dataesr/scientific-paragraphs-categorization huggingface dataset:
( fellowship IFI18/00006) of the Instituto de Salud Carlos III (ISCIII), co-funded by the European Social Fund (ESF). AV is supported by the US Department of Defense grant (CA150272P3) and the Tisch Cancer Institute (Cancer Center grant P30 CA196521). SPP is supported by the ISCIII through the Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016 and 2017-2020 co-funded by the European Regional Development Fund (ERDF) (PI16/00111 and PI19/00036). JZR's team is supported by Inserm, Labex OncoImmunology Investissement d'Avenir and is "Equipe labellisée par la Ligue Nationale Contre le Cancer». XF is supported by the ISCIII through the Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016 and 2017-2020 co-funded by the ERDF (PI18/00079), by the Secretaria d'Universitats i Recerca del Departament d'Economia i Coneixement (grant 2017_SGR_1753) and by CERCA Programme/Generalitat de Catalunya.
mRNA expression analysis Total RNA was reverse transcribed using Maxima First-Strand cDNA Synthesis kit (Fermentas, Thermoscientific, France), and subjected to quantitative real-time PCR on the LightCycler® Carousel-Based System (Roche Diagnostics, France) using the LightCycler® Fast Start DNA Master SYBR® Green I kit and specific primers designed using Oligo 6 software (MedProbe, Norway). Protocol consisted of a hot start step (8 min at 95 • C) followed by 40 cycles including a 10 s denaturation step at 95 • C, a 10 s annealing step, and an elongation step at 72 • C varying from 3 s to 11 s. The primer sequences and PCR conditions are given in Table  1. Relative gene expression levels were normalized to YWHAZ (tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein), and PPIA (peptidylprolyl isomerase A) transcripts and determined using the 2 -ΔΔCt method.
Membranome data analysis Transcriptional changes were previously monitored in 7 biological replicates, each consisting of osteoblasts grown alone and cocultured osteoblasts after 9 h or 48 h, using the Agilent Human SurePrint G3 Microarray, 8 × 60 K microarray chip (Agilent Technologies)  [7]. Microarray data have been deposited in the Array Express data base at EMBL-EBI (https://www. ebi. ac. uk/arrayexpress/experiments/E-M TAB-8849/). Probe-level data were preprocessed using LIMMA  [9]  by applying the normexp background correction method followed by withinarray normalization using the robust spline method, and between-array normalization using the Aquantile method. The totality of the genes expressed in osteoblasts were selected for membranome identification from transcriptomic data using a background expression cutoff corresponding to two times the overall median log2-transformed probe intensity.
Experimental study design and workflow To identify adipocyte-secreted factor(s) causing a shift of osteoblasts towards an adipocyte-like phenotype, we used an integrative Omics strategy combining proteomic and transcriptomic data from both cell types as shown in Fig. 1. We first identified proteins contained in adipocyte culture media using LC MS/MS and Label Free methods and selected actively secreted proteins using Gene Ontology terms. In parallel, by combining gene expression data experimentally determined in osteoblasts and data from the in silico human surfaceome, we identified protein coding genes that are part of the osteoblast membranome. Physical interactions between adipocyte secreted proteins and osteoblast membrane proteins were then investigated using data from StringDB. Finally, significant pathways leading to osteoblast transcriptional changes were identified using gene set enrichment analysis (GSEA).
Osteoblast membranome identification Gene expression analyses of the osteoblastic population were previously performed by microarray in 7 biological replicates  [7]. Using those available data, we applied a cutoff corresponding to two times the overall median log2-transformed probe intensity (i. e. 8. 72) and thus selected 2885 genes expressed in osteoblasts. Gene-encoding membrane proteins were then selected based on their association with the in silico human surfaceome database. We found that 454 protein coding genes expressed in osteoblasts were associated with gene products located on the surface membrane (Supplementary Table  S2 ).
Nevertheless, their results support the hypothesis that tropical insect species are at higher risk of extinction than temperate insects  (Deutsch et al. , 2008). For example, the leaf beetle Cephaloleia belti includes high-and low-elevation mitochondrial haplotypes locally adapted to cold and warm temperatures. Both haplotypes interbreed in a hybridization zone at mid elevation. Demographic models, combined with long-term temperature datasets, show that, with an increase of just 2 C, trailing-edge populations will decline, and middle elevations will become refuges to global warming  (García-Robledo & Baer, 2021a , 2021b). As long as population losses at lower latitudes or elevations are compensated by range expansions at other boundaries of the range, most species may be able to persist  (Deutsch et al.
