dataesr/scientific-paragraphs-categorization huggingface dataset:
Analysis of gene functions differentially regulated by BaP treatment A comparative analysis of differentially regulated gene functions in BaP-treated and control animals was performed on the genes showing a significant differential transcription at at least one time point. We manually assigned all detected transcripts to different biological categories based on their Ensembl annotation. Genes were classified into 10 categories: cell death/proliferation, cytoskeleton and extracellular matrix, detoxification enzymes, DNA interaction and repair, carbohydrate metabolism and ATP production, lipid metabolism, protein metabolism, immunity, signaling and transport, and hypothetical proteins. In order to compare the transcription patterns of genes from each category, hierarchical clustering was performed by loading fold transcription values into TM4 Multi experiment Viewer (MeV) software  [68].
Functional annotation enrichment Differentially expressed genes were subjected to annotation enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway mapping using the online functional annotation tool DAVID (Database for Annotation, Visualisation and Integrated Discovery, http://david. abcc. ncifcrf. gov/) [70, 71]  with all genes detected in our experiment as background (10,483 genes). Xenopus gene identifiers were transformed into their human orthologs to improve the richness of the output  [72, 73]. In order to differentiate functional enrichment due to up-and downregulated genes, the DAVID tool was used on any gene found up-or down-regulated at at least one time point from the genes found to be differentially transcribed at any time point. Significance was calculated using a modified Fisher's Exact test (p < 0.
mRNAseq data validation using RT-qPCR The transcription levels of ten genes (ENSXETG000 00021546 -Cytochrome P450 26B1; ENSXETG00000 003432 -Cytochrome P450 51A1; ENSXETG0000002 1140 -Cytochrome P450 1A1; ENSXETG00000011124jun oncogene; ENSXETG00000023050squalene epoxidase; ENSXETG00000007561catalase; ENSXETG000 00011266 -phosphoenolpyruvate carboxykinase 1; EN SXETG00000024078 -heat shock 70 kDa protein; ENSX ETG00000018041potassium large conductance calciumactivated channel M alpha1; ENSXETG00000017419pyruvate dehydrogenase phosphatase catalytic subunit 2) found to be over-transcribed at at least one time point were validated using real-time quantitative PCR (RT-qPCR). We treated 1. 6 μg of the total RNA per Xenopus and per time point with DNAse I (Invitrogen, Carlsbad, CA, USA) and used cDNA synthesis with superscript III and OligodT20 primer (Invitrogen) according to the manufacturer's instructions, the resulting cDNAs were diluted 50 times.
Acknowledgments Isabelle Worms  was the recipient of a fellowship from the  Rhône-Alpes Region. This work was funded by a research grant from the  Grenoble-Alps University  and  PEPS2012-CNRS. We also acknowledge funding from the federative structure Environmental and  Systems Biology (BEeSy) of Grenoble -Alps University. We thank  Dr. E. Coissac  and  Dr. F. Boyer  for their help with data deposition. We thank  Dr. JP Vert  ( Mines ParisTech ) for his help with bioinformatics and statistics. We are grateful to the  CC LBBE/PRABI  for providing access to its computing resources and for the technical support provided. We thank  Dr. B. Manship  and  Dr. I. Akhouary  for the English language review of the manuscript. We thank  Kim Barrett  ( Version Originale ) for the English review of the revised manuscript.
DNA trajectories We illustrate the ion analysis developed here using microsecond long molecular dynamics trajectories for two 18-mer oligomers belonging to the so-called ABC (Ascona B-DNA Consortium) database  (24) (25) (26). Both oligomers are based on tetranucleotide repeats, respectively, AGCT and ATCG, flanked by GC ends to reduce fraying during the simulations. Their sequences are GCCTAGCTAGCTAGCTGC and GCGCATGCATGCATGCGC (where the tetranucleotide repeat used to refer to the oligomers has been underlined). Each oligomer was simulated using the AMBER program suite  (27)  and with the current ABC protocol  (24). This involves the parm99 force field  (28, 29)  with the bsc0 modifications  (30). The oligomers were simulated using periodic boundary conditions with a truncated octahedral cell and a solvent environment consisting of SPC/E water molecules (31) (with a minimum thickness of 10 Å around the solute), leading to 11 621 and 11 546 water molecules for AGCT and ATCG, respectively).
