dataesr/scientific-paragraphs-categorization huggingface dataset:
Mice liver samples Paraffin-embedded liver sections (3 mm thick) were stained with H&E (haematoxylin, RAL diagnostics; and Eosin, RAL diagnostics), Picro-sirius (Sirius red, RAL diagnostics; picric acid, VWR Prolabo), rabbit anti-CD3 antibody (DAKO, #A0452) and a monoclonal rat anti-mouse CD68 antibody (AbD Serotec, MCA1957). Steatosis was defined as the percentage of hepatocytes containing large or medium-sized lipid droplets. Steatosis was scored on H&E stained sections by a single experienced pathologist (VP) who was blinded to mice groups. The following scale was used: 0 (<5%), 1 (5-33%), 2 (34-66%) and 3 (>67%). The Sirius red positive area was measured in at least 10 fields without vessels or capsule (original magnification, ×200) and quantified using ImageJ software. Inflammatory cells (CD3 + or CD68 + ) were counted in ≥15 microscope views (original magnification, ×200).
Western blot About 50 mg of frozen mouse liver tissue samples were homogenised in 650 mL of TNE buffer (10 mmol/L Tris-HCl ( pH 7. 5), 1% Nonidet P40, 150 mmol/L NaCl, 1 mmol/L EDTA) containing a cocktail of protease and phosphatase inhibitors (Roche). Lysates were incubated at 4°C for 45 min and then centrifuged at 12 000 g for 5 min. Protein content was quantified in the supernatant using BCA Protein Assay (Thermo Fisher). Lysates were mixed with the reducing sample buffer (Bio-Rad) for electrophoresis and subsequently transferred onto polyvinylidene fluoride membranes (Thermo Fisher). Equal loading (50 mg) was verified using Ponceau red solution. Membranes were incubated with anti-PPARα (1:1000, Ab24509, Abcam) antibody. After secondary antibody incubation (anti-rabbit, 1:3000; Amersham, GE Healthcare, Courtaboeuf, France), immunodetection proceeded using an enhanced chemiluminescence kit (Clarity Western ECl Substrate, BioRad, France) and bands were revealed using the Las-4000 Imaging System and Image MultiGauge software (Fujifilm, Tokyo, Japan).
Patients Twenty-five patients who underwent a percutaneous liver biopsy between 2008 and 2013 were selected from a pathological database: 6 had no or mild abnormalities at liver histological examination, 8 had bland steatosis and 11 had NASH as defined using the SAF (for steatosis, activity and fibrosis) score. 17 None of the patients had detectable anti-hepatitis C virus antibodies or HBs antigen. Alcohol consumption was below 21 drinks on average per week in men and 14 drinks on average per week in women. 1  None of the patients had a history of TIPS or of liver transplantation. Liver biopsy specimens were reviewed by a single experienced pathologist (VP) who was blinded to the clinical and laboratory data.
Statistics Quantitative variables were expressed as median (interquartile range, IQR). Comparisons between groups of independent quantitative variables were performed using Mann-Whitney test. Spearman's correlation coefficient was used to test the correlation between liver miR-21 expression levels and pathological features. All tests were two-sided and used a significance level of 0. 05. Data analysis was performed with SPSS V. 17. 0 (SPSS, Chicago, Illinois, USA) and figures were designed using GraphPad Prism 5 software.
Funding This work was supported by ' appel à projets scientifiques de Sorbonne Paris Cité 2012 ',  ANR   11-META-004-02   MIRA ,  Fondation de France  ( 2012-00029497 ) and  Institut National de la Santé et de la Recherche Médicale (INSERM). XL was supported by ' Fondation pour la Recherche Médicale' (FRM) , ' Fondation Lefoulon-Delalande ' and ANR-Meta-2011-miR-A. P-ER was supported by 'bourse  Robert Tournut ' of the  Société Nationale Française de Gastroentérologie (SNFGE)  and the  Association Française pour l'Etude du foie (AFEF) , and A-CV by a grant from  Région Ile-de-France (CODDIM).
