates. With this analysis we found several suggestive associations of SNPs to secondary maternal phenotypes (Table  5 ; Tables  S18-S20 ). These findings point to several genetic regions containing relevant genes associated with pregnancy and the complex biology of preeclampsia, as detailed below. Due to the low sample size of this unique dataset, only two of these associations reached standard genome-wide significance (p ¼ 5EÀ08).
Acknowledgments We extend our deepest gratitude to the people of Puno, Peru who participated in this study at  Hospital Regional Manuel Nun ˜ez Butron and Universidad del Altiplano. We are tremendously grateful to the  Mendoza Revilla  family, who provided lodging and logistics support in Lima during fieldwork seasons of the technical team. We thank  Victor Acun ˜a Alonzo  from the  Instituto Nacional de Antropologı ´a e Historia  and  Samuel Canizales  from the  Instituto Nacional de Medicina Genomica of Mexico  for providing access to the CANDELA cohort data and  Colleen Julian  for providing helpful commentary on this manuscript. This work was supported in part by the  National Science Foundation (NSF) SBE   Postdoctoral Research Fellowship  Award No. 1711982  awarded to M. A. N. C. ;  NSF   Graduate Research Fellowship Program  Grant No.
DGE-1147470  awarded to K. M. B. R. (fellow no. 2014187481); an  A. P. Giannini Foundation postdoctoral fellowship , a  Stanford Child Health Research Institute postdoctoral award , and a  Stanford Dean's Postdoctoral Fellowship  awarded to E. T. Z. ; the  Chan Zuckerberg Biohub Investigator Award  to C. D. B. ; a  Burroughs Welcome Prematurity Initiative Award  to J. C. B. ;  Mexico CONACyT  fellowship  PNPC 003649  to R. G. B. ; and the  George Rosenkranz Prize for Health Care Research in Developing Countries , the  International Center for Genetic Engineering and Biotechnology (ICGEB, Italy)  grant  CRP/MEX15-04_EC , and  Mexico's CONACYT  grant  FONCICYT/50/2016 , each awarded to A. M. E. Further funding was provided by the  Sandler Family Foundation , the  American Asthma Foundation , the  RWJF Amos Medical Faculty Development Program ,  Harry Wm.
| Patients We enroled 25 pwCF, and 12 non-CF individuals (WT). Patients' genotype was classified according to genetic mechanism of the variant (class 1: nonsense, deletion or severe splicing; class 2: misfolding; class 3: activation defect; class 4: conductance defect; class 5: splicing with residual function). 1 All participants signed an informed consent form for sweat collection and analysis (no. Eudract 2016-A00309-42; Comité de Protection des Personnes IDF2). Clinical status of the patient at the study visit was assessed by pancreatic status, microbiology of sputum and Respiratory Volume in 1 s (FEV1), expressed as percentage predicted (ppFEV1). Sweat induction was performed by pilocarpine iontophoresis, as previously reported. 6 Sweat was collected using the Wescor Macroduct (ELITech, Puteaux, France) and chloride concentration was assessed by coulometry (chloridometer 926S Sherwood, Servilab, Le Mans, France).
| LC-MS/MS analysis For each sample, 400 ng of peptides were injected on a nanoelute (Bruker Daltonics, Germany) highperformance liquid chromatography system coupled to a timsTOF Pro (Bruker Daltonics, Germany) mass spectrometer. High-performance liquid chromatography separation (Solvent A: 0. 1% FA in water, 2% ACN; Solvent B: 0. 1% FA in ACN) was carried out at 250 nL/ min using a packed emitter column (C18, 25 cm � 75 μm 1. 6 μm) (Ion Optics, Australia) with a gradient elution (2%-11% solvent B during 19 min; 11%-16% for 7 min; 16%-25% for 4 min; 25%-80% for 3 min and finally 80% for 7 min to wash the column). Mass-spectrometric data were acquired using the parallel accumulation serial fragmentation (PASEF) acquisition method. The measurements were carried out over the m/z range from 100 to 1700 Th.
| Data analysis The data were analyzed using MaxQuant version 2. 0. 17. 0 and searched with Andromeda search engine against the UniProtKB/Swiss-Prot Homo sapiens database (release 02-2021, 20 396 entries). To search parent mass and fragment ions, we set a mass deviation of 3 and 20 ppm respectively. The minimum peptide length was set to 7 amino acids and strict specificity for trypsin cleavage was required, allowing up to two missed cleavage sites. Carbamidomethylation (Cys) was set as fixed modification, whereas oxidation (Met) and N-term acetylation were set as variable modifications. The false discovery rates at the protein and peptide level were set to 1%. Scores were calculated in MaxQuant as described previously. 7 The reverse and common contaminants hits were removed from Max-Quant output.
Proteins were quantified according to the MaxQuant label-free algorithm using lable free quantification intensities, providing a label-free normalization; protein quantification was obtained using at least 2 peptides per protein. Match between runs was allowed. Data filtering and imputation was performed using the Prostar Software. Proteins were retained in the analysis if they were detected in at least 70% of the patients in at least one group. Statistical analyses were conducted in R. Data in pwCF and WT conditions were compared by Student t-test. Benjamini-Hochberg corrections were applied to account for multiple testing. A q-value <0. 01 combined with a log2 fold change >0. 5 was considered statistically significant.
| Proteomic analysis Protein concentration ranged between 0. 25 and 0. 70 μg/μL of sweat with no difference between pwCF and WT. Nine hundred ninety-one proteins were retained for the analysis. Data are available via Pro-teomeXchange with identifier PXD032894. Fifty-seven proteins were found differentially abundant between pwCF and WT subjects: 25 were upregulated in CF sweat, and 32 were downregulated (Figure  1 , Table  2 ). Hierarchical analysis confirmed that these proteins clearly separated the pwCF from the WT as shown in Figure  2. Sweat proteome profile of F508del homozygous patients was not significantly different from that of compound F508del heterozygotes or patients with other genotypes, as shown in the dendrogram (Figure  2 ). Furthermore, patients carrying 2 class 1 variants had a similar profile to those carrying at least one class 2 variant.