dataesr/scientific-paragraphs-categorization huggingface dataset:
As a human endeavour, manual literature curation is imperfect, prone to errors in interpreting published experimental results or in choosing applicable ontology terms. In particular, the language used in publications is often less specific, or more prone to multiple interpretations, than the precisely defined ontology terms used in annotations. Furthermore, because manually curated annotations are widely propagated to support computed annotations, any inaccuracy in core manual annotation risks being transferred and amplified. Efficient ways to identify and correct errors are therefore highly valuable. GO and model organism database (MOD) curators have developed a set of best practices to guide manual annotation, encompassing recommendations for interpreting various experimental results, selecting appropriate GO terms, applying evidence, and using annotation extensions  [8, 18, 19].
Once created, annotations are subject to a series of automated quality control (QC) checks that flag errors for correction, such as incorrect term-evidence combinations, missing metadata or file format problems  [3]. Nevertheless, there is still ample scope for additional QC measures to improve the accuracy of the GO annotation corpus. Accordingly, we have developed a novel approach to annotation QC based on our observations of patterns of co-occurrence of different biological process terms used to annotate the same genes. In biology, each gene may be involved in a wide variety of processes, and some have multiple functions; these are represented in GO as multiple annotations for a single gene. Owing to spatial, functional or temporal constraints, however, certain combinations of functions or processes are not likely to be carried out by the same genes.
Term Matrix annotation query tool For each pair of GO terms analysed, annotations were retrieved from the GO database by querying for gene products annotated to 'Term1 AND Term2' directly or by transitivity (i. e. inferred over transitive relations in the ontology; by default, is_a and part_of are included). We developed a new tool, Term Matrix, which queries all pairwise combinations of a specified set of GO terms. Users can filter annotations by organism or annotated entity type (gene, protein, ncRNA, etc. ) and can opt to include or exclude the regulates relations (regulates, positively_regulates, negatively_regulates) when traversing the GO graph to retrieve annotations inferred by transitivity. Results are displayed in a grid-based view (the 'matrix') that shows the number of gene products annotated to each pair of GO terms.
Clicking the annotation count retrieves the annotation details for manual inspection. Term Matrix uses the JavaScript D3 library. The code is released under the BSD 3-Clause 'New' or 'Revised' License, the same as the parent AmiGO application  [20]. The tool works by querying the AmiGO Solr index, which uses precomputed graph closures that enable fast calculation of intersection counts for any term pair. Term Matrix is available directly  [21] , and accessible from the GO tools menu on the GO website  [22].
Rule generation for annotation validation Co-annotation QC rules were generated using the annotations retrieved in Term Matrix for the cross-species case study described above. After the correction of annotation errors, term pairs with no annotated gene products in common (mutually exclusive processes) were used to establish a set of rules capturing 'Term1 is not usually coannotated with Term2' statements. Rules are expressed in a simple tab-delimited text format, as described in table  1. The set of rules that have been incorporated into GO's annotation validation pipeline  [3]  is available at GO's GitHub site  [26] , which also includes the runner code and additional tests and documentation  [27]. Reports for the currently deployed GO release are available from 9 August 2018 onwards.
We observed that the number of annotated genes shared by GO term pairs reflected biology: whereas large intersections between gene sets such as those annotated to 'transcription' (GO:0006351) and 'chromatin organization' (GO:0006325) are readily explained, biologically unrelated processes such as 'tRNA metabolic process' (GO:0006399) and 'protein folding' (GO:0006457) tended to yield few or no shared genes. Figure  1  illustrates the scale of annotation changes over time using annotation matrix 'snapshots' based on data from 2012 and 2020 for 21 of the term pairs studied (before 2012, individual annotation error corrections were not systematically recorded). Individual annotation corrections derived from this analysis since 2012 are included in the electronic supplementary material, table  S1.
From these phenotypes, SLBP was erroneously annotated to terms 'DNA replication' (GO:0006260) and 'cell cycle phase transition' (GO:0044770), despite playing no role in either process in a normally functioning cell. Indirect effects are frequently seen, and most at risk of misinterpretation, in high-throughput datasets where candidate genes may be annotated without data from follow-up validation experiments. (2) Term interpretation and usage: errors in manual curation can arise from misinterpretation of experimental results or the meaning of a GO term. For example, we found 12 examples of genes annotated to 'transmembrane transport' (GO:0055085) where 'nucleocytoplasmic transport' (GO:0006913) would instead be correct, because during nucleocytoplasmic transport the lipid bilayer is not traversed.
