cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.26249174
title: Innovative construction of the first reliable catalogue of bovine circular RNAs

description: The aim of this study was to compare the circular transcriptome of divergent tissues in order to understand: i) the presence of circular RNAs (circRNAs) that are not exonic circRNAs, i.e. originated from backsplicing involving known exons and, ii) the origin of artificial circRNA (artif_circRNA), i.e. circRNA not generated <i>in-vivo</i>. CircRNA identification is mostly an <i>in-silico</i> process, and the analysis of data from the BovReg project (https://www.bovreg.eu/) provided an opportunity to explore new ways to identify reliable circRNAs. By considering 117 tissue samples, we characterized 23,926 exonic circRNAs, 337 circRNAs from 273 introns (191 ciRNAs, 146 intron circles), 108 circRNAs from small non-coding genes and nearly 36.6K circRNAs classified as other_circRNAs. Furthermore, for 63 of those samples we analysed in parallel data from total-RNAseq (ribosomal RNAs depleted prior to library preparation) with paired mRNAseq (library prepared with poly(A)-selected RNAs). The high number of circRNAs detected in mRNAseq, and the significant number of novel circRNAs, mainly other_circRNAs, led us to consider all circRNAs detected in mRNAseq as artificial. This study provided evidence of 189 false entries in the list of exonic circRNAs: 103 artif_circRNAs identified by total RNAseq/mRNAseq comparison using two circRNA tools, 26 probable artif_circRNAs, and 65 identified by deep annotation analysis. Extensive benchmarking was performed (including analyses with CIRI2 and CIRCexplorer-2) and confirmed 94% of the 23,737 reliable exonic circRNAs. Moreover, this study demonstrates the effectiveness of a panel of highly expressed exonic circRNAs (5–8%) in analysing the tissue specificity of the bovine circular transcriptome.
DOI: 10.6084/m9.figshare.26249174.v1
title: Innovative construction of the first reliable catalogue of bovine circular RNAs

description: The aim of this study was to compare the circular transcriptome of divergent tissues in order to understand: i) the presence of circular RNAs (circRNAs) that are not exonic circRNAs, i.e. originated from backsplicing involving known exons and, ii) the origin of artificial circRNA (artif_circRNA), i.e. circRNA not generated <i>in-vivo</i>. CircRNA identification is mostly an <i>in-silico</i> process, and the analysis of data from the BovReg project (https://www.bovreg.eu/) provided an opportunity to explore new ways to identify reliable circRNAs. By considering 117 tissue samples, we characterized 23,926 exonic circRNAs, 337 circRNAs from 273 introns (191 ciRNAs, 146 intron circles), 108 circRNAs from small non-coding genes and nearly 36.6K circRNAs classified as other_circRNAs. Furthermore, for 63 of those samples we analysed in parallel data from total-RNAseq (ribosomal RNAs depleted prior to library preparation) with paired mRNAseq (library prepared with poly(A)-selected RNAs). The high number of circRNAs detected in mRNAseq, and the significant number of novel circRNAs, mainly other_circRNAs, led us to consider all circRNAs detected in mRNAseq as artificial. This study provided evidence of 189 false entries in the list of exonic circRNAs: 103 artif_circRNAs identified by total RNAseq/mRNAseq comparison using two circRNA tools, 26 probable artif_circRNAs, and 65 identified by deep annotation analysis. Extensive benchmarking was performed (including analyses with CIRI2 and CIRCexplorer-2) and confirmed 94% of the 23,737 reliable exonic circRNAs. Moreover, this study demonstrates the effectiveness of a panel of highly expressed exonic circRNAs (5–8%) in analysing the tissue specificity of the bovine circular transcriptome.
DOI: 10.6084/m9.figshare.25225290
title: MRM-BERT: a novel deep neural network predictor of multiple RNA modifications by fusing BERT representation and sequence features

