dataesr/scientific-paragraphs-categorization huggingface dataset:
761 No function/No homology found in the databases No PFCIRM121_08040 slpC1 PSE T2 (-) 0. 231 (-) 0. 745 Cell wall/surface protein containing 1 SLH domain predicted by HMM Pfam No PFCIRM129_00525 hsdM3 C T3 (+ ) 0. 175 (+ ) 0. 744 DNA restriction and modification/Type I site-specific DNA methylase Yes PFCIRM129_07765 lacl1 C T4 (+ ) 0. 693 Transcription regulation/transcriptional regulator, arabinose operon epressor No PFCIRM121_08300 acn C T5 (-) 0. 683 TCA cycle/Aconitase No PFCIRM129_10930 pouf 10930 C G1 (+ ) 0. 263 (+ ) 0. 518 No function/No homology found in the databases No PFCIRM129_10925 pep C G2 (+ ) 0. 263 Metabolism of amino acids and related molecules/peptidase Yes PFCIRM129_04790 pouf 04790 C G3 (+ ) 0. 261 (+ ) 0. 603 No function/No homology found in the databases No PFCIRM121_08235 pouf 08235 PSE G4 (-) 0.
Flow cytometric analyses All experiments were carried out with a Becton Dickinson Facscan equipped with a 488 nm Laser, and fluorescence signals were measured on three channels: FL1 (530/30 nm), FL2 (585/42 nm) and FL3 (670 nm LP). Data were acquired with the Cellquest software (BD Biosciences, Le Pont De Claix, France) and analyzed with the Cyflogic Software (http://www. cyflogic. com).
Propidium Iodide assay After treatment with menadione and/or Lcarnitine, the culture medium was removed and stored in a 15 mL tube; cells were rinsed with DPBS and trypsinized. The culture medium, the DPBS used for washing and the trypsinized cells were collected and centrifuged at 600 g for 3 min. The supernatant was discarded and the cells resuspended in DPBS. Propidium iodide was added to the cell suspension at a final concentration of 5 µg/mL. After a 5 min incubation, cells were analyzed with a flow cytometer  [21]. FL3 fluorescence was recorded for 10000 cells. Lethal dose 50 was determined with the BioStat 2009 software (AnalystSoft) as described in  [22].
Inversely, KLF2, TNFAIP3 and MYD88 mutations were rare (KLF2, 2%) or absent (TNFAIP3 and MYD88) in SDRPL compared with SMZL. 24 These recent data highlight the genetic differences between these entities, which provides the possibility of developing novel therapeutic approaches. In the first-line setting for patients, PNA are the mainstay of HCL therapy for physically fit and symptomatic HCL patients, conferring in most cases a long overall survival (OS). The treatment is based on either cladribine (2-CdA) or pentostatin (DCF). However, no randomized trials have compared pentostatin and cladribine and, to our knowledge, there is no evidence in the literature to date that demonstrates the superiority of any one drug. In a large representative US database that included 749 HCL patients, cladribine was utilized in more than 75% of patients requiring first-line treatment.
| BCR inhibitors Ibrutinib, a first-in-class oral inhibitor of Bruton's tyrosine kinase, is approved for treating patients with relapsed or refractory B-cell malignancies, such as CLL. 45 A multicenter phase 2 study of ibrutinib is ongoing for treatment of relapsed HCL (NCT01841723). Ibrutinib FIG URE  3  Therapeutic algorithm for treatment of patients with hairy cell leukemia-variant (HCL-V) and splenic diffuse red pulp lymphoma (SDRPL) seems to represent a future alternative treatment in relapsed/refractory HCL. 46.
An in-house software developed by D. Boichard (personal communication) was used to detect incompatible genotypes for each sire/ dam/progeny triplet. Each incompatibility with either the sire, the dam, or both was recorded. When the total number of incompatible SNPs exceeded 2% of all SNPs, another in-house software, also developed by D. Boichard (personal communication), was used to assign the most probable parent(s) to each progeny. About 10% of pedigree errors were identified and corrected. When the pedigree was confirmed, only a few incompatible SNP genotypes remained for some F2 individuals compared to their F1 parents, and these were considered as genotyping errors rather than as pedigree errors and replaced by missing values for the corresponding F2 individuals. The final data included genotypes for 844 individuals (F0, F1, and F2) and 40,618 SNPs, distributed on 28 autosomes, two linkage groups and the Z sex chromosome.
