dataesr/scientific-paragraphs-categorization huggingface dataset:
Alternative exons regulated by SRSF1 were obtained from 4 GEO datasets (GSE26463, GSE52834, ENCSR094KBY and ENCSR066VOO) as described previously  (79). We modeled the proximity to a CTCF peak according to the different groups of exons using the glm function, with family = binomial ('logit') in R software (R Core Team (2018), R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna). A CTCF peak was considered as close to an exon if its center is located within an exon or within an interval of 1 base to 2 kb upstream or downstream of the exon. To test the differences between the groups of exons, a Tukey's test was used (with R, emmeans function from emmeans library). Density figures were created to represent the genomic distance of the nearest peak located from 1 bp to approximately 100 kb upstream or downstream of : (i) the first and last exons of genes with a 3 -end processing defect upon DDX5/DDX17 depletion, or all other genes; (ii) exons repressed or activated by DDX5/DDX17 and SRSF1 or all other internal exons.
The orientation of the nearest CTCF peaks upstream and downstream of human exons was computed using Gim-meMotifs version 0. 16. 0 (80) with a motif score cutoff of 0. 8 and reporting only the best match (options -c 0. 8 and -n 1). The MA0139. 1 CTCF motif file used in this analysis was downloaded from JASPAR (https://jaspar. genereg. net/matrix/MA0139. 1/)  (81). We could assign an orientation for 74. 4% of CTCF peaks. Then, we computed the proportion of convergent, divergent or tandem pairs of CTCF sites which flank looped exons as defined by ChIA-PET analyses (see the corresponding section for more details). For this analysis, only the 6 datasets corresponding to CTCF/Cohesin ChIA-PET were used (weight = 2), and we analysed the CTCF pairs corresponding to: (i) loops between the first and last exon of genes presenting a 3 end processing defect upon DDX5/DDX17 depletion, or their control loops; (ii) loops between a DDX5/DDX17-dependent internal exon and the first exon of its cognate gene, or their control loops (loops involving SRSF1-dependent exons or other internal exons).
Analysis of ChIA-PET data Processed ChIA-PET BED files were retrieved from the GEO database (Supplementary Table  S5  for the list of 20 datasets) and were used to determine which exons interact in pairs. To do so, the genomic coordinates of each exon were retrieved from the FasterDB database  (78)  and were modified to include a 200 bp window on each side of each exon. We used the BEDTools intersect tool  (82)  to determine which extended exon matches with a PET anchor. Then we defined a list of extended exons interacting with another exon for each dataset (a weight was attributed to each exon pair based on the number of PETs that report the association), and stored this information in a database. For subsequent analyses, exon pairs found in at least 3 ChIA-PET datasets with a weight of 2 (i.
e. 2 PETs supporting the interaction) were recovered from the ChIA-PET database. As small genes do not contain many reliable intragenic interactions, we only considered exons from genes of size ≥ 15 kb. To avoid considering self ligation PETs, only interactions between exons distant from at least 10 kb were kept for the statistical analysis. These parameters reduced the number of useable PET-tags, especially those connecting internal exons, but they make our analysis more significant. A logistic regression analysis was performed to test if the last exons of DDX5/DDX17-dependent genes (n = 473) or other last exons (control exons, n = 11 025) have a different proportion of interactions with the first exon of their corresponding gene. We used the same statistical methods and functions as those described above to analyse the proximity to CTCF binding sites.
Weblogo analysis To check if some motifs are enriched around the PAS, we analysed 400 nucleotides-long sequences centered on the 3 end of the last exon of genes presenting a 3 end processing defect upon DDX5/DDX17 depletion (n = 744), or all other genes (n = 16 046), using the MEME v5. 4. 1 program of the MEME suite (https://meme-suite. org/meme/)  (83). The following parameters were used: '-mod zoopsnmotifs 10 -minw 6 -maxw 20 -brief 200000 -dna -nostatus'. Then the SEA program of the MEME suite was used to check if the MA0139. 1 CTCF motif was enriched in those sequences. The same procedure was applied for 200 nucleotides-long sequences centered on the 3 SS (n = 2256) and the 5 SS (n = 2256) of internal exons regulated by DDX5/DDX17 or other internal exons (n = 181 294).
