dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical Analysis of Mass Spectrometry-Based Proteomic Data Statistical analysis on the proteins from 4G10 dataset was performed using ProStaR  (Wieczorek et al. , 2017)  to determine differentially abundant proteins between WT and ΔCK2β MCF10A cells. Protein sets were filtered out if they were not identified and quantified in at least two biological replicates of one condition. Reverse protein sets and contaminants were also filtered out. These filters downsized the dataset to 249 proteins. After log2 transformation, the data were normalized with VSN method. POV missing values were imputed with slsa method and MEC ones with 1-percentile value of each sample. Statistical testing was conducted using limma test. Differentially expressed proteins were sorted out using a log2 (fold change) cut-off of 1 and a p-value inferior to 0.
05. Proteins with more than three imputed values in the upregulated condition were invalidated. For each replicate, pTyrphosphopeptides were declared upregulated in ΔCK2β cells (or, respectively, WT cells) if they were detected only in this condition or if their log2 (fold change) was higher than 1 (respectively, lower than -1). The peptides upregulated in at least two replicates were further considered. For the PMAC protein dataset, after log2 transformation, missing values were imputed with 1-percentile value. Log2 (fold change) was calculated and normalized.
Pathway Enrichment Biological pathway enrichment analyses were carried out by Gene Set Enrichment Analysis (GSEA, FDR <0. 05) from the 249 proteins quantified by proteomic analysis and kept during statistical analysis (Supplementary Table  S1 ). The enrichGO and the cnet (category net plot used for visualization) functions were executed by ClusterProfiler v4. 2. 2  (Wu et al. , 2015)  (Bioconductor 3. 14, https://www. bioconductor. org/, accessed on XX February 2022, R v4. 1. 2 (2021-11-01)). The enrichment analyses were performed using the biological pathway annotations from the Gene Ontology (GO, GO. db_v3. 14. 0 (2021-09-01), Bioconductor v3. 14 Rv4. 1. 2 (2021-11-01), and KEGG databases (release 101, 2022/01)  (Kanehisa and Goto, 2000). Gene sets consisting of at least 10 genes and less than 500 genes were retained for analysis.
A pathway enrichment analysis by GSEA was carried out to identify deregulated molecular processes from proteomics quantification data (Supplementary Table  S1 ). The enrichment analysis was performed from the log2 (fold change) values computed according to protein abundances between both phenotypes (ΔCK2β/WT). Figure  2D  shows the seven significant cellular components enriched using the pathway annotation from the GO database (Supplementary Table  S3 ). Significance was defined as each cellular component with a p-value less than 0. 05 after the Benjamini-Hochberg correction. We observed that among them, the focal adhesion, anchoring junction, cell-substrate junction, microtubule cytoskeleton, and microtubule organizing center were significantly enriched in upregulated proteins. These enrichment insights support the idea that the overabundance of phosphoproteins involved in these biological processes following CK2β depletion leads to a revamping of the cell adhesion mechanisms.
The ranking factor is the final (median) kinase score. This score is based on a combined sensitivity score (difference between "treatment" and "control" groups) and specificity score (for a set of peptides to kinase relationship derived from current databases). The kinase statistics indicates the group differences, with effect size (values) and direction (+ or >0 = activation;or 0 = inhibition). (C) Signaling pathways and biological process analysis in ΔCK2β MCF10A cells compared with WT cells (GProfiler, Gene Ontology Biological Processes).
FUNDING FJ is supported by the  KATY EU program Horizon   2020/H2020-SCI-FA-DTS-2020-1  (contract number  101017453 ). The proteomic experiments were partially supported by  Agence Nationale de la Recherche  under projects  ProFI (Proteomics French Infrastructure ,  ANR-10-INBS-08 ) and GRAL, a program from the  Chemistry Biology Health (CBH) Graduate School of University Grenoble Alpes  ( ANR-17-EURE-0003 ). This research was supported in part by  Institut National de la Santé et de la Recherche Médicale (Inserm) ,  Commissariat à l'Energie Atomique et aux Energies Alternatives (CEA) , the  Ligue Nationale contre le Cancer , the  Ligue Comité de la Loire , and  Université Grenoble-Alpes (UGA).