SAXS measurement The SAXS experiments were performed with an upgraded apparatus at BL10C of the Photon Factory, at the Institute of Materials Structure Science (IMSS), High Energy Accelerator Research Organization (KEK), Tsukuba, Japan. The wavelength of the incident X-ray beam was tuned to 1. 488 Å, and the reciprocal space ranging from 0. 008 to 0. 26 Å À 1 was measured with a two-dimensional position-sensitive detector, PILATUS3 2M. By using the software for SAXS image data processing, SAngler, incident beam normalization, transmission correction, solvent subtraction, and circular average were applied, and then the scattering profiles of the nucleosomes were obtained. The analyzed nucleosomes were the canonical H2A nucleosome and the variant H2A. Z. 1 nucleosome. For each SAXS experiment, we prepared for 0.
Moreover, in a 40% D 2 O solution, we selectively observe the DNA by the same concept of contrast matching with the exchange of the roles. We used two SANS spectrometers, TAIKAN at J-PARC/MLF, Tokai, Ibaraki, Japan for mainly structural check of nucleosomes and also D11 instrument, installed at the Institut Laue-Langevin (ILL), Grenoble, France for CV-SANS experiment. In the CV-SANS experiments with D11, the SANS intensities were measured in the qrange of 0. 0085-0. 13 Å À 1 at a constant temperature of 25 °C. The observed SANS intensities were corrected for the background, empty cell and buffer scatterings, and the transmission factors, using the standard D11 data processing software, GRASP, and finally converted to one-dimensional scattering profiles. The samples were the canonical H2A nucleosome and the variant H2A.
Acknowledgments We would like to thank  Dr. Nobutaka Shimizu  for his kind assistance with the SAXS experiment at BL10C of  KEK-PF. The SAXS measurements were performed with the approval of the  Photon Factory Program Advisory Committee  (Proposal nos. 2014G127  and  2014G162 ). We would also like to express our appreciation to  Dr. Takashi Oda  for his help analysis about the SAXS data. The SANS measurements were performed under proposal No. 8-03-838 at ILL and also under No. 2014A0060  at J-PATC/MLF. This work was partly supported by  MEXT KAKENHI  Grant nos. 26116509  and  26102524  (to M. S. ), and  25116002  (to H. K. ) and  JSPS KAKENHI  Grant nos. 24310068  and  15H02042  (to M. S. ). This work was partly supported by the  Platform Project for Supporting Drug Discovery  and  Life Science Research (Platform for Drug Discovery, Informatics , and  Structural Life Science) , from the  Ministry of Education, Culture, Sports, Science and Technology (MEXT) , and a grant from the  Japan Agency for Medical Research and Development (AMED)  (to H.
2, INSDC Assembly, Aug 2011) led to the development of commercially available pig cDNA microarrays that could be used to detect changes in swine genes expression. In the present study, we aimed at deciphering the sequence of molecular events and pathways leading to the progression of ventricular dysfunction and arrhythmias, by studying gene expression profiles of repaired Fallot (rTOF) pigs' hearts compared to control animals (shamoperated), in four different localizations of the heart (epicardium and endocardium of right and left ventricles), by a whole genome approach. Some transcripts were selected to verify the accuracy and reproducibility of the microarray data by real-time qRT-PCR.
Microarray Data Analysis Hierarchical clustering of the significantly differentially expressed (DE) genes from microarray data was carried out by the Genespring software (Agilent Technologies), with a p-value corrected by Benjamini Hochberg False Discovery Rate (FDR). Different softwares were used to interpret the biological functions and canonical pathways of the gene lists: Visualization and Integrated Discovery Online platform (http://david. abcc. ncifcrf. gov/) [8] , using Gene ontology (GO) terms and the Database for Annotation, with a threshold of a minimum three genes annotated at each node, Panther Pathway (http://www. pantherdb. org/pathway/) and Ingenuity Pathway Analysis (IPA, http://www. ingenuity. com).
0°C, 40 cycles of denaturation at 95°C for 30 s followed by 30 s annealing and elongation at 60°C. Efficiency of primer pairs was previously evaluated. Melting curves were obtained at the end of each run to confirm a single PCR product. All samples were run in triplicate. Non-template controls were included in each run to exclude contamination and nonspecific amplification. Expression levels of samples were normalized by using a normalization factor calculated by the software CFX Manager (BIO-RAD). This normalization factor was calculated based on RT-qPCR results for two selected reference genes, HPRT1 and GUSB. This allowed quantification of the target gene in one sample relative to that in another (the calibrator) using the "2 -ΔΔCt method" of calculating fold changes in gene expression.
