dataesr/scientific-paragraphs-categorization huggingface dataset:
Incorporation of input control data improves crosslink site detection We expect the observed pulled-down fragment densities to be biased by different factors, among others by RNA transcript abundances. The published eCLIP datasets have input control experiments  [7] , which provide information about the non-specific background signal, i. e. , RNA fragments crosslinked to background proteins. We observe significant correlations between the fragment density of the eCLIP target dataset and the input dataset with Pearson correlation coefficients ranging from 0. 36 to 0. 42 (p values < 2. 2 × 10 -16 ) (see Additional file 1: Figure  S12a ). Therefore, the incorporation of the input signal into the PureCLIP framework gives us the possibility to indirectly normalize for transcript abundances, crosslinking preferences, and other local biases.
In detail, the PureCLIP framework uses the eCLIP input signal to model the emission probabilities of the nonenriched and enriched states for the observed data, i. e. , the pulled-down fragment densities. This means that instead of using one global emission probability distribution for the non-enriched or enriched states, the position-wise input signal is used to model the expected mean parameter of each of the two emission probability distributions (see Additional file 1: Figure  S12b ). With this, we aim to reduce the number of false positives, for example, within highly abundant RNAs while increasing the sensitivity within lowly abundant RNAs. The evaluation based on bona fide binding regions from real data shows that incorporating the input signal improves the precision of PureCLIP for all eCLIP datasets and over all sensitivity thresholds (Fig.
PureCLIP has a higher agreement of called sites between eCLIP replicates compared to previous strategies Besides using known binding regions for evaluation, we aimed to assess the performance of the different methods independently of that information, since in the end the exact binding regions and crosslink sites remain unknown. For this reason, we explored each method's precision based on the agreement of called crosslink sites between eCLIP replicates, assuming that target-specific binding events are more likely to be observed in both replicates than non-specific noise. We applied all methods to the individual eCLIP replicate datasets and measured for each sensitivity threshold how many of the x called crosslink sites in replicate 1 overlap with the top x ranking crosslink sites in replicate 2.
We refer to this measurement as the bias-corrected replicate agreement (see "Methods" for details). To further prevent a contribution from common nonspecific crosslinks, for PUM2 and RBFOX2 we counted only sites to the bias-corrected agreement that are not located within CL motif occurrences. Since the target motifs of these two proteins are clearly distinct from CL motifs, we expect that we do not miss relevant targetspecific sites by this. The U2AF2 iCLIP data are excluded from this evaluation, since no input control experiment is available and thus, the bias-corrected replicate agreement cannot be computed. Our evaluations show that PureCLIP has a higher biascorrected replicate agreement for the top-ranking sites compared to previous strategies, in all four PureCLIP settings and over all three eCLIP datasets, as depicted in Fig.
b) c) Fig. 5  Agreement of called sites between replicates. For each eCLIP dataset (a: PUM2, b: RBFOX2, c: U2AF2), we report for each given number of called sites x in replicate 1 (corresponding to a certain p value or score threshold), the percentage that were also called within the top x ranking sites in replicate 2 after correcting for different biases (see "Results" and "Methods"). The leftmost point of each curve corresponds to the number of calls associated with the lowest p value or highest score the strategy can report. rep1 replicate 1 Additional file 1: Figures  S17f  and S18f  ). Further, the results show that these strategies also call far more sites within known common background binding regions than PureCLIP, even when not incorporating covariates. Moreover, the other strategies have far less bias-corrected agreeing calls between the two eCLIP replicates (Fig.
For the analysis of PUM2 and RBFOX2 data, note that for the 50 top-ranking sites (Fig. 3a , b, right panel), the precision of PureCLIP including the input signal is much higher in comparison to PureCLIP in basic mode or to previous strategies. The results indicate that the top-ranking sites called by other strategies are likely to be caused by a non-specific background signal, which is resolved by PureCLIP when incorporating the input signal. PureCLIP incorporating CL motif scores strictly outperforms all other strategies over all four datasets. In fact, PureCLIP's precision in this setting increases, especially for the eCLIP datasets of proteins whose sequence motifs do not coincide with CL motifs, namely PUM2 and RBFOX2 (Fig. 3a, b ). For RBFOX2 eCLIP data, the increase is particularly remarkable.
