dataesr/scientific-paragraphs-categorization huggingface dataset:
In the future, the sensitivity can be improved by using higher depth of coverage data and combining the paired-end datasets with reads produced with the mate-pair protocol or with long PacBio reads. The proposed framework can be applied in the future to any type of cancer. Oncology Group sites that submitted samples for patients on this study in the United States. In total we obtained material for 22 neuroblastoma patients (tumor tissue at diagnosis, relapse and constitutional DNA, Suppl. Table  1 ). Whole-genome sequencing of neuroblastoma samples. In the framework of this study, we carried out Illumina paired-end sequencing for 7 novel neuroblastoma patients (corresponding to 21 samples). Data for 15 patients were taken and reanalyzed from the previous study  (Eleveld et al. , 2015). DNA from 7 patients from the previous study and 7 new ones have been sequenced using Illumina HiSeq 2500 instruments to an average depth of coverage of 80× by Beijing Genomics Institute (BGI) and the Centre National de Génotypage (CNG) respectively.
For 8 patients out of 15 previously reported, whole-genome sequencing was performed by Complete Genomics with an average read depth of coverage of 50×. DNA material for each patient (lymphocytes, primary tumors and relapse tumors) was in each case sequenced using the same sequencing platform (see Suppl. Table  1  for more detail). Data processing. Sequenced reads were mapped to the human genome hg19 using BWA and the internal Complete Genomics tools for Illumina and Complete Genomics datasets respectively. Reads from datasets sequenced using the Illumina platform were realigned around indels with the Genome Analysis ToolKit (GATK)  (McKenna et al. , 2010) , followed by a base recalibration. Due to the inherent structure of Complete Genomics reads, which contain an eective deletion relative to their corresponding genomic library, the indel realignment step was skipped for the Complete Genomics samples.
Finally, we only kept mutations located in regions where the genotype evaluated by Control-FREEC was available. To obtain a set of high condence mutations, we required the minimal depth of coverage of 50×. We ltered out variants corresponding to polymorphisms present in more than 1% of the population (snp138, 1000Genomes, esp6500) except if it was a known cancer related variant (COSMIC database for coding and non-coding mutations). Strand bias was also tested by the Fisher exact test (in addition to the test in Varscan2) to reduce the number of informative mutations to a maximum of 500. We restricted the analysis to AB regions in case when after such ltering we kept more than 100 mutations. where θ is the cellular prevalence, N Ch the number of copies of the corresponding locus, N C the number of chromosomal copies bearing the mutation, and P the tumor purity.
QuantumClone-Single is a modication of QuantumClone that assigns to all variants a single copy state. QuantumClone-Alpha uses the same EM algorithm as the default version of QuantumClone, except for the selection of the most likely mutation where it chooses the possibility maximizing the quantity q: q f,θ,p = N Ch N C × P (f |θ). Analysis of mutation enrichment in signaling pathways and gene modules ACSNMineR (Deveau P. , Barillot E. , Boeva V. , Zinovyev A. , Bonnet E. , In Press) version 0. 16. 01. 29 was used to compute gene modules and pathways enriched in deleterious mutations. Gene modules included by default in ACSNMineR come from the manually curated Atlas of Cancer Signalling Networks (ACSN)  (Kuperstein et al. , 2015). In addition to the ACSN modules, we calculated mutation enrichment in a set of neuritogenesis genes frequently mutated in neuroblastoma  (Molenaar et al.
(2012) , Suppl. Table  8 ). We called deleterious mutations as stop-gain mutation or variants that were predicted as possibly damaging or deleterious by SIFT  (Ng and Heniko, 2003) , PolyPhen-2  (Adzhubei et al. , 2013) , or FunSeq2  (Khurana et al. , 2013). To get a list of genes to use as an input to ACSNMineR, we pooled mutations from all neuroblastoma patients; genes mutated at least once were included in the nal list. Modules with a p-value lower than 0. 05 after Benjamini-Hochberg correction were considered as enriched.