DNA conformational analysis The DNA conformation and the ion distribution were analyzed directly from the AMBER trajectory output using Curves+  (37, 38). In the case of DNA, this analysis yields a curvilinear helical axis, and a full set of helical, backbone and groove parameters that can subsequently be used to generate time series or time-averaged probability distributions using the program Canal. As discussed below, a new option 'ions' is now available in Curves+ that triggers the calculation of the position of ions, water molecules, or any desired solute atoms in the trajectory. This output can then be analyzed with the Canion utility program, which is freely.
This file is then read by the Canion program and the ions positions are accumulated in a 3D histogram with a bin size in curvilinear helicoidal space of 0. 5 Å in R, 1/6 of a base pair step (≈0. 5 Å) in D and 5 • in A. We then analyze the ion distributions in 1D (R, D or A), in 2D (RA, DA, DR) or in 3D Cartesian space (see below). Note that in the case of a 2D RA analysis, we use polar coordinate plots to make the results easier to understand. Ion distributions can be obtained for the entire space surrounding the oligomer, or for any selected zone, defined by fixing lower and upper limits on R, D or A. Note that, by default, we use an upper limit of R = 30 Å since beyond this point the solute molecule has little impact on the ion distribution and the helicoidal coordinate analysis ceases to be of interest.
In order to recreate a 3D Cartesian space distribution, the helicoidal ion coordinates can be mapped back to Cartesian space with respect to a chosen helical axis, with typical choices being the axis of the average nucleic acid structure from the corresponding molecular dynamics trajectory, or a linear axis corresponding to a regular helix with userdefined twist and rise. Lastly, when calculating ion densities close to DNA, it seems reasonable to exclude the volume occupied by the solute molecule itself. This has been made possible in the Canion program by determining which CHC volume elements lie inside the van der Waals envelope of the solute molecule (using standard Pauling values, P: 1. 9 Å, C: 1. 6 Å, N: 1. 5 Å, O: 1. 4 Å, H: 1. 2 Å). While taking the solute excluded volume into account is optional in the Canion program, it is particularly useful when comparing the ion molarities in the two grooves of helical nucleic acids, and this option has been selected in all the results presented here.
FUNDING Centre National de la Recherche Scientifique (CNRS)  and the  Agence Natoinale de la Recherche (ANR)  project  CHROME  [ ANR-12 BSV5-0017-01  to R. L. and K. Z. ];  Swiss National Science Foundation  for funding under award  2000020 143613/1  (to J. H. M. and M. P. ). Open access publication costs were covered by the project  CHROME.
1998; Zhu et al. 2003). Development of several platforms for detecting SNPs has led to the refinement of the technology and now genomewide SNP databases can be generated at reduced cost and in a much shorter time compared to 10 years ago. Many crop species now have high-density SNP maps available which have proven to be valuable resources for practical applications in gene mapping, association mapping, and genomic selection  (Jing et al. 2007; Vignal et al. 2002; Varshney et al. 2005). Until recently in pea, however, only limited SNP resources have been available  (Aubert et al. 2006; Deulvot et al. 2010). SNP discovery and mapping carried out by  Bordat et al. (2011) , using 214 EST-derived markers, resulted in a resource to deploy in silico mapping of 5,460 pea unigenes using genomic resources developed in the model legume Medicago truncatula.
Illumina GoldenGate OPA design From the 8,822 target SNPs identified in 4,194 contigs and selected for potential array development, an additional filtering step was employed to remove potentially heterozygous SNPs by identifying those SNPs that showed less than 100 % of the reads matching either the reference or the alternate allele (1,018 SNPs; Supplementary Table  S2 ). These SNPs were removed to avoid potential issues with assay reliability for genotyping in targeted RILs. Sequence data for the contigs surrounding the SNPs were checked for the number of base pairs to the end of the contig, to the next SNP, or to the closest splice site. Those with less than 60 bp in this flanking region were eliminated since they cannot be used for Illumina GoldenGate arrays (Illumina Inc. , San Diego, CA, USA).
SNP genotyping and construction of consensus pea genetic map A total of 586 RILs from five mapping populations (Table  2 ; Supplementary Table  S3 ) were genotyped using the Ps1536 OPA using a standard GoldenGate protocol following the instructions provided by the supplier (http:// www. illumina. com/technology/goldengate_genotyping_ assay. ilmn). The PCR products generated were scanned for genotying using an Illumina HiScan (Illumina Inc. , San Diego, CA, USA). GenomeStudio software 2010. 3 (Illumina Inc. , San Diego, CA, USA) was used for data clustering and allele calls were visually inspected for errors in automatic allele calling and corrected where deemed necessary. Any calls that were not clearly one allele or the other were reported as missing data to avoid errors. The segregation data in individual RIL populations were subjected to Chi square test to determine deviations from balanced segregation ratios.
