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Enregistrement W4393401674 · doi:10.5281/zenodo.3956522

Characteristics of human and viral RNA binding sites and site clusters recognized by SRSF1 and RNPS1

2020· dataset· en· W4393401674 sur OpenAlexaff
Peter K. Rogan, EJ Mucaki, BC Shirley

Notice bibliographique

RevueZenodo (CERN European Organization for Nuclear Research) · 2020
Typedataset
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueRNA Research and Splicing
Établissements canadiensWestern University
Organismes subventionnairesnon disponible
Mots-clésBinding siteRNAComputational biologyBiologyGeographyEvolutionary biologyGeneticsGene

Résumé

récupéré en direct d'OpenAlex

This dataset was developed for the following article: Rogan PK, Mucaki EJ and Shirley BC. A proposed molecular mechanism for pathogenesis of severe RNA-viral pulmonary infections [version 1; peer review: awaiting peer review]. F1000Research 2020, 9:943 (https://doi.org/10.12688/f1000research.25390.1) Section 1. Extended Data Tables This archive contains the extended data tables for the research article "A proposed mechanism for molecular pathogenesis of severe RNA-viral pulmonary infections". These tables provide SRSF1, RNPS1 and hnRNP A1 binding site and information-dense cluster counts across various RNA viral genomes [including multiple SARS-CoV-2 and influenza strains] and the human transcriptome, the estimated SARS-CoV-2 doubling time necessary for viral genome SRSF1 binding site availability to exceed sites within the host transcriptome, and an analysis of influenza, dengue, and aplastic anemia patients misdiagnosed as irradiated by established radiation gene signatures.These tables are: Section 1 - Table 1. RNPS1 and hnRNPA1 binding sites and Information-Dense Clusters for RNPS1 and hnRNPA1 in RNA Virus Genomes Section 1 - Table 2A. Detailed Analysis of Information-Dense Clusters for SRSF1 (Replicate 1) in RNA Virus Genomes Section 1 - Table 2B. Detailed Analysis of Information-Dense Clusters for SRSF1 (Replicate 2) in RNA Virus Genomes Section 1 - Table 2C. Detailed Analysis of Information-Dense Clusters for RNPS1 in RNA Virus Genomes Section 1 - Table 2D. Detailed Analysis of Information-Dense Clusters for hnRNP A1 in RNA Virus Genomes Section 1 - Table 3. Binding Site Analysis of Multiple Coronavirus Strains (Both Strands) Section 1 - Table 4A. Binding Site Analysis of Multiple Influenza A (H3N2) Strains (Negative Strand Only) Section 1 - Table 4B. Binding Site Analysis of Multiple Influenza A (H3N2) Strains (Both Strands) Section 1 - Table 5. SRSF1, RNPS1 and hnRNPA1 Binding Sites and Information-Dense Clusters by Gene Section 1 - Table 6A. Transcriptome-Wide Information Dense Clusters Intersecting DRIP- and DRIPc-seq Intervals Section 1 - Table 6B. Exome-Wide Information Dense Clusters within DRIP- and DRIPc-seq Intervals Section 1 - Table 6C. Transcriptome-Wide Scan of Strong Binding Sites Intersecting DRIP- and DRIPc-seq Intervals Section 1 - Table 6D. Exome-Wide Scan of Strong Binding Sites within DRIP- and DRIPc-seq Intervals Section 1 - Table 7. Rate of False Positives for Influenza, Dengue Virus and Aplastic Anemia Using Radiation Signatures Section 1 - Table 8. Radiation Model Genes Contributing to False Positives for Patients with Influenza A, Dengue Virus, and Aplastic Anemia Section 1 - Table 9A. Doubling Time of SARS-CoV-2 Needed to Exceed Host Transcriptome SRSF1 Binding Sites (Positive-Strand Sites Only) Section 1 - Table 9B. Doubling Time of SARS-CoV-2 Needed to Exceed Host Transcriptome SRSF1 Binding Sites (Both Strands Considered) Section 2. All SRSF1, hnRNPA1 and RNPS1 binding site tracks for human and viral genomes We provide bedgraph tracks which provide the location and strength of binding sites (and binding site clusters) for SRSF1, RNPS1 and hnRNPA1 across the human transcriptome (GRCh37), the human exome (including +/-300nt surrounding the exon; non-intergenic only), and for all viral genome investigated in this study (Coronavirus, Dengue, HIV-1 [two strains] and Influenza [two strains]). Note that if no clusters were found for a particular viral genome, a file for said genome will not be present in the Zenodo archive. Folder “Cluster-to-DRIPseq-Intersection-Tracks” contain tracks which indicate where binding site clusters have been identified, intersected with DRIP-seq and DRIPc-seq intervals which indicate where there is evidence of R-Loop formation in the human genome. The DRIP-seq dataset (GSE68845) is not strand specific. DRIPc-seq (GSE70189) is strand specific, and has been taken into account in the intersection (e.g. tracks only list positive strand clusters found in positive-strand DRIPc-seq intervals). Due to sheer size, the human transcriptome and exome tracks which indicate the location of individual binding sites are split into two separate files (separated by strand). While the custom tracks containing human binding site information are designed to be uploaded to the UCSC Genome Browser, files containing transcriptome-wide binding site information may be too large to be uploaded and may require further filtering (i.e. by chromosome). To be classified as a cluster, binding sites on the same strand must have Ri values which sum to >50 bits, each binding site must have a neighboring site within 25nt, and all binding sites in the cluster must have Ri greater than a minimum bit