description: RNA modifications play crucial roles in various biological processes and diseases. Accurate prediction of RNA modification sites is essential for understanding their functions. In this study, we propose a hybrid approach that fuses a pre-trained sequence representation with various sequence features to predict multiple types of RNA modifications in one combined prediction framework. We developed MRM-BERT, a deep learning method that combined the pre-trained DNABERT deep sequence representation module and the convolutional neural network (CNN) exploiting four traditional sequence feature encodings to improve the prediction performance. MRM-BERT was evaluated on multiple datasets of 12 commonly occurring RNA modifications, including m<sup>6</sup>A, m<sup>5</sup>C, m<sup>1</sup>A and so on. The results demonstrate that our hybrid model outperforms other models in terms of area under receiver operating characteristic curve (AUC) for all 12 types of RNA modifications. MRM-BERT is available as an online tool (http://117.122.208.21:8501) or source code (https://github.com/abhhba999/MRM-BERT), which allows users to predict RNA modification sites and visualize the results. Overall, our study provides an effective and efficient approach to predict multiple RNA modifications, contributing to the understanding of RNA biology and the development of therapeutic strategies.
DOI: 10.6084/m9.figshare.25225290.v1
title: MRM-BERT: a novel deep neural network predictor of multiple RNA modifications by fusing BERT representation and sequence features

description: RNA modifications play crucial roles in various biological processes and diseases. Accurate prediction of RNA modification sites is essential for understanding their functions. In this study, we propose a hybrid approach that fuses a pre-trained sequence representation with various sequence features to predict multiple types of RNA modifications in one combined prediction framework. We developed MRM-BERT, a deep learning method that combined the pre-trained DNABERT deep sequence representation module and the convolutional neural network (CNN) exploiting four traditional sequence feature encodings to improve the prediction performance. MRM-BERT was evaluated on multiple datasets of 12 commonly occurring RNA modifications, including m<sup>6</sup>A, m<sup>5</sup>C, m<sup>1</sup>A and so on. The results demonstrate that our hybrid model outperforms other models in terms of area under receiver operating characteristic curve (AUC) for all 12 types of RNA modifications. MRM-BERT is available as an online tool (http://117.122.208.21:8501) or source code (https://github.com/abhhba999/MRM-BERT), which allows users to predict RNA modification sites and visualize the results. Overall, our study provides an effective and efficient approach to predict multiple RNA modifications, contributing to the understanding of RNA biology and the development of therapeutic strategies.
DOI: 10.6084/m9.figshare.25907040
title: Assessment of different enrichment methods revealed the optimal approach to identify bovine circRnas

description: Although circular RNAs (circRNAs) play important roles in regulating gene expression, the understanding of circRNAs in livestock animals is scarce due to the significant challenge to characterize them from a biological sample. In this study, we assessed the outcomes of bovine circRNA identification using six enrichment approaches with the combination of ribosomal RNAs removal (<b>Ribo</b>); linear RNAs degradation (<b>R</b>); linear RNAs and RNAs with structured 3′ ends degradation (<b>RTP</b>); ribosomal RNAs coupled with linear RNAs elimination (<b>Ribo-R</b>); ribosomal RNA, linear RNAs and RNAs with poly (A) tailing elimination (<b>Ribo-RP</b>); and ribosomal RNA, linear RNAs and RNAs with structured 3′ ends elimination (<b>Ribo-RTP</b>), respectively. RNA-sequencing analysis revealed that different approaches led to varied ratio of uniquely mapped reads, false-positive rate of identifying circRNAs, and the number of circRNAs per million clean reads (<i>P</i><sub><i>adj</i></sub> &lt;0.05). Out of 2,285 and 2,939 highly confident circRNAs identified in liver and rumen tissues, respectively, 308 and 260 were commonly identified from five methods, with Ribo-RTP method identified the highest number of circRNAs. Besides, 507 of 4,051 identified bovine highly confident circRNAs had shared splicing sites with human circRNAs. The findings from this work provide optimized methods to identify bovine circRNAs from cattle tissues for downstream research of their biological roles in cattle.
DOI: 10.6084/m9.figshare.25907040.v1
title: Assessment of different enrichment methods revealed the optimal approach to identify bovine circRnas