A meta-gene analysis showed an increase of RNAPII density downstream of the polyadenylation site (PAS) of genes whose termination is altered in absence of DDX5/DDX17 (Figure  1D ). Second, the quantification of metabolically labelled nascent transcripts also showed an increase in nascent transcription downstream of the annotated PAS (Supplementary Figure  S1E ). Third, the pull-down of NCKAP5L or SH3TC1 transcripts using antisense oligonucleotides targeting their last annotated exon specifically recovered the downstream region, indicative of a deficient cleavage of the pre-mRNA after the PAS (Figure  1E ). Altogether, these results showed that DDX5/DDX17 depletion impaired the 3 end processing of nascent transcripts and/or transcription termination of a large group of genes. Of note, the consensus sequence of the PAS of DDX5/DDX17-impacted genes matched the usual AAUAAA consensus although the score of the second position was slightly reduced compared to control genes (Supplementary Figure  S1F ).
We also looked for enriched motifs around the PAS of these exons, but apart from a higher frequency of G/C-rich sequences, that are also found around control exons, we did not identify a unique motif that would clearly differentiate them from unregulated exons (Supplementary Figure  S1G ). We also quantified splicing variations in our RNA-seq dataset and found important changes upon DDX5/DDX17 silencing, affecting nearly 20% of expressed genes (Figure  1F , Supplementary Table  S2 ). Focusing on simple cassette exons, we identified more than 2300 DDX5/DDX17dependent exons, a large majority of which were more skipped in absence of both helicases compared to the control condition (Figure  1F , Supplementary Table  S2 ), consistent with our previous studies  (60, 73). Analysing the expression of 226 splicing regulators did not reveal any significant impact of DDX5/DDX17 depletion, reducing the possibility of indirect effects (Supplementary Figure  S1I , Supplementary Table  S3 ).
Moreover, the consensus CTCF binding motif is enriched around the PAS and splice sites regulated by DDX5/DDX17 (Supplementary Figure  S2C ). Using public CTCF ChIP-seq datasets, we counted the CTCF binding sites localized within DDX5/DDX17regulated genes and found their average number was significantly higher than in other genes (Supplementary Figure  S2D ). We next built a statistical model to test the probability of finding one CTCF binding site overlapping with, or within 2 kb of, DDX5/DDX17-dependent exons. We found a strong enrichment of CTCF binding at the level of the last exon (in purple) of DDX5/DDX17-regulated genes, compared to unaffected genes (Figure  2A ). A highly significant enrichment was also observed 2 kb downstream, and to a lesser extent 2 kb upstream, of those terminal exons.
Frequent intragenic looping in DDX5/DDX17-regulated genes We next asked whether CTCF binding at different intragenic locations along DDX5/DDX17-regulated genes could reflect the existence of chromatin contacts between those specific sites. To address this question, we used existing ChIA-PET (Chromatin Interaction Analysis by Paired-End Tag sequencing) datasets, which identified the DNA fragments that are brought together in close spatial proximity by a given factor. In agreement with CTCF ChIP-seq data (Figures 2A and C), we found a significantly higher proportion of PET-tags connecting the first and last exon of genes whose 3 end processing was altered upon DDX5/DDX17 depletion than for unaffected genes (Figure  2D , left panel). Similarly, we observed a higher frequency of contacts be-tween the first and last exon within the group of genes containing a DDX5/DDX17-dependent alternative exon, and not in those containing a SRSF1-dependent exon (Figure  2D , middle panel).
This deletion reduced CTCF binding to background level specifically at this site (Figure  3E ). In normal culture conditions, these cells displayed a 1. 7fold increase in the amount of read-through SH3TC1 transcripts compared to the parental cell line, whereas other genes remained unaffected (Figure  3F ). This indicated that CTCF binding indeed contributes to correct 3 end processing and transcription termination. Yet, RNA helicases are still required for this process in the modified cell line, since the depletion of DDX5/DDX17 enhanced transcriptional read-through even further, to a significantly higher level compared to depleted parental cells (Figure  3G  and Supplementary Figure  S3B ).
