dataesr/scientific-paragraphs-categorization huggingface dataset:
This algorithm performed the normalization of the MSdata in the total peptide ion signals. The normalization factors is calculated between the different cell condition and replicates  (19). Analysis of the proteins identified was performed using Perseus software (http://www. perseus-framework. org/) (version 1. 6. 5. 0)  (20). The file containing the information from identification was used with hits to the reverse database, and proteins identified with modified peptides and potential contaminants were removed. Then, the LFQ intensity was logarithmized (log2[x]). Categorical annotation of rows was used to define different groups depending on the cell line. Multiple-samples tests were performed using an ANOVA test with a p-value of 0. 05 and preserved grouping in randomization. The results were normalized by Z-score and represented as hierarchical clustering.
Sub-Network Enrichment Pathway Analysis Using Elsevier's Pathway Studio (version 11. 0//Elsevier), all relationships between the differentially expressed proteins among all conditions were depicted based on the Ariadne ResNet  (22)  For proteins identified in the shotgun analysis, the Subnetwork Enrichment Analysis (SNEA) algorithm was used to detect the statistically significant altered biological pathways in which the identified proteins are involved. This algorithm uses Fisher's statistical test to detect any non-random associations between two categorical variables organized by a specific relationship. Also, this algorithm starts by creating a central "seed" from all the relevant identities in the database and builds connections with associated entities based on their relationship with the seed.
Mutation Identification Protein identification was also performed using the mutationspecific database. 32 XMan v2 database contains 2 539 031 mutated peptide sequences from 17 599 Homo sapiens proteins (2 377 103 are missense and 161 928 are nonsense mutations). The interrogation was performed with Proteome Discoverer 2. 3 software and Sequest HT package, using an iterative method. The precursor mass tolerance was set to 15 ppm and the fragment mass tolerance was set to 0. 02 Da. For high confidence results, the false discovery rate (FDR) values were specified to 1%. A filter with a minimum Xcorr of 2 was applied. The generated result file was filtered using a Python script to remove unmutated peptides. All mutations were then manually checked based on MSMS spectra profile. The structure of mutated proteins was constructed with PremPS (https://lilab.
Druggable Genome Database and Clinical Trials To identify drug candidates targeting the surfaceome proteins specific to GBM, the relationship between protein-coding genes and drugs was analyzed from different sources including Drug Central (https://drugcentral. org), DrugBank (https://go. drugbank. com/), ApexBio (https://www. apexbt. com/), DGIdb (https://www. dgidb. org/), FDA Approved Drugs (https://www. accessdata. fda. gov/scripts/cder/daf/), ClinicalTrials. gov, and/or PharmGKB (https://www. pharmgkb. org/). To determine whether drug candidates were investigated in therapeutic trials for GBM patients, ClinicalTrials. gov web-based resource was used (https://clinicaltrials. gov/ct2/home). This resource provides information on publicly and privately supported clinical studies on a wide range of diseases and conditions and is maintained by the National Library of Medicine at the National Institutes of Health (US).
Patient Samples and Consent Patients with newly diagnosed glioblastoma were prospectively enrolled between September 2014 and November 2018 at Lille University Hospital, France. Patients were adult, had no medical history of other cancers or previous cancer treatment, no known genetic disease potentially leading to cancer and no neurodegenerative disease. Tumors samples were processed within 2 hours after sample extraction in the surgery room to limit the risk of degradation of proteins. Characteristic of the cohort has been published in  (24)  (Supplementary Table  1 ). Approval was obtained from the research ethics committee (ID-RCB 2014-A00185-42) before initiation of the study. The study adhered to the principles of the Declaration of Helsinki and the Guidelines for Good Clinical Practice and is registered at NCT02473484.
RESULTS Malignant cells adopt a complete proteomic makeover, especially on their surface. Surfaceome study allows emphasizing the changes in the interactions between cells and with their environment. Here, we compared the surfaceome of two different human glioblastoma cell lines, U87 and NCH82, with a human astrocyte cell line. Cell-surface proteins were labelled and captured with membrane-impermeable EZ-Link Sulfo-NHS-SS-Biotin from intact cells. Shotgun proteomic analysis of these three cell lines yielded 2,920 protein identifications across all the samples (Supplementary Data 1). We applied a filter to retain only the proteins found in at least two of the three replicates. Then, the variations in protein expression were analyzed between the three cell lines. As a criterion of significance, we applied an ANOVA test with a significance threshold of p < 0.