The pre-requirements are sucient number of candidate mutations (at least 50 mutations per sample) and a minimal read depth of coverage of 50×. These requirements are usually met by WGS or whole exome sequencing datasets. Our simulation results show that increasing the number of mutations used for clonal reconstruction above 50 does not improve signicantly the clonal reconstruction accuracy provided that mutations specic for every clone are present in the input. MethodsDatasetsPatient selection and collection of tumor samples. The inclusion criteria for this study were histopathological conrmation of neuroblastoma at original diagnosis and the presence of biopsy material from a subsequent relapse specimen. Patients were included in this study after an informed consent was obtained from parents or guardians, with oversight from the ethics committees 'Comité de Protection des Personnes Sud-Est IV', reference L07-95/L12-171, and 'Comité de Protection des Personnes Ile-de-France', reference 0811728 in France, the review board at the Children's Hospital of Philadelphia and review boards at other Children's.
Acknowledgments GS and her team were supported by the  Annenberg Foundation  and the  Nelia and Amadeo Barletta Foundation. Funding was also obtained from  SiRIC/INCa  (Grant INCa -DGOS-4654 ) and from the  CEST of Institut Curie. This study was also funded by the  Associations Enfants et Santé ,  Association Hubert Gouin Enfance et Cancer ,  Les Bagouz à Manon ,  Les amis de Claire. VB and her team were supported by the  ATIP-Avenir.
Data access The whole-genome sequencing data have been deposited at the European Genome-phenome Archive (EGA) under accession number EGAS00001001184 for the French cases sequenced at BGI and under accession number EGAS00001001825 for the French cases sequenced at CNG. Sequence data for the US cases are available in the database of Genotypes and Phenotypes (dbGaP) under accession number phs000467. QuantumClone is available at https://github. com/DeveauP/QuantumClone/ and can be downloaded as an R package from the CRAN repository.
FEM model In this research, the commercial FEM software Abaqus is used to model film delamination using the traction separation mechanism at the interface between a 20 nm gold film and an 833 nm silicon substrate, which is implemented using cohesive elements in the simulation. The thickness of the cohesive elements layer is 5 nm. Figure  9  below illustrates of silicon side laser ablation model for gold film. Table 1   (CES EduPack, 2011). The plastic properties of the gold film and material properties of ductile damage model for gold film are shown in Table  2   (Timpano K. , 2005)  and Table  3 , respectively. The damage evolution law for the gold film can be specified in terms of equivalent plastic displacement, which is 0. 2 nm in this research. Because laser induced plasma expansion at the gold film/silicon interface can delaminate the gold film from the substrate, traction separation behaviors at the interface are modeled using cohesive elements.
The known parameter values are listed in Table  4. A and are determined based on data from the Refractive Index Database  (Polyanskiy, 2015). To obtain the beam radius r 0 , the transverse intensity distribution is approximated with a Gaussian profile, and the radius at which the intensity drops to 60% of its peak value is used to specify the beam size in this study. Because of the difficulty in determining the energy loss in the silicon substrate, the average laser fluence incident on the gold film after passing through the silicon wafer is measured using our imaging setup and a calibration of the pixel response in air. For the pulse energy of 157 nJ incident on the front surface we found that the laser fluence yielded at the backside is only 0. 035 J/cm 2 , which is assumed to be absorbed by the gold film.
Motivation: Flow, hyperspectral and mass cytometry are experimental techniques measuring cell marker expressions at the single cell level. The recent increase of the number of markers simultaneously measurable has led to the development of new automatic gating algorithms. Especially, the SPADE algorithm has been proposed as a novel way to identify clusters of cells having similar phenotypes in high-dimensional cytometry data. While SPADE or other cell clustering algorithms are powerful approaches, complementary analysis features are needed to better characterize the identified cell clusters. Results: We have developed SPADEVizR, an R package designed for the visualization, analysis and integration of cell clustering results. The available statistical methods allow highlighting cell clusters with relevant biological behaviors or integrating them with additional biological variables.
Introduction Cytometry is an experimental technique used to characterize cell properties at the single cell level. Flow cytometry is the most common technique and allows measuring simultaneously up to 18 cell markers. Thanks to mass and hyperspectral cytometry techniques, the number of simultaneously measurable cell markers has increased up to 50  (Bendall et al. , 2011; Gregori et al. , 2014). This increase of measurable cell markers has led to the development of new automatic gating algorithms, such as SPADE  (Qiu et al. , 2011)  or ACCENSE. The aim of these algorithms is to identify group of cells, also named cell clusters, having similar expressions in the whole dataset for selected markers. Cell cluster behaviors are then analyzed in terms of variations of associated cells among the different biological samples and conditions.