Acknowledgements MDEM acknowledges the initiative of  Excellence IDEX-Unistra  ( ANR-10-IDEX-0002-02 ) from the  French national program  " Investment for the Future", Agence Nationale pour la Recherche  ( ANR-20-CE05-0005-01 ,  ANR-18-CE092-0040-01 ),  CNRS MITI interdisciplinary programs ,  CNRS  and  Université de Strasbourg  for funding. JS thanks  ENS-PSL university  for PhD grant. The authors acknowledge the NMR, mass spectrometry and X-ray diffraction common services ( Nathalie Gruber ) from  Federation de Chimie "Le Bel" -FR 2010 and FrenchBIC network.
Here, we report on our experience on the implementation of RNA-seq into clinical diagnostics using patientderived skin fibroblasts (Fig. 1 ). We demonstrate the application of our validated computational workflow, DROP  [29] , which integrates preprocessing and quality control steps, as well as modules for detecting aberrant expression, aberrant splicing, and MAE (Fig. 1 ), and to which we have added a new module for RNA-seq-based variant calling. We apply this workflow to a compendium of WES and RNA-seq samples of 303 individuals suspected of having a mitochondrial or another Mendelian disease, the largest such dataset to date. For each type of aberrant event, we examine their genetic background and provide diagnostic guidance through case studies. While our analysis is based solely on fibroblast-derived material and with the majority of individuals suspected of having a mitochondrial disorder, our study also addresses the value of other clinically accessible tissues for the diagnosis of Mendelian disorders.
First, individuals suspected of a Mendelian disorder are recruited for DNA sequencing. In addition, patient biopsy material is collected during the routine medical examination and prepared for RNA extraction. The sample preparation process can take from hours for biopsies to weeks for establishing a cell culture. RNA sequencing is then performed followed by alignment and quality control. The generated data go through DROP which consists of quality control steps and detection of aberrant RNA expression events. The results are then interpreted by sample, including the association of aberrant RNA expression events with rare variant(s) and the function of affected genes with the patient phenotype, which can lead to new diagnoses or candidates. Experience-based estimated durations are provided for each step and deletions (< 200 bp), were detected with SAMtools v0.
Variant annotation and handling Variants were annotated for consequence, location, minor allele frequencies (from the 1000 Genomes Project  [35]  and gnomAD  [36]  cohorts), and deleteriousness scores using the R interface to the Ensembl Variant Effect Predictor (VEP) v1. 32. 0  [16, 37]. For variants that fell on multiple transcripts and had therefore multiple predicted consequences, the one with the highest predicted impact was selected  [38]. We considered a variant to be rare if the maximum minor allele frequency across both cohorts was lower than 0. 001 and the frequency of the variant in our cohort was lower than 0. 01. Variants are reported using the Human Genome Variation Society (HGVS) recommendations  [39]. In order to detect whether a genomic position is expressed, we computed the RNA coverage using the coverage function from the GenomicAlignments R package v1.
Variant calling in RNA-seq data Variants were called on RNA-seq data using GATK best practices for RNA-seq short variant discovery  [33]. Variants with a ratio of quality to depth of coverage < 2 that were strand biased (Phred-scaled fisher exact score >30) or belonging to an SNP cluster (3 or more SNPs within a 35 bp window) were filtered out, as suggested by GATK. Furthermore, variants not contained in a repeat masked region (as defined by RepeatMasker v4. 1. 0 [42] ) and with 3 or more reads supporting the alternative allele were prioritized. For the benchmark analysis, 210 RNA-seq samples derived from suprapubic skin of the GTEx project were used. Only genomic positions with an RNA coverage of at least 3 reads were considered.