2 V and a scan rate of 40 mV s À1. The fill factor FF, short-circuit current J sc , open-circuit voltage V oc , and power conversion efficiency PCE parameters were then deduced from J-V measurements, using a home-made program on Spyder software. The structure and texture of the films were determined with a PANalytical X'Pert PRO MPD X-Ray diffractometer using Cu-Ka source. Match! and DIFFRAC. EVA softwares were used to identify the phases and determine the lattice parameters. Absorption measurements in the UVÀvisible range were performed with a Shimadzu UV-2600 spectrophotometer coupled with an integrating sphere, in the 300-1100 nm range, with a Glass/ITO substrate as a reference. Optical bandgaps were calculated from Tauc plots drawn using the absorbance data. PL spectra were acquired over the 600À900 nm range on a Shimadzu RF-6000 spectro fluorophotometer with a 520 nm excitation wavelength.
Acknowledgements The authors thank  I-MEP2  doctoral school for the MESR PhD grant (M. A. K. ) attribution and the platform of  Consortium des Moyens Technologiques Communs (CMTC) of Grenoble INP for laboratory assistance and laboratory equipment. This work has been partially funded by the  EU   Framework Program for Research and Innovation HORIZON 2020 (Cofund ERA-NET Action , No  691664 ). This work has also been partially funded by the " PROPER" project supported by "EIG Concert Japan " and financed from the  French National Centre for Scientific Research  under the funding number "IRUEC 222437".
Positive controls were glutaraldehyde (0¢8%)-treated RBC, which are completely retained; negative controls were unstained control RBC. For each subject, the mean value of RER was calculated after exclusion of outliers, and results were normalized to the negative control value. Experiments were performed 15 to 17 days after RBC collection, after exclusion of unsuitable samples (haemolysis; n = 58). Cytometric data were acquired with FACSDIVA software v6. 1. 3 (BD Biosciences) and analyzed with FlowJo v10. 0 software (FlowJo, LLC).
Genotyping and data quality control Genotyping was conducted at the Centre National de Recherche en G enomique Humaine (CNRGH, CEA, Evry, France). Before genotyping, a quality control was systematically performed on each DNA sample. All samples were quantified by fluorescence, in duplicate, using the Quant-It kits (Thermo Fischer Scientific). The lowest values systematically underwent a second measurement before any sample was excluded. The quality of material sent was estimated using about 10% of the total samples received (selected randomly throughout the collection) by performing: i) a quality check by migration on a 1% agarose gel to ensure the samples were not degraded, ii) a standard PCR amplification reaction on the samples to ensure that the genomic DNA was free of PCR inhibitors, iii) a PCR test to verify the gender of the individual.
Fragmentation and hybridization of the DNA on the chips were performed in a dedicated (« post-PCR ») laboratory. Several quality controls were systematically included during the process, such as visual inspection of the DNA pellets after precipitation, visual inspection of the deposited cocktail of reagents for hybridization, and systematic verification of the temperature of the heating block during the extension and imaging steps. Reading of the chips was performed on iScan+ scanners (Illumina Ò ). Primary analysis of the genotyping results was done using the GenomeStudio software (Illumina Ò ). The analysis of the internal controls provided by Illumina and the randomly distributed positive controls allowed the validation of the technological process. Standard quality control metrics were then applied to the genome-wide data of the 347 genotyped samples using PLINK v1.
90b4. 6 software. 42 Single nucleotide polymorphisms (SNPs) with a call-rate < 0¢98 (n = 96,834), a minor allele frequency (MAF) < 0¢01 (n = 1,740,582) or displaying significant departure from Hardy-Weinberg equilibrium (HWE) (P < 10 À4 ) (n = 1,714) were removed from the dataset, as well as duplicated (n = 70,807) and non-autosomal SNPs (n = 109,008). For DNA samples, individuals with discordant sex information (n = 16), an autosomal heterozygosity rate more than three standard deviations on either side of the mean (n = 4), and sample duplicates (n = 4) were excluded. All remaining samples had a call rate >0¢98 (mean = 0¢998) and were kept for analysis. These quality control checks resulted in a final set of 2,281,899 high-quality SNPs genotyped in 323 individuals. The data sets generated and analyzed during the current study are available from the DataSuds repository (https://doi.
Genotype imputation Genome-wide imputation was performed using the IMPUTE2 v2. 3. 2 algorithm. 43 First, genotypes were prephased into best-guess haplotypes with SHAPEIT v2 to increase the computational efficiency of downstream steps. 44À46  We used a window size of 2 Mb, an effective population size of 17,469 (as recommended by the software providers for African populations) and the duoHMM method that allows to incorporate the known pedigree information to improve phasing. 47 Then, IMPUTE2 was run on the prephased haplotypes using the cosmopolitan panel of reference haplotypes from the 1000 Genomes Project Phase 3 (October 2014 release; n = 2,504). 48 The genome was split up into non-overlapping segments of 5 Mb. We used imputation parameter settings of k =500, Ne= 20,000 and an internal buffer region of 250 kb.