Phenotypic comparisons The normality of trait distribution in the F2 population was checked by the SAS ® UNIVARIATE procedure (Statistical Analysis System, Version 9. 3, SAS, Institute Inc. , Cary, NC, USA). Trait differences between parental lines were studied by analysis of variance using the GLM procedure in SAS ® and including the effects of sex and line. For the F2 population, the GLM procedure was applied to estimate the fixed effects of sex, dam (full-sib family effect), and batch, taking into account BW00 as a covariate for all other body weight measurements, as well as for SL08 and CA16. For the SRBC traits, the model included BW08 instead of BW00 as a covariate, in addition to the fixed effects.
The linear mixed model used in this study was applied for each chromosome, with sex, batch, and dam as fixed effects and by incorporating random genetic effects with genomic relationships to correct for genetic structure in the experimental population. The analysis was implemented on all data for the autosomes, while data from males and females were separated for the analyses on the Z chromosome. P value thresholds were computed based on the number of independent SNPs and LD blocks  [17, 18] , resulting in a P-value threshold of 5% Bonferroni genome-wide significance threshold of 4. 84 × 10 -6 (0. 05/10329) and a suggestive linkage threshold of 9. 68 × 10 -5 (1/10329). Manhattan plots of the GWAS results for each trait were produced with the qqman package available from R (Version 3. 1. 2) [19].
QTL mapping Single QTL detection analyses were carried out with the QTLMap software  [20] , which was developed to handle data on F2 individuals for designs from outbred populations. QTLMap is an interval mapping method, in which QTL are detected using a likelihood ratio test (LRT) calculated under the hypothesis of one versus no QTL linked to the given SNPs. The linkage disequilibrium linkage analysis (LDLA) option of QTLMap was applied to single trait  [17]. No assumptions were made on the fixation of alleles in the grand-parental lines (L2 and R-) and on the number of segregating alleles at the QTL. The family structure of the F2 population consisted of six half-sib families, and the minimum size for considering dam families was set at 20 progeny, which was the case for 15 of the 51 dam families.
Fixed effects of dam, sex, batch, as well as covariates (BW00 or BW08 depending on the trait studied) were taken into account in the model. For the Z chromosome, an interaction between the QTL and the fixed effect of sex was added to the model. The LRT value was calculated by scanning chromosomes for QTL with a step of 1 cM. The method proposed by Churchill and Doerge  [23]  was used to estimate empirical thresholds taking into account the family structure (pedigree) and genotype data (SNPs). Chromosomewide significance of LRT values was obtained by 10,000 simulations under the null hypothesis (no QTL) to calculate 1 and 5% significance thresholds  [21]. Genome-wide thresholds were defined by applying the Bonferroni correction across chromosomes: where n is the number of chromosomes used in the study.
Confidence intervals for QTL were estimated by the one LOD drop-off method  [22]. The QTL substitution effect was assessed in each sire family at the position of the LRT maximum, and significance was assessed by Student's t test. The additive value of a QTL effect was calculated as the mean of the absolute value of the sire substitution effects (P value < 0. 05). SNP positions and information were annotated using the GalGal4 genome assembly  [24]. Information on potential candidate genes in each QTL region was searched for in the NCBI and Ensembl databases  [24, 25]. Furthermore, for genes that were associated with significant SNPs, we performed a search of the gene ontology database  [26]  to draw hypotheses about the biological processes and molecular functions that likely influence the trait of interest.
Bioinformatics In previous research, whole-genome sequence data was produced for one L2 chicken  [27]  and for two R-chickens  [28]. The sequenced animals were chosen at random in a recent generation for each line and constitute a resource for identifying SNPs that differ between the lines but were not used as F0 animals in the current F2 design. Sequence alignment between the reference genome assembly GalGal4 and the whole-genome sequences of the L2 and R-individuals was performed to identify SNPs that differ between the two lines. Sequencing fragments with a Phred quality score lower than 15 and the adapter P genome-wide = 1 -1 -P chromosome-wide n , sequences were removed by Trimmomatic  [29]  before alignment to the chicken reference genome assembly Galgal4. 84  [25]. Trimmed reads that were shorter than 36 bp were then eliminated.