(5) Advances in biology: older findings can be supplanted by new knowledge, especially paradigm shifts in biology. For example, the Elongator complex was long thought to act as a histone acetyltransferase (GO:0004402), based on assays that have since been shown to be artefacts (e. g. [33] ). Instead, the Elongator complex is actually involved in tRNA modification (GO:0006400), and all observed phenotypes can be attributed to this role  [34, 35]. Annotations need to be adjusted to reflect this new knowledge. For errors affecting automated annotations based on mappings (InterPro and SPKW), we report two counts. One represents errors found in the entire set of 800 million annotations provided by the Gene Ontology Annotation (GOA) project at EBI (hereafter the 'Gene Ontology Consortium (GOC) database').
Cohesin complex We next conducted two case studies to investigate whether the use of co-annotation analysis for annotation QC would hold for species other than the well-annotated fission yeast. In the first, we examined co-annotations to the GO CC term 'cohesin complex' (GO:0008278) with each of 35 selected GO BP terms. Erroneous annotations fell into the same categories identified for fission yeast annotations. Figure  2  shows co-annotation counts before and after corrections, and a breakdown of annotation errors by type and database. Across multiple MODs plus UniProt, 35 experimental annotations were deleted (listed in the electronic supplementary material, table  S2 ). Finally, one InterPro2GO mapping affecting over 7000 computationally inferred annotations was removed (see electronic supplementary material, table  S3 ).
Gene Ontology biological process subset In the second cross-species study, we narrowed the number of species to six (fission yeast, budding yeast, worm, fly, mouse and human; see Methods), but broadened GO coverage to pairwise combinations of five core cellular level biological processes ('amino acid metabolism' (GO:0006520), 'cytoplasmic translation' (GO:0002181), 'ribosome biogenesis' (GO:0042254), 'tRNA metabolism' (GO:0006399) and 'DNA replication' (GO:0006260)) against a set of 40 core cellular level biological process GO terms (electronic supplementary material, table  S4 ). As a result, 182 manual annotations were corrected or removed directly. Correcting 19 ontology paths affected over 45 000 annotations in the GOC database and over two million annotations in the GOA database. Fifty-four InterPro2GO mappings were corrected, affecting over 5900 annotations in the GOC database and over 380 000 inferred annotations across all species in the GOA dataset (calculated from family size in Inter-Pro version 77).
Annotation outliers, defined as intersecting sets with low numbers of annotated gene products, are critically inspected for validity. Annotation errors are identified and corrected, usually by assessing the original experimental data. Table  2  summarizes the corrections we made to manual annotations, mappings and ontology paths as part of establishing the Matrix QC workflow. Annotation intersections which yield empty sets can royalsocietypublishing. org/journal/rsob Open Biol. 10: 200149 be used to generate co-annotation QC rules of the form 'genes annotated to process A are not usually annotated to process B'. (2) New and existing annotations that violate annotation co-annotation QC rules are reported to contributing databases via standard GO Consortium QC pipelines. (3) Upon reviewing reported errors, the contributing database may either make corrections or provide evidence that validates annotations.
Propagating error correction Errors in ontology relationships and in mappings between ontology terms and other classification systems such as Inter-Pro can introduce systematic errors in annotation datasets, as royalsocietypublishing. org/journal/rsob Open Biol. 10: 200149 often every gene product annotated to a misplaced term, or associated with a particular domain or keyword, is affected. Systematic errors may also originate from experimentally supported annotations produced by curators, because these manually curated data are then used to develop mappings between GO and protein families, and in phylogenetic propagation. For example, experimental annotations assigned by MOD and UniProt curators are used to establish InterPro2GO mappings, and routinely propagated to orthologues across thousands of species by Ensembl Compara pipelines  [37]  and PAINT.
Furthermore, widely propagated annotation errors affect common uses of GO data. For example, it has been demonstrated that new annotations can produce more informative results in enrichment analyses  [12, 38, 39] , so we expect that the removal of many incorrect annotations to a given term will similarly improve enrichments. Correcting widely propagated annotations thus results in improvements throughout the GO annotation corpus-affecting hundreds or even thousands of annotations-and correcting ontology errors also benefits analyses that use annotation datasets.
Acknowledgements. We thank  Peter D'Eustachio  for Reactome updates and the  InterPro group  for InterPro2GO mapping updates. We thank  Nomi Harris  for constructive comments on the manuscript. We also thank the many biocurators, editors and other members of the  GO Consortium  who have contributed to GO annotations and to the development of the Gene Ontology, and PomBase principal investigator  Stephen G. Oliver  for ongoing guidance and support of all PomBase activities.