Detection of aberrant expression Detection of aberrant expression was fully based on DROP v1. 0. 3  [29]. We used as reference genome the GRCh37 primary assembly, release 29, of the GENCODE project  [44]  which contains 60,829 genes. We used the summarizeOverlaps function from the GenomicAlignments  [40]  R package to count reads that are paired with mates from the opposite strands (singleEnd = FALSE). We only considered reads that fell completely within an exon or span two exons from the same gene via splicing (mode = intersectionStrict). Reads that overlapped more than one feature were assigned to each of those features instead of being removed (inter. feature = FALSE). Genes with a 95th percentile FPKM < 1 were considered to be not sufficiently expressed and filtered out. Expression outliers were found using OUTRIDER  [45] , which uses a denoising autoencoder to control for latent effects and returns multiple-testing corrected p-values (FDR) for each gene and sample.
Detection of aberrant splicing Splicing outliers were obtained using the aberrant splicing DROP module based on FRASER  [47] , an annotationfree aberrant splicing detection algorithm. FRASER uses a denoising autoencoder to control for latent effects and estimates splice-site level and gene-level multiple-testing corrected p-values for percent spliced-ins and splicing efficiencies. Exon-exon and exon-intron junctions with < 20 reads in all samples and for which the total number of reads at the donor and acceptor splice site is 0 in more than 95% of the samples were filtered out. From the FRASER output, splicing outlier genes were defined as those with Holm's adjusted p-value across junctions of the tested gene < 0. 1. Outlier junctions are defined as those in splicing outlier genes, with an FDR < 0.
Detection of mono-allelic expression For mono-allelic expression analysis, only heterozygous single-nucleotide variants from WES were considered. Reads assigned to each allele were counted using the ASEReadCounter function from GATK v4. 0  [48]. Positions with less than 10 reads in total were filtered out. Afterward, the negative binomial test described in Kremer et al. [23]  was performed. This fully corresponds to the MAE module of DROP v1. 0. 3. ANEVA-DOT was run using the ANEVADOT_test function from its R package and the provided pre-calculated genetic variations from fibroblasts from GTEx  [49]. Significant variants were defined as those with FDR ≤ 0. 05.
The study cohort consists of 106 WES-diagnosed cases used to establish a reference dataset of gene expression (some of which have been published as single-gene studies  [4, ) and 197 cases that remained inconclusive after WES (Additional file 2: Fig. S1 ). A single RNA-seq assay was performed per individual at a median sequencing depth of 90 million reads (range 50-165 million reads, Additional file 2: Fig. S2A ). A total of 101 samples were sequenced following a non-strand-specific protocol and 202 following a strand-specific one using automated protocols minimizing sample handling and allowing highly reproducible results (Methods). We provide the gene expression count matrices, as well as the privacy-preserving count matrices of split and unsplit reads overlapping annotated splice sites via Zenodo independently for the non-strand-specific (https:// zenodo.
org/ record/ 46468 23  [79] ) and the strand-specific datasets (https:// zenodo. org/ record/ 46468 27  [80] ). These matrices can be integrated by external users through DROP  [29]. After alignment, RNA-seq data were analyzed using the computational workflow DROP  [29] , which ensures reproducibility, robustness, and scalability (Fig. 1 , Methods). All the samples had a high percentage of high-quality reads aligned (> 80% for all samples) and expressed more than 11,000 genes (Additional file 2: Fig. S2B-C ). DROP also computes the percentage of matching DNA-RNA variants to control for sample mismatches, which allowed us to reassign ten RNA-seq samples to their corresponding DNA (Additional file 2: Fig. S3 , Methods). Afterward, through DROP, we called aberrant expression, aberrant splicing, and MAE using the statistical methods OUTRIDER  [45] , FRASER  [47] , and a negative binomial test  [23] , respectively.
This yielded a median of 25 aberrant genes per sample, including eight where variants have been reported to cause a Mendelian disease in humans (OMIM  [81] , Additional file 2: Fig. S4A-B ). From RNA isolation until candidate identification via data analysis, the workflow has been streamlined by applying standardized protocols and semi-automated analysis pipelines which, in principle, allow to call outliers within 1 week (Fig. 1 ). Aberrant events involving known disease-associated genes were then inspected manually in a case-by-case fashion by comparing patient phenotype information with the phenotypes and mode of inheritance associated with the disease-associated gene, following the flow diagram shown in Fig. 2. For plausible candidate genes, we next inspected the sequencing data and searched for causative variants called by either WES or RNA-seq, and in some cases performed WGS followed by segregation analysis of the likely pathogenic variants.
