dataesr/scientific-paragraphs-categorization huggingface dataset:
Actually, log BB is dened as the calculated ratio of the concentration of the compound in the brain to the concentration of the compound in the blood at steady state (log([brain]/ [blood])) and the cut-off we chose for the selection of candidate molecules was set at the optimal value of log BB > À0. 3. 36 rom this screening, the list of candidates was reduced to about 700 compounds showing the required features. These compounds were used for the following step of molecular docking with the biological target, a pentamer of Ab (PDB entry: 2BEG). 18 The analysis of molecular docking poses revealed the preference of curcumin to interact with the lateral oligomer region, near the 17-21 amino acid residues (see Fig. 2 ), as was previously experimentally observed for curcumin as well as for other aggregation inhibitors.
Acknowledgements SAXS experiments were performed on beamline BM29 at the  European Synchrotron Radiation Facility (ESRF), Grenoble, France. We are grateful to  Petra Pernot  at the  ESRF  for providing assistance in using the beamline. We thank  Paolo Moretti  for assistance in the SAXS experiments and  Ranieri Bizzarri  for stimulating discussion in designing the experiments.
Conclusions This multi-platform genome-wide dataset provides the most comprehensive database of DDLPS, MFS, UPS, and LMS genomic and epigenomic alterations to date. The depth and breadth of alterations reveals the decidedly heterogeneous nature of adult soft tissue sarcomas and highlights their presumed dependence on SCNAs, rather than targetable activating point mutations. In both integrated and platform-specific analyses, the one fusion-associated sarcoma type in the study (SS) was the most dissimilar to other sarcomas, but the analyses also revealed distinct differences among LMS and the other complex karyotype sarcomas. Salient differences include MDM2, CDK4, JUN, and TERT amplifications in DDLPS; MYOCD amplification, PTEN mutations/deletions, and AKT, IGF1R, and MTOR pathway activation in LMS; and VGLL3 amplification and Hippo pathway activation in UPS/MFS.
Additional novel somatic mutation calls submitted by the Broad Institute, BCGSC, and UCSC to the TCGA Data Coordinating Center were downloaded and filtered to remove known, common germline dbSNP sites. Remaining variants were annotated using Gencode 19 transcripts from Ensembl release 74, and non-coding sites were removed. Read counts were generated for all remaining novel putative variants, and said variants were incorporated into the final mutation annotation format (MAF) if they met the same minimum coverage, maximum coverage, and variant allele frequency requirements described below. A separate MAF was delivered for the somatic mutations called in whole-genome sequence data; it used the same alignment and detection parameters described for exome analysis with no restriction for coverage or protein translational effect.
Single-nucleotide variant and indel callers Single-nucleotide variant (SNV) calling and analytic pipelines were performed as follows in an institution-specific manner: Washington University: Single-nucleotide variant (SNV) callers included: Samtools v0. 1. 16 (Li et al. , 2009)  (samtools pileup --cv -A -B), SomaticSniper v1. 0. 4  (Larson et al. , 2012)  (bam-somaticsniper -F vcf -G -L -q 1 -Q 15), Strelka v0. 4. 6. 2  (Saunders et al. , 2012)  (with default parameters except for setting is SkipDepthFilters = 1), and VarScan v2. 2. 6  (Koboldt et al. , 2012 ) (--mincoverage 3 --min-var-freq 0. 08 --p value 0. 10 --somatic-p value 0. 05 --strand-filter 1). The Washington University analytic strategy was as follows: First, Samtools calls were retained if they met all of the following rules, inspired by the mapping short DNA sequencing reads and calling variants using the mapping quality scores (MAQ) software algorithm  (Li et al.
, 2008) : d Site is greater than 10 bp from a predicted indel of quality 50 or greater d The maximum mapping quality at the site is R 40 d Fewer than 3 SNV calls in a 10 bp window around the site d Site is covered by at least 3 reads and fewer than 1,000,000,000 reads d Consensus quality R 20 d SNP quality R 20 After these filters were applied, Samtools and SomaticSniper calls were unioned using joinx v1. 9 (https://github. com/genome/ joinx; joinx sort --stable --unique). The resulting merged set of variants were additionally filtered to remove likely false positives  (Larson et al. , 2012; Li et al. , 2009). Bam-readcount v0. 4 (https://github. com/genome/bam-readcount) with a minimum base quality of 15 (-b 15) was used to generate metrics, and sites were retained based on the following requirements: VarScan variants passing these criteria were then filtered for likely false positives using bam-readcount v0.