Validation of Gene Expression Pattern from Microarray Data Using qRT-PCR Representative genes were selected for validation via qRT-PCR, using the same tissue samples used in the microarray. For these fourteen genes, the fold changes obtained via microarray analysis demonstrated substantial agreement with the fold change values determined via qRT-PCR (Pearson correlation coefficient R>0. 7, p<0. 05) thereby confirming the microarray data (Fig  5B ). Two genes were no longer significantly deregulated using qPCR: FHL1C and MYOT. This could be due to different isoforms not tested by qPCR (9 targets deregulated in microarray for each of these two genes).
Discussion To our knowledge, this study represents the first characterization of myocardial transcriptomic profiles in a long-term swine model of surgically repaired Tetralogy of Fallot. Not surprisingly, the most statistically differentially expressed genes were identified in the endocardium of the right ventricle, as expected in a model of RV primitive pathogenesis. According to Panther Pathway software, the analysis of the 54 most DE genes in the RV endocardium revealed two main biological processes: cellular process and metabolic process. Ingenuity Pathway software pointed out the most significant canonical pathways including oxidative stress, energy metabolism, cell communication (gap junction signaling), cell adhesion and reshaping in response to external stimuli, angiogenesis. Molecular networks were also formed with IPA software involving the lipid metabolism that may reflect the changes in energy metabolism associated with ventricular hypertrophy.
Acknowledgements The work was performed within the  Laser Processing Platform for Multifunctional Electronics on Flex  ( LEAF -ANR-11-EQPX-0025 )  EQUIPEX  project -funded by the  French Government. The authors thank  Dr Emmanuel Dubois  (Head of the  LEAF  project) for the use of equipment purchased within the project. This work was partly supported by the  French RENATECH network.
As whole-genome sequencing for cancer genome analysis becomes a clinical tool, a full understanding of the variables affecting sequencing analysis output is required. Here using tumour-normal sample pairs from two different types of cancer, chronic lymphocytic leukaemia and medulloblastoma, we conduct a benchmarking exercise within the context of the International Cancer Genome Consortium. We compare sequencing methods, analysis pipelines and validation methods. We show that using PCR-free methods and increasing sequencing depth to B100 Â shows benefits, as long as the tumour:control coverage ratio remains balanced. We observe widely varying mutation call rates and low concordance among analysis pipelines, reflecting the artefact-prone nature of the raw data and lack of standards for dealing with the artefacts.
Benchmark data sets and analytical tools  [13] [14] [15] [16] [17]  have been developed for variant calling on normal genomes, while those for cancer have largely focused on SSM detection from exome sequencing  12, 18. Benchmarking of mutation calling from exome data from The Cancer Genome Atlas has raised concerns about biased inferences and highlights the need for benchmark data sets  19. In our study we set out to investigate the factors that need to be considered to generate high-quality whole-genome sequence data and high-confidence variant calls from tumournormal pairs, including new sources of bias and pitfalls not encountered in exome data. We explored several benchmarking strategies. First, we evaluated somatic mutation calling pipelines using a common set of 40 Â WGS reads of average quality corresponding to a case of chronic lymphocytic leukaemia (CLL).
WGS data generation. We conducted a first benchmark exercise using WGS data generated from a CLL tumour-normal pair and then a second using a case of MB. Both tumour types were expected to have relatively low mutational load and not very pervasive structural changes: CLL has a few known translocations and large copy number variants, and MB exhibits a large degree of tetraploidy but is otherwise typically free of complex rearrangements. The quality of the CLL data was below today's standards but typical for the time it was produced, while the quality of the MB library preparation and produced sequence were of high quality. The validation strategies also differed. For CLL we chose to validate submitted mutations by target capture and sequencing with two independent platforms (MiSeq and IonTorrent).
For these reasons, we present here only the results for the MB benchmark. Evaluation of sequencing library construction methods. Several different protocols were used for generating sequencing libraries at the five contributing sequencing centres, which varied in their reagent supplier, methods for selecting the fragment size of library inserts and use of amplifying PCR steps (Table  1  and Supplementary Table  1 ). Interestingly, these differences resulted in marked variation in the evenness of coverage genome-wide as well as in key regions of interest such as exons. PCR-free libraries were found to give the most even coverage, with very little effect of GC content on coverage levels (Fig. 1a ). Evenness is directly correlated with coverage of the genome: when we downsampled each data set to an average 30 Â tumour and control coverage (libraries sequenced to less than 28 Â , L.