05. A heatmap was created from which 190 proteins showed a significant difference in LFQ expression between astrocytes, NCH82, and U87 cell lines (Figure  1A , Supplementary Data 2, 3). Surface proteins were then identified by cross-checking the total list of heatmap proteins with the lists of surface proteins provided by the cell surface protein atlas (CSPA)  (13)  and the list of predicted surfaceome proteins  (14). Gene Ontology enrichment allowed the identification of some additional surface proteins. We found 57 surface proteins thanks to CSPA and in silico databases and 14 additional proteins being described as membrane proteins for a total of 71 surface proteins with differential expression between the three cell lines (Figure  1B ). Four clusters were highlighted, cluster 1 regroups under-expressed surface proteins in the two GBM cell lines and cluster 2 overexpressed proteins (Figure  1A ).
Over-Expressed and Exclusive Surface Proteins in Immortalized Astrocyte Cell Line We were first interested in the proteins under-expressed or unexpressed in GBM cells compared to astrocytes. On the heatmap (Figure  1A ), cluster 1 includes 21 proteins that are under-expressed in NCH82 and U87 compared to the astrocyte cell line (Supplementary Data 2). Among these proteins, 12 are known to be expressed at the plasma membrane. We compared these proteins to the surface proteins described in GBM cell line and primary culture in CSPA (GBM CSPA) database. Four proteins were found in GBM CSPA databases and 8 additional proteins were found in astrocyte cell line (Figure  1C , Supplementary Data 2). Among these proteins, we found Cell adhesion molecule 3 (cadm3) involved in intercellular adhesion and Fibrillin-2 (fbn2), an extracellular matrix component.
Stomatin (stom) which regulates ion transport was also found in this cluster of under-expressed surface proteins. We then examined the proteins exclusive to each cell line and among the 15 proteins only expressed by astrocytes cell line, 11 were membrane-bound and so, unexpressed in GBM cell lines (Figure  2A ). 7 of these 11 surface proteins are described in GBM CSPA databases (Supplementary Data 4, Figure  2B ). In addition to these proteins, we also found 4 more cell surface proteins not described in the lists above (Supplementary Data 4). Among these proteins, we found the desmosomal protein plakoglobin (jup) which plays a central role in the structure and function of submembranous plaques  (25). We have also identified NHE-RF1 (slc9a3r1), known to be localized to the plasma membrane in normal astrocytes and showing a cytoplasmic shift within GBM tumor cells  (26).
Finally, Caveolin-1 (cav1) could serve as a biomarker to predict response to chemotherapy. Indeed, its over-expression seems to confer sensitivity to the most commonly used chemotherapy for glioblastoma, temozolomide  (40). Moreover, 69 proteins described as membrane proteins were found among the 241 proteins common to both NCH82 and U87 glioblastoma cell lines and not expressed by astrocyte cell line (Supplementary Data 4, Figure  2C ). Among these proteins, 43 are found in the GBM CSPA database. We found 26 additional surface proteins that are not included in the lists of surface proteins previously mentioned (Supplementary Data 4). Among them, we found Glioma pathogenesis-related protein 1 (glipr1), Receptor of activated protein C kinase 1 (rack1 or gnb2l1), Protein disulfideisomerase (p4hb), and Carbonic anhydrase 9 (ca9) known to be over-expressed in glioblastoma and to promote tumor growth  (41) (42) (43) (44).
Expression of Mutated Surface Proteins in the Glioblastoma Cell Lines Several studies have reported the high number of protein mutations related to tumor progression in GBM  (47) (48) (49). In this context, we investigated the mutations of surface proteins in GBM cell lines. For that purpose, we used a human database combined with the XMan v. 2 database (15). This database contains information about mutated peptides that can be found in some cancers, extracted from the COSMIC database. After applying statistic filters, 114 mutations were identified. The MSMS spectra of these 114 mutations were manually checked for specific fragmentation of the mutated amino acid. Thus, thirtythree mutated peptides were identified among which 9 are only detected in both NCH82 and U87 GBM cell lines compared to astrocyte cell line.