, 2011). The MuTect algorithm version 1. 1. 6 (Cibulskis et al. , 2013)  was used to generate somatic mutation calls, which were subsequently filtered as previously described  (Costello et al. , 2013)  to remove any spurious calls due to shearing-induced generation of 8-oxoguanine. Indels were identified using the indelocator algorithm. Details and tools are available at https://www. broadinstitute. org/cancer/cga. Functional annotation of mutations was performed with Oncotator  (Ramos et al. , 2015)  using Gencode V18. UCSC: Single-nucleotide somatic mutations were identified by RADIA (RNA AND DNA Integrated Analysis), a method that combines the patient-matched normal and tumor DNA whole exome sequencing (WES) with the tumor RNA sequencing (RNA-Seq) for somatic mutation detection  (Radenbaugh et al.
, 2014) , software available at: https://github. com/aradenbaugh/radia/. The inclusion of the RNA-Seq data in RADIA increases the power to detect somatic mutations, especially at low DNA allelic frequencies. By integrating the DNA and RNA, we can rescue some mutations that would be missed by traditional mutation calling algorithms that examine only the DNA. RADIA classifies somatic mutations into 3 categories depending on the read support from the DNA and RNA: 1) DNA calls -mutations that had high support in the DNA, 2) RNA Confirmation calls -mutations that had high support in both the DNA and RNA, 3) RNA Rescue calls -mutations that had high support in the RNA and weak support in the DNA. Here RADIA identified 32,573 DNA mutations, 5,785 RNA Confirmation mutations, and 843 RNA Rescue mutations.
BCGSC: Strelka  (Saunders et al. , 2012 ) (v1. 0. 6) was used to identify somatic single-nucleotide variants and short insertions and deletions from the TCGA SARC exome dataset. All parameters were set to defaults, with the exception of ''isSkipDepthFilters,'' which was set to 1 in order to skip depth filtration, given the higher coverage in exome datasets. We analyzed 259 pairs of libraries. When a blood sample was available, it served as the matched normal specimen; otherwise, the matched normal tissue was used. The variants were subsequently annotated using SnpEff  (Cingolani et al. , 2012) , and the COSMIC (v61)  (Forbes et al. , 2010)  and dbSNP (v137)  (Smigielski et al. , 2000)  databases.
Identification of significantly mutated genes The entire set of 206 sarcomas and those sarcoma types with greater than 15 samples (LMS, DDLPS, UPS, and MFS) were separately analyzed for significantly mutated gene (SMG) detection. Variants from the MAF (described previously) were used to define the region of interest (ROI) file. Coverage for each sample over an ROI was collected using the corresponding tumor/normal bam file. MAF files for each cancer type and a MAF combining all available samples were created. Mitochondrial and RNA genes were removed from this analysis. We performed MuSiC2 (https://github. com/ding-lab/MuSiC2) frequency-based SMG identification  (Dees et al. , 2012). SMGs were defined as genes significantly mutated with FDR < 0. 05 using a convolutions test.
7. 5  (Wang et al. , 2010). Gene expression was quantified for the transcript models corresponding to the TCGA GAF2. 1 (https://gdc-api. nci. nih. gov/v0/data/a0bb9765-3f03-485b-839d-7dce4a9bcfeb) using RSEM  (Li and Dewey, 2011) , and gene expression was normalized within-sample to a fixed upper quartile. Fusion gene alignments were determined using MapSplice  (Wang et al. , 2010). For further details on this procedure, refer to Description file at the Genomic Data Commons (GDC) data portal under the V2_MapSpliceRSEM workflow (https://gdc-api. nci. nih. gov/legacy/data/e34a93ee-d3c4-44c7-8bfa-0c19c6df0866). Putative TRIO fusions were selected from discordant paired end reads in which the non-TRIO end mapped to an identified gene. BAM files and expression data can be found at the Genomic Data Commons (https://gdc-portal.