In line with this, we found that a high proportion of transcripts presenting a 3 extension also present splicing alterations, suggesting a global perturbation of RNA processing of these transcripts, consistent with the recent demonstration that splicing defects are often associated with inefficient 3 end cleavage  (20). CTCF binds frequently to chromatin at or near DDX5/DDX17-dependent internal and terminal exons, and it co-regulates at least a subset of those exons. CTCF binding is promoted by DDX5/DDX17 and deleting a CTCF binding site as far as 1. 2 kb downstream of a PAS is sufficient to increase transcriptional read-through of the gene, which highlights the prominent role played by CTCF. In these conditions, DDX5/DDX17 are still required for 3 end processing and termination, indicating a complex relationship between these helicases and CTCF, as discussed also below.
Our results indicate that DDX5/DDX17-dependent exons, in particular those associated to CTCF binding, are often engaged in chromatin loops, which establishes a link between gene looping, alternative splicing and 3 end processing, in line with earlier reports  (51, 53, 54). However, we now provide the first direct demonstration that gene looping is instrumental for splicing and 3 end processing regulation at the RNA level. One remarkable finding is the high frequency of contacts between first and the last exons of genes whose 3 end processing and/or splicing is regulated by DDX5/DDX17, which suggests that this 3D conformation is a feature of DDX5/DDX17-dependent genes. Note that the analysed ChIA-PET datasets and our experiments were performed in different biological contexts, so the link between the specific 3D organization of genes and their regulation by DDX5/DDX17 may be underestimated.
ACKNOWLEDGEMENTS We thank  Dr Kevin C. Wang  ( Stanford University School of Medicine ) and  Dr Philippe Mangeot  ( CIRI, Lyon ) for the generous gift of plasmids. We are grateful to LBMC members for fruitful discussions and advice, especially  Dr Vincent Vanoosthuyse  for his critical reading of the manuscript. We acknowledge the contribution of AniRA lentivectors production facility from the  CELPHEDIA Infrastructure  and  SFR Biosciences (UMS3444/CNRS ,  US8/Inserm ,  ENS de Lyon, UCBL) , the  GenomEast sequencing platform of IGBMC (Illkirch, France)  for highthroughput sequencing, and the  PSMN  ( P ôle Scientifique de Modélisation Numérique)  of the  ENS-Lyon  for computing resources.
FUNDING Agence Nationale pour la Recherche  [ ANR-16-CE12-0009-01 ];  Fondation ARC  [ PGA120140200853 ];  Ligue contre le Cancer (Equipe labelisée, Comités du Rh ône et de la Loire) ;  Institut National du Cancer ;  AFM-Téléthon  and  Association Hubert Gouin 'Enfance et Cancer '; S. T. , J. V. , A. L. and L. B. A. received doctoral fellowships respectively from  AFM-Téléthon  (to S. T. and A. L. );  French Ministery of Research  (to J. V. );  Ligue contre le Cancer  (to J. V. and L. B. A. ); G. G. and E. C. were supported by the  Agence Nationale pour la Recherche ; J. B. C. was supported by  Fondation de France. Funding for open access charge:  CNRS.
9234FFigure 2. 2 Figure 2. (A)Relative distribution of CTCF binding sites at and around the first (F) and last exons of genes showing siDDX5/DDX17-induced defect in 3 end cleavage (purple) compared to unregulated genes (Ctrl, grey). (B) Relative distribution of CTCF binding sites at and around the exons that are skipped (red, top panel) or included (blue, bottom panel) upon DDX5/DDX17 depletion. Control sets of exons include exons skipped or included upon depletion of the SRSF1 splicing regulator, and all internal exons that are neither dependent on DDX5/DDX17, nor dependent on SRSF1 (Ctrl exons). (C) Relative distribution of CTCF binding sites at and around the first (F) and last exons of genes containing at least one internal DDX5/DDX17-dependent alternative exon (in red). (D) Frequency of intragenic looping in genes harboring DDX5/DDX17-dependent exons, based on ChIA-PET datasets (for CTCF, Cohesin and RNAPII).
Left panel: looping between the first and last exon of genes with siDDX5/DDX17-induced defect in 3 end cleavage (purple), compared to unregulated genes. Central panel: looping between the first and last exons of genes containing DDX5/DDX17-dependent alternative exons (red), SRSF1dependent exons or other exons. Right panel: looping betwen the internal exon regulated by DDX5/DDX17 or SRSF1 and the first exon of its cognate gene. (E) Orientation of the pairs of CTCF sites corresponding to chromatin loops analysed in D between the first and internal or last exons. Only exon pairs found in ChIA-PETs datasets using CTCF, SMC1 or RAD21 antibodies were selected for this analysis, with a weight 2 in at least one dataset. The different orientations of CTCF site pairs are depicted on the right. (F) Basal steady-state expression of transcripts of which 3 end cleavage is regulated (light purple) or not (grey) by DDX5/DDX17.