Tracking Identified Surface Proteins in Patients In this study, proteomic analyses were performed on three different replicates of cell samples. Some variation in the protein expression can be observed between replicates of a same cell sample (Figure  1A ). Since the number of replicates was limited, we wanted to validate the expression in GBM patients of all the surface proteins identified in this study. To do so, we compared the proteins of each heatmap clusters (Figure  1A , Supplementary Data 2) as well as the proteins exclusive to each cell line and the proteins common to both NCH82 and U87 cell line (Figure  2A , Supplementary Data 4) to the ones identified in patient's glioblastoma using spatial proteomic guided by MALDI-mass spectrometry  (24). Characteristics of the GBM cohort are presented in (Supplementary Table  1 ).
It has allowed to identify 66 of our surface proteins expressed in at least 70% patient's GBM from this cohort. Of all the surface proteins we identified in this study (Supplementary Data 2 and 4), 67 are not described in the GBM cell lines in the CSPA database. It was therefore necessary to confirm the expression of these additional surface proteins in patient's GBMs. Thus, we can confirm the expression of 32 surface proteins in 70% of the GBM patients cohort  (24)  among the 67 additional proteins not founded in the GBM CSPA data (Supplementary Data 2 and 4). Finally, among the surface proteins identified in the GBM cell lines and in the patient's GBM we found 5 proteins not described in healthy brain tissues according to human protein atlas (Table  1 , Supplementary Table  3 ). In which we have HSPD1 already used in clinical trials as described before and LGALS3BP which have no known drug interaction yet.
Clinical Investigation for the Selected Specific Glioblastoma Targets We selected 78 surface proteins only found in each GBM cell lines (Figure  2C ) or overexpressed (Cluster 2, Figure  1A ) in both NCH82 and U87 GBM cell lines (Supplementary Table  2 ) and the 9 mutated surface proteins only expressed by NCH82 or U87. We analyzed the expression of these proteins inside healthy tissues and cancers thanks to Human Protein Atlas and Catalogue Of Somatic Mutation In Cancer (COSMIC) databases. Thus we highlighted 25 proteins not expressed in healthy brain tissues (Supplementary Table  3 ). The druggability of the selected surface proteins specific to GBM was assessed using drug and compound databases as described in methods. Our approach identified among targetable proteins CPM, CYBA, EGFR, HLA-A, HLA-B, HSPD-1, P4HA2 which were druggable with either chemotherapy, tyrosine kinase inhibitors, vaccines, anti-CTLA4 or vitamins.
It is a new tool to explore specific tumor development mechanisms through a non-traditional approach. The surfaceome study is a useful tool for fundamental research to understand the mechanisms of tumor growth. It also enables the discovery of new biomarkers for clinical purposes. Indeed, in this data, we found 78 surface proteins over-expressed or exclusive to GBM cell lines compared to astrocyte cell lines (Cluster 2, Figures  1A, 2C , and Figure  2C ). Among these proteins, 28 are not described in GBM CSPA database. It may be an opportunity to complete databases after localization validation of these proteins with the literature. On the other hand, we screened the human protein atlas for these 78 surface proteins to highlight potential therapeutic targets for immunotherapy. Thus, 25 surface proteins showed low expression on brain-healthy tissues (Table  S1 , proteins highlighted in blue), and 6 of these proteins are described in GBM tissues (Table  S1 , proteins highlighted in purple) according to the human protein atlas.
This work showed that exploring the surfaceome of GBM cells has the potential to identify new therapeutic targets including mutated surface proteins exclusive to GBM. However, it is important to be aware that these results are derived from surfaceome analyses of immortalized cell lines in a limited range of replicates. The cross-validation of our results with GBM patients proteomics data and with the Human Protein Atlas data helped us to select the more specific potential targets which still need to be validated on a more relevant study model such as patients'-derived organoids and an increased number of replicates. Using patients' derived tumoroids in order to select the right antigens expressed by the tumor cells could help to translate this kind of approach into the clinic in order to perform personalized therapies.
Differential RNA expression between JMML patients and normal bone marrow controls. In total, 167,569 genes were identified of which 58,096 corresponded to known Ensembl genes. Of these, 19,979 were coding, and 38,117 non-coding genes. Using edgeR, we identified 227 and 419 differentially expressed coding and non-coding genes, respectively. The LNCipedia database was used to further identify 8559 known lncRNAs showing expression of ≥ 1 count per million in at least 2/3 of the patients or PN BM samples  21. Principal component analysis (PCA), using this subset of lncRNAs showed clustering of PN BM samples and divergent of JMML patients (Fig. 1A ). Within the JMML samples, heterogeneity can be observed. Moreover, a lncRNA signature was established, consisting of 185 differentially expressed lncRNAs genes (131 up-and 54 downregulated) between JMML and PN BM (Fig.