description: Although circular RNAs (circRNAs) play important roles in regulating gene expression, the understanding of circRNAs in livestock animals is scarce due to the significant challenge to characterize them from a biological sample. In this study, we assessed the outcomes of bovine circRNA identification using six enrichment approaches with the combination of ribosomal RNAs removal (<b>Ribo</b>); linear RNAs degradation (<b>R</b>); linear RNAs and RNAs with structured 3′ ends degradation (<b>RTP</b>); ribosomal RNAs coupled with linear RNAs elimination (<b>Ribo-R</b>); ribosomal RNA, linear RNAs and RNAs with poly (A) tailing elimination (<b>Ribo-RP</b>); and ribosomal RNA, linear RNAs and RNAs with structured 3′ ends elimination (<b>Ribo-RTP</b>), respectively. RNA-sequencing analysis revealed that different approaches led to varied ratio of uniquely mapped reads, false-positive rate of identifying circRNAs, and the number of circRNAs per million clean reads (<i>P</i><sub><i>adj</i></sub> &lt;0.05). Out of 2,285 and 2,939 highly confident circRNAs identified in liver and rumen tissues, respectively, 308 and 260 were commonly identified from five methods, with Ribo-RTP method identified the highest number of circRNAs. Besides, 507 of 4,051 identified bovine highly confident circRNAs had shared splicing sites with human circRNAs. The findings from this work provide optimized methods to identify bovine circRNAs from cattle tissues for downstream research of their biological roles in cattle.
DOI: 10.6084/m9.figshare.24886227
title: CCAR-1 works together with the U2AF large subunit UAF-1 to regulate alternative splicing

description: The Cell Division Cycle and Apoptosis Regulator (CCAR) protein family members have recently emerged as regulators of alternative splicing and transcription, as well as having other key physiological functions. For example, mammalian CCAR2/DBC1 forms a complex with the zinc factor protein ZNF326 to integrate alternative splicing with RNA polymerase II transcriptional elongation in AT-rich regions of the DNA. Additionally, <i>Caenorhabditis elegans</i> CCAR-1, a homolog to mammalian CCAR2, facilitates the alternative splicing of the perlecan <i>unc-52</i> gene. However, much about the CCAR family’s role in alternative splicing is unknown. Here, we have examined the role of CCAR-1 in genome-wide alternative splicing in <i>Caenorhabditis elegans</i> and have identified new alternative splicing targets of CCAR-1 using RNA sequencing. Also, we found that CCAR-1 interacts with the spliceosome factors UAF-1 and UAF-2 using mass spectrometry, and that knockdown of <i>ccar-1</i> affects alternative splicing patterns, motility, and proteostasis of UAF-1 mutant worms. Collectively, we demonstrate the role of CCAR-1 in regulating global alternative splicing in <i>C. elegans</i> and in conjunction with UAF-1.
DOI: 10.6084/m9.figshare.24886227.v1
title: CCAR-1 works together with the U2AF large subunit UAF-1 to regulate alternative splicing

description: The Cell Division Cycle and Apoptosis Regulator (CCAR) protein family members have recently emerged as regulators of alternative splicing and transcription, as well as having other key physiological functions. For example, mammalian CCAR2/DBC1 forms a complex with the zinc factor protein ZNF326 to integrate alternative splicing with RNA polymerase II transcriptional elongation in AT-rich regions of the DNA. Additionally, <i>Caenorhabditis elegans</i> CCAR-1, a homolog to mammalian CCAR2, facilitates the alternative splicing of the perlecan <i>unc-52</i> gene. However, much about the CCAR family’s role in alternative splicing is unknown. Here, we have examined the role of CCAR-1 in genome-wide alternative splicing in <i>Caenorhabditis elegans</i> and have identified new alternative splicing targets of CCAR-1 using RNA sequencing. Also, we found that CCAR-1 interacts with the spliceosome factors UAF-1 and UAF-2 using mass spectrometry, and that knockdown of <i>ccar-1</i> affects alternative splicing patterns, motility, and proteostasis of UAF-1 mutant worms. Collectively, we demonstrate the role of CCAR-1 in regulating global alternative splicing in <i>C. elegans</i> and in conjunction with UAF-1.
DOI: 10.21233/4cae-nv58
title: Kazanie testate amoebae surface sample dataset

description: Raw data for the Kazanie testate amoebae surface sample dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.15468/dl.tsmreu
title: Occurrence Download

description: A dataset containing 4 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "PublishingCountry is Colombia",
 {
 "and" : [
 "Country is Colombia",
 {
 "not" : {
 "Issue" : [
 "is Country coordinate mismatch"
 ]
 }
 }
 ]
 }
 ]
 },
 {
 "and" : [
 "TaxonKey is one of (Momotus momota (Linnaeus, 1766))",
 "DatasetKey is one of (Aves de la desembocadura del caño Teracay en el río Tomo, Puerto Carreño, Vichada - Proyecto Colombia Bio)"
 ]
 }
 ]
}

