dataesr/scientific-paragraphs-categorization huggingface dataset:
7. These datasets were used in the GWAS analyses. We then selected only the imputed SNPs from the two datasets and merged them into a single dataset in which all SNPs had been imputed. This dataset was used to compare imputation accuracies of SNPs according to their location relative to known genes. References used for the imputation were downloaded prior to the imputation process from the Impute website (http://mathgen. stats. ox. ac. uk/impute/impute. html). The references were labeled on the website as International HapMap project phase II release #22 (HMII) and 1000G phase I. A total of 1,417,081 SNPs from 90 Chinese and Japanese samples were used from HMII with an additional 39,343,900 SNPs from 1092 worldwide sample populations in the combined reference from 1000G. PanSNPdb shared 47,870 SNPs with the HapMap reference and 51,849 with the 1000G reference.
SNP selection and annotation All SNPs from the dengue dataset were grouped based on their location in genes. Gene annotation was collected from Illumina sample sheets (Illumina Inc. , San Diego, CA) and the NCBI database of genetic variation  [25]. Targeted gene regions included coding, intergenic, intronic, and untranslated regions (UTR). Some SNPs mapped to more than one location and were marked as being in a complex region. There were 53,277 SNPs that were not in any of these 5 groups and were subsequently discarded from further analysis. The difference between imputed and genotyped data in the dengue dataset was evaluated by looking at several properties of the SNPs. Differences in accuracy and yield for each gene region were measured by varying the posterior probability threshold from 0.
5 to 1. 0. MAFs and p-values from chi-square tests for each SNP were compared between imputed and genotyped datasets. Coefficients of determination (r 2 ) were calculated for each comparison to estimate the concordance of imputed and genotyped SNPs. Pairwise LD, which is measured as r-squared, was calculated within a region of < 1 Mb around each SNP. The calculation was performed in PLINK v1. 07 using the option -r2 with -ld-window-r2 0 and -ld-window-kb 1000. Average R-squared values for each region were calculated and plotted in Microsoft Excel. R-squared values were also plotted against distance and averaging over 1 kb bins using R software (http://www. r-project. org/).
3a ). The secondary axis (C2) mainly described the difference between 18 samples of the Thai Mlabri ethnic group (from Nan Province, Thailand) to the rest of the Thai samples. The third axis (C3) also mainly described the variation within Thai samples, whereas the fourth axis (C4) showed variation within PI, ID and SG (Fig. 3b ). Fig. 1  Boxplot of accuracies and yields for imputation results across all populations. Five percent of randomly removed SNPs were imputed with IMPUTE2 using either the 1000 Genomes project phase I (1000G) or combined Chinese and Japanese haplotypes from the International HapMap project phase II (HMII) as a reference. The imputed SNPs were tested for accuracy with the previously removed SNPs. The same set of the removed SNPs was applied to all population dataset. The technique was repeated five times.
a Boxplot of accuracy comparing populations and references. b Boxplot of yield comparing populations and references. Abbreviations: Indonesia (ID), Malaysia (MY), the Philippines (PI), Singapore (SG), and Thailand (TH) Fig. 2  Imputation accuracy and yield by chromosome. The results derived from 5% randomly removed SNPs. Imputation with IMPUTE2 was accomplished to recover the removed SNPs. The imputed SNPs were tested for accuracy with previously removed SNPs. The same set of the removed SNPs was applied to all population dataset. This process was repeated five times. a Imputation accuracy by chromosome using HMII as a reference. b Imputation accuracy by chromosome using 1000G as a reference. c Imputation yield by chromosome using HMII as a reference. d Imputation yield by chromosome using 1000G as a reference.
Effects of SNP location in a gene on imputation Using the dengue GWAS dataset to evaluate the effects of SNP locations within a gene on the quality of imputation, we found 415,710 SNPs located within genes (Additional file 2: Table  S1 ). As expected, intergenic regions contained the most SNPs, while complex regions, where SNPs have been associated with more than one gene, had the fewest. Varying the threshold settings in IMPUTE2 for accepting an imputed genotype showed that increasing the threshold led to an increase in accuracy but a decrease in yield (Additional file 1: Figure  S2 ). Imputation results for SNPs in coding regions showed the highest yield and accuracy. Intronic and intergenic regions led to the second and third highest yield and accuracy, respectively. At a threshold of 0.