This is also the only dataset where PureCLIP without incorporating CL motifs shows a lower precision than strategies that make use of the CITS crosslink site detection score (Fig. 3b ). The main reason is that in basic mode, PureCLIP is more sensitive than CITS in also calling non-specific crosslink sites, in particular for this dataset (see Additional file 1: Figure  S18d ). In general, a high sensitivity is desired, since we also want to detect crosslink sites for low-coverage regions, for example within lncRNAs or for proteins with lower binding affinity. In addition, the number of false positives can be reduced by incorporating CL motifs. Interestingly, when incorporating both the input signal and CL motifs simultaneously, the precision of PureCLIP increases even further, highlighting the huge benefit of the incorporation of both covariates into the model.
Compared to previous strategies, PureCLIP achieves higher agreement in calling RBP-bound sites between eCLIP replicates for bona fide crosslink sites. These are sites where the fragment coverage is enriched over the input signal and they do not overlap known background binding regions and, for PUM2 and RBFOX2, they are not located within CL motif occurrences. Interestingly, the simple threshold method, which detects crosslink sites by applying a cutoff on the read start counts, has the worst performance of all on both simulated and real data, as expected, but by far the highest replicate agreement for all datasets when not explicitly accounting for biases. This indicates that, beside target-specific crosslink sites, other factors also contribute to this raw replicate agreement, and that to obtain a meaningful evaluation of all methods, we need to compute a bias-corrected replicate agreement.
These results also strongly suggest that it would be valuable in the analysis of iCLIP/eCLIP data to include replicate information explicitly (as already suggested by  [15] ) but, importantly, this needs to be done carefully while addressing possible sources of biases. It is also important to stress that for all analyzed eCLIP datasets, PureCLIP calls far fewer crosslink sites within regions of high fragment density (see Additional file 1: Figures  S17f, S18f,  and S19f  ) and within known common background binding regions  [11]  (see Additional file 1: Figures  S17e, S18e,  and S19e ) compared to all other strategies. This even holds for PureCLIP in basic mode. Taken together with PureCLIP's general higher precision, these findings demonstrate how important it is in the analysis of CLIP-seq data not only to call peaks but also to model accurately the counts of individual read starts, which indicate potential truncation events.
Conclusion More and more high-resolution CLIP-seq datasets are being generated, but the precise determination of protein-RNA interaction sites from iCLIP/eCLIP has been challenging so far. Extensive evaluations demonstrated the superiority of PureCLIP over several previous strategies in detecting target-specific crosslink sites, for both simulated data as well as real datasets. PureCLIP is able to capture protein-RNA interaction footprints precisely, while not relying on the highest peaks, and it is able to correct for biases, such as transcript abundances, background binding, and crosslinking sequence preferences. It, therefore, provides a promising method for analyzing these datasets, and also for proteins with lower binding affinities or proteins binding to low abundant RNAs, such as lncRNAs.
Preprocessing of iCLIP/eCLIP datasets We analyzed three published eCLIP datasets targeting the proteins PUM2, RBFOX2, and U2AF2 and one iCLIP dataset targeting U2AF2 (see Additional file 1: Table  S1  for details). First, any possible adapter contamination at the 3 ends was removed using TrimGalore on the iCLIP dataset  [28]  (v0. 4. 2, based on cutadapt), and by running cutadapt twice on the eCLIP datasets  [29]  (v1. 12). The latter was done to correct for possible double ligation events  [7]. Reads shorter than 18 bp were discarded. Next, the reads were mapped against the human genome (hg19) using STAR  [30]  (v2. 5. 1b), a read aligner designed for RNA-seq data with setting -alignEndsType EndToEnd, -scoreDelOpen -1 for gap penalty, and -outFilterMultimapNmax 1 to discard reads mapping to multiple locations.
PCR duplicates were removed based on the read mapping positions and the random bar-code sequences (also called UMIs). This is important, as PCR amplification rates are high, in particular for iCLIP datasets. To address this, we used UMI-tools  [31] , a network based de-duplicating method (with setting -paired), which is able to handle errors within bar-code sequences. All evaluated datasets come as two replicates. When assessing each method's ability to recover bona fide binding regions, we pooled the reads of the two replicates, whereas they were analyzed separately when evaluating the agreement between called sites. Due to the differences in the two library preparation protocols  [3, 7] , we used either the 5 -end read (iCLIP) or the 3 -end read (eCLIP) of the sequenced fragment for the analysis.