Genome-wide association study The genome-wide association analysis was performed in R (version 2. 15. 1; http://CRAN. R-project. org) using the GRAMMARÀGC (Genome-wide Rapid Association using Mixed Model and Regression-Genomic Control) method  49  implemented in the GenABEL software (version 1. 8-0). 50 This method is suitable for the analysis of data in which both ethnic admixture and close family relationships are present while successfully controlling the overall genomic inflation factor to an appropriate level to avoid false-positive results. Phenotypes are regressed on genotypes one SNP at a time in GRAM-MAR-GC in three steps. First, phenotypes were corrected by accounting for population stratification and familial dependence among individuals by using the genomic kinship matrix (pairwise kinship coefficients amongst sampled individuals estimated on the basis of genome-wide SNP data).
Gene mapping and biological prioritization Loci that showed evidence of association at P5£10 À6 with the deformability of circulating RBC (as assessed by microsphiltration, after adjustment for relevant covariates) were mapped to genes using the SNP2GENE tool implemented in FUMA (http://fuma. ctglab. nl/ 51 ), a web plateform designed for post-processing of GWAS results and prioritizing of genes. FUMA annotates candidate SNPs in genomic risk loci and subsequently maps them to prioritized genes based on (i) physical position mapping on the genome, (ii) expression quantitative trait loci (eQTL) mapping, and (iii) 3D chromatin interactions (chromatin interaction mapping). It incorporates the most recent bioinformatics databases, such as Combined Annotation-Dependent Depletion score (CADD score  52  ), regulatory elements in the intergenic region of the genome (RegulomeDB  53  ), Genotype-Tissue Expression (GTEx), and 3D chromatin interactions from HiÀC experiments.
Criteria to define deleterious SNPs were a CADD score >12¢37 which indicates potential pathogenicity or an RDB score 4 which indicates that the SNP likely lies in a functional location (categories 1 and 2 of RegulomeDB classification identifed as "likely to affect binding"). EQTL mapping was performed using eQTL data from tissue types considered as relevant for our pheno- Chromatin interaction mapping was performed from Hi-C experiments data of three tissues identifed as relevant for our phenotype of interest: aorta, spleen and mesenchymal stem cell (GSE87112), as well as from the FANTOM EP correlations cell type and organ databases. Candidate genes were selected whenever their promoter region (250bp upstream and 500bp downstream of the TSS) belonged to a locus significantly interacting with candidate lead SNPs or SNPs in LD with them in the selected tissues.
GERP score ranges from À12¢3 to 6¢17, where higher scores indicate higher evolutionary constraint and a score greater than 2 can be considered constrained. To further investigate the functional role of genes most associated with RER values, we searched for known functions in the gene database of the National Center for Biotechnology Information (https://www. ncbi. nlm. nih. gov/gene/); each gene name, and its potential aliases (according to GeneCards, https://www. genecards. org/), was also entered into a PubMed search through the following algorithm: (GENE) AND ((malaria) OR (Plasmodium) OR (spleen) OR (red blood cell) OR (erythrocyte) OR (smooth muscle) OR (smooth muscular cell) OR (endothelium) OR (endothelial cells) OR (macrophage)) to identify any known association between these genes, malaria, and processes involved in RBC production or clearance, with a focus on cellular and biomechanical control of red blood cell deformability.
In case of association between the considered gene and endothelial physiology, search was focused on articles mentioning the spleen. Regarding association with macrophage, we focused on articles mentioning their phagocytic function. We ranked genes according to the number and relevance of potential links between known gene functions and observed phenotypic features (namely, RBC deformability, splenomegaly, and innate processes involved in the quality control of RBC), as follows: no link (red boxes in Supplementary Table  4 ), potential and indirect link (orange boxes), direct link (green boxes). The relative abundance of each gene transcript in the spleen was estimated through RPKM, retrieved from the NCBI database.