Validation of immune infiltration scores In order to validate the sarcoma-specific immune infiltration scores identified in our cohort, we repeated our analysis using RNA-seq data from a publically available dataset including 42 UPS, 36 LMS, 18 DDLPS, 15 MFS, and 2 SS  (Lesluyes et al. , 2016). Only fresh frozen tissue samples were included in the analysis. As with our cohort, median immune infiltration scores were calculated for each of the 24 immune signatures across all sarcoma histologies. For each sarcoma type, a scatterplot was generated where the median immune infiltration score for each immune signature served as the x-or y-axis for our data and the  Lesluyes et al. (2016)  data, respectively, and the Spearman correlation coefficient was calculated. Correlation of UPS/MFS morphology with mRNA Percent myxoid component (described above in Pathology Review) was used to group UPS and MFS samples into three classes: no myxoid stroma (class 0), 1%-49% myxoid stroma (class 1), or 50%-100% myxoid stroma (class 2).
MicroRNA libraries and sequencing We generated microRNA sequence (miRNA-seq) data using methods described previously  (Chu et al. , 2016). Briefly, reads were aligned to the GRCh37/hg19 reference human genome, and read count abundance was annotated with miRBase v16 stemloops and mature strands. While the read counts included only exact-match read alignments,. bam files at CGHub (http://cghub. ucsc. edu)  (Wilks et al. , 2014)  included all sequence reads. We used miRBase v20 to assign 5p and 3p mature strand (miR) names to MIMAT accession IDs. Cluster analysis of miRNA mature strands To identify subtypes within the various sarcoma cohorts, we used unsupervised hierarchical clustering with pheatmap v1. 0. 2 in R. The input was a reads-per-million (RPM) data matrix for the 303 (top 25%) miRBase v16 5p or 3p mature strands that had the largest variances across the cohort.
DNA Methylation Analysis Sample preparation and hybridization The Illumina Infinium HM450 array  (Bibikova et al. , 2011)  was used to assay the 206 TCGA sarcoma samples using standard protocols. Briefly, genomic DNA (1 mg) for each sample was treated with sodium bisulfite, recovered using the Zymo EZ DNA methylation kit (Zymo Research, Irvine, CA) according to the manufacturer's specifications, and eluted in 18 ml volume. After passing quality control, bisulfite-converted DNA samples were whole-genome amplified, fragmented enzymatically, hybridized overnight to BeadChips, then subjected to locus-specific base extension with labeled nucleotides (Cy3 and Cy5). BeadArrays were scanned and the raw data imported into custom programs in R computing language for pre-processing and calculation of DNA methylation beta value for each probe and sample.
Estimation of leukocyte fraction We estimated leukocyte fraction in each tumor by calculating the leukocyte signature from methylation results as described  (Carter et al. , 2012). As a source of leukocyte methylation level, we used DNA methylation data of peripheral blood mononuclear cells (PBMC) from six healthy donors  (Reinius et al. , 2012)  (GEO: GSE35069).
Slides were probed with 192 validated primary antibodies (Table  S7 ) followed by corresponding secondary antibodies (goat anti-rabbit IgG, goat anti-mouse IgG, or rabbit anti-goat IgG). Signal was captured using a Dako Cytomation-catalyzed system and DAB colorimetric reaction. Slides were scanned in a CanoScan 9000F. Spot intensities were analyzed and quantified using Array-Pro Analyzer (Media Cybernetics Washington DC) to generate spot signal intensities (Level 1 data). The software SuperCurveGUI  (Hu et al. , 2007) , available at http:// bioinformatics. mdanderson. org/Software/supercurve/, was used to estimate the EC 50 values of the proteins in each dilution series (in log 2 scale). Briefly, a fitted curve (''supercurve'') was plotted with the signal intensities on the y axis and the relative log 2 concentration of each protein on the x axis using the non-parametric, monotone increasing B-spline model  (Tibes et al.
, 2010). These antibodies were assessed for specificity, quantification, and sensitivity (dynamic range) in their application for protein extracts from cultured cells or tumor tissue. Antibodies were labeled as validated and used with caution based on the degree of validation by criteria previously described  (Hennessy et al. , 2010). RPPA arrays were quantitated and processed (including normalization and load controlling) as described previously, using MicroVigene (VigeneTech, Inc. , Carlisle, MA) and the R package SuperCurve (version-1. 3), available at http://bioinformatics. mdanderson. org/main/SuperCurve:Overview (Hu et al. , 2007; Tibes et al. , 2006). Raw data (level 1), SuperCurve nonparametric model fitting on a single array (level 2), and loading-corrected data (level 3) were deposited at the GDC.