Evaluation on real data based on bona fide binding regions To assess the performance of the different strategies in detecting target-specific crosslink sites for the PUM2 and RBFOX2 datasets, we used the sequence motifs that were described for each of those proteins (Additional file 1: Figure  S1 ). FIMO  [23]  (-thresh 0. 001 -norc) was used to compute genome-wide motif occurrences. Next, for each called crosslink site, the distance to the closest motif start site was identified. The precision was defined as the percentage of all called sites that are located within 2 bp of a motif occurrence (Fig. 3 ). For the protein U2AF2, its known predominant binding site ∼11 nt upstream of 3 splice sites was used for evaluation. Ensembl release 75 annotations were used to compute the distance of each called crosslink site to the closest 3 splice site.
Computation of bias-corrected replicate agreement For the evaluation based on the replicate agreement, only sites with calls at the exact same nucleotide position in both replicates were considered as agreeing. In all comparisons, the replicate dataset with the larger library size was chosen as a reference for the evaluation and this is referred to as replicate 2 in the following. We report for each given number of called sites x in replicate 1 (corresponding to a certain sensitivity threshold), the percentage that were also called within the top x ranking sites in replicate 2. To compute the bias-corrected replicate agreement, we count only sites that additionally (1) have sufficient enrichment over the input signal and (2) are not contained in common background regions  [11]  or in CL motifs (for PUM2 and RBFOX2).
To determine the sites whose pulled-down fragment densities are enriched over the input, we chose an individual threshold for each protein dataset based on its distribution of log-fold enrichment (for details, see Additional file 1: Section 7). CL motif occurrences were obtained with FIMO as described previously, while using all matches with a score >0. Common background binding regions were taken from  [11] , using only regions observed in at least six different CLIP-seq datasets, and extending them upstream and downstream by 200 bp.
2002). However, recent reports demonstrated that patients with an AB genotype have a third variant leading to genotype AAB or ABB  (Font-Llitjos et al. 2005; Chillaron et al. 2010). One hundred fifty-one and 103 variants are reported in SLC3A1 and SLC7A9 genes, respectively, in the public Human Genome Mutation Database accessed on October 2016  (Stenson et al. 2012). Some pathogenic variants are commonly found such as c. 808C>T -p. Arg270*  (Pras et al. 1995) , c. 1400T>C -p. Met467Thr  (Calonge et al. 1994) , c. 647C>T -p. Thr216Met in SLC3A1 gene, and c. 313G>A -p. Gly105Arg and c. 1445C>T -p. Pro482Leu in SLC7A9 gene  (Chillaron et al. 2010). Of note, large-scale rearrangements (deletions and duplications) represent a significant proportion of SLC3A1 and SLC7A9 mutated alleles in cystinuria patients ranging from 11%  (Bisceglia et al.
Indeed, among the 254 SLC3A1 and SLC7A9 variants listed in public Human Gene Mutation Database (HGMD â , 2017), 23 are considered as splicing mutations. The large majority of them are located in the most conserved intronic sequences of the 5 0 or 3 0 splice sites, that is, À2, À1/+1, +2 and are today systematically classified as pathogenic mutations. So far, if these specific dinucleotides are excluded, very few intronic or exonic SLC3A1 and SLC7A9 variants have been experimentally shown to induce an alteration of 5 0 or 3 0 splice sites  (Pras et al. 1998; Font-Llitjos et al. 2005; Barbosa et al. 2011)  or to be responsible for a modification of exonic splicing regulatory elements (ESR)  (Kummer et al. 2014). Molecular studies of cystinuria have been reported in patients from many countries in Europe except France.
PCR was terminated after a final cycle at 72°C for 5 min. Direct PCR product sequencing of SLC3A1 and SLC7A9 genes, was performed with an ABI prism Bigdye Terminator cycle Sequencing Ready Reaction Kit (PE Applied Biosystem/and ABI model 3130xl Genetic Analyser). Patients' genomic sequences comparison with the reference sequence was done by Variant Reporter software (Applied Biosystem).