A total of 19,185 genes were analyzed. A multiple testing-corrected P value threshold of 2¢61 £ 10 À6 was applied to identify genes significantly associated with RBC deformability. No gene score was above this significance threshold (Figure  4b2 ; see the corresponding quantile-quantile plot in Supplementary Figure  3b ). Nevertheless, we analysed the top ten genes that displayed P-values between 3¢84£10 À6 and 8¢25£10 À5 (Figure  4b2  and Supplementary Table  4 ). A thorough search and analysis of available information was performed to identify potential associations between these 10 genes and malaria, erythrocytes biology, spleen physiology, smooth muscle, endothelium, and macrophages (Supplementary Table  4 ). According to the Gene database of the National Center for Biotechnology Information, 5 of the top 10 genes (CHERP, CEBPZ-AS1, IL10RB, PALLD, SPARC) displayed a significant splenic  expression (RPKM values ranging from 10¢66 to 123¢8).
Acknowledgements We thank all the subjects who participated in the study;  Micha€ el Dussiot ,  Micka€ el Marin  and  Nathalie Chartrel  for outstanding technical support; our research team in Benin for excellent technical and logistical achievements:  S everin Vignigbe ,  P epin Kounou ,  Sabi N'Dah Kouagou ,  Wilson Houssou ;  Jehane Fadlallah  for her help in the recruitment of control patients;  Nicolas Br  echot for his logistical help;  Genevi eve Milon ,  Adrian Luty  for giving scientific guidance and participating in discussions during the course of this research. This research was supported by a grant from  Labex GR-Ex  and from the  National Geographic Society Committee for Research and Exploration  (Grant  9804-15 ). The labex  GR-Ex , reference  ANR-11-LABX-0051  is funded by the program Investissements d'avenir of the  French National Research Agency , reference  ANR-11-IDEX-0005-02.
| RADseq data analysis The sequenced reads were demultiplexed with the process_radtags program in stacks v. 2. 10 (Rochette et al. , 2019). All reads were truncated to 140 bp and only paired reads were retained for downstream analyses. The demultiplexed reads were mapped to the 141 primary scaffolds of the reference genome of P. nigrolimitatus SigPhenig9 (https://mycoc osm. jgi. doe. gov/Pheni1) with bowtie2  (Langmead & Salzberg, 2012). The paired-end reads were mapped with a maximum distance of 1000 bp allowed within the pair. The resulting sam files were viewed and ordered in Samtools and converted into bam files. Prior to variant calling, each bam file was assigned a read group and duplicate reads were marked with Picard tools  (Broad Institute, 2015). Variants were called with using the gvcf workflow in gatk v.
| Genetic diversity and divergence For each location sampled, we calculated summary statistics, such as the expected and observed heterozygosity and F IS with genodive  (Meirmans & Van Tienderen, 2004). Nucleotide diversity indices (theta and π), and estimates of the departure from neutrality estimate (Tajima's D) were calculated with angsd  (Korneliussen et al. , 2014). In angsd, the site frequency spectrum (sfs) for each location was first estimated from the paired-end bam files and the resulting sfs was then used as a prior for the calculation of each statistic per site with the doTheta command. Only SNPs with a coverage above 8 in 50% of the individuals within a location were retained, while SNPs with low quality (minQ < 20 and minMapQ < 30) were excluded from the analysis. We calculated Tajima's D and π within 50 kb windows, with a step size of 10 kb, with the thetaStat program in angsd.
, 2015). We visualized the geographic distribution of the genetic groups identified in admixture by plotting the average membership for each sampling location with qgis v. 2. 18. 5 (QGIS Development Team, 2018. Geographic Information System. Open Source Geospatial Foundation Project. http://qgis. osgeo. org). We performed a DAPC analysis with the R package adegenet  (Jombart & Ahmed, 2011) , according to the instructions provided in the manual. We used the find. cluster function to identify the number of clusters and included all the principal components (PCs) to capture the maximum variation in the data set. The Bayesian information criterion (BIC) was used to identify the most meaningful number of clusters in the data set. We also applied the dapc function to transform the data by PCA and we performed discriminate analysis (DA) on the retained PCs.