Discussion In this study, we focused on traits that were measured during the growing period and are likely to play an important role in the adaptation of chickens to sub-optimal conditions, particularly to tropical climate. Since the lines used for the F2 design were original experimental lines, our findings may be specific to these genetic backgrounds and/or to the sub-optimal environmental conditions that differ from other studies. Yet, most of the QTL regions detected for growth traits overlapped with previously published QTL, which suggests that these QTL have an effect across a range of environmental conditions and could be particularly useful for selection. Several genome-wide QTL were detected for BW00, a trait that is strongly influenced by maternal effects. Since data on egg weight of the F1 dams were not available at the time of reproduction, this effect could not be separated from the additive dam effect on chick weight.
The differences between GWAS and QTL mapping-LDLA may be due to GWAS detecting associations based on LD across the F2 population, whereas QTL mapping-LDLA also exploits the information provided by within-family segregation, in order to estimate SNP effects on the trait variation. Legarra et al. [50]  compared LDLA and EMMA methods (such as GEMMA) using real and simulated datasets and showed good agreement for the location of QTL between them. In general, these datasets consisted in a large number of small families, except for the sheep dataset, in which five F1 sires were mated to a large number of F1 dams, so that the number of half-sib families was similar to the design in the current study but the proportion of full-sibs was much lower in the sheep dataset. In our case, differences between the GWAS and LDLA results could be due to the fact that within-family segregation provides the most relevant information for QTL detection.
Genome-wide DNA methylation profiling Methylation analysis was performed using Illumina Infinium HumanMethylation450 BeadChip. DNA methylation data from all participants were generated at the same time. DNA samples from both groups were randomized across the chips. For each participant, DNA methylation data at more than 485,512 sites was obtained. Four participants (3 controls and 1 meditator) with poor methylome quality were removed, leaving a data set of 17 meditators and 17 controls.
Funding This work was supported by  CNRS PEPS INEE 2015 (RC) ,  NCCAM (NIH)  ( P01-AT004952  (RJD and AL),  Fetzer Institute ,  John Templeton Foundation , anonymous donor (RJD),  LABEX CORTEX ANR-11-LABX-0042 ,  Université de Lyon   ANR-11-IDEX-0007  and  ERC -Consolidator 617739-BRAINandMINDFULNESS (AL). We thank  Claire Dandine ,  Hervé Perdry  and  Alice Urvoy  for helpful discussions.
Variant analysis and prioritization Output paired-end sequencing data were processed through two independent bioinformatical pipelines including read alignment, achieved by either BWA or CASAVA, variant calling by either GATK or CASAVA, and annotation by Alamut Batch. Variants with a minor allele frequency (MAF) of less than 1% in available were visualized on Alamut Visual software (Interactive Biosoftware) and classified in 5 classes from 1-benign to 5-pathogenic, based on MAF, predicted effect on protein, predicted effect on splicing, databases of pathogenic variations, according to ACMG recommendations.
Bioinformatic Resources Primers for Sanger sequencing were designed with Primer3 software (http://bioinfo. ut. ee/primer3-0. 4. 0/). MAF was obtained on ExAC database (http://exac. broadinstitute. org/), EVS (http://evs. gs. washington. edu/EVS/) and 1000 genomes (http://www. 1000genomes. org/). Two databases of pathogenic variations, HGMD (http:// www. hgmd. cf. ac. uk/ac/index. php) and ClinVar (http:// www. ncbi. nlm. nih. gov/clinvar/), were consulted.
Genetic analyses Targeted NGS analysis in foetus IV-3 from family 1 (Foetus 1) identified a homozygous deletion of the first 4 nucleotides at the 5' end of exon 11 of the MPDZ gene (NM_003829. 4): c. 1291_1294del. Splice prediction software showed no significant alteration of the 3' splice site of intron 10. Thereby, this outof-frame deletion is predicted to result in a frameshift leading to a stop codon after 14 aberrant codons: p. (Val431Metfs*14). Sanger sequencing confirmed this homozygous mutation in foetus IV-3 as well as in her affected sib, foetus IV-5 (Foetus 2), with both parents being heterozygous carriers (Family 1; Fig. 1 ). This mutation was absent from the control population databases ExAC, EVS and 1000 Genomes as well as from the pathogenic variation databases HGMD and ClinVar.