The PCR consisted of 25 cycles of 94°C for 15 sec, 50°C for 15 sec, and 72°C for 15 sec, preceded by a 5-min initial denaturation step at 94°C and followed by a 5-min final extension at 72°C. One lL of the PCR product was resuspended in a mix containing 10 lL of deionized formamide, 0. 5 lL of GeneScan-500 Rox (PE Applied Biosystems, Foster City, CA, USA). After denaturation for 2 min 30 sec at 94°C, 2 lL of each sample was loaded on an Applied Biosystems model 3130xl Genetic Analyzer automated sequencer (PE Applied Biosystems). Data were analyzed using the Genescan software (PE Applied Biosystems,). Electropherograms were superimposed to those generated from a normal control DNA by adjusting the peaks obtained for the control amplicon and the heights of the corresponding peaks to the same level.
Allele frequency analysis The frequency in human population of the novel singlenucleotide variants identified in this study was evaluated using different databases (Exome Aggregation Consortium  (Lek et al. 2016) , Exome Sequencing Project (ESP)  (Auer et al. 2016) , the 1000 Genomes Project  (Auton et al. 2015) , and the dbSNP  (Kitts et al. 2013). Only the variants with frequency below 1% were considered for further analyses.
Bioinformatics predictions of splicing variants The in silico tool MaxEntScan  (Yeo and Burge 2004)  was used to predict variant-induced alterations of 3 0 and 5 0 splice sites. This algorithm was interrogated using the integrated software Alamut (Interactive Biosoftware, France). One newly developed bioinformatics approach was used to evaluate the potential impact of variants on exonic splicing regulatory elements (ESR). This method is based on the reported quantitative evaluation of all RNA hexamers as potential ESR  (Ke et al. 2011) , which allows the calculation of total ESRseq score changes (DtESRseq) for each variant, as previously described  (Di Giacomo et al. 2013; Soukarieh et al. 2016).
2010 ) (Table  S2 ). Hence, two missense variants in SLC7A9, c. 26G>A (p. Arg9Gln) and c. 397T>C (p. Ser133-Pro), were classified as variants of unknown significance because they were predicted nonpathogenic by all or by three out of four algorithms, respectively. For the synonymous and intronic variants located outside the most conserved sequences of splice sites, their functional impacts on splicing were assessed using splicing minigene assays. In addition, variants previously reported as splicing mutations without functional data were included in this analysis.
Indeed, SLC3A1 c. 1011G>A (p. =), SLC3A1 c. 1617+5G>A, and SLC7A9 c. 1399+3_+6del were predicted to alter reference 5 0 splice sites, whereas SLC7A9 c. 120G>A (p. =) could create a new 3 0 splice site. The impact on RNA splicing of these selected variants was then assessed using a functional minigene splicing assay (Fig. 1A ). In this assay, our results showed that all the processed RNA expressed from the wild-type SLC3A1 exon 5 minigene retained SLC3A1 exon 5, whereas the major spliced transcripts expressed from the mutant SLC3A1 c. 1011G>A minigene exhibited the skipping of exon 5 (Fig. 1B ). A second minor aberrant transcript, corresponding to the inclusion of SLC3A1exon 5 deleted for the last 21 bp, was also detected. These data are consistent with the bioinformatics predictions assessing the effect of SLC3A1 c.
The synonymous variant SLC7A9 c. 120G>A -p. (=), located in exon 3, was identified in patient P96, who also carries in trans a missense variant in this exon, c. 209C>T -p. (Ala70Val) (Table  S3 ). Both variants were then tested in the minigene splicing assay. This study revealed that the wild-type SLC7A9 exon 3 minigene generated two different transcripts: a major transcript corresponding to the normal inclusion of exon 3 and a minor transcript lacking this exon (Fig. 1E ). In contrast, minigene constructs carrying either the SLC7A9 c. 120G>A or c. 209C>T variants produced a major transcript without exon 3. These data were surprising because the bioinformatics predictions dedicated to splice site analysis suggested that the variant c. 120G>A would create a new 3 0 splice site whereas no effect was predicted for the variant c.