Since no JMML cell lines are available to evaluate GapmeRs, we screened publicly available RNA-seq data from 934 human cancer cell lines from the Cancer Cell Line Encyclopedia (CCLE) to identify hematopoietic cell lines with overexpression of the lncRNAs validated. For all these lncRNAs, at least one cell line from another pediatric or adult hematopoietic malignancy was selected and expression verified with qPCR (Supplemental Figure  S4 ; Supplemental Table  S6 ). After 24 h incubation of cells with individual GapmeRs at a concentration of 5 µM, expression of the targeted lncRNA was determined with qPCR and compared with cells treated with empty mock control. For six lncRNAs (lnc-THADA-4, lnc-ACOT9-1, NRIR, MIR222HG, LINC02217, and MEG3) a downregulation ≥ 70% of the level of expression of the targeted lncRNA for two or more GapmeRs could be observed (Supplemental Figure  S5 ).
Methods Patient samples. Diagnostic BM (n = 15) and PB (n = 4) samples from 19 children with JMML and BM of 3 healthy children selected for sibling graft donation (PN) were used for RNA-seq. Sequencing results were verified with qPCR in a validation cohort consisting of 12 additional JMML patients (9 BM and 3 PB) and 6 PN BM samples. Additionally, living cells from 3 JMML patients, derived from spleen or PB, were used for primary cell culture generation. JMML samples were obtained from different institutions throughout Europe (Belgium, the Netherlands, France, the Czech Republic and Germany) (Supplemental Table  S1 ). Out of 33 JMML patients, 17 are registered in the European Working Group of Myelodysplastic Syndromes in Childhood (EWOG-MDS) studies EWOG-MDS98 and EWOG-MDS2006 (National Institutes of Health trials registered as #NCT00047268 and #NCT00662090 at www.
Acknowledgements This work was supported by the  Foundation against Cancer  ( 2016-113 , BDM), the  Cancer Plan , action 29 (KP_29_020, JP), vzw Kinderkankerfonds-a non-profit childhood cancer foundation under Belgian law (Grant to TL), the  Foundation Against Cancer (STK , Grant  2018-109 ) and the  German Research Foundation  ( ER599/3-2  to ME). The sponsors of this study are public or nonprofit organizations that support science in general. They had no role in gathering, analyzing, or interpreting the data. We thank the  Center for Biological Resources (CRB-cancer)  ( BB-0033-00076 ) of the Robert Debré hospital. MH is a PhD candidate at  Ghent University  and this work is submitted in partial fulfilment of the requirement for the PhD.
MinION Sequence Assembly For the reads obtained through the two MinION protocols, accurate basecalling was performed using Guppy (v4. 09 or 4. 015; [55]). Demultiplexing and adapter removal was then performed using Porechop v0. 2. 4  [56]. Quality of the reads was investigated using NanoPlot v1. 33. 0  [57]. The demultiplexed reads obtained from RCA-MinION procedure were assembled with FLYE 2. 6  [58] , using the "meta" and "plasmid" parameters and when possible circularized as monomers using a homemade script. Contigs were then subjected to a BLASTn search against a CRESS DNA reference sequence database obtained from GenBank. CRESS DNA contigs were then polished using Medaka v1. 2. 2  [59]. PCR-MinION sequences higher than 1500 nt for one run (TrV1-C) and 2000 nt for the two other run (TrV1-B), were assembled using Canu v1.
Sequence Comparison and Phylogenetic Analysis All the available capulavirus full genome sequences were downloaded from GenBank on 4 March 2021 and aligned with the sequences of strains TrV1-B and TrV1-C obtained after Sanger sequencing using MAFFT v7. 475  [62]. A maximum likelihood (ML) phylogenetic tree was inferred using FastTree2  [63]  using the "gtr" and "gamma" parameters. Branch supports were tested using SH-like local supports. Tree edition was performed using the ape R package  [64]. In order to properly classify the sequences obtained, an analysis that include a subset of representative capulavirus from GenBank and the Sanger sequence obtained in this study was performed using SDT1. 2  [65]. Sequences obtained using the three distinct protocols (i. e. , Sanger, PCR-MinION, and RCA-MinION procedures) were aligned together with MAFFT v7.