Discussion Imputation of genotyped datasets is a common practice when performing genome-wide association studies. This technique is used to fill in missing genotypes and to increase the density by adding information from SNPs that are not present in the original dataset  [5, 26]. Imputation of SNPs that are not available in the dataset serves several purposes. First, if available SNP arrays are designed based on a specific population, such as Europeans, the SNPs may not cover the areas of interest for another population. SNPs important to populations from Southeast Asia might therefore be underrepresented or missing from these arrays. This situation has been reported for populations from Africa  [27]  and Mexico  [28]. Second, if data are collected independently between groups of case and control populations, the datasets might have been genotyped on different SNP sets.
At the threshold 1. 0, the result showed a large drop in yield but only a limited increase in accuracy. At the threshold 0. 9, the slope of the yield and accuracy were significantly changed. This threshold might be a good starting point for using IMPUTE2 for Southeast Asian populations. Genotype imputation of the Thai population had the highest accuracy in the current study. Previous work has shown that in the PanSNPdb database, the Thai population had the highest relationship to the Chinese and Japanese populations out of the other four study populations  [16]. The increased accuracy can therefore be explained by this closer relationship. To determine if population diversity influenced the average imputation accuracy, classical MDS was used to display the variation within Southeast Asian populations.
This is an important factor to consider when discussing significant results because imputed SNPs in coding regions will have a higher accuracy compared to SNPs in less conserved areas. This trend does not mean that we can omit non-coding SNPs because they have been shown to be associated with phenotypes in more than one-third of GWAS  [33] [34] [35]. Our study also compared GWAS results from a dataset from Thai dengue fever patients to see how imputation affected the reported results. Imputation appeared to systematically reduce the allele frequency of the initially minor allele for most imputed SNPs (Additional file 1: Figure  S5 ). This effect was more pronounced for SNPs with an initial low MAF and will make imputing low frequency alleles difficult. Our results demonstrated that imputation tended to increase the common allele.
This outcome was especially problematic if the genotypes are very rare variants  [26]. Even if the most significant SNPs had higher significance in the imputed dataset, this trend was not seen when comparing p-values from GWAS before and after imputation. The imputed genotypes were also subjected to QCfiltering, which is similar to the QC being performed on the raw genotype data. It was previously shown that post-imputation QC did not influence the imputation outcome  [36]. However, we observed an improvement in the correlation between measured MAF and p-values from imputed data and genotyped data. Without the SNPs failing the QC, the correlation between imputed data and genotyped data was close to one for allele frequency and had improved p-values. Even if over half the SNPs were removed in this step, the remaining data had higher quality and were more trustworthy.
Funding This research was supported by  National Research Council of Thailand (NRCT)  (grant number  92/2550 ). Additionally, the research was partial funded by the  Office of the Higher Education Commission and Mahidol University  under the National Research Universities Initiative. W. Lert-itthiporn was supported by a scholarship from the  Medical Scholars Program, Mahidol University. P. Malasit was supported by  NSTDA   Chair Professor Grant. P. Suriyaphol  was supported.
FITC-and PI-labeled cell populations were analyzed by flow cytometry (FACS Calibur, Becton Dickinson, Franklin Lakes, NJ, USA) and CellQuest software (Bekton Dickinson), and expressed as percentage. Annexin V+ cells correspond to the total of Annexin V+ and Annexin V+/PI+ cells. For some experiments, cell death was measured with an Incucyte ZOOM® system (Essen BioScience, Michigan, USA) analyzing sytox green (SG)positive cells. The 100% SG-positive cells were achieved by permeabilization of the cells using 0. 02% Triton X-100, and the percentage of cell death was calculated as (induced SG+ cells-background SG+ cells) / (maximal SG+ cells-background SG+ cells) × 100.
Anesthetized mice were killed and tumors samples were fixed in 10% buffered formalin, embedded in paraffin, and then 3-4-µm-thick tumor sections were prepared. Immunostainings of mouse tumor specimen were performed on a Discovery XT Ventana autostainer (Ventana Medical System, Tucson AZ, USA), using the avidin-biotin complex protocol and the Ventana detection DabMap kit. Tissue sections were incubated with rabbit polyclonal anti-CD31 antibody (Anaspec, Fremont, CA, USA) at the dilution 1:200, or with rabbit monoclonal anti-Ki67 antibody (clone SP6, Spring bioscience, Pleasanton, CA, USA) at the dilution 1:200, or with rabbit monoclonal anti-cleaved caspase-3 antibody (clone 5A1E, Cell signaling, Beverly, MA, USA) at the dilution 1:200, with 40 min antigen retrieval in CC1 buffer. Image analysis was performed by using a light microscope (Eclipse E400, Nikon France, Champigny, France) equipped with a tri-CDD video camera (Sony, Japan), and a morphometric analysis software (Histolab, Microvision Instruments, Evry, France).
