dataesr/scientific-paragraphs-categorization huggingface dataset:
rockefeller. edu/BPHunter and https://github. com/casanova-lab/BPHunter. branchpoint j splicing j intronic variant j disease genetics j software Pre-mRNA (messenger RNA) splicing is a necessary step for protein-coding gene expression in eukaryotic cells. The biochemical process is initiated by the recognition of the branchpoint (BP) in an intron, followed by the identification and ligation of the 5 0 splice site (5 0 ss = donor site) and 3 0 splice site (3 0 ss = acceptor site) to join two exons and the removal of the intervening intron as a circular lariat, to yield a mature mRNA of coding sequence for protein translation (Fig. 1A ). Splicing takes place in the nucleus and is regulated by an array of cis-acting elements (the RNA sequences with their splicing codes) and trans-acting elements (proteins and small nuclear RNAs (snRNAs) that bind to the cis-acting elements), which together constitute a garden around the splice sites promoting the formation of the complex and dynamic spliceosome  (1) (2) (3).
Significance The search for candidate variants underlying human disease in massive parallel sequencing data typically focuses on coding regions and essential splice sites, mostly ignoring noncoding variants. The RNA spliceosome recognizes intronic branchpoint (BP) motifs at the beginning of splicing and operates mostly within introns to define the exon-intron boundaries; however, BP variants have been paid little attention. We established a comprehensive genome-wide database and knowledgebase of BP and developed BPHunter for systematic and informative genome-wide detection of intronic variants that may disrupt BP and splicing, together with an effective strategy for prioritizing BP variant candidates. BPHunter not only constitutes an important resource for understanding BP, but should also drive discovery of BP variants in human genetic diseases and traits.
Since lariats are transient and only exist at low abundance, their detection is technically difficult, hence large-scale data are required for identifying BP positions. Spliceosome iCLIP experimentation identifies cross-links of spliceosomal factors on pre-mRNA sequences at nucleotide-level resolution to pinpoint BP positions  (18). Some machine-learning models have also been developed to predict BP positions based on one or two experimentally identified BP datasets  (19) (20) (21). However, these individual BP studies have yet to be integrated into a comprehensive database and knowledgebase of human BP. The functional role of splicing makes it a critical process for the correct assembly of gene products, while its complexity renders it vulnerable to deleterious variants that can perturb any part of the splicing machinery.
Data from RNA-seq or other studies of mRNA occasionally lead to the detection of intronic or exonic variants that disrupt splicing  (27). However, the spliceosome operates mostly within intronic regions to define the exon-intron boundaries and hence the coding sequences. It follows that introns probably harbor a substantially larger number of pathogenic variants than has so far been appreciated  (28). The BP, involved in the initiation of splicing through its interactions with spliceosome elements, constitutes a key vulnerability of splicing by virtue of its potential variants. Employing the Human Gene Mutation Database (HGMD) Professional version  (25)  in concert with an in-depth literature review, we identified 48 BP variants reported in 43 genes underlying 33 human diseases whose pathogenicity has been supported by accompanying experimental evidence.
Results Collection of Experimentally Identified BP Data. We first collated five datasets of experimentally identified BP (eBP), based on RNA-seq and iCLIP data: eBP_Mercer (59,359 BP from total Fig. 1. Schematics of a splicing event, the two types of spliceosome, and typical consequences of a BP variant. (A) Schematic of the biological processes of transcription, splicing, and translation from DNA to pre-mRNA to mature mRNA and protein. In pre-mRNA splicing, the BP is first recognized, two exons are then joined, and the intervening intron is finally released as a circular lariat. (B) Schematic of the major (U2-type, on the left) and minor (U12-type, on the right) spliceosomes, with an illustration of the interaction between pre-mRNA sequence and U2/U12 snRNA. (C) Schematic of the potential molecular consequences of a BP variant, including complete/ partial exon skipping and complete/partial intron retention.
The percentage in parentheses refer to the observed fraction of each category from the published pathogenic BP variants (note that one variant could result in more than one missplicing consequence). RNA-seq) (5), eBP_Taggart  (27, 795  BP from total RNA-seq)  (15) , eBP_Pineda (138,313 BP from total RNA-seq)  (16) , eBP_Talhouarne (240 BP from cytoplasmic RNA-seq)  (17) , and eBP_Briese (43,630 BP from iCLIP) (18) (Table  1 ). In addition to the published BP datasets, we revisited our RNA-seq data derived from DBR1-mutated patients with brainstem viral infection  (30). DBR1 encodes the only known lariat debranching enzyme, and the patients with DBR1 variants, who were characterized by a residual DBR1 activity of 3 to 10%, have been reported to exhibit an elevated cellular level of lariats  (30).