1), including 6 disease genes (Fig. 4A ). Three samples had a high number of splicing outlier genes, out of which two corresponded to the ones with the highest sequencing depth. This indicates that sensitivity to splicing outliers could increase with deeper sequencing, which is in line with analyses in simulated and real datasets  [29, 90]. Power analysis for FRASER has been described in the original publication on suprapubic skin tissue from the GTEx dataset, which has a similar sequencing depth to this study's dataset. In brief, in junctions with low mean coverage (lowest third, mean junction coverage ≤ 16 reads), FRASER was able to recall 55% of simulated outliers that have a differential  around 0. 5. The recall increased to 90% in junctions with higher coverage in the analyzed dataset  [47].
S10B ). However, only 12. 5% of SpliceAI and 10% of MMSplice predicted variants in expressed genes were supported with an aberrant splicing call. Altogether, direct experimental observations of aberrant splicing by RNA-seq are still far from being accurately predicted by variant annotation tools. Moreover, besides scoring a junction, RNA-seq reveals the consequence of the splicing defect on the resulting transcript isoform (e. g. , frameshift or exon truncation), which is crucial for diagnostics. A particular value of RNA-seq lies in the quantification of different transcript isoforms. This is especially useful for transcripts with physiological presence of several alternative isoforms and in cases of aberrant splicing with a complex pattern, exemplified by a case with a homozygous splice region variant (NM_022915.
64, respectively, Fig. 3C ). Each of these genes had heterozygous protein-truncating variants suggesting that NMD acted on the transcript originating from the alternative allele. This hypothesis was confirmed by MAE of the reference alleles (88%, 91%, 75%, respectively). Moreover, segregation analysis in each case confirmed that the protein-truncating variants occurred de novo. Altogether, these results demonstrate that aberrant expression callers controlling for hidden confounders such as OUTRIDER  [45]  are sufficiently sensitive to detect aberrant expression when only one allele is affected and may discover the pathological variant in autosomal dominant disorders, particularly those presenting haploinsufficiency. We identified MAE in nine disease-associated SNVs with a median alternative allelic ratio of 0.
This intronic variant was also found associated with the inclusion of the same cryptic exon in another unrelated RNA-seq diagnosed case from our compendium, where it is in trans with a heterozygous frameshift (NM_024120. 4:c. 605dup) which causes aberrant expression. Notably, variant calling in RNA-seq data fails in intergenic and intronic regions, as well as in genes that are not expressed. Thus, the increased power of WGS in calling all genetic variation is still unquestionable, though the interpretation of cumbersome WGS datasets could be streamlined through the incorporation of RNA-seq data.
Reanalysis of the cohort published in 2017  [23]  provided a genetic diagnosis to an additional five cases (two NDUFAF5, LPIN1, TAZ, NDUFA10; Table  1 ). The splicing defect in NDUFAF5 was not detected by the previously used LeafCutter method  [114]  but was found with FRASER. LeafCutter did find aberrant splicing in the LPIN1 and TAZ genes, but no variants were found to Fig. 7  RNA-seq captures a broad spectrum of mechanisms of action of pathogenic variants. Summary of variants and their effect on transcript across 33 cases from our cohort, where the capture of a transcript event by RNA-seq enabled establishing a genetic diagnosis in 32 and rejecting a candidate gene in one case, highlighting the value of transcriptomics as a tool in diagnostics. Each gene represents one case, except for NFU1, which belongs to two categories.
This work aims at investigating the impact of some GNSS processing parameters on IWV retrieval from a shipborne antenna in PPP mode. The studied parameters are cutoff angle, random walk of the estimated delays, and observation weighting. Data were collected for 2 months in 2018 by the GNSS antenna of a vessel operating in the Bay of Brest, France. The impact of the parameters is assessed by comparing the shipborne GNSS-derived IWV to the IWV estimated from a close GNSS ground station, and those computed by the ERA5 reanalysis and operational radiosonde profiles from the nearest Météo-France station. The most satisfying parameterization is shown to have Root Mean Squared (RMS) differences of 0. 5 kg m 2 , 0. 9 kg m 2 , and 1. 2 kg m 2 compared to GNSS ground station, ERA5, and radiosonde respectively.