org). Data aggregation and analysis in Firehose began in the Broad Institute GDAC Firehose, an automated, high-throughput analysis pipeline designed to systematize analyses from The Cancer Genome Atlas. Firehose first normalized clinical annotations and molecular sample data, in the form of one file per sample, into analysis-ready aggregates (a single file with many samples) that can be immediately fed into scientific codes with no further processing. These data packages were documented in detailed sample reports and released to the public (https://gdac. broadinstitute. org/runs/sampleReports/latest), then fed into the analysis workflow (https://gdac. broadinstitute. org/Analyses-DAG. html). Primary analyses were automatically performed for every data type of every cohort, such as: significance assessment of mutations with MutSig and copy number alterations with GISTIC; functional analysis with Mutation Assessor; analysis of mutagenesis by APOBEC cytidine deaminases; miR, mRNA, and protein expression cluster assignments using both consensus hierarchical and consensus NMF methods; and pathway analyses with PARADIGM and GSEA.
Statistical associations were automatically generated between many of these results, and to the entire suite of clinical variables (e. g. , survival) available for the given cohort. q-values corresponding to FDR for individual gene mutations were computed by Mutsig2CV  (Lawrence et al. , 2013). All results were integrated and made publicly available as: (a) online HTML reports citable in the literature by DOIs (https:// gdac. broadinstitute. org/runs/analyses__latest/reports), (b) the firebrowse. org portal and APIs (http://firebrowse. org/api-docs) and (c) visual exploration tools such as IGV, the UCSC Genome Browser and cBioPortal. In the context of this paper, Firehose served as first-pass analysis mechanism for the Sarcoma Analysis Working Group. In subsequent passes, custom data were integrated from outside the Firehose automated data flow, including the more accurate cluster and pathway analysis results manually curated by the Analysis Working Group.
Regulome Explorer Integrated analysis & interactive exploration To gain greater insight into the development and progression of sarcomas, we integrated all of the data types produced by TCGA and described in this paper into a single ''feature matrix. '' From this single heterogeneous dataset, significant pairwise associations were inferred using statistical analysis and can be visually explored in a genomic context using Regulome Explorer, an interactive web application (http://explorer. cancerregulome. org). In addition to associations that are inferred directly from the TCGA data, additional sources of information and tools were integrated into the visualization for more extensive exploration (e. g. , NCBI Gene, miRBase, the UCSC Genome Browser, etc. ). Feature matrix construction A feature matrix was constructed using all available clinical, sample, and molecular data for 206 unique patient/tumor samples.
Pairwise statistical significance Statistical association among the diverse data types in this study was evaluated by comparing pairs of features in the feature matrix. Hypothesis testing was performed by testing against null models (i. e. , absence of association), yielding a p-value. P-values for the association between and among clinical and molecular data types were computed according to the nature of the data levels for each pair: categorical versus categorical (Fisher's exact test in the case of a 2x2 table, otherwise chi-square test); categorical versus continuous (Kruskal-Wallis test) or continuous versus continuous (probability of a given Spearman correlation value). Ranked data values were used in each case. To account for multiple-testing bias, the p-values were adjusted using the Bonferroni correction.
Integrated pathway analysis Integration of copy number, mRNA expression, and pathway interaction data was performed on the 206 SARC samples using the PARADIGM software  (Sedgewick et al. , 2013). Briefly, this procedure infers integrated pathway levels for genes, complexes, and processes using pathway interactions and genomic and functional genomic data from each patient sample. Expression and gene copy number data were obtained from Firehose. One was added to all expression values, which were then log 2 -transformed and mediancentered across samples for each gene. The log 2 -transformed, median-centered mRNA data were rank-transformed based on the global ranking across all samples and all genes, then discretized (+1 for values with ranks in the highest tertile, À1 for values with ranks in the lowest tertile, and 0 otherwise) prior to PARADIGM analysis.
Pathways were obtained in BioPax Level 3 format and included the NCIPID and BioCarta databases from https://pid. nci. nih. gov and the Reactome database from http://reactome. org. Gene identifiers were unified by UniProt ID then converted to Human Genome Nomenclature Committee's HUGO symbols using mappings provided by the committee (https://www. genenames. org/). Altogether, 1524 pathways were obtained. Interactions from all of these sources were then combined into a merged Superimposed Pathway (SuperPathway). Genes, complexes, and abstract processes (e. g. , ''cell cycle'' and ''apoptosis'')  were retained and henceforth referred to collectively as pathway features. The resulting pathway structure contained a total of 19504 features, representing 7369 proteins, 9354 complexes, 2092 protein families, 82 RNAs, 15 miRNAs, and 592 abstract processes.