Acknowledgements We thank  Nicolas Gadot  and  Julie Valantin  ( Plateforme Anatomopathologie Recherche-Centre Léon Bérard-Lyon, France ) for their support with immunohistochemistry,  Isabelle Grosjean  for her assistance with cell culture,  Isabelle Durand  ( CLB-CRCL Flow cytometry platform ) for her support with Flow cytometry,  Flora Nguyen Van Long  for her assistance with tumor lysis,  Isabelle Goddard  and  Armelle Chappot De La Chanonie  ( Tumor Model Laboratory, Centre Léon Bérard-Lyon, France ) for their support with mice xenografts experiments,  Cédric Duret  for his assistance for the manuscript revision experiments, and our colleagues in the laboratory for their restless support and encouragement. We are grateful to  Dr. Christophe Caux  for insightful and constructive discussions.
This work was supported by a grant from the  Cancéropôle Lyon Auvergne Rhône Alpes, France  ( PDC 016  CITCAP, CLARA, Lyon) (SL), from the  Institut National de la Recherche contre le Cancer (INCA) France  ( PAIR CHC 2009-140 ) (SL), and from  La ligue Contre le Cancer region Rhône Alpes. B. V. was supported by the  SIRIC  project (LYRIC, grant no. INCa_4664 ). O. M. was supported by grants from the program " Investissements d'Avenir " with reference  ANR-11-LABX-0021-01-LipSTIC Labex , the  Conseil Regional de Bourgogne , the  FEDER  ( Fonds Européen de Développement Régional ;  BG0003196 ), and the  INCa (Institut National du Cancer ,  PLBIO14-007 ).
Under these conditions, the PRKCA D463H cancer cell fraction was equal to 1 for all cases thus suggesting that the mutation is clonal. Given the high frequency of the PRKCA D463H mutation in patients with ChG, we next investigated whether the same variant was also present in other tumors occurring in the brain or elsewhere. First, we queried the Catalog of Somatic Mutations in Cancer-COSMIC  6  , and cBioPortal database characterizing 31,920 cancer samples and found no PRKCA D463H mutation referenced to date. Furthermore, we sequenced 283 primary brain tumor samples from the Onconeurotek tumor bank that includes several subtypes of gliomas including some that display juxta-ventricular location or expansion from circumventricular organs and that might be considered to be closely related to ChG.
Again, none of the samples we investigated carried the mutation (Supplementary Data 2). Genomic landscape of ChG. We examined the 346 identified somatic mutations in the four G1 samples in order to examine for other recurrent or driver mutations. In contrast to PRKCA D463H that was present in all four G1 samples, the two other recurrent mutations were only present in 2/4 G1 samples and are likely to be passenger and functionally irrelevant changes: (i) PABPC3: L530P which is not predicted to have a functional impact (Sift and Polyphen scores) and (ii) chr6:26745595 which is not located within known genes. With the exception of the SMARCA4 that was found to be mutated twice at two different intronic positions (both predicted to have no functional impact), no other cancerrelated gene was found to be recurrently mutated in the G1 group (Fig.
1 which contains NOL4 and ASXL3 genes (Supplementary Fig. 1 ; Supplementary Data 4, 5). The ten samples of groups G1 and G2 were analyzed for expression levels and for pathways using ssGSEA. Among the top cancer-related pathways most significantly enriched in ChG were: upregulation of (i) Pi3K-AKT-mTOR, (ii) EIF2 pathway, and (iii) RAS signaling pathway (the complete pathways measures are given in Supplementary Data 6). Next, Ingenuity Pathway Analysis (IPA) was performed on differential expression analysis of G1+G2 ChG samples, compared to TCGA grade II wild-type IDH which are closely related tumor with high PKCα expression (Supplementary Fig. 2 ). The top 10 activated pathways involve the biological processes of translation, proliferation, apoptosis, cell survival, and immune response (Supplementary Data 7 and Supplementary Fig.
3 ). The most significantly activated pathway was EIF2 pathway (p = 1e-11, Fisher exact test, Supplementary Data 8). EIF2 pathway is critical for translation initiation  9  , and of particular interest as PKCα regulates translation by phosphorylating elements of the EIF complex  [10] [11] [12]. These kinase relations were also confirmed using harmonizome  13  search for PKCα kinase substrates. We therefore performed an IPA analysis of expression data from PRKCA knockout vs. wild-type in umbilical human cells experimental data set (GEO:GSE27869) and found that the EIF2 pathway was activated (p = 0. 004, Fisher exact test) in PRKCA knockout cells suggesting that PKCα D463H mutation acts on EIF2 complex as a loss of function. In addition EIF2 pathway regulates activation of RAS and AKT/mTOR pathways (the latter was also among the 10 most significant pathways upregulated in ChG compared to grade II gliomas), thus strengthening the results obtained by ssGSEA described above.