Introduction The QCD parton model has been used with great success to make predictions for experiments at SLAC, HERA, TeVatron, RHIC and LHC. However, the parton distribution functions necessary for this cannot be determined perturbatively and the current best option is to fit them in global QCD analyses. Especially in the case of nuclear PDFs, analyses including only deep inelastic scattering (DIS) and the Drell-Yan (DY) process data struggle to put strong constraints on the gluon, since these processes are not directly sensitive to gluons at leading order. To address this problem we investigate the impact of single inclusive hadron production data, which is dominated by gluon initiated subprocesses in the kinematic range of the available data.
Available data Table  1  lists the nuclear data sets included in this analysis. The data from STAR and PHENIX is taken at s N N = 200 GeV and the data from ALICE at 5. 02 TeV and 8. 16 TeV. Cutting the data below p T = 3 GeV leaves us with 77 (out of 174) ALICE and 32 (out of 77) RHIC data points. We exclude data of other light hadrons from the analysis due to their larger uncertainties, but the effect of including eta meson production is investigated in  [6].
PDF fits with SIH data The fits are performed with the same data and cuts as nCTEQ15(WZ) for the DIS, DY and WZ data, while adding the new SIH data with a cut at p T = 3 GeV. We use DSS fragmentation func-   shown as the ratio of theory over data for PHENIX  [7]  in the upper panel and ALICE  [8]  in the lower panel. The theory predictions are calculated using nCTEQ15 proton PDFs with DSS FFs. + p 0 + X i = pT, r = pT, f = pT i = pT, r = pT, f = pT 2 i = pT, r = pT 2 , f = pT i = pT, r = pT 2 , f = pT 2 i = pT 2 , r = pT, f = pT i = pT 2 , r = pT, f = pT 2 i = pT 2 , r = pT 2 , f = pT i = pT 2 , r = pT 2 , f = pT 2 ALICE data PHENIX data 10 0 10 1 PT [GeV].
Resulting PDFs Figure  2  shows the gluon PDFs in lead for the fits with SIH data and their respective baselines. In both cases the inclusion of SIH data raises the low-x gluon while causing a suppression in the medium-x region. This effect is less pronounced in the nCTEQ15WZ based fit since the WZ production data puts additional constraints on the gluon. Adding the DSS uncertainties to the systematic unceratinties of the data leads to a negligible shift in the fits' central values, but does increase the final uncertainty band slightly. Other flavors do not change much in terms of central values but the uncertainties in the nCTEQ15 based fits increase due to the newly opened strange quark parameters. A more detailed discussion of those flavors is provided in  [6].
Quality of the fits To investigate how well the fits describes the data we take a look at the resulting χ 2 /N do f values for each process. Table  2  shows these χ 2 /N do f values and the total for the two baseline  fits and the fits including the DSS uncertainties. We see that nCTEQ15 does not deliver a good description of the WZ data. The value of 1. 23 for SIH data does not look bad at first sight, but including the data sets in the fit shows a tremendous improvement to 0. 38 while also improving the description of the WZ production data. Similarly, adding only the WZ data to the nCTEQ15 fit yields a large improvement in the χ 2 /N do f for this process and some improvement for SIH data suggesting a similar impact on the gluon PDF. Finally, including both data sets allows a good description of all processes and the best total χ 2 /N do f = 0.
Conclusion New data on single inclusive hadron (SIH) production from ALICE and RHIC has been incorporated into our PDF analysis. We investigated the fragmentation functions uncertainties and choice of scales, and identified a p T region where reliable perturbative predictions can be made to help constrain the PDFs. The resulting impact is consistent with that of the colorless weak boson production data introduced in the nCTEQ15WZ analysis. Good χ 2 /N do f values are obtained for all data sets and a noticeable impact on the gluon PDF at low to medium x is observed despite the restrictive p T cut. The gluon distribution flattens out in the region x ≈ 0. 05 and the uncertainties are reduced in this region. The obtained nCTEQ15WZ+SIH PDFs for a selection of nuclei will be available through LHAPDF and others can be obtained upon request.