Analysis in foetus V-3 from family 2 3) identified a homozygous substitution in position +1 within intron 5, affecting the canonical 5' donor splice site of intron 5: c. 533 + 1G > T. This splice mutation is therefore predicted to result in a frameshift transcript. This variant is very rare in control populations with a single mutated allele from the 120,068 present in ExAC database, in a non-Finish European heterozygous carrier. Sanger sequencing confirmed the variation at a heterozygous state in both parents (Family 2; Fig. 1 ). Unfortunately no DNA sample was available from affected patient V-1. Analysis in foetus II-3 from family 3 (Foetus 5) identified a homozygous nonsense mutation in exon 17 of MPDZ: c. 2248C > T; p. (Arg750*). This nonsense variant is also rare with 8 occurrences among the 120,618 ExAC alleles, resulting in a minor allele frequency of 6.
To assess this prevalence, we examined the ExAC database variants in the MPDZ gene. Among 2,210 MPDZ variants in the 60,706 ExAC control individuals, 80 are canonical splice site, nonsense or frameshift mutations. These 80 inevitably truncating variants have been found in 155 heterozygous individuals, resulting in an estimation of heterozygous carriers of ~1/624, highlighting the rarity of loss-of-function MPDZ alleles in the general population. Accordingly, MPDZ constitutes a rare cause of congenital hydrocephalus and should be considered first and foremost in consanguineous families. Nevertheless, the phenotype associated with less severe mutations such as missense or non-canonical splice variants may be different and remains to be described. In humans, HSAS encompasses approximately 5-15% of congenital hydrocephalus cases with a genetic cause.
The iCLIP and eCLIP techniques facilitate the detection of protein-RNA interaction sites at high resolution, based on diagnostic events at crosslink sites. However, previous methods do not explicitly model the specifics of iCLIP and eCLIP truncation patterns and possible biases. We developed PureCLIP (https://github. com/skrakau/PureCLIP), a hidden Markov model based approach, which simultaneously performs peak-calling and individual crosslink site detection. It explicitly incorporates a non-specific background signal and, for the first time, non-specific sequence biases. On both simulated and real data, PureCLIP is more accurate in calling crosslink sites than other state-of-the-art methods and has a higher agreement across replicates.
Like iCLIP, it provides single-nucleotide resolution by capturing truncated cDNAs but, due to the optimization of several steps, it improves the specificity of called binding sites. To date, eCLIP datasets for more than 120 different proteins have been published by the ENCODE consortium  [8, 9]. While previous CLIP-seq experiments often had matched IgG control experiments, which suffer from sparsity and high amplification rates  [7] , the eCLIP-seq protocol is designed to generate a size-matched input control. This input control is sampled prior to the immunoprecipitation and thus, contains the signal of a non-specific background. To infer target-specific RBP binding regions from iCLIP/eCLIP data, it is crucial to account for different sources of biases, such as transcript abundances, crosslinking sequence preferences  [4] , and mappability.
The crosslinking sequence bias can also be observed within the eCLIP input data, since it "represent[s] RNAs crosslinked to many different RBPs and should reflect the sequence preferences at crosslink sites that are common to a mixture of RBPs"  [5]. Haberman et al. showed that certain polypyrimidine-rich k-mers, which they call crosslink-associated (CL) motifs, are enriched at read start sites in both input and target eCLIP data compared to upstream regions  [5]. Besides background noise, such as the signal from sticky RNA fragments or non-specific crosslink events within CL motifs, the binding of background proteins is a major challenge in the analysis of CLIP-seq data. A recent study analyzing previously published PAR-CLIP datasets showed that if no control dataset is used for correction, up to 45 % of the called binding sites overlap with background binding sites  [10].
Background binding regions that are common to several CLIP-seq datasets have been systematically identified  [11]  and can be used to validate called binding sites. These findings demonstrate the importance of control experiments, such as input experiments, to reduce the number of false positives at such regions. Several tools have been developed for the computational analysis of HITS-CLIP and PAR-CLIP data  [12] [13] [14] , but very few tools have been developed that are tailored for the specific analysis of iCLIP/eCLIP data. In addition, previous methods for CLIP-seq data analysis do not fully take into account possible sources of bias, such as transcript abundances and non-specific CL motifs, which heavily affect iCLIP and eCLIP data  [5, 15] , thereby they return a high number of false calls.