threshold. For human transcriptomes and exomes, this bit minimum was set to Rsequence. The bit minimum for viral binding sites was set to 0.1 * Rsequence. The information density-based clustering algorithm utilized in this work is described in Lu and Rogan 2018 (https://f1000research.com/articles/7-1933/v2) and archived source code is available through Zenodo (https://dx.doi.org/10.5281/zenodo.1892051). Section 3. Binding site clusters - lollipop plots Lollipop plots present the genomic coordinates and information densities of clusters across the human transcriptome, human exome, and viral genomes (Coronavirus, Dengue, HIV-1 [two strains] and Influenza [one strain]). The height of the "lollipop" corresponds to the information density of a cluster. Labels above "lollipops" present the start and end genomic coordinate (GRCh37) of the cluster followed by the number of sites in the cluster enclosed in brackets. Lollipop plots associated with human transcriptomes/exomes each contain a single gene. Influenza has 8 segments and each segment requires its own plot, other viral genomes examined are presented in a single plot. File naming convention for human plots: RBP_Gene.png e.g. RNPS1_ADK.png File naming convention for viral plots (elements in square brackets do not always appear): Virus[.InfluenzaSegment].RiThreshold.Strand.RBP.png e.g. Wuhan-Hu-1.complete-genome.4.2-bits.PosStrand.hnRNPA1.png The specified Ri threshold indicates all binding sites which comprise a cluster have Ri greater-than or equal to the threshold. Section 4. Ri(b,l) matrices for all binding sites scanned The information theory-based position weight matrices for the following RNA binding proteins (RBP) used in this study: SRSF1, hnRNPA1 and RNPS1. We investigated binding using two different RNPS1 binding models. While similar, these two models contained binding site information on opposing sides of the binding site motif which is why we found it prudent to scan with both models. Structure of each file: Line #1: Start position, End position and Rsequence [average strength of sequences used to generate the model] Subsequent lines describe the information on each position of the binding site: First four columns: Ri contribution of nucleotide at this position of the matrix [A, C, G, T] Row #5: Position of the matrix Last four columns: Number of binding sites used to generate model with a particular nucleotide at this position of the matrix [A, C, G, T] Example: -2.965775 1.282153 0.034225 -4.906891 0 1 19 8 0 At zero position of the matrix (first nucleotide), a ‘C’ would have a positive contribution to binding site strength, a ‘G’ would be relatively neutral, and an ‘A’ or ‘T’ would negatively contribute to binding site strength. Generation of Ri(b,l) matrices and computation of Ri values and can be accomplished by utilizing the Delila package (https://alum.mit.edu/www/toms/delila/delilaprograms.html). Section 5. Ri and intersite distance - histograms Two sets of histograms present Ri distribution and intersite distance distribution across the human transcriptome, human exome, and viral genomes (Coronavirus, Dengue, HIV-1 [two strains] and Influenza [one strain]). File naming convention for human plots (elements in square brackets do not always appear): [IntersiteDistancesThreshold-]Human-[DRIPc]-AllChrs-RBP[-RiThreshold].png e.g. IntersiteDistances500-Human-AllChrs-hnRNPA1-4.6-bits.png File naming convention for viral plots (elements in square brackets do not always appear): [IntersiteDistancesThreshold-]Strand-RBP-Virus[.InfluenzaSegment][-RiThreshold].png e.g. IntersideDistances1000-PosStrandOnly-SRSF1-top50000sitesReplicate1-HIV-1-Strain-B.png Intersite distance thresholds of 500 or 1000 were assigned for all intersite distance histograms. Any distances above the corresponding threshold were excluded from the plot. Plots presenting Ri distributions contain a dashed line indicating Rsequence if it is visible within the scope of the plot. Section 6. Perl Scripts and Descriptions This archive contains all Perl scripts discussed in this archive's associated manuscript and a document file which describes them ("Perl-Script-Descriptions-Page.docx"). The programs and their general functions are as follows: “ClusterToDRIPseqAnalysisProgram.pl” – reports which information-dense clusters are located within DRIPc- and/or DRIP-seq intervals (individually and by gene) “ClusterToDRIPseqAnalysisProgram.GeneDensityFinder.pl” – uses the output from script “ClusterToDRIPseqAnalysisProgram.pl” to determine the number

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,005
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Jeu de données · Signal consensuel: Jeu de données
Score de désaccord entre enseignants0,036
Score d'incertitude au seuil0,121

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,005
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0020,004
Études des sciences et des technologies0,0010,000
Communication savante0,0010,001
Science ouverte0,0010,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0360,028

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,019
Tête enseignante GPT0,249
Écart entre enseignants0,230 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreJeu de données

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2020
Routes d'admission1
Résumé présentoui

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