DNA were extracted from blood samples using NucleoSpin ® Blood L (740954, Macherey-Nagel). Sanger sequencing. Separate primers were designed for FFPE and frozen samples (seq primer in Supplementary Data 11). PCR were performed in the following conditions: 94 °C for 3 min, 45 cycles of 94 °C × 15 s, 60 °C × 45 s and 72 °C × 1 min, and a final step at 72 °C for 8 min. The PCR products were purified using NucleoFast ® 96 PCR plates and NucleoVac 96 Vacuum Manifold (Macherey-Nagel) or Agencourt ® AMPure ® XP (Beckman Coulter) for short amplicons, and submitted to Sanger sequencing. The forward and reverse sequences were visualized using Chromas Lite software. Copy number variation analysis. CNV analysis was performed using the Illumina HumanCoreExome-24 array according to manufacturer protocols.
Normalized intensity signals were generated from the Illumina GenomeStudio software. We used CBS  36  for CNV analysis. GISTIC2. 0 37 was utilized in order to identify significantly repeated CNV. Exome sequencing. Exome sequencing was performed for DNA from frozen material (N = 10) and paired blood samples when available (N = 4/10). Library was prepared using Kapa Library Preparation Kit for Illumina Sequencing System (07137974001, Roche) and exome capture was performed using SeqCap EZ Exome Enrichment Kit v2. 0 (44 Mb 05860504001, Roche) paired-end (2*75 bp) for 13 samples and by SeqCap EZ Exome Enrichment Kit v3. 0 (2*150 bp) for one blood sample. Paired-end sequencing was performed by HighOutput kit on NextSeq500 (Illumina). The data analysis consisted of the following steps: (1) adapter sequence was removed using Cutadapt 38 , (2) poor-quality reads were removed using Trimomatics 39 , (3) alignment was performed by BWA  40  with hg19 as the reference genome, (4) quality control was performed by Qualimap 41 , (5) deduplication was done using samtools 42 , (6) variant calling was performed using GATK pipeline  43, 44  for the samples without germline information (N = 6), (  7 ) somatic mutation analysis was done using Mutect  45  for the four samples with germ line information, we excluded potential Covaris-induced mutations as per Costello et al.
46  using inhouse scripts  47  , (8) annotation was performed using Oncotator  48  including mutation functional prediction by Sift 7 and Polyphen 8. RNA sequencing. Libraries were generated from total RNA using TruSeq Stranded mRNA LT Kit (Illumina RS-122-2101). Paired-end sequencing (2 × 150 bp) was performed by Nextseq500 using High Output kit. The data analysis consisted of the following steps: first, the quality of raw reads has been assessed with FastQC  49. Trimming was done with Trimmomatic 39 : adapters were cut and reads <40 bp were excluded. The processed reads were aligned on human reference genome hg19 with Tophat2 50. The expression levels were evaluated by CuffLinks. Fusion identification and analysis were performed by three different algorithms: (1) Tophatfusion 51 , (2) FusionCatcher 52 , (3) PRADA  53.
We excluded the putative fusion pairs which did not fulfill the following minimal criteria: (i) at least two read pairs aligned to both sides of the breakpoint and (ii) at least one read aligned on the breakpoint. Clonal analysis. Clonal analysis was performed using Absolute  54  , using CNV profiles and somatic mutation profiles that were produces by CBS  36  and MuTect  45  , respectively. We used the program default parameters and also reviewed manually the selected estimated solutions. Pathway analysis. Pathway analysis was performed using single sample Gene Set Enrichment Analysis (ssGSEA)  55  using GenePatterns  56. We used Biocarta, PID, and KEGG pathways lists from MsigDB  57. Pathway analysis was also carried out for the differential expression analysis of the 10 G1+G2 ChG samples and the grade II glioma IDH wt cohort of TCGA.
The raw counts from both cohorts were jointly normalized using the EDASeq protocol with the full-quantile option in the between normalization step  58. Differential expression analysis was carried out using R's LIMMA package  59. The pathway analysis was performed for the top 2000 most significant differentially expressed genes, using IPA  60. The top 600 most significant differentially expressed genes in the PRKCA knockout vs. wild-type in umbilical human cells (GEO: GSE27869) were obtained using Harmonizome  13. Structural analysis of mutant PKCα. PKCα's structure and homology modeling of the mutant: The crystal structure of PKCα wt was available in RCSB database (www. rcsb. org21: PDB ID 3IW4). The structure of the mutant was produced using homology modeling server named I-TASSER (Iterative Threading ASSEmbly Refinement)  [61] [62] [63] [64].