Data availability KnowPulse (http://knowpulse2. usask. ca/portal) is a repository of legume genetic and genomic data for pulse crop breeding. The assembled 454 sequencing data for the pea reference cultivar CDC Bronco is available with all polymorphic loci and associated markers (KASP and Illumina GoldenGate arrays) indicated. Potential homologues of a gene of interest can be identified using a BLAST interface based on sequence similarity. This allows for identification of existing markers associated with the gene of interest, as well as export information on specific loci in a variety of formats to aid in marker design. A comparative GBrowse [Generic Model Organism Database (GMOD), http://gmod. org] with a Medicago genomic backbone graphically displays sequence similarity-based homology between legume species providing an alternative approach to finding candidate markers.
For an overview of all data made available through this project, visit the project page (http://kn owpulse2. usask. ca/portal/node/3214575). All raw read data have been submitted to the NCBI-NIH Short Read Archive (BioProject ID: PRJNA237996; CDC Bronco (SRX555302); Alfetta (SRX555303); Nitouche SRX555304, Cooper (SRX555305), Striker (SRX555306), Orb (SRX555307), PI358610 (SRX555310) P651 (SRX555311)).
This set of non-redundant and high-quality SNPs comprised of 5,667 transitions (64 %) and 3,079 transversions (36 %) with 76 SNPs that could not be classified due to ambiguity of bases in the reference. An average SNP frequency of 1 SNP per 667 bp in P. sativum cultivars was observed, while an average frequency of 1 SNP per 99 bp was observed when the P. fulvum and P. sativum spp. abyssinicum accessions were included. The average minor allele frequency for all SNP loci was 0. 3 when all accessions were considered and 0. 19 for the P. sativum cultivars alone. Five (16 %) of the 32 SNPs tested for validation failed across all genotypes (Supplementary Table  S4 ). The other 27 SNPs were successfully separated by KASP assay, with 97 % successfully matching SNP calls with the 454 sequence data in each genotype.
2006). For this reason comparative mapping using CMap software was conducted to more efficiently visualize marker order in four pea RILs; an approach that aided in assessing and confirming the overall congruency of marker positions and order but also hinting at the possibility of some rearrangements on some LGs. In our study, the markers showed varied segregation distortion in different RIL populations with PR-19 exhibiting the highest segregation distortion. Such variation in segregation distortion in individual mapping populations and inconsistencies in linear marker order in individual population maps and consensus maps have been reported previously  (Gautami et al. 2012 ). To address these issues, several studies have reported the use of framework markers with known positions to divide linkage groups into segments representing bins which hold defined locations on chromosomes.
DDX5/DDX17 and hnRNP H/F Regulate the Same Subset of Exons Because class S exons are highly included in control cells despite their weak 5 0 ss, we hypothesized that 5 0 ss activators may enhance their inclusion in a DDX5/DDX17-dependent manner. To test this hypothesis, we computed the density of previously reported intronic splicing enhancers (ISE) downstream of the 5 0 ss regulated by DDX5 and DDX17  (Lim and Burge, 2001; Zhang et al. , 2005). As shown in Figure  S1E , there is an enrichment of ISE downstream of class S exons compared with control sets of alternative and constitutive exons. We then used the SFmap software  (Paz et al. , 2010)  to search for specific ISE motifs. Because class S 5 0 ss were embedded in GC-rich sequences (Figure  1G ), we focused our attention on splicing factors with a known affinity for GC-rich motifs, such as hnRNP H/F  (Huelga et al.
Bioinformatics Microarray analyses were performed as described previously  (Mallinjoud et al. , 2014). Exon numbering of mouse and human genes was based on the FasterDB annotation (https://fasterdb. lyon. unicancer. fr/index. php). MaxEntScan was used to measure the scores of the 5 0 ss and 3 0 ss. SFmap web server (http://sfmap. technion. ac. il/)  (Paz et al. , 2010)  was used to predict and map known splicing-factor binding sites. The UCSC LiftOver tool (http:// genome. ucsc. edu/cgi-bin/hgLiftOver) was used to look at conservation between human and mouse exons. Publicly available CLIP-seq data sets were retrieved from the NCBI Gene Expression Omnibus for hnRNPH1 and hnRNPF (GSE23694 and GSE34993), and from ArrayExpress for TIA and TIAL1 (E-MTAB-432). The number of reads that overlapped the regions of interest was computed using coverageBed (Bedtools).