We designed a computational program to detect BP positions from RNA-seq reads (Methods), which identified 8,682 BP sites (eBP_BPHunter) from 15 RNA-seq samples from the fibroblasts of three DBR1-mutated patients (SI Appendix, Fig. S2A  and Table  S1 ). In these six eBP datasets, the most abundant BP nucleotides were eBP_Mercer (79% A), eBP_Taggart (68% A), eBP_Pineda (75% A), eBP_Talhouarne (84% C), eBP_Briese (100% A), and eBP_BPHunter (46% A) (Table  1 ). This showed that adenine-BP were the most frequent in lariats from total RNAseq, whereas cytosine-BP were the most frequent in the stable lariats in cytoplasm albeit in very low amounts  (17). DBR1mutated patients had 22% stable cytosine-BP. The iCLIPidentified BP were invariably adenine. In total, we obtained 210,986 unique eBP. Collection of Computationally Predicted BP Data.
We also collated three datasets of computationally predicted BP (cBP): cBP_BPP (232,828 BP)  (19) , cBP_Branchpointer (352,322 BP)  (20) , and cBP_LaBranchoR (203,914 BP) (21) (Table  1 ). These studies trained machine-learning models based on one/ two eBP datasets and performed predictions in the region [À21, À34], [À18, À44] and [À1, À70] nt of 3 0 ss respectively. These cBP datasets supplemented the BP positions that were not detected from experimental data and hence increased the BP coverage across the human genome. These computational methods overlapped the predictions in [À21, À34] nt of 3 0 ss, with less attention being paid to in the region closer to 3 0 ss, which is generally considered to comprise polypyrimidine tracts (PPT). However, the use of a hard distance cutoff for PPT or the use of an algorithm to estimate PPT to skip BP prediction could miss potentially genuine BP sites.
In these four cBP datasets, the most abundant BP nucleotides were cBP_BPP (99% A), cBP_Branchpointer (99% A), cBP_LaBranchoR (98% A), and cBP_BPHunter (100% A) (Table  1 ), showing that the prediction models favored adenine-BP. In total, we obtained 479,366 unique cBP. Please note that cBP does not denote cytosine-BP. When we mention the nucleotides of BP, we fully spell out the nucleotide (e. g. , adenine-BP, cytosine-BP). Consensus-Guided Positional Adjustment of BP and Integration of BP Datasets. We tested the established BP consensus sequence YTNAY (4) in eBP but found only 23. 5% eBP matched this pattern precisely. Considering the noncanonical 2 0 -to-5 0 linkage between 5 0 ss and BP, and the possibility of mutations being introduced by reverse transcriptase when traversing the 5 0 ss-BP junction in RNA-seq  (5, 15) , we anticipated that a number of eBP may have been mislocated in the raw dataset (SI Appendix, Fig.
S2B ), which could also have led to some mislocated cBP predictions. We therefore screened a window of [À2, +2] nt from each BP position for consensus sequence matching and adjusted the raw BP position to its closest neighbor that perfectly matched YTNAY (SI Appendix, Fig. S2C ). As a result, about 6% of raw BP positions were adjusted (SI Appendix, Table  S2 ), and multiple neighboring raw BP positions were merged into a single adjusted position, yielding 198,256 eBP (Δ = À12,730) and 451,215 cBP (Δ = À28,151) after adjustment (Table  1 ). By integrating eBP and cBP, we assembled a comprehensive collection of 546,559 BP in the human genome, with 102,912 mutually shared BP (mBP) (Fig. 2A , Table  1 , SI Appendix, Fig. S3  and Table  S3 , and Dataset S1). This BP database contained 90. 4% adenine-BP, 4.
1B ), it is important to distinguish intron types when studying BP and splicing. Major introns were dominated by GT-AG (98. 9%) as the 5 0 ss-3 0 ss terminal dinucleotides, whereas 71% and 24% of minor introns were GT-AG and AT-AC, respectively. We mapped 546,559 BP onto 200,059 introns, retained the BP within [À3, À100] nt of 3 0 ss (3 0 -proximal introns), and finally obtained 386,209 BP associated with 200,059 introns (Fig. 2B  and Datasets S2 and S3). We excluded the distal BP in this study, as more complicated splicing mechanisms may occur (SI Appendix, Fig. S1 ). Among the intron-associated BP, 57% of them were located within a 10-nt window [À21, À30] nt of 3 0 ss, and 88% were located within a 25-nt window [À15, À40] nt of 3 0 ss (Fig. 2C ); 32%, 41%, and 18% of 3 0 -proximal introns had one, two, and three BP mapped, respectively, while 9% of 3 0 -proximal introns harbored more than three BP (Fig.