2b ). This effect was even more striking when considering mutation calls per chromosome, which clearly shows the difference between low and high coverage when looking for late-occurring mutations after whole-chromosome copy number changes (Supplementary Fig. 3 ). Effect of tumour purity on mutation calling. Since MBs tend to show a very high tumour cell content (usually above 95%, and for this sample B98%, because of their nature as masses of small, round, tightly packed tumour cells), the high coverage data set also provided an opportunity to model the dynamics of mutation calling with increasing coverage and with increasing proportions of 'contaminating' normal tissue. We found that the mutation calls with increasing coverage were accurately modelled with a Michaelis-Menten equation, reaching 'saturation' (no or minimal additional mutations called as coverage increases) at around 100 Â (Fig.
5a, b ). At lower control coverage levels, a notable increase is observed in the overall number of mutations reported because of a sharp rise in those called with a low allele fraction. Since these mutations are not called in the 250 Â versus 250 Â set, it is almost certain that they are sequencing artefacts arising in a very small proportion of calls, which appear to be somatic when the control coverage is insufficient to show the same phenomenon. These new calls are dominated by a T4G base change arising in a particular sequence context (GpTpG, Supplementary Fig. 5c ). Indeed, performing a motif analysis on the wider context of these changes revealed that the majority occur at a thymine base within a homopolymer run of guanines (Supplementary Fig. 5d ). Keeping the ratio of tumour:normal coverage closer to one therefore appears to play a role in maintaining the accuracy of mutation calling with standard pipelines, since any systematic artefacts are then balanced out in both the tumour and control data sets.
While it may be possible to apply additional filters to account for the new false positives (FPs) seen in unbalanced comparisons, this would potentially come at the cost of a reduced sensitivity for detecting true mutations with low allele frequencies (that is, tumour subpopulations), which are of particular interest when increasing sequencing coverage depth. Curation of a Gold Set of somatic mutations. We used the highcoverage (314 Â :272 Â ) data set from the sequencing benchmark to curate a Gold Set of verified somatic mutations (Methods). Gold Set mutations were classified (Table  2 ) according to the potential issues that may lead to an incorrect call: Tier 1 mutations have a mutant AFZ10%, Tier 2 mutations have AFZ5%, Tier 3 includes all verified mutations supported by unambiguous alignments, while Tier 4 includes additional mutations with more complicated or ambiguous local alignments and Tier 5 includes those with unusually high or low read depth.
Evaluation of somatic mutation calling pipelines. A submission and revision process was set up for the MB benchmark with guidelines for the mutation call format. Participating centres were provided with the best-quality sequence data set from the sequencing benchmark (L. A). Using these FASTQs, they produced SSM and SIM calls and submitted them for evaluation. We received 18 SSM and 16 SIM submissions. Submissions were compared among themselves and to the Gold Set. Figure  3  shows the overlap of mutation call sets (private calls are shown in Supplementary Figs  6  and 7 ). We found that only 205 SSMs and one SIM were agreed upon by all submitters (Fig. 3  and Supplementary Figs  6  and 7 ). Agreement among SSM sets was much greater than agreement among SIMs in general. In Fig. 4  we show the precision versus recall of the submitted mutation calls.
Some notable exceptions can be observed among the FP SIMs, where MB. L1 and MB. L2 cluster as do MB. F and MB. N. These concordant FPs may indicate incompleteness of the Gold Set and/or similarities in the pipelines. In this case, MB. L1 is a filtered subset of MB. L2, explaining the high degree of overlap. MB. F and MB. N both use Strelka  27  to call SIMs, possibly explaining the overlap in FPs. Indeed, some overlap of MB. F and MB. N FP SIMs is seen with MB. L2, which also uses Strelka. For TP SIMs, pipelines that share components tend to have higher overlap, for example, among Strelka calls or among GATK SomaticIndelDetector calls. Sometimes, we observe higher Jaccard index values for pipelines using different software, for example Platypus  28  and Atlasindel  29  , which are two of the most sensitive mutation callers.
The extent of observed pipeline customization (Supplementary Methods and Supplementary Data 1) did not allow for exhaustive testing of all potentially important analysis steps; however, three pipeline components were selected for closer inspection because of their expected high impact: mapper, reference genome build and mutation caller. Four mappers (Novoalign2, BWA, BWA-mem and GEM), two SSM callers (MuTect  31  and Strelka) and three versions of the human reference genome (b37, b37 þ decoy and 'hg19r'-a reduced version of hg19, with unplaced contigs and haplotypes removed) were selected for testing, based on their usage by the benchmarking groups (Supplementary Methods for software versions and settings). To limit the effect of non-tested software on the produced mutation sets, a simple SSM-calling pipeline was established.