Clinical characterization A cohort of 112 patients from 99 families was characterized and a questionnaire addressing, age at first symptoms, stone recurrence rates, metabolic evaluations, and follow-up was completed. Sufficient data were provided for 109 patients belonging to 98 families (Table  4 ). The male/female ratio was 58 (53. 2%)/51 (46. 8%). Consanguinity was documented in 7/91 (7. 7%) families. Eight out of 109 patients did not develop any symptoms due to their young age (the oldest being 8 years old) and/ or to preventive therapy. Median age at first clinical event was 17 years (0 to 57) (mean 17 AE 2) and 44/101 patients (43. 6%) had less than 15 years at first symptoms. For males, median age at first symptoms was 17 years (0 to 57) (mean age 15 AE 3), whereas it was 17 years (2 to 40) (mean age 17 AE 3) for females (P = 0.
Interestingly, Dello Strologo et al. reported that type B female patients had higher urinary excretion of cystine while male patients had on average a higher stone event rate than female patients  (Dello Strologo et al. 2002). Six cases with an antenatally diagnosed hyperechogenic colon were included in this study. No specific genotype was associated to this very early manifestation of cystinuria. Indeed, the genotype was AA in 4/6 (P2, P6, P20, P23), AAB in 1/6 (P7), and BB in 1/6 (P77). Only one study reported molecular characterization of a case presented with hyperechogenic colon; a homozygous variant in SLC3A1 gene (SLC3A1 NM_000341. 3:c. 833T>Cp. (Phe278Ser) was identified  (Buxmann et al. 2014). The lack of genetic testing data impedes the correlation between the prenatal finding and the corresponding genotype.
The first group of splicing mutations alters directly the reference 5 0 splice sites resulting into total or partial exon skipping. It includes two intronic variants and one synonymous variant located at the last base of the exon. The effects on splice sites of these three variants were correctly predicted by the in silico tool MaxEntSan. These data confirm the good performance of this type of bioinformatics prediction, as previously shown, in particular for large datasets of BRCA1 and BRCA2 variants  (Houdayer et al. 2012). More surprisingly, the second group of splicing mutations identified in this study induces exon skipping without alteration of the splice sites. It included two synonymous and one missense variants, located at distance from the splice sites. Their effects on splicing are mediated through the potential modification of exonic splicing regulatory elements (ESR).
2016). A good correlation has been previously described between the DtESRseq values and the experimental minigene data obtained for two large sets of variants located either in BRCA2 exon 7  (Di Giacomo et al. 2013)  or in MLH1 exon 10  (Soukarieh et al. 2016). Interestingly, the three SLC7A9 variants which experimentally induced exon 3 skipping are associated with negative DtESRseq values (Table  3 ), predictive of ESE loss and/or ESS gain. These data confirm the predictive power of this new in silico approach for the detection of ESR mutations. Of note, in the patient P97, two sequence variants were identified in the SLC7A9 gene: one deleterious missense variant c. 1166C>T p. (Thr389Met) and one synonymous nucleotide change, c. 1032C>T p. (=), with unknown functional effect. Our in silico analysis revealed that this variant is associated with a negative DtESRseq value, predictive of a perturbation on splicing regulation.
Indeed, the splicing minigene data confirmed that this variant induces the complete skipping of exon 10 which would result into a pathogenic frame shift. It would be of importance to confirm all these splicing defects from patient RNA but these samples were not available. Altogether, these results suggest that the number of splicing mutations in SLC3A1 and SLC7A9 genes, especially those resulting into ESR alterations, might be currently underestimated. It has been estimated that approximately one third of all disease-causing mutations alter splicing  (Lim et al. 2011). Today, in diagnostic context, attention is given to intronic variants that could directly disturb reference splice sites. Moreover, special consideration should also be given to the potential splicing effects of exonic variants, whatever their predicted consequences at the protein level (missense, synonymous, or even nonsense).
Acknowledgments We thank the families for their participation and contribution to this research. The authors are grateful to H el ene Dranguet,  Carine Tardivel-Pilon  ( Rouen University Hospital ), and  Charlotte Grout  ( University of Rouen, France ) for their technical assistance. This work was supported by " Plan Maladie Rares ". This project was also supported by grants from the  "Association Nationale de la Recherche et de la Technologie" (ANRT)  in the context of public-private partnership between INSERM and  Interactive Biosoftware  ( CIFRE PhD fellowship  to H. T. ), the  OpenHealth Institute  (H. T. ), and the  "Groupement des Entreprises Franc ßaises dans la Lutte contre le Cancer" (Gefluc)  (A. M. ).