We further estimated the binding energy between BP and snRNA and found that BP in major introns displayed weaker binding with U2-snRNA than that between BP and U12-snRNA in minor introns (Fig. 3C  and SI Appendix, Table  S5 ). This may imply that the BP-U2 interaction in the major spliceosome can be complemented by other spliceosomal elements (e. g. , SF3B1, SF3B6, PHF5A, SFA2, SFA3)  (6) , and hence a weaker wobble BP-U2 base pairing could be acceptable. Since the minor spliceosome forms a U11-U12 di-snRNA complex to bind to 5 0 ss and BP simultaneously, it requires more perfect BP-U12 base pairing, which is also reflected in more conserved 5 0 ss and BP motifs in minor introns (Fig. 3A ). Population Variants in BP. We obtained human population variants from the gnomAD database r3. 1. 1  (32) , with which to compare the occurrence of population variants in the BP region, essential/flanking splice sites, and intronic/exonic background.
g. , effective population size, demography)  (38). Pathogenic BP Variants. Variants in BP may lead to reduced, altered, or abolished binding to spliceosome elements during the initiation of splicing, leading to the disruption of splicing and giving rise to misspliced gene products with the consequent loss of expression/function (Fig. 1C ). Through the HGMD database and primary literature, we identified 48 pathogenic BP variants (42 single-nucleotide variants [SNVs] and 6 microdeletions) reported in 43 human genes underlying 33 different disorders that had been experimentally confirmed (Table  2  and SI Appendix, Table  S8 ), from the first report of a BP variant in the L1CAM gene causing X-linked hydrocephalus in 1992  (29)  to a recent study of BP variants in hereditary cancer genes in 2021  (39).
Thus, neither the advent of MPS technology (since 2010) nor the publication of large-scale human BP datasets (since 2015) has resulted in a major increase in the identification of pathogenic BP variants. In fact, paradoxically, the proportion of pathogenic BP variants among all pathogenic variants discovered since the 1990s has steadily decreased. The detection efficiency of pathogenic BP variants is rather low when compared with other types of variant. Therefore, there is an urgent need to bridge BP data with MPS data, thereby extending the utility of the MPS data, making more practical use of the BP data and contributing to pathogenetic discovery. BPHunter: Detection of Intronic Variants That Disrupt the BP. Variants that affect BP recognition may be individually rare, but their impact on splicing, gene expression, and protein function can be highly deleterious.
Therefore, BPHunter constitutes a computational method capable of detecting candidate BP-disrupting variants and a platform for collating the negative results from BP genetics studies that should not be wasted. Validation of BPHunter on the Reported Pathogenic BP Variants. We deployed BPHunter retrospectively on the 48 reported pathogenic BP variants and successfully captured 40 of them (Fig. 4A , Table  2 , SI Appendix, Fig. S17 , and Dataset S4). There were 4 deletions (8/17/20/22 nt) that removed the entire BP motif, 2 singlenucleotide deletions that removed one BP site and one BPÀ2 site, respectively, 22 SNVs that disrupted BP sites, 11 SNVs that disrupted BPÀ2 sites, and 1 SNV that disrupted a BPÀ1 site (Fig. 4B ). In 24 cases, the variants disrupted the only BP in their 3 0proximal introns, in 9 cases, the variants disrupted the first BP, and in 7 cases, the variants disrupted the nonfirst BP (Fig.
" if no variant found) • conservation scores GERP and PhyloP at BP and BPÀ2 positions • intron type (major, minor), and intron length • transcript ID and intron number (e. g. , ENST123456789_ IVS10) • BPHunter score (SI Appendix, Fig. S20 ) In addition, the BPHunter webserver provides a negative BP reporting platform, which requires the submitters to provide variant information, cell type, assay type, and experimental evidence following the template (Dataset S6). located very close ([À9, À17] nt) to 3 0 ss and 6 of which were published before 2004 (Fig. 4A ). The eight introns harboring these variants all have BP documented in the BPHunter, but none of these pathogenic variants disrupted the BP. Interestingly, by extracting and aligning their wild-type (wt) and mutated (mt) sequences using SeqTailor (43), we found that all of them had created an AG-dinucleotide in the region between BP and 3 0 ss (SI Appendix, Fig.