The SPADE algorithm, which stands for Spanning-tree Progression Analysis of Density-normalized Events, was developed to identify cell clusters in the context of mass cytometry data. In summary, SPADE is a hierarchical clustering-based algorithm combined to a density-based down-sampling procedure. SPADE results can be mainly summarized by two matrices: the cluster abundance matrix which contains the number of cells associated to each cluster for each sample and the cluster phenotype matrix which contains the marker median expressions for each cluster of each sample. While SPADE is a powerful approach, the interpretation of the behaviors or phenotypes of the identified cell clusters can be challenging, in particular in the scope of a whole dataset. For instance, SPADE has no methods allowing to highlight cell clusters with a cell abundance statistically different between two biological conditions or associated with an additional biological variable.
Moreover, SPADE lacks of visualization methods to deeply characterize the phenotypes of the cell clusters in the whole dataset. We have developed SPADEVizR, an R package to visualize, analyze and integrate results provided by SPADE. This package extends the original SPADE outputs with techniques such as volcano plots, streamgraphs, parallel coordinates, heatmaps, or distograms. Moreover, several statistical methods allow the identification of clusters with important biological behaviors. SPADEVizR also has features allowing the quantification and the visualization of the quality of clustering results and can be used with results generated by algorithms different from SPADE. We illustrate the capabilities of our package using a mass cytometry dataset  (Pejoski et al. , 2016) , obtained in a macaque vaccination study (Supplementary Fig.
CpG islands (CGI) marked by bivalent chromatin in stem cells are believed to be more prone to aberrant DNA methylation in tumor cells. The robustness and genome-wide extent of this instructive program in different cancer types remain to be determined. To address this issue we developed a user-friendly approach to integrate the stem cell chromatin signature in customized DNA methylation analyses. We used publicly available ChIP-sequencing datasets of several human embryonic stem cell (hESC) lines to determine the extent of bivalent chromatin genome-wide. We then created annotated lists of high-confidence bivalent, H3K4me3-only and H3K27me3only chromatin regions. The main features of bivalent regions included localization in CGI/promoters, depletion in retroelements and enrichment in specific histone modifications, including the poorly characterized H3K23me2 mark.
DNA methylome studies in a variety of cancer cells have benefitted from the development of cost-effective and normalized tools, such as the Infinium HumanMethylation450 (HM450K) BeadChip Arrays or the recently released Infinium MethylationEPIC Arrays (both from Illumina) that allow comparing results between laboratories. However, the analysis of chromatin signatures in stem cells is hampered by the bioinformatics skills needed to handle the publicly available genomewide ChIP-sequencing datasets. Alternatively, already processed lists of chromatin signatures could highly facilitate this kind of approach. Three initial studies have provided a list of the bivalent chromatin regions in human embryonic stem cells (hESCs)  [19] [20] [21]. In these three studies, data generated following genome-wide chromatin immunoprecipitation using antibodies against H3K4me3 or H3K27me3 were merged to identify genes carrying both modifications.
Although all three studies limited their analysis to the regions surrounding the transcription start site (TSS), the number of identified bivalent promoters greatly varies, from 1798 in  [20] , 2500 in  [21]  and more than 5500 in  [19]. Moreover, these three lists show an incomplete and limited overlap  [22]. This discrepancy can be explained by the different experimental approaches (i. e. , sequencing vs microarray), insufficient sequencing depth  [23]  and the use of relaxed statistical criteria. It could also reflect the variability between hESC lines, because each study used a different line: H1  [19] , hES3  [20]  an H9  [21]. A more recent study precisely improved the accuracy and characterization of bivalent regions in both mouse and human genomes by relying on datasets for several independent ESC lines  [24].