The retention of too many PCs can affect the DA, we therefore followed the procedure of  Miller et al. (2020). We performed the DAPC in two rounds, in which we first retained 30 PCs and applied the optim. a. score function to identify the optimal number of PCs, which we then used in the second DAPC. For further investigations of population structure, we performed PCA implemented in the R package snprelate, with the dosage mean value used for the imputation of missing data  (Zheng et al. , 2012). The degree of genetic differentiation between locations was estimated with the fixation index (F ST ). F ST values between all pairs of locations were calculated with a custom script in R, using the weighted estimator developed by  (Hudson et al. , 1992). We assessed isolation by distance (IBD), by calculating the correlation between genetic differentiation (linearized genetic differentiation: F ST 1 -F ST ) and geographic distance in the R package adegenet  (Jombart & Ahmed, 2011; Rousset, 1997)  and estimating the significance of the correlation in a Mantel test  (Mantel, 1967) , with the mantel.
| Genotype-environment associations The associations between genotypes and environmental variables were explored by two different approaches, (i) a multivariate analysis for identifying outlier loci in a redundancy discriminate analysis (RDA) and (ii) a univariate logistic regression as implemented in samβada v. 0. 7  (Stucki et al. , 2017). The environmental metadata included the 19 standard bioclimatic variables for Worldclim2 (http://world clim. org/version2) with a resolution of 30 seconds. We also inferred the level of ammonia NHx, which has been reported to impact the distribution of saprophytic fungal species  (Andrew et al. , 2018)   (Forester et al. , 2018). We performed RDA with the Vegan R package  (Oksanen et al. , 2019)  and identified outlier loci on all significant RDA axes.
All loci with a loading exceeding three standard deviations from the mean were considered significantly associated with environment  (Forester et al. , 2018). In samβada, we calculated the univariate association of each SNP genotype, coded as present or absent, with the set of 20 environmental variables. Despite collinearity, we retained all variables in the samβada analyses, which are univariate, implying that the analysis of one variable does not affect the result for another variable. The resulting p-values were adjusted for false discovery rate, by calculating q values in the R package qvalue  (Storey et al. , 2019) , and the genotype-environmental associations with a q-value below 0. 05 were retained. To visualize the potential effect of the environmental associations on the allele frequencies and the pattern of the associations, we calculated the Spearman's correlation coefficients between the allele frequencies per population from each SNP significantly association with the environmental variable in either samβada or the RDA, using the rcorr function in the Hmisc R package  (Harrell, 2021).
pair mix, followed by end repair and the ligation of SMRT adapters with the PacBio SMRTbell Template Prep Kit (PacBio). The PacBio sequencing primer was annealed to the SMRTbell template library and the Sequel Binding kit 2. 0 was used for sequencing. The library was sequenced on a Pacific Biosystem's Sequel sequencer with the v3 sequencing primer, 1 M v2 SMRT cells, and v. 2. 0 sequencing chemistry, with 6-and 10-hour sequencing run times. Filtered subread data were assembled, with Falcon v. 1. 8. 8 (https://github. com/ Pacif icBio scien ces/FALCON), and error corrected with Arrow version SMRTLink v5. 0. 1. 9578 (https://github. com/Pacif icBio scien ces/ Genom icCon sensus). The transcriptome was sequenced by Illumina RNASeq methods with PolyA selection. Plate-based RNA sample preparation was F I G U R E 1 Maps showing the 24 sampling locations from which dead wood fungus Phellopilus nigrolimitatus was collected in sprucedominated forests in Northern Europe.
Department of Energy Joint Genome Institute, a  DOE Office of Science User Facility , supported by the  Office of Science of the U. S. Department of Energy  under Contract No. DE-AC02-05CH11231. We thank  Anna Lipzen  for the transcriptome assembly,  Chris Daum 's group for DNA and RNA sequencing of the reference genome and  Kerrie Barry  for coordinating sequencing. The RAD libraries were sequenced by the  Norwegian Sequencing Center  hosted by the  University of Oslo  and supported by the  "Functional Genomics" and "Infrastructure" programs of the Research Council of Norway  and the south-eastern Regional Health Authorities. SM thanks the group of  Guido Barbugiani ,  University of Ferrara , for hosting her research stay funded by a  Kristine Bonnevies grant  from the  University of Oslo.
DATA AVA I L A B I L I T Y S TAT E M E N T The genome sequence of Phellopilus nigrolimitatus and its annotations are available from https://mycoc osm. jgi. doe. gov/Pheni1. This Whole Genome Shotgun project has been deposited at DDBJ/ENA/ GenBank under the accession JAJOHG000000000. The version described in this paper is version JAJOHG010000000. The Fastq files and BAM files generated from the mapping to the P. nigrolimitatus genome are available from the European Nucleotide Archive (project PRJEB48923). The filtered VCF file, environmental metadata and customs scripts used for the population genetic analyses are available from Dryad (doi https://doi. org/10. 5061/dryad. 7d7wm 37wr).