ACKNOWLEDGMENTS We are grateful to  D. Furling  and  J. Marie  for their comments on the manuscript. We thank  C. Vanbelle  and  C. Bouchardon  ( CIQLE-SFR SanteLyon-Est ) and  Dr. Joe ¨l Lachuer  ( ProfileXpert-LCMT Facility, Lyon, France ) for their assistance with imaging and microarray analyses, respectively. This work was supported by grants from  INSERM ''Plan Cancer   2009-2013 ,''  INCa , the  Fondation pour la Recherche Medicale (FRM)  and  LNCC , and  AFM-Telethon. It was also supported by doctoral fellowships from the  LNCC (E. D. ) ,  AFM  (M. P. E. ),  French Ministere de l'Enseignement Superieur et de la Recherche  (L. F. and M. D. ), and FRM (E. Z. ), and postdoctoral fellowships from the  Centre Leon Be  ´rard (H. N. ), FRM (H. M. ), and  Fondation ARC  (S. G. , M. P. L. , F.
Results The study population has a mean age of 62. 9 (611. 1) (Table  1 ). The healthy control group has a mean age of 62. 40 (611. 82), compared to 63. 62 6 (9. 98) for the T2DM patients. Sixty-three percent of the individuals were males with 40. 05 percent suffering from T2DM. The mean BMI was of 28. 1 (64. 6) according to standard measures. 52 samples that failed genotype call rate (,95%)  27  and that showed relatedness (kinship coefficient range. 0. 0442)  28  , were removed. Imputation. 37995922 sites were imputed. The final set included 5891794 SNPs kept after QC (MAF. 0. 05, AIMs removal). The accuracy of the genotype imputation depends on the array SNP coverage and the similarity of haplotypes between the study dataset and the reference panels. The Lebanese population was previously shown to be genetically very close to Europeans  29  that are well represented in the 1000 Genomes Project references.
All participants signed an informed consent and data and blood samples were obtained from each individual. By taking part in the two recruitment campaigns, participants (1) answered a detailed questionnaire, (2) gave a blood sample for DNA analysis and (3) gave a blood sample for HbA1C, fasting blood glucose (FBS), and lipid profile measures after 12 hours fasting. For the remaining FGENTCARD 2,292 participants, a detailed questionnaire was duly filled, a blood sample was obtained for DNA and metabolites analysis and annotations were coded from medical charts for data such as laboratory tests, prescribed medications, and presence of clinical conditions. Table  1  describes clinical and demographic characteristics of the study participants  49. Previously reported  51  analysis of these subjects showed association with recruitment for questionnaire variables, such as ''exercise level,'' ''smoking usage''.
However, blood panel associations showed very consistent odds ratios for T2DM and coronary artery disease risk independently of recruitment. Selection of Patients and Controls. For the 998 participants selected through the two recruitment campaigns, an HbA1C of 48 mmol/mol (6. 5%) was used as the cut-off point for diagnosing T2DM in line with the World Health Organization definition of type 2 diabetes mellitus diagnosis  52. For the 2,292 participants from the FGENTCARD project, T2DM was diagnosed by an ascertained physician supported by subjects HbA1C levels and or their two-hour plasma glucose concentration after an oral glucose tolerance test as documented in their medical records. For the control dataset, 85. 23% of the participants were $50 years old and have no history of T2DM. Body mass index (BMI) was calculated according to standard measurements.
Selection criteria for DNA analysis were based on the complete availability of the genotyping data and the T2DM-relevant characteristics, resulting in the selection of 3,286 subjects. Genotyping and imputation. DNA was extracted using a standard phenolchloroform extraction procedure. Samples were genotyped using Illumina HumanOmniExpress-12V1-1 Multi-use (623 controls, 839 cases, ,700,000 SNPs) and Illumina Human610-660W Quad BeadChips (1279 controls, 545 cases, ,550,000 SNPs). Plink 27 was used for data management and quality control keeping 3,286 samples with call rate $95%, SNPs call rate $98%, Hardy-Weinberg p. 10 26 , MAF $ 1%. Genotype data was converted to NCBI genome build 37 using LiftOver (http://genome. ucsc. edu/cgi-bin/hgLiftOver). Chromosomes were phased with SHAPEIT  53  using the 1000 Genomes Phase I haplotypes  54  provided on the IMPUTE2 website (1000 Genomes Phase I integrated haplotypes (produced using SHAPEIT2), September 2013) based on sequence data for 1,092 TGP samples from release 20110521.