Sanger Sequences A total of 46 sequences were obtained after cloning and Sanger sequencing. These sequence groups in two distinct clades (Figure  S1 ) that share a mean identity of 90. 3%. Within the first clade (n = 19), sequences present with identity ranging from 99. 5 to 100% with each other and were most similar to the isolate BG2_capuz_47 of the B strain of TrV1 (GenBank accession number MW698819) with a minimum identity of 99. 7%. Within the second clade (n = 27), sequences present identity ranging from 99. 0 to 100% with each other and were most closely related to the isolate BG2_coro_02-2 of the C strain of TrV1 (GenBank accession number MW698820) with a minimum identity of 99. 1%. Therefore, our two groups of sequences belong to two distinct strains (TrV1-B and TrV1-C) of the capulavirus Trifolium virus 1 species (Figure  S1 ).
Otherwise, for other applications, such as virus discovery, virus classification, or recombination analysis, MinION assemblies represent a competitive alternative to Sanger sequencing. Although, similarly to other NGS protocols, MinION based studies require the use of sophisticated bioinformatics tools for data management and analysis and despite the specific drawbacks on sequence quality, bench top sequencer such as the MinION would probably become routinely used in the laboratory. It allows a real-time detection and diagnostic of multiple viral strains or virus species in a single run. For niche applications, such as the exploration of geminivirus genomes, which do not exceed 10 kb, nanopore sequencing is poised to push the cost and performance limits of sequencing technologies. The high reactivity offered by the platform would make it more and more democratized as a mobile real-time plant disease diagnostic tool.
Sequencing of ITS2 for molecular determination of species. Mosquito species was determined molecularly via PCR amplification and Sanger sequencing of the ribosomal DNA internal transcribed spacer region (ITS2; see supplementary information for PCR conditions). All sequence reads were obtained using standard Sanger sequencing methods and resulting sequence traces were visually verified prior to being submitted to nucleotide blast. Resulting sequences were blasted against the NCBI GenBank database. If a given sample gave at least 250 bps of clean sequence and the percent identity of the input ITS2 sequence with a single published (preferably voucher) sequence was 98% or higher, a species determination was made. We were able to confidently assign species for 774 of the 844 samples this way. In the rare event samples where species determination was not possible (~ 8% of total, 67 samples/841 total samples with species calls) either using ITS2 sequence (n = 2), or obtaining ITS2 sequence was technically problematic (n = 65), amplification and sequencing of the mitochondrial cytochrome oxidase subunit 1 (CO1) gene was carried out using Sanger sequencing with quality control as for ITS2, discussed above (see supplementary information for PCR conditions).
Landscape indices. Land use classification over the Keav Seima district was based on Landsat imagery (Landsat 4-8, 30 m resolution, 2017; https:// landc overm apping. org/ en/). A buffer zone of 2 km around each mosquito collection site was used to determine the percentage of the different land cover classes defined as: cropland, evergreen broadleaf, forests, mixed forests, plantation forests (see supplementary information for land cover class description). Landscape metrics computation was performed on Fragstats software, version 4. 2. 1  48. To reflect landscape diversity and heterogeneity, we computed Shannon's diversity index (SHDI)  49  , Simpson's diversity index (SIDI)  50  , patch density (PD) and edge density (ED) at landscape level. SHDI and SIDI allow quantification of diversity (see supplementary information for details), whereas PD and ED indices are fragmentation indices quantifying heterogeneity.
Statistical analyses. Day biting rate-We tested the effect of collection site (forest, village, forest near the village, plantation), season (dry vs. rainy) and their interactions on the day biting rate (number of Anopheles collected between 6 am and 6 pm over the total number of females collected over 24 h) using a General Linearized Mixed Model (GLMM) with a binomial error structure and Site coded as a random factor to account for repeated measurements. Data for the CBNTs and HBNTS were analysed separately. Malaria infection rates-We tested the effect of collection site, season and daytime biting as well as the interaction between collection site and season and the interaction between collection site and daytime on the prevalence of infectious mosquitoes in the CBNTs using a GLMM with a binomial error structure and Site coded as a random factor to account for repeated measurements.