Funding information The work of P. D. , M. K. and K. K. was funded by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ) -project-id 273811115 -SFB 1225. L. A. H. , M. K. , K. K. and K. F. M. also acknowledge support of the DFG through the  Research Training Group GRK 2149. A. K. acknowledges the support of  Narodowe Centrum Nauki  under Grant No. 2019/34/E/ST2/00186. The work of T. J. was supported by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ) under grant  396021762 -TRR 257. F. O. acknowledges support through  US DOE  grant No. DE-SC0010129 , and the  National Science Foundation  under Grant No. NSF   PHY-1748958. The work of I. S. was supported by the  French CNRS  via the  IN2P3  project  GLUE@NLO.
Genetic data quality control and processing Genetic data in the discovery samples underwent standard quality control (QC; Plink v1. 9) and ancestry determination (SNPweights v2. 1; Fig. S1 )  [57]. Only non-Hispanic subjects of European ancestry (representing the vast majority of samples) were selected for processing. Data were restricted to those providing coverage of the rs439401 variant. Principal component analysis of genotyped SNPs provided principal components (PCs) capturing population substructure (PC-AiR, Fig. S2 )  [58]. Identity-by-descent (IBD) analyses reliably identified kinship down to 3rd degree relatedness (PC-Relate, Fig. S3 )  [58]. Sparse genetic relationship matrices (GRM) were constructed to enable analyses including related individuals  [59]. SNP array data were used to perform genotype imputation with regard to the TOPMed imputation reference panel  [60, 61].
Ascertainment of rs439401 The rs439401 variant was originally included in our analyses as it had a cross-cohort genotyping rate >80% in the discovery samples. Genotypes were considered from either the direct call on the SNP array data (i. e. , called from probe intensity data) or the call from WGS data. We specifically relied solely on directly genotyped data rather than using imputed data in order to obtain unbiased results. This choice was additionally motivated reasoning that putative rare haplotypes may not be accurately imputed, particularly when using the commonly younger (non-AD) individuals in imputation reference panels  [60, 62, 63]. Genotype reliability for the variant was verified by cross-correspondence across 3804 duplicate samples in the discovery and by assessing genotype intensity data on the SNP microarray in EADB.
Ascertainment of APOE genotypes Throughout, we will refer to APOE*2/3/4 genotypes as APOE genotypes. APOE genotypes were available from (1) cohort demographics (i. e. , "provided" APOE), which generally had APOE genotype status determined through various direct genotyping methods (detailed elsewhere  [33, 54] ), (2) directly from WES/WGS calls, or (3) through imputation of rs429358 (which captures the APOE*4 allele) and rs7412 (which captures the APOE*2 allele). It is relevant to note that rs429358 was never directly available on the SNP microarrays. It is further relevant to note that for the current WES data from ADSP, rs7412 was not available, with only rs429358 being reliably called in most subjects. The WES data could thus be used only to verify subjects with a provided APOE*3/3, 3/4, or 4/4 status (cf.
APOE genotype filtering criteria To our understanding, common criteria across prior studies regarding APOE genotypes can be summarized as giving priority to provided APOE genotypes when available (as direct genotyping methods are generally considered the gold standard), followed by using APOE genotypes derived from rs429358 and rs7412 when directly called on a SNP microarray, followed by inference of APOE genotypes through (high quality) imputation of rs429358 and rs7412. There is no clear consensus on whether or how any discrepancies across available APOE genotypes for a given subject should be adjudicated. Furthermore, with the recent increasing availability of WGS/ WES data in the AD field  [42, 46, 51] , these data can now also be used to verify APOE genotypes. When high-quality WGS/WES calls are available for rs429358 and rs7412 (i.
Robustness assessment: limitations to APOE filtering approach 1 After the initial analyses, we assessed the robustness of the primary discovery findings. This appeared particularly relevant considering the very low frequency of rs439401 carriers in APOE*4/4 controls in EADB versus other cohorts, suggesting potential biases in the controls across the cohorts. The concordance rate of rs439401 from duplicate samples across microarrays and WGS (99. 97%) supported genotype reliability (Table  S7 ). Similarly, the variant appeared confidently called from the EADB microarray intensity data (Fig. S4 ). Overall, we concluded there were no specific genotyping issues for rs439401. Another important consideration is that some error rate is expected for the different direct APOE genotyping methods used across cohorts.