(already the MB. E pipeline, which uses SomaticSniper, was replaced with the MB. F pipeline, which uses Strelka, based on the analysis of the CLL benchmark results). Using the Gold Set to devise and tune a set of filters for various metrics (Supplementary Table  7 ), including mapping quality and distance from the end of the read alignment block (Supplementary Fig. 20 ), we are able to outperform (in terms of F1) all other MB SSM submissions (Fig. 7a  and Supplementary Table  8 ). Despite choosing reasonably conservative thresholds, we were still worried about the possibility of overfitting; thus, we tested the adjusted pipeline on the CLL benchmark data set. We achieved similar results (Fig. 7b ), demonstrating that the filter settings work well on at least one other cancer type. Removal of the repeat copy filter in Strelka also improve both MB and CLL SIM sensitivity without greatly affecting precision (Supplementary Fig.
The calls were then sorted according to the number of centres that made this particular call using a custom Perl script. The resulting file was plotted using a custom R-script (both available on request). Merging of the bam files to get the 300 Â files. To create the high coverage B300 Â bam files, the raw fastq files were aligned using bwa 0. 6. 2-r126-tpx aln -t 12 -q 20, followed by bwa-0. 6. 2-tpx sample -P -T -t 8 -a 1000 -r. The bam files for each centre/library were merged, and duplicates were marked using Picard tools MarkDuplicates Version 1. 61. Finally, all merged per-centre bam files were merged using picard-1. 95 MergeSamFiles and the header was adjusted using samtools-0. 1. 19 reheader. Since only reads from different libraries were merged at this step, duplicates were not marked.
Duplicates were removed with Picard, indels realigned at 1,000 genomes indel target locations and all indels were left-aligned using GATK. The pileups at SSM positions were extracted using samtools mpileup with base-quality threshold Z13. Read depth and base counts were extracted using a custom script. Mutant allele and normal counts were compared using in-house software snape-cmp-counts  38  , which compares alternate and reference allele counts in tumour and normal and then assigns a score according to the probability that they are derived from different beta distributions. Mappabilities with 0, 1 and 2% mismatches were computed for the reference genome (h37d5). The average mappabilities in 100-bp windows preceding each candidate mutation were stored as tracks for visualization in IGV. In addition, the segmental duplication annotation from the UCSC browser was loaded into IGV.
Only positions were considered, not the genotypes reported. For calculation of precision, all Tier 3 mutations plus ambiguously aligned mutations (class 4) were included so as to not penalize difficult to align but otherwise convincing differences between tumour and normal samples. For SIMs, no stratification into tiers 2 or 3 was performed; for recall, Tier 1 SIMs were used while, for precision, Tier 4 SIMs were used. To compare overall performance, we used a balanced measure of accuracy, the F1 score, defined as 2 Â (Precision Â Recall)/(Precision þ Recall). Overlap calculations for the purpose of clustering and heat map generation were performed using Tier 3 for SSMs and Tier 1 for SIMs. The Jaccard index is defined as the intersection divided by the union. Correspondence analysis was performed using the 'ca' package in R on a table where each row corresponds to a genomic position at which at least one submission calls a somatic mutation in the Gold Set.
Acknowledgements We thank the  DKFZ Genomics and Proteomics Core Facility  and the  OICR Genome Technologies Platform  for provision of sequencing services. Financial support was provided by the  consortium projects READNA  under grant agreement  FP7 Health-F4-2008-201418 ,  ESGI  under grant agreement  262055 ,  GEUVADIS  under grant agreement  261123  of the European Commission Framework Programme 7, ICGC-CLL through the  Spanish Ministry of Science and Innovation (MICINN) , the  Instituto de Salud Carlos III (ISCIII)  and the  Generalitat de Catalunya. Additional financial support was provided by the  PedBrain Tumor  Project contributing to the ICGC, funded by  German Cancer Aid  ( 109252 ) and by the  German Federal Ministry of Education and Research (BMBF , grants # 01KU1201A , MedSys # 0315416C  and NGFNplus # 01GS0883 ), the  BMBF -funded ICGC projects on early-onset prostate cancer and malignant lymphoma (# 01KU1001A , # 01KU1002B ) and via the  BMBF-funded de.