BPHunter therefore successfully detected all 40 published pathogenic variants that truly disrupted BP. When these 40 variants were tested with existing cBP datasets, we found that 36, 30, and 37 can be detected using BPP, Branchpointer, and LaBranchoR, respectively. This confirms that the detection output from BPHunter is an inclusive outcome that outperforms the use of any single BP dataset. Comparison of the Pathogenic BP Variants with Common BP Variants in the General Population. Focusing on SNVs at BP and BPÀ2 positions, we compared 33 pathogenic variants (22 variants on BP and 11 variants on BPÀ2) against 659 common population variants (386 variants on BP and 273 variants on BPÀ2, with MAF ≥1%) (SI Appendix, Fig. S19A ). We found that common variants occurred significantly less frequently in 3 0 -proximal introns with single BP (6%) versus pathogenic variants (58%, P < 0.
98e-6, exhibited a GERP of À0. 257 and a PhyloP of 0. 152, and was supported by five data sources. These features satisfied our prioritization strategy requirements for a promising candidate BP variant, and it had a BPHunter score = 6, even though the missplicing/deleteriousness predictions were not suggestive of its pathogenicity (SpliceAI = 0, MMSplice = À0. 4, CADD = 10. 1). We therefore hypothesized that this intronic variant might disrupt BP recognition, leading to missplicing of STAT2 transcripts. To assess its impact on mRNA splicing, we performed an exon trapping assay. Compared to the wild-type control, mRNA extracted from cells transfected with this variant displayed a majority of abnormally spliced STAT2 transcripts: 6% normal transcripts in the mutant vs. 70% in the control, 82% of complete exon-6 skipping in the mutant vs.
We studied RNA-seq data of this tumor sample derived from the patient, as well as another three tumor biopsies from random DLBCL patients who do not carry this deletion, and observed complete retention of intron-3 specifically in this patient (Fig. 6  B and C). Hence, we concluded that this intronic deletion, which would have been filtered out in the absence of BPHunter, resulted in aberrant splicing of ITPKB, which consequently contributed to the lymphomagenesis in this patient. This case study successfully demonstrated the use of BPHunter for the detection and exploration of somatic variants. In addition, we screened the noncoding somatic variants in COSMIC (56), focused on 1,148 genes known to be associated with cancer formation and progression (Methods), and identified 129 candidate variants that may disrupt BP (106 SNVs and 23 deletions, in 101 genes, from 121 patients involving 19 cancer types) (Dataset S5).
g. , missense, stop-gain, essential splice site) to guide investigators in assessing their pathogenicity. With BPHunter, we are now able to detect intronic variants that may disrupt BP recognition, and hence splicing and gene expression/function, in a systematic, efficient, and informative manner. It provides an opportunity for medical genetics studies to explore patients' MPS data more deeply, by identifying candidate BP variants in known disease-associated genes, or alternatively in new and functionally promising candidate genes. It also provides an opportunity to systematically investigate the biomolecular effect of BP variants, at least in some genes, particularly in those 3 0proximal introns containing multiple BP. Such studies will delineate the intron-specific molecular associations between BP and splicing.
ACKNOWLEDGMENTS. We thank  Y. Nemirovskaya ,  M. Woollett ,  L. Lorenzo ,  D. Liu , and  E. Williams  for administrative support;  A. Cobat ,  E. Jouanguy , and  S. Boisson-Dupuis  for assistance; and  Z. Yang  and  Y. Nemirovskaya  for artwork design. The  Laboratory of Human Genetics of Infectious Diseases  is supported by the  Howard Hughes Medical Institute , the  Rockefeller University , the  St. Giles Foundation , the  NIH  ( R01AI088364 ), the  National Center for Advancing Translational Sciences (NCATS) ,  NIH Clinical and Translational Science Award (CTSA) program  ( UL1 TR001866 ), the  Yale Center for Mendelian Genomics  and the  GSP Coordinating Center  funded by the  National Human Genome Research Institute (NHGRI)  ( UM1HG006504  and  U24HG008956 ), the  French National Research Agency (ANR)  under the  "Investments for the Future" program  ( ANR-10-IAHU-01 ),  ANR  grants ( ANR-14-CE14-0008-01 ,  ANR-18-CE15-0020-02 , and  ANR-20-CO11-0001 ), the  Integrative Biology of Emerging Infectious Diseases Laboratory of Excellence  ( ANR-10-LABX-62-IBEID ), the  French Foundation for Medical Research (FRM)  ( EQU201903007798 ), a  Fast Grant  from  Emergent Ventures ,  Mercatus Center at George Mason University , the  Fisher Center for Alzheimer's Research Foundation , the  Meyer Foundation , the  JPB Foundation , the  FRM  and  ANR   GENCOVID  project ( ANR-20-COVI-0003 ),  ANRS Nord-Sud  ( ANRS-COV05 ), the  Square Foundation, Grandir-Fonds de solidarit e pour l'enfance , the  SCOR Corporate Foundation for Science, The Foundation du Souffle, Institut National de la Sant e et de la Recherche M edicale (INSERM)  and the  Paris Cit e University.