 The dataset includes 4 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0026725-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.25762990
title: Truncating the spliceosomal ‘rope protein’ Prp45 results in Htz1 dependent phenotypes

description: Spliceosome assembly contributes an important but incompletely understood aspect of splicing regulation. Prp45 is a yeast splicing factor which runs as an extended fold through the spliceosome, and which may be important for bringing its components together. We performed a whole genome analysis of the genetic interaction network of the truncated allele of <i>PRP45</i> (<i>prp45</i>(1–169)) using synthetic genetic array technology and found chromatin remodellers and modifiers as an enriched category. In agreement with related studies, H2A.Z-encoding <i>HTZ1</i>, and the components of SWR1, INO80, and SAGA complexes represented prominent interactors, with <i>htz1</i> conferring the strongest growth defect. Because the truncation of Prp45 disproportionately affected low copy number transcripts of intron-containing genes, we prepared strains carrying intronless versions of <i>SRB2</i>, <i>VPS75</i>, or <i>HRB1</i>, the most affected cases with transcription-related function. Intron removal from <i>SRB2</i>, but not from the other genes, partly repaired some but not all the growth phenotypes identified in the genetic screen. The interaction of <i>prp45</i>(1–169) and <i>htz1</i>Δ was detectable even in cells with <i>SRB2</i> intron deleted (<i>srb2</i>Δi). The less truncated variant, <i>prp45</i>(1–330), had a synthetic growth defect with <i>htz1</i>Δ at 16°C, which also persisted in the <i>srb2</i>Δi background. Moreover, <i>htz1</i>Δ enhanced <i>prp45</i>(1–330) dependent pre-mRNA hyper-accumulation of both high and low efficiency splicers, genes <i>ECM33</i> and <i>COF1</i>, respectively. We conclude that while the expression defects of low expression intron-containing genes contribute to the genetic interactome of <i>prp45</i>(1–169), the genetic interactions between <i>prp45</i> and <i>htz1</i> alleles demonstrate the sensitivity of spliceosome assembly, delayed in <i>prp45</i>(1–169), to the chromatin environment.
DOI: 10.6084/m9.figshare.25762990.v1
title: Truncating the spliceosomal ‘rope protein’ Prp45 results in Htz1 dependent phenotypes

description: Spliceosome assembly contributes an important but incompletely understood aspect of splicing regulation. Prp45 is a yeast splicing factor which runs as an extended fold through the spliceosome, and which may be important for bringing its components together. We performed a whole genome analysis of the genetic interaction network of the truncated allele of <i>PRP45</i> (<i>prp45</i>(1–169)) using synthetic genetic array technology and found chromatin remodellers and modifiers as an enriched category. In agreement with related studies, H2A.Z-encoding <i>HTZ1</i>, and the components of SWR1, INO80, and SAGA complexes represented prominent interactors, with <i>htz1</i> conferring the strongest growth defect. Because the truncation of Prp45 disproportionately affected low copy number transcripts of intron-containing genes, we prepared strains carrying intronless versions of <i>SRB2</i>, <i>VPS75</i>, or <i>HRB1</i>, the most affected cases with transcription-related function. Intron removal from <i>SRB2</i>, but not from the other genes, partly repaired some but not all the growth phenotypes identified in the genetic screen. The interaction of <i>prp45</i>(1–169) and <i>htz1</i>Δ was detectable even in cells with <i>SRB2</i> intron deleted (<i>srb2</i>Δi). The less truncated variant, <i>prp45</i>(1–330), had a synthetic growth defect with <i>htz1</i>Δ at 16°C, which also persisted in the <i>srb2</i>Δi background. Moreover, <i>htz1</i>Δ enhanced <i>prp45</i>(1–330) dependent pre-mRNA hyper-accumulation of both high and low efficiency splicers, genes <i>ECM33</i> and <i>COF1</i>, respectively. We conclude that while the expression defects of low expression intron-containing genes contribute to the genetic interactome of <i>prp45</i>(1–169), the genetic interactions between <i>prp45</i> and <i>htz1</i> alleles demonstrate the sensitivity of spliceosome assembly, delayed in <i>prp45</i>(1–169), to the chromatin environment.
DOI: 10.6084/m9.figshare.26520333
title: Identification of whole-cell dsRNA-binding proteins by phase separation