We validate a subset of splicing events by RT-PCR (Fig. 1c ). To test whether Tax-induced splicing modifications occurred in vivo, we took advantage of a publicly available RNA-seq dataset (EGAS00001001296  24  ) from three healthy control donors, three asymptomatic carriers (AC), and 55 ATLL patients (Supplementary Data 1). Although lowly expressed, Tax mRNA was detected in the vast majority of infected samples (Supplementary Fig. 1 ). It is important to note that ATL can sporadically express Tax in bursts  25, 26  ; thereby explaining, at least in part, their Tax-like features even though Tax is poorly detected in ATL samples  24. Based on this, we examined the percent spliced-in (PSI) values of exons expressed in samples from donors, carriers, and patients with ATLL (Supplementary Data 1).
3d ). Functional insight of Tax and DDX17-dependent spliced genes. Gene ontology (GO) analysis of quantitatively altered genes revealed several signaling pathways that are well described in Tax-expressing cells, including NF-κB, TNF, and MAPK signaling (Supplementary Fig. 3C )  11, 35. In contrast, genes modified at the splicing level belong to membrane-related regulatory processes including focal adhesion and ABC transporters (Supplementary Fig. 3C ). In particular, the term Focal Adhesion was shared between Tax splicing targets identified in infected samples (RNA-seq datasets, Supplementary Data 1) and those regulated by DDX17 (Supplementary Data 3) (Supplementary Fig. 3C ). In addition, using an exon ontology (EO) approach that we recently developed to estimate enrichment in protein features encoded by exons  36  , we found that Tax-and DDX17-regulated exons encoded for regions involved in functionally validated posttranslational modifications (PTM), protein structure and binding functions (Supplementary Data 4 and Supplementary Fig.
These results suggested that Tax-mediated effect on exon v10 splicing could depend on RELA binding in the vicinity of the alternative v10 exon. Supporting this hypothesis, the analysis of publicly available RELA ChIP-seq datasets revealed that intragenic RELA peaks are significantly closer to alternative exons than to constitutive exons (Supplementary Fig. 4D ). More particularly, alternative exons regulated by Tax, including those sensitive to DDX5/17 depletion, were identified in genomic regions enriched in NF-κB-binding sites compared to alternative exons non-regulated by Tax (Fig. 4e ). TaxM22 and siRNA-mediated RELA depletion were used in order to assess the dependency of splicing events on NF-κB activation. Histograms represent the results of exon-specific quantitative RT-PCR measurements computed as a relative exon inclusion (alternatively spliced exon vs constitutive exon reflecting the total gene expression level).
Methods Cell culture and transfections. Peripheral blood mononuclear cells (PBMCs) were obtained by Ficoll separation of whole blood from HTLV-1-infected individuals in the context of a Biomedical Research Program approved by the Committee for the Protection of Persons, Ile-de-France II, Paris (2012-10-04 SC). All individuals gave informed consent. PBMCs were cloned by limiting dilution in RPMI 1640 supplemented with penicillin and streptomycin, sodium pyruvate, non-essential amino acids, 2-mercaptoethanol, 10% filtered human AB serum, 100 U/ml recombinant IL-2 (Chiron Corporation), and 75 μM HTLV-1 integrase inhibitor L-731,988. Clones were phenotyped by flow cytometry using antibodies against CD4 (Dako-Cytomation) and isotype-matched controls on a FACScan system using CellQuest software (Becton Dickinson).
HTLV-1-positive clones were assessed by PCR. The human embryonic kidney 293T-LTR-GFP cells  53  , which contain an integrated GFP reporter gene under the control of the Tax-responsive HTLV-1 LTR, were cultured in DMEM + glutamax medium supplemented with 10% heat-inactivated FBS and 1% penicillin/streptomycin. This cell line was used to measure transfection efficiency in Tax and TaxM22 conditions. In standard transfection experiments, siRNAs (Supplementary Data 5) and/or expression vectors (pSG5M empty, pSG5M-Tax-WT, pSG5M-M22) were mixed with JetPrime (Polyplus Transfection) following the manufacturer's instructions, and cells were harvested 48 h after transfection. TNFα exposure consisted in treating cells with 10 ng/ml of TNFα for 24 h. HTLV-1-chronically infected lymphocytes ATL2 (kind gift from Masao Matsuoka (Kyoto University, Kyoto, Japan) and Roberto Accolla (Università degli Studi dell'Insubria, Varese, Italia)), C91PL (kind gift from Cynthia Pise-Masison (National Cancer Institute, NIH, Bethesda, MD)) and Renaud Mahieux (Center for Research in Infectious Diseases, Lyon, France) and non-infected MOLT4 cells (CRL-1582, ATCC) were grown in RPMI 1640 medium (Gibco, Life Technologies) supplemented with 10% heat-inactivated fetal calf serum, 20 IU/ml penicillin, 20 μg/ml streptomycin, and 25 mM HEPES.