This study provided the chromatin signature of all annotated promoters in hESCs and identified 4979 of them as being bivalent  [24]. However, none of these four studies gave the exact genomic coordinates of the bivalent regions nor investigated the presence of a bivalent signature genomewide. In addition, the spreadsheet-based files used to present the results are not easy to handle for integrative analyses using DNA methylation array data. These observations stress the need to establish an annotated list of high-confidence (HC) bivalent regions in hESCs that can be easily used by researchers and clinicians to investigate the relationship between the bivalent chromatin signature in hESCs and methylation defects in cancer cells. To this aim, we used publicly available ChIP-sequencing datasets of several hESC lines to determine and characterize bivalent chromatin regions genome-wide.
Creation of a list of HC bivalent genomic regions in hESCs To establish an exhaustive list of the genomic regions marked by H3K4me3, H3K27me3 or both (i. e. , bivalent) in hESCs, we took advantage of publicly available ChIP-sequencing (ChIP-seq) datasets. In total, we collected 11 dataset series (i. e. , input; H3K4me3 and H3K27me3) each produced by the same laboratory and using the same hESC sample (Supplementary Table  S1 ). By combining the dataset series for the same hESC line, we obtained pairs of input, H3K4me3 and H3K27me3 ChIP-seq datasets for five hESC lines: HUES48, HUES64, HUES6, I3 and H1 (Supplementary Table  S2 ). As the sequencing depth is critical for obtaining statistically significant results  [23] , we ensured that each ChIP-seq dataset included at least 20 million uniquely mapped reads (Supplementary Table  S2 ).
For each hESC line, peaks were called in the H3K4me3 and H3K27me3 datasets using Macs1. 4. 2. Genome-wide, we identified between 26 354 and 50 312 peaks for H3K4me3 and between 56 163 and 92 099 for H3K27me3 (Figure  1A  and Supplementary Table  S3 ). We next isolated regions enriched for both histone marks and defined as bivalent domains all regions where H3K4me3 and H3K27me3 peaks overlapped for at least 1Kbp (Figure  1B ). The number of bivalent domains ranged from 7 756 in I3 cells to 10 266 in HUES6 cells (Figure  1C , Supplementary Table  S3 ), giving a merged list of 12 402 bivalent domains. This variation between hESC lines could be explained by different culture conditions or antibodies used for ChIP and also the sequencing depth across samples (Supplementary Table  S2 ). Indeed, the number of uniquely mapped reads correlated with the number of identified peaks (for H3K27me3, Pearson's correlation (r) = 0.
Genes with bivalent promoters tend to be expressed in a tissue-specific manner It has been proposed that bivalent promoters poise genes for activation or repression. To investigate whether genes with bivalent promoters were more likely to be differentially expressed upon differentiation, we collected RNA sequencing data for 34 tissues and primary cell lines (Figure  4A ). Most of the genes with H3K4me3-only promoters showed a strong expression in all analyzed tissues/cell lines, whereas most genes with H3K27me3only promoters were not expressed. These findings suggest that the stem cell expression pattern of these two gene categories tend to be maintained also following differentiation. Genes associated with bivalent promoters showed a more dynamic expression pattern. While most of them displayed a low basal expression in hESCs, their expression was variable (from silent to highly expressed) in the different tissues/cells under study, indicating that such genes tend to be expressed or firmly repressed in a tissue-specific manner.
Integration of the hESC chromatin signature features in customized DNA methylation analyses using Illumina arrays To develop a user-friendly approach to integrate these hESC chromatin signature features in customized DNA methylation analyses, we created a text file (Supplementary File S4) ready for uploading onto GenomeStudio, the software dedicated to methylation analyses performed with the HM450K and MethylationEPIC arrays from Illumina. These arrays are widely used in laboratories worldwide and are becoming the gold standard for high-throughput DNA methylation analyses of human specimens. The HM450K array covers 482421 CpG sites and allows interrogating 98. 9% of HC bivalent regions (mean coverage, MC: 10. 9 probes per region), 88. 1% of H3K4me3-only regions (MC: 8. 7 probes/region) and 55.