The tool Piranha  [13]  performs strand-specific peak-calling. It supports the incorporation of covariates, but does not explicitly normalize for a non-specific background signal. It models the underlying bin-wise read count distribution to compute a genome-wide significance threshold above which peaks are called. CLIPper  [7]  is also a strand-specific peakcalling method designed by members of the ENCODE Consortium to analyze published eCLIP datasets. It incorporates annotations from the reference genome and computes significance thresholds gene by gene. Both tools, Piranha and CLIPper, are peak-calling methods that do not detect individual crosslink sites. Their limitation is that they potentially miss low-affinity binding regions with a clear iCLIP truncation pattern due to the arbitrary setting of a threshold on the number of reads.
Our method uses a non-homogeneous hidden Markov model (HMM) to incorporate additional factors into the model, such as a non-specific background signal from input experiments and CL motifs, to reduce the number of false positives. We have exhaustively validated the superiority of PureCLIP over several existing methods in various settings. First, we designed a realistic iCLIP/eCLIP simulation setup and demonstrated that, over a wide range of simulation parameters, PureCLIP is up to 7-15 % more precise than other methods in detecting target-specific crosslink sites. Second, due to the lack of an experimental gold standard, we selected four datasets of published iCLIP/eCLIP data for evaluation where the RBP motif or the predominant binding region of the RBP is known. We consistently observed that PureCLIP is better than other methods in determining bona fide binding site locations.
Evaluation of the performance of PureCLIP in comparison to previous strategies Evaluating a method's performance in analyzing CLIPseq data is not trivial, since no gold standard of binding regions or crosslink sites exists. To address this:  (1)  We assess the precision and recall of PureCLIP in basic mode, i. e. , without additional covariates, in calling individual crosslink sites on simulated data. (2)  We then use real iCLIP and eCLIP datasets of proteins with known binding characteristics, such as known sequence motifs or known predominant binding regions. We assess the ratio of sites called by each method that fall within these motifs or inside those binding regions. Called crosslink sites within such regions are defined as true positives. Here we applied PureCLIP in four different settings: in basic mode, incorporating the input signal, incorporating CL motifs, and incorporating both the input signal and CL motifs simultaneously.
To evaluate the performance of PureCLIP under different conditions, we produced three different datasets. For these, we used varying pull-down rates for the target signal, i. e. , either 100 or 50 % of the RNA fragments that overlap a target binding region are selected and further modified where required. Reducing the pull-down rate enables us to get an idea for how the different methods perform for proteins with overall lower binding affinities. Additionally, we simulated non-specific background binding for two of the datasets (see Fig. 2 ). For the evaluation, we define a called crosslink site as a true positive if a target crosslink site was simulated at the same position. The precision of a method is calculated as the fraction of true positives among the called crosslink sites. We first investigated the precision versus the number of true positive crosslink sites.
PureCLIP detects bona fide binding regions with higher precision compared to previous strategies We used publicly available eCLIP (PUM2, RBFOX2, and U2AF2)  [7]  and iCLIP (U2AF2)  [21]  datasets to measure the performance of the different strategies in calling crosslink sites within bona fide binding regions. For PUM2 and RBFOX2, these binding regions were defined by their known sequence motifs (see Additional file 1: Figure  S1 ), while for U2AF2, we make use of its known predominant binding region ∼11 nt upstream of 3 splice sites  [21]. Here, a sequence motif based definition of the binding region is not applicable, since U2AF2 binds to poly(U) tracts, which coincide with non-specific CL motifs. For the PUM2 data, all strategies revealed an accumulation of called crosslink sites at the 5 end of PUM2 motif Fig.
Performance of PureCLIP without incorporating external data as covariates We first investigated the precision of PureCLIP in basic mode, i. e. , without the incorporation of any covariates, where calls are considered true positives if they fall within the motif area or upstream of 3 splice sites. We observed that PureCLIP outperforms all other methods even without covariates in three out of four datasets, as shown in Fig. 3  (right panel). Interestingly, when applying strategies that merge results from peak-calling tools and CITS using the peak-calling scores for ranking (Piranha sc + CITS and CLIPper sc + CITS), we always get a lower precision than when using the CITS crosslink site detection score for ranking (Piranha + CITS sc and CLIPper + CITS sc ).