Methods Collection of Eight Published BP Datasets, Creation of Two New BP Datasets, and Consensus-Guided Positional Adjustment of BP. We collected five datasets of experimentally identified BP (5, 15-18) and three datasets of computationally predicted BP  (19) (20) (21). We created eBP_BP-Hunter, by analyzing RNA-seq data of three DBR1-mutated patients  (30). We created cBP_BPHunter, by developing a high-precision machine-learning model to scan the region [À3, À40] nt of 3 0 ss. We screened a window of [À2, +2] nt from each raw BP position for consensus sequence matching and adjusted the position to its closest neighbor that perfectly matched the consensus. Human Population Variants. We obtained human genetic variants from the gnomAD database (32) v3. 1, which contained 76,156 WGS samples.
6101204T > A À20 von Willebrand disease Complete skipping of exon-38 #1/2 0 XPC g. 14209889A > T À9 Xeroderma pigmentosum Complete skipping of exon-4 N. D. XPC g. 14209904T > C À24 Xeroderma pigmentosum Complete skipping of exon-4 #1/1 0 All references are available in SI Appendix, TableS8. Full information on these variants with BPHunter's annotations are available in Dataset S4. (The genomic positions are on the hg19/ GRCh37 human genome assembly. Hit position: BP positions disrupted by the variant. N. D. : not detected. ).
7 cm and 10 cm depending on the imaging range. The sensor line has a field of view of 14°, which requires two scans to cover the entire height of the outcrops and a section of overlap between the images. Two spectral calibration targets (50% and 99% reflectance) were placed on the outcrops and scanned within the images for later spectral correction  (Kurz et al. , 2013). Validation data from ground rock sampling were acquired as described below.
Registered hyperspectral images were textured as additional layers in the photogrammetric models (Fig. 3 ) and visualized using the LIME software  (Buckley et al. , 2019). As an additional step in the 2-D-3-D data integration, the RGB information from the digital outcrop models was back-projected into 2-D images with the same dimensions and pixel size as the hyperspectral image  (Denk et al. , 2015; Fig. 3D) , which provided additional spectral information outside of the SWIR. The RGB bands of the Canon EOS 6D camera are centered at ca. 470 nm (blue), ca. 525 nm (green), and ca. 600 nm (red) and are much wider than the SWIR hyperspectral bands (typically >100 nm). RGB bands were combined with the SWIR data during spectral mapping, which resulted in a wider spectral range for analysis and visualization  (Denk et al.
Plug Sampling and Laboratory Sample Analysis Rock samples (plugs) were drilled at seven accessible areas of the outcrop to provide independent mineral calibration of the HySpex imagery. Figure  6  shows the sample locations within the photogrammetric outcrop model. Note that samples were collected as part of a wider geological field campaign across the Barranco del Mortero area; original sample numbering is retained to keep the link to the acquired data set. To address the scale of the hyperspectral imagery (based on GSD) and localized geological heterogeneity, each location was sampled within a 30 × 30 cm square with four plugs in the corners and one in the center (Fig. 7 ) for a total of 35 plugs. Sample plugs had a diameter of 4 cm and a length between 2 cm and 7 cm. For subsequent analyses, each plug was divided into three zones, where Zone A (outcrop surface) was used for laboratory spectrometry for characterizing the weathered and fresh sample surfaces, Zone B was used for thin section preparation, and Zone C for laboratory bulk mineral analyses (grain density and XRD; Fig.
ACKNOWLEDGMENTS This research was funded by  TotalEnergies SE. We thank  TotalEnergies' laboratory at CSTJF Research and Scientific Centre and Panterra Geoconsultants (Pau, France) , and especially  Sergio Fernandez , for providing laboratory services. Benjamin Dolva  processed ground-based photogrammetric 3-D models, and  Nicole Naumann  is thanked for several discussions on the hyperspectral processing. Kari Ringdal ,  Joris Vanbiervliet , and  Bowei Tong  are thanked for their work on the LIME software, which is also supported by the  Research Council of Norway  ( SkatteFUNN  project # 266740 ). The authors are grateful to the geo-tourism park of Oliete for support during fieldwork.