7% of H3K27me3-only regions (MC: 2. 4 probes/regions). Coverage is higher with the EPIC arrays that allow interrogating 863904 CpG sites, with 99. 5% of HC bivalent regions (MC: 12. 4 probes/ region), 93. 3% of H3K4me3-only regions (MC: 10. 3 probes /regions) and 73. 5% of H3K27me3-only (MC: 3. 1 probes/region) (Supplementary Table  S7 ). After uploading in GenomeStudio, our text file (Supplementary File S4) allows selecting the probes that belong to all or a selected bivalent, H3K4me3-only or H3K27me3-only region from any loaded HM450K or MethylationEPIC dataset. This text file also allows selecting probes belonging to one of the three clusters we defined for bivalent promoters. In addition, functionalities already present in GenomeStudio can be used to filter for probes associated with a variety of genomic features [e.
g. , CGI (core, shore, shelf), promoters, intergenic…] in order to perform fully customized and detailed analyses (Figure  5A ). By using this text file, we first analyzed the DNA methylation pattern at CGIs relative to their chromatin signature in hESCs. HM450K data for H1 hESCs were downloaded from Encode (see Material and Method section). This array covered a total of 21 177 CGIs analyzed by nearly 146 000 probes. About 36% of these probes were located in H3K4me3-only regions, 26% in bivalent regions and only 1. 3% in H3K27me3-only regions. This further confirms that most CGIs are not marked by H3K27me3 alone. The remaining probes (36%) were located in CGIs not marked by any of these three chromatin signatures in hESCs (referred as "noneregions"). In agreement with the unmethylated status of most CGIs, most of these probes were unmethylated (β value < 0.
Bivalent CGIs associated with genes that are less prone to be expressed upon differentiation are the main target of aberrant hypermethylation in cancer As a proof of concept, we next evaluated the relationship between hESC chromatin signatures and DNA methylation in cancer. To this aim, we downloaded from the TCGA data portal the HM450K data for eight solid tumor types and their matched normal controls (from 21 matched couples for bladder urothelial carcinoma up to 90 matched couples for breast invasive carcinoma) (Supplementary Table  S8 ). The overall DNA methylation pattern observed in hESCs is maintained in normal tissues with the majority of CGIs marked by bivalency and H3K4me3-only in hESCs remaining unmethylated (Supplementary Figure  S6 ). Compared with normal tissues, in tumor samples we detected DNA methylation changes at CGIs predominantly in bivalent (black) and none-regions (gray), with an overall gain of methylation (A) Expression level in tissues and primary cell lines of genes with H3K4me3-only (upper panel), bivalent (middle panel) and H3K27me3only (lower panel) promoters in hESCs.
Finally and although less prominent than hypermethylation, we also observed hypomethylated probes at CGIs in tumors (delta β value < 0. 25 tumor vs control, FDR <0. 05), mainly in none-regions. We next determined the genomic features associated with hypermethylated probes in the eight tumor types (Figure  6D ). The distribution of hypermethylated probes was similar in CIMP-positive and CIMP-negative tumors and they were mainly located in promoter regions. This distribution mainly reflects that of all probes in HM450K, indicating that aberrant hypermethylation is equally present at CGIs located in promoter regions and in intergenic regions or gene bodies. The only exception was the H3K4me3-only regions where a relative depletion of hypermethylated probes was observed at promoter regions. We then collected publicly available RNA sequencing data for healthy liver, lung, breast and colon tissues to determine whether the gene transcriptional status in healthy tissues can influence the gene CGI/ promoter methylation status in the corresponding tumor.
DISCUSSION The currently available lists of genomic regions marked by bivalent chromatin in hESC lines are limited to TSS, are only partially overlapping and are poorly informative and not adapted to determine genome-wide stem cell chromatin signatures that pre-mark CGIs for hypermethylation in cancer cells. With the objective to facilitate studies on the molecular bases and the impact of aberrant DNA methylation in cancer cells, we integrated publicly available datasets obtained from five independent hESC lines to establish annotated lists of HC bivalent (n = 5 766), H3K4me3-only (n = 11 966) and H3K27me3-only regions (n = 16 361). These numbers are in agreement with a recent (B) Distribution of hyper-and hypo-methylated probes in tumor and matched control tissues according to their chromatin signatures in hESCs (none: gray; bivalent: black; H3K4me3-only: blue; H3K27me3-only: red).