Complexes were washed with 5 different buffers: Wash 1 (1% Trition, 0. 1% NaDOC, 150 mM NaCl, 10 mM Tris-HCl pH 8), Wash 2 (1% NP-40, 1% NaDOC, 150 mM KCl, 10 mM Tris-HCl pH 8), Wash 3 (0. 5% Trition, 0. 1% NaDOC, 500 mM NaCl, 10 mM Tris-HCl pH 8), Wash 4 (0. 5% NP-40, 0. 5% NaDOC, 250 mM LiCl, 20 mM Tris-HCl pH 8, 1 mM EDTA), and Wash 5 (0. 1% NP-40, 150 mM NaCl, 20 mM Tris-HCl pH 8, 1 mM EDTA). The immunoprecipitated chromatin was purified by phenol-chloroform extraction, and quantitative PCR was performed using Rotor-Gene 3000 cycler (Corbett) or LightCycler 480 II (Roche, Mannheim, Germany). Values were expressed relative to the signal obtained for the immunoprecipitation with control IgG. Primers used for ChIP experiments were designed for exon/intron junction (Supplementary Data 5). For TALE ChIP experiments, DDX17 and RelA enrichment were normalized to the signal observed with V5 antibody corresponding to TALE recruitment.
The TALE-GFP condition was used as control and set to 1. Note that in TALE assays qChIP experiments and exon-specific RT-qPCR were carried out at 24-h post-transfection. RNA extraction, PCR, and real-time quantitative PCR. Total RNAs were extracted using TRIzol (Invitrogen). RNAs (2. 5 μg) were retro-transcribed with Maxima First Strand cDNA Synthesis Kit after treatment with dsDNase (Thermo Scientific) following the manufacturer's instructions. PCRs were performed using 7. 5 ng of cDNAs with GoTaq polymerase (Promega, Madison, WI, USA). PCR products were separated by ethidium bromide-labeled agarose gel electrophoresis. Band intensity was quantified using the ImageLab software (BioRad). Quantitative PCR was then performed using 5 ng of cDNAs with SYBR® Premix Ex Taq TM II (Tli RNaseH Plus) on LightCycler 480 II.
Relative levels of the target sequence were normalized to the 18 S or GAPDH gene expression (ΔCt), and controls were set to 1(ΔΔCt). The inclusion rate of alternative exons was calculated as 2 -ΔΔCt (included exon)/2 -ΔΔCt (constitutive exon). Oligonucleotide sequences used are listed in Supplementary Data 5. RNA-seq and bio-informatic analysis. RNA-seq analyses were performed with poly-A transcripts extracted from 293T-LTR-GFP cells transfected with pSG5M-Tax or pSG5M empty vectors and knocked down or not for DDX5-17. RNA-seq libraries were generated at Aros Applied Biotechnology (Aarhus, Denmark) using Stranded mRNA Sample Prep kit (Illumina) and sequenced using illumina HiSeq 2500 technology. Each sample had in average 6 × 10 7 of paired-end pairs of reads. RNA-seq data were analyzed using FaRLine, a computational program dedicated to analyzing alternative splicing with FasterDB database  23, 54.
The gene expression level in each sample was calculated with HTSeq-count (v0. 7. 2)  55  , and differential expression between conditions was computed with DESeq2 (v1. 10. 1) (abs(log2-FoldChange) ≥ 0. 4, p ≤ 0. 05)  56. Tax expression in the RNA-seq dataset EGAS00001001296 was examined by Kallisto 57 using the nucleotide sequence of Tax/Rex sequence (coordinates 6951-8078) from NC_001436. 1 as a reference. Ontology analysis were performed using DAVID software for gene ontology and Exon Ontology v1. 5. 0  36. In silico screening of NF-κB-responsive elements in the CD44 promoter sequence was carried out using the PROMO database (based on TRANSFAC v8. 3)  58. The MEME-ChIP suite was used to discover the regulatory motifs in the NF-κB ChIP-seq data  39. To predict splice site strengthes, scores were computed using MaxEntScan  59  for the sequence covering both sides of the splicing site (using 3 bases into the exon and 6 bases into the intron for 5′ splice sites, and 20 bases into the intron and 3 bases into the exon for 3′ splice sites).