In this study, we use insights from our previous studies on population stratifications in the Lebanese to avoid type 1 errors in the GWAS. We apply a method that uses available 1000 Genomes Project reference panels to impute a resolved ancestry dataset  29  as well as the current T2DM cohort to the same SNP coverage resolution. We next use two model-based clustering methods to unambiguously classify subjects into groups (Figure  5 ):. 1-mclust (http://www. stat. washington. edu/mclust/) uses model-based hierarchical clustering and Bayesian Information Criterion to classify samples into related groups. 2-ADMIXTURE  57  assigns individuals in a model-based manner into ancestral populations using maximum likelihood estimates. The two methods identified similar population structures and assigned samples into three ancestral groups (Figure  5 ) similar to previous ancestry reports on the Lebanese  29.
Acknowledgments We thank the patients for agreeing to participate in the study. We thank  Dr. Bouchra Douaihy  and  Mrs. Hana Sbeite  for helping in the campaigns. We thank the  dispensaire de la maison maronite de soins et d'hebergement-Ain El Remmaneh, Ehden Governmental Hospital -Ehden ,  Caritas Liban -Dahr el Ain ,  Lebanese Red Cross -Amioun ,  Saint Joseph dispensary -Zgharta , and dispensary of  Kfarhabou -Donnieh  for hosting the campaigns. This publication was made possible by a grant from the  Qatar National Research Fund  under its  National Priorities Research program  (NPRP) award number  NPRP 09-215-3-049. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the  Qatar National Research Fund.
Background: Tuberculosis (TB) still constitutes a major public health problem in Malaysia. The identification and genotyping based characterization of Mycobacterium tuberculosis complex (MTBC) isolates causing the disease is important to determine the effectiveness of the control and surveillance programs. Objectives: This study intended a first assessment of spoligotyping-based MTBC genotypic diversity in Malaysia followed by a comparison of strains with those prevailing in neighboring countries by comparison with an international MTBC genotyping database. Methods: Spoligotyping was performed on a total of 220 M. tuberculosis clinical isolates collected in Kelantan and Kuala Lumpur. The results were compared with the SITVIT2 international database of the Pasteur Institute of Guadeloupe. Results: Spoligotyping revealed 77 different patterns: 22 corresponded to orphan patterns while 55 patterns containing 198 isolates were assigned a Spoligo International Type (SIT) designation in the database (the latter included 6 newly created SITs).
Based on recent reviews on Mycobacterium tuberculosis complex (MTBC) molecular typing methods  [7]  and strategies and innovations in the broad field of TB molecular epidemiology  [8] , we opted for spoligotyping which is one of the most widely used methods for an initial assessment of MTBC genotypic diversity. This polymerase chain reaction (PCR)-based technique relies on the presence or absence of 43 unique spacer sequences in the direct repeat (DR) locus of the M. tuberculosis genome, useful both for simultaneous detection and typing of M. tuberculosis strains  [9]. This methodology is ideal for assigning MTBC strains in major phylogenetic lineages thanks to the SITVIT2 international database, which further allows one to compare strains worldwide and draw distribution maps  [10]. We selected MTBC isolates from TB patients residing in 2 different geographic locations (Kelantan and Kuala Lumpur), and looked for significant associations between phylogenetic lineages vs.
Patient population and clinical isolates M. tuberculosis isolates were collected from tertiary hospitals in Kelantan (n5184) and Kuala Lumpur (n536), Malaysia between 2009 and 2013 (1 isolate per patient). Diagnostic specimens were cultured and isolated on Ogawa media after decontamination. Drug Susceptibility testing (DST) against first-line anti-TB drugs (rifampicin, isoniazid, streptomycin, and ethambutol) was done by The National Public Health Laboratory (NPHL) using the proportional method  [11]. Clinical and demographic data (date of isolation, isolation city, age, sex, ethnicity, first line-drug susceptibility patterns) were retrieved from TBIS 101A registration book and Laboratory Information System (LIS).