Nuclear regions were identified by applying morphological reconstruction to the hematoxylin image, and a watershed transformation was applied to separate closely packed nuclei. The area of each nucleus was then measured in pixels and the area of each nucleus in each slide recorded. A histogram of nuclear areas was created for each patient with 100 bins of 12 pixels each. Statistical moments of these histograms were calculated to capture the mean, variance, negative skew, and kurtosis of nuclear areas for each patient. Histologic-genomic analysis Several statistics were calculated for measures of pleomorphism against genomic features, using copy number data from SNP arraybased methods (see SNP-based copy number analysis, above). Specifically, ploidy, subclonal genome fraction (which measures the fraction of tumor genome that is not part of the ''plurality'' clone, and therefore is a reflection of increasing genomic complexity), and genome doublings were calculated using the ABSOLUTE algorithm  (Carter et al.
ACKNOWLEDGMENTS We thank the patients and families who contributed to this study. This project is supported by the following  NIH  grants:  U54 HG003273 ,  U54 HG003067 ,  U54 HG003079 ,  U24 CA143799 ,  U24 CA143835 ,  U24 CA143840 ,  U24 CA143843 ,  U24 CA143845 ,  U24 CA143848 ,  U24 CA143858 ,  U24 CA143866 ,  U24 CA143867 ,  U24 CA143882 ,  U24 CA143883 ,  U24 CA144025 ,  U24 CA211006 , and  P30 CA016672. A. D. C. and M. M. receive funding from  Bayer AG.
Fine-tuning parameters, available in most standard quantification tools, are missing (e. g. overlap type and library type, see infra). Last, it involves several Perl files and thus is slow, not appropriate for large datasets involving many RNA-Seq experiments. In this paper, we present a new tool that implements the aforementioned method, together with state-of-the-art characteristics in terms of usability, especially speed and options. We believe that this tool will be useful to the RNA-Seq users, because it is simple (a drop-in replacement of the highly used featureCounts  [1]  or htseq-count  [3] ) and more informative, with no drawback. The rest of the paper is organized as follows. We will first detail the implementation of our tool. Then, we will Fig. 1  Possible bioinformatics pipe-line for RNA-Seq differential expression analysis.
Results We tested our method on recent data, taken from  [4]. Briefly, this article uses RNA-Seq of human brain to find genes that are differentially expressed in individuals diagnosed with bipolar disorder. The article uses two sequencing protocols (GA-IIx and HiSeq) and microarray to validate the results. Our aim is not to reproduce their pipe-line (parameters and software versions have not been provided in the article), but to show the differences between the most used gene quantification tools (htseqcount and featureCounts), and our tool. We focused on the HiSeq dataset, that contained 6 replicates in each condition, because HiSeq is probably the most used sequencing machine now. Reads are 100 bp (base pairs) long, each sample is paired-end and contains approximately 200 millions reads.
Admittedly, this dataset is challenging because duplicated genes are known to play a major role in human brain  [5]. Our pipe-line uses STAR v2. 5. 0a (with parameters recommended by ENCODE) and DESeq2 v1. 14. 1  [6]  with default options and adjusted p-value at 5%, whereas  [4]  used TopHat, htseq-count and DESeq (arguably the reference tools at the time of publication). In the original paper, the author found only 11 differentially expressed genes (with adjusted p-value at 5%). We found all of them with our pipe-line and all the quantification tools, except one, a pseudo-gene (probably due to a problem of TopHat, which, on the earliest versions, was known to favor pseudo-genes with no intron compared to mature transcripts). htseq-count was used in the "union" mode because it is the recommended one.
We then considered the 33 merged genes with adjusted p-value < 1%, which represented very good candidates. These merged genes included 75 genes that were not detected otherwise (neither by htseq-count nor fea-tureCounts, nor in the non-merged genes found by mmquant). This gene list includes new excellent candidates with putative links to bipolar disorder, including ADK  [8] , GTF2I  [9] , hnRNP-A1  [10] , HTRA2  [11] , PKD1  [12]  and RERE  [13] , which have been linked to various brain-related diseases (see Additional file 2). Some of these genes have complex regulation systems in cis: ADK and HTRA2 contain overlapping processed pseudogenes and antisense transcripts or genes, and mmquant merges these annotations on the fly. Other genes, like GTF2I, hnRNP-A1, PKD1, and RERE, are duplicated genes, or have produced a pseudo-gene in another locus.