MaxEntScan uses Maximum Entropy Models (MEMs) to compute log-odds ratios. The minimum free energy was computed from exon-intron junction sequences using RNAFold from the ViennaRNA package (v 2. 4. 1; http://rna. tbi. univie. ac. at/cgi-bin/RNAWebSuite/RNAfold. cgi). Analyzed sequences include 25 nucleotides within the intron and 25 nucleotides within the exon. The GC-content was calculated for exons defined in FasterDB  54. The distribution of RELA peaks across alternative and constitutive exons, and the average distance between RELA peaks and Tax exon targets was measured using ChiPseq datasets from GEO  60  , ENCODE  61  , and CISTROME 62 databases: from GEO GSE63736, GSM1239484, GSM486271, GSM486293, GSM486298, GSM486318, GSM847876, GSM847877, GSM2394419, GSM2394421, GSM2394423, from ENCODE ENCFF002CPA, ENCFF002CQB, ENCFF002CQJ, ENCFF002CQN, ENCFF580QGA, and from CISTROME 53597, 5388, 5389, 4940, 36310, 36316, 4971.
For another GEO dataset, GSM2628088, reads were mapped to the hg19 build of the human genome with Bowtie2  63  and RELA peaks were identified with Macs2  64. Alternative and Constitutive spliced exons were obtained from FasterDB 54. To focus on intragenic RELA peaks, we used the bedtools  65  intersect command to remove all intergenic RELA peaks and all RELA peaks localized on first exon (or at least at less than 500 nt distance) for each gene. A Perl script was specifically created to measure the distance between RELA peaks and Tax-regulated exons. Briefly, RELA peaks and exons are provided as BED files, and the script reports for each exon the distance in nucleotides of the nearest RELA peak. Closest peak distances from the 710 Tax-regulated exon-cassettes were compared to closest peak distances from 710 exons chosen by chance (10 5 runs).
RNA-seq data produced in this study have been deposited on NCBI GEO under the accession number GSE123752. The source data underlying Figs. 1c, 2b-f, 3a, b, d, 4a, c-f, 5a-d, 6a-f, and Supplementary Figs. 1, 2b-d, 3a, b, e, 4a-g, 5a-d, and 6a-c are provided as a Source Data file. All data supporting the findings of this study are available within the article and its supplementary information files and from the corresponding author upon reasonable request. Source data are provided with this paper.
Acknowledgements This study makes use of data ( EGAS00001001296 ) generated by  Department of Pathology and Tumor Biology, Kyoto University   24. We gratefully acknowledge support from  Laurent Modolo  at the LBMC for bio-informatic advice, and from the  PSMN (Pôle Scientifique de Modélisation Numérique)  of the  ENS de Lyon  for computing resources. This work was supported by the  Ligue Contre le Cancer  ( Comité de la Savoie, de la Drome et du Rhône ), the  Fondation ARC  ( ARC PJA20151203399 ), and the  Agence Nationale pour la Recherche  (program EPIVIR and CHROTOPAS). L. B. A. was supported by  Ligue Contre le Cancer ; G. G. , by  ANR  CHROTOPAS; M. T. , by a bursary from the  French Ministry of Higher Education and Science ; F. M. , C. F. B, and D. A. , by INSERM; and E. W. , by  Hospices Civils de Lyon and Lyon I University (France).
Climatic Data Climatic data were collected through a sampling process in 50 weather stations located in central and remarkable points in Galicia. These weather stations sample temperature, relative humidity, and wind speed variables with a sampling frequency from about 5 to 10 min  [12, 23]  and an error margin of 0. 1 • C, 0. 2%, and 0. 1 m/s, which can be considered acceptable. Moreover, these weather stations were chosen for the purpose of this study because they ignore buildings and other conditions that could possibly interfere with the data.
Due to the fact that the ISO procedure needs few calculations and the curve fitting needs the highest number of values possible, an iterative process was developed with more than 60,000 iterations. However, after a curve fitting in accordance with more than 60,000 mathematical models, a Weibull density distribution was selected in accordance with the determination factor obtained. The curve fitting process was done with the CFtool of Matlab software. Despite this, the codification was implemented by the authors to develop this task automatically, and to order the obtained curves by its determination factor. Within the 10 more accurate equations obtained, the Weibull model was selected due to its well-known relation with processes that change its probability density function during its life, as it used to be in maintenance models.