A high cut-off angle on satellite elevation will reduce the multipath effect; a low value will help to decorrelate ZWD and height estimates. An elevation dependent weighting of observations will also permit to mitigate the multipath. Finally, a suitable choice of the random walk on the ZWD will help the algorithm to decorrelate height and ZWD estimates as well. This study aims at investigating the impact of these GNSS processing parameters on the IWV retrieval from a shipborne GNSS antenna. Here we processed only GPS raw data acquired on the French survey vessel Panopée. It was operating in the Bay of Brest during 49 days between March, 30th and May, 17th of 2018. The datasets used in this study and the data processing will be detailed in Sect. 2. The Sect. 3 will present the results of the height and IWV retrieval from the shipborne GNSS antenna, and their comparison to other water vapor datasets described in Sect.
Shipborne Antenna Dataset The GNSS antenna PANO is onboard the French survey vessel Panopée operating in the Bay of Brest. The antenna is mounted on a pole located on the echosounder well at the back of the vessel, as illustrated in Fig. 1. This location was chosen in order to reduce the multipath effects that could affect the antenna, due to the vessel structure. The chosen dataset has a length of 49 days from day 89 to day 137 of year 2018  (March, 30th to May, 17th). During this period, the vessel Panopée was operating in the Brest harbor for hydrographic purposes. It was docked more than 90% of the time, leaving for at most a couple of hours straight. The Gipsy-Oasis II v. 6. 4 software (hereafter GIPSY) developed by the Jet Propulsion Laboratory (JPL) of the National Aeronautics and Spatial Agency (NASA), is used to process the PANO dataset  (Zumberge et al.
1997). GNSS constellations other than GPS are not supported by GIPSY for kinematic PPP processing with ambiguity resolution  (Bertiger et al. 2010) , only the GPS data are studied here. The data are processed with a time resolution of 30 s with highresolution final clocks and orbit products from JPL. To avoid edge effects, it was processed in a 30 h window centered on each day from which the 00-24 h parameters were extracted. The International Earth Rotation and Reference Systems Service (IERS) conventions for solid Earth tides were applied  (Petit and Luzum 2010). The Finite Element Solution tide model FES2004  (Lyard et al. 2006 ) was also applied for ocean tide loading effects, using the coefficients computed by the ocean tide loading provider. 1 The a priori values for ZHD and ZWD, and the coefficients for the mapping functions were extracted from the Technische Universität Wien (TU Wien) VMF database.
1 , are tested for each of the three following tested parameters: • cut-off angle of the satellite elevation, for decreasing the multipath effect and the correlation between ZTD and antenna height estimates; • random walk on the ZWD modeling, for constraining the ZWD variations to decorrelate ZTD and antenna height estimation; • satellite elevation weighting on a 10 mm uncertainty for phase observations, for limiting the multipath effect impacting the signal. The method used to derive the IWV from the ZTD is fully described in  Bosser et al. (2021). The IWV is computed from the ZWD with a semi-empirical function using the mean temperature of the air column above the antenna  (Bevis et al. 1992 ). The values for mean temperature were extracted from the TU Wien database. 3 To estimate the wet part, the hydrostatic part must be first computed thanks to the pressure at the antenna height.
Comparison Datasets Reference Ground Based GNSS Antenna As the vessel Panopée is evolving in the harbor of Brest, it is located close to the Brest ground reference antenna BRST (operated by IGN 4  ), with a distance smaller than 10 km. A cut-off angle of 7 ı with a uniform weighting on phase elevation of 10 mm, and a random walk on the ZWD of 5 kg m 2 are used to process the BRST data. To stay consistent with the PANO processing, the time resolution is 30 s with highresolution final satellite products in the GIPSY settings. As BRST is a ground reference station, the data is processed in static PPP mode, in contrast with the kinematic mode used for processing the moving PANO antenna. This processing strategy has already been applied in many studies  (Bock et al. 2016 (Bock et al. , 2021;; Bosser and Bock 2021 ) and has therefore already been validated.
Penfeld Tide Gauge The harbor of Brest houses the Penfeld digital tide gauge operated by the Shom. 5 It provides a reference set of data to qualify the calculation of the PANO antenna height by comparison. It will then be used in order to assess the proper estimation of height and ZTD in the GNSS analysis despite of their strong correlation. The tide gauge measurement has a 10-min time resolution. Consequently, to compare the PANO height to the tide gauge, we will use the nearest time method. This method consists of taking the PANO height value of the closest time to each tide gauge measurement time.