description: Interactions between double-stranded RNA (dsRNA) and proteins play an important role in cellular homeostasis by regulating the editing, stability, and splicing of intracellular RNA. The identification of dsRNA-binding proteins (dsRBPs) is key; however, it has long been challenging to purify dsRBPs from cells. In this study, we developed a novel method, dsRBPC (dsRNA-binding protein capture), to purify cellular dsRBPs based on classic phase separation purification procedures. A global dsRNA-binding proteome of LLC-PK1 cells was obtained, and we identified 1326 dsRBPs, including 1303 putative novel dsRBPs. Functional analyses suggested that these enriched dsRBPs are mainly associated with rRNA processing, RNA splicing, transcriptional regulation, and nucleocytoplasmic transport. We also found that the ARM (armadillo/beta-catenin-like repeats) motif is a previously unknown dsRNA-binding domain, as demonstrated by biochemical experiments. Collectively, this study provides a useful approach for dsRBP identification and the discovery of a global dsRNA-binding proteome to comprehensively map the dsRNA – protein interaction network.
DOI: 10.6084/m9.figshare.26520333.v1
title: Identification of whole-cell dsRNA-binding proteins by phase separation

description: Interactions between double-stranded RNA (dsRNA) and proteins play an important role in cellular homeostasis by regulating the editing, stability, and splicing of intracellular RNA. The identification of dsRNA-binding proteins (dsRBPs) is key; however, it has long been challenging to purify dsRBPs from cells. In this study, we developed a novel method, dsRBPC (dsRNA-binding protein capture), to purify cellular dsRBPs based on classic phase separation purification procedures. A global dsRNA-binding proteome of LLC-PK1 cells was obtained, and we identified 1326 dsRBPs, including 1303 putative novel dsRBPs. Functional analyses suggested that these enriched dsRBPs are mainly associated with rRNA processing, RNA splicing, transcriptional regulation, and nucleocytoplasmic transport. We also found that the ARM (armadillo/beta-catenin-like repeats) motif is a previously unknown dsRNA-binding domain, as demonstrated by biochemical experiments. Collectively, this study provides a useful approach for dsRBP identification and the discovery of a global dsRNA-binding proteome to comprehensively map the dsRNA – protein interaction network.
DOI: 10.6084/m9.figshare.25848836
title: Nm-Nano: a machine learning framework for transcriptome-wide single-molecule mapping of 2´-O-methylation (Nm) sites in nanopore direct RNA sequencing datasets

description: 2´-O-methylation (Nm) is one of the most abundant modifications found in both mRNAs and noncoding RNAs. It contributes to many biological processes, such as the normal functioning of tRNA, the protection of mRNA against degradation by the decapping and exoribonuclease (DXO) protein, and the biogenesis and specificity of rRNA. Recent advancements in single-molecule sequencing techniques for long read RNA sequencing data offered by Oxford Nanopore technologies have enabled the direct detection of RNA modifications from sequencing data. In this study, we propose a bio-computational framework, Nm-Nano, for predicting the presence of Nm sites in direct RNA sequencing data generated from two human cell lines. The Nm-Nano framework integrates two supervised machine learning (ML) models for predicting Nm sites: Extreme Gradient Boosting (XGBoost) and Random Forest (RF) with K-mer embedding. Evaluation on benchmark datasets from direct RNA sequecing of HeLa and HEK293 cell lines, demonstrates high accuracy (99% with XGBoost and 92% with RF) in identifying Nm sites. Deploying Nm-Nano on HeLa and HEK293 cell lines reveals genes that are frequently modified with Nm. In HeLa cell lines, 125 genes are identified as frequently Nm-modified, showing enrichment in 30 ontologies related to immune response and cellular processes. In HEK293 cell lines, 61 genes are identified as frequently Nm-modified, with enrichment in processes like glycolysis and protein localization. These findings underscore the diverse regulatory roles of Nm modifications in metabolic pathways, protein degradation, and cellular processes. The source code of Nm-Nano can be freely accessed at https://github.com/Janga-Lab/Nm-Nano.