CHARMM for molecular simulations: CHARMM  65  was used to produce 70 nanoseconds of molecular simulation in water for PKCα wt and PKCα D463H. The analysis was performed at pH 7 (characteristic of Gliomas 66 ). CHARMM scripts were obtained using CHARMM-GUI (http://www. charmm-gui. org). CHARMM calculates and predicts protein's different conformations around its stable conformation using leapfrog verlet integrator algorithm  65. While looping through each conformational frame in PKCα wt and PKCα D463H simulations, MDtraj was used to identify hydrogen bonds involving D463 and H463  67. Baker and Hubbard algorithm identifies hydrogen bonds using their respective atom distances and atom angles for each frame  67. The threshold of atom angles/distances must be respected in at least 80% of total number of the frames by default.
Protein sequence homology: Position-Specific Iterated BLAST (PSI-Blastp) 68 was used to find homologous proteins for PKCα. The query sequence was the kinase domain sequence of the mutant PKCα. Alignment parsing and analyzing scripts written in python were used to identify the position and amino acid homologous to position 463 of PKCα in the homologous proteins. RT-PCR. RT-PCR detection of PRKCA D463H mutant cDNA: cDNA were retrotranscribed from tumor RNA samples (1 µg) with Maxima First Strand cDNA Synthesis Kit for RT-qPCR (Thermo Fisher Scientific K1641) according to manufacturer instructions, then amplified with PCR primers VG-229 and VG-623 by using Fast Start PCR Master (Roche 04710452001). PCR reactions were split and half was digested with BstYI (New England Biolabs), when the other half was incubated in the digestion mix without BstYI, and left over night at 60 °C.
Fragments were analyzed on 2% agarose gel or by using LabChip GX bioanalyzer (Caliper LifeSciences, Villepinte, France). PRKCA RT-qPCR was performed using LightCycler ® 480 Probes Master mix and Universal Probe Library TM probes specific to each genes. Primers and probe number are indicated in Supplementary Data 11. The reference primers amplified PPIA (Peptidylprolyl isomerase A = Cyclophilin A). Real-time qPCR reactions were performed according to the manufacturer's instructions. The 2 -ΔCT method was used to determine the relative expression, where ΔCT = CT target gene -CT PPIA. PRKCa WT cDNA synthesis for plasmid construction (see below): RNA was extracted from a glioblastoma spheroid culture, non-mutated on PRKCA. First Strand cDNA Synthesis Kit (Fisher Scientific, Illkirch, France) was then used to generate cDNA.
1/myc-His B plasmid (V800-20, Thermo Fisher Scientific), to construct the PRKCa wt pcDNA plasmid. The D463H missense mutation was substituted by PCR mutagenesis with primers MD pcDNA-D463H-F and MD pcDNA-D463H-R on the wild-type PRKCA pcDNA plasmid by using Q5 Site-directed Mutagenesis kit (New England Biolabs E0554S) (seq primer in Supplementary Data 11). PCR products containing the CMV promoter and PRKCa wt and PRKCa D463H cDNA sequences fused to myc/His tag were amplified from pcDNA expression vectors with adapter primers attB1-CMV-F et attB2-pcDNA3-R, then pEntry vectors were generated with Gateway BP Clonase II Enzyme Mix (11789, Thermo Fisher Scientific) by Gateway recombination (Life Technologies 12535). The T638A missense mutation was substituted by PCR mutagenesis with primers MD pcDNA-T638A-F and MD pcDNA-T638A-R on the wild-type PRKCA pEntry vector by using Q5 Site-directed Mutagenesis kit (New England Biolabs E0554S).
The following primary antibodies were used: anti-PKCα (Santa Cruz, SC-8393; diluted 1:500), anti-Myc (Cell Signaling, #2276; diluted 1:1000), anti-β-actin (Cell Signaling, #3700; diluted 1:1000), anti-Aquaporin 4 (Sigma, #HPA014784; diluted 1:500), anti-GFAP (Sigma, #G3893; diluted 1:1000). Secondary antibodies (Odyssey IRDye 800CV goat anti-rabbit or mouse secondary antibodies, Science Tec, Courtaboeuf, France) were diluted 1:5000 in SuperBlock Blocking Buffer in TBS (Pierce, Brebieres, France). Blots were scanned and quantified on the Odyssey CLx (Science Tec, Courtaboeuf, France). Quantification values were normalized to the corresponding β-actin band. The unscropped scans (raw data western blots) are provided in the Supplementary Information file (Supplementary Fig. 8 ). Tanycytes isolation, culture and EdU counting.