SplicingFactory – Splicing diversity analysis for transcriptome data

MicroRNAs (miRNAs) are known to modulate gene expression, but their activity at the tissue-specific level remains largely uncharacterized. To study their contribution to tissue-specific gene expression, we developed novel tools to profile putative miRNA targets in the Caenorhabditis elegans intestine and body muscle. We validated many previously described interactions and identified ∼3500 novel targets. Many of the candidate miRNA targets curated are known to modulate the functions of their respective tissues. Within our data sets we observed a disparity in the use of miRNA-based gene regulation between the intestine and body muscle. The intestine contained significantly more putative miRNA targets than the body muscle highlighting its transcriptional complexity. We detected an unexpected enrichment of RNA-binding proteins targeted by miRNA in both tissues, with a notable abundance of RNA splicing factors. We developed in vivo genetic tools to validate and further study three RNA splicing factors identified as putative miRNA targets in our study (asd-2, hrp-2, and smu-2), and show that these factors indeed contain functional miRNA regulatory elements in their 3′UTRs that are able to repress their expression in the intestine. In addition, the alternative splicing pattern of their respective downstream targets (unc-60, unc-52, lin-10, and ret-1) is dysregulated when the miRNA pathway is disrupted. A reannotation of the transcriptome data in C. elegans strains that are deficient in the miRNA pathway from past studies supports and expands on our results. This study highlights an unexpected role for miRNAs in modulating tissue-specific gene isoforms, where post-transcriptional regulation of RNA splicing factors associates with tissue-specific alternative splicing.

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jSplice: a high-performance method for accurate prediction of alternative splicing events and its application to large-scale renal cancer transcriptome data

Bioinformatics ◽

10.1093/bioinformatics/btw145 ◽

2016 ◽

Vol 32 (14) ◽

pp. 2111-2119 ◽

Cited By ~ 9

Author(s):

Yann Christinat ◽

Rafał Pawłowski ◽

Wilhelm Krek

Keyword(s):

Alternative Splicing ◽

Renal Cancer ◽

High Performance ◽

Large Scale ◽

Accurate Prediction ◽

Transcriptome Data ◽

Cancer Transcriptome ◽

Alternative Splicing Events

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Novel Observations Concerning Differentiation of Bloodstream-Form Trypanosomes to the Form That Is Adapted for Growth in Tsetse Flies

mSphere ◽

10.1128/msphere.00533-18 ◽

2018 ◽

Vol 3 (5) ◽

Author(s):

Christine Clayton

Keyword(s):

Cold Shock ◽

Tricarboxylic Acid ◽

Bloodstream Form ◽

Tsetse Flies ◽

Data Sets ◽

Glucose Medium ◽

Transcriptome Data ◽

Procyclic Form ◽

Link Type ◽

Highly Efficient

ABSTRACT Salivarian trypanosomes grow in mammals, where they depend on glucose, and as procyclic forms in tsetse flies, where they metabolize proline. Differentiation of bloodstream forms to nongrowing stumpy forms, and to procyclic forms, has been studied extensively, but reconciling the results is tricky because investigators have used parasites with various differentiation competences and different media for procyclic-form culture. Standard protocols include lowering the temperature to 27°C, adding a tricarboxylic acid, and transferring the parasites to high-proline medium, often including glucose. A 20°C cold shock enhanced efficiency. Y. Qiu, J. E. Milanes, J. A. Jones, R. E. Noorai, et al. (mSphere 3:e00366-18, 2018, https://doi.org/10.1128/mSphere.00366-18) studied this systematically, and their results call long-established protocols into question. Importantly, highly efficient differentiation was observed after cold shock and transfer to no-glucose medium without tricarboxylic acid; in contrast, glucose made differentiation tricarboxylic acid dependent and inhibited procyclic growth. New transcriptome data for stumpy and procyclic forms will enable informative comparisons with biochemical observations and with other RNA and protein data sets.

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Thousands of exon skipping events differentiate among splicing patterns in sixteen human tissues

F1000Research ◽

10.12688/f1000research.2-188.v1 ◽

2013 ◽

Vol 2 ◽

pp. 188 ◽

Cited By ~ 131

Author(s):

Liliana Florea ◽

Li Song ◽

Steven L Salzberg

Keyword(s):

Alternative Splicing ◽

Gene Diversity ◽

Exon Skipping ◽

Human Tissues ◽

Rna Seq ◽

Gene Splicing ◽

Link Type ◽

Normal Human ◽

Splicing Patterns

Alternative splicing is widely recognized for its roles in regulating genes and creating gene diversity. However, despite many efforts, the repertoire of gene splicing variation is still incompletely characterized, even in humans. Here we describe a new computational system, ASprofile, and its application to RNA-seq data from Illumina’s Human Body Map project (>2.5 billion reads). Using the system, we identified putative alternative splicing events in 16 different human tissues, which provide a dynamic picture of splicing variation across the tissues. We detected 26,989 potential exon skipping events representing differences in splicing patterns among the tissues. A large proportion of the events (>60%) were novel, involving new exons (~3000), new introns (~16000), or both. When tracing these events across the sixteen tissues, only a small number (4-7%) appeared to be differentially expressed (‘switched’) between two tissues, while 30-45% showed little variation, and the remaining 50-65% were not present in one or both tissues compared. Novel exon skipping events appeared to be slightly less variable than known events, but were more tissue-specific. Our study represents the first effort to build a comprehensive catalog of alternative splicing in normal human tissues from RNA-seq data, while providing insights into the role of alternative splicing in shaping tissue transcriptome differences. The catalog of events and the ASprofile software are freely available from the Zenodo repository(http://zenodo.org/record/7068; doi:10.5281/zenodo.7068) and from our web site http://ccb.jhu.edu/software/ASprofile.

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Assessing Conservation of Alternative Splicing with Evolutionary Splicing Graphs

10.1101/2020.11.14.382820 ◽

2020 ◽

Author(s):

Diego Javier Zea ◽

Sofya Laskina ◽

Alexis Baudin ◽

Hugues Richard ◽

Elodie Laine

Keyword(s):

Alternative Splicing ◽

Protein Function ◽

Human Genetics ◽

Scaling Up ◽

Computational Tool ◽

Protein Coding ◽

Link Type ◽

Clear Link ◽

Functional Relevance ◽

Definition Of

AbstractUnderstanding how protein function has evolved and diversified is of great importance for human genetics and medicine. Here, we tackle the problem of describing the whole transcript variability observed in several species by generalising the definition of splicing graph. We provide a practical solution to building parsimonious evolutionary splicing graphs where each node is a minimal transcript building block defined across species. We show a clear link between the functional relevance, tissue-regulation and conservation of AS events on a set of 50 genes. By scaling up to the whole human protein-coding genome, we identify a few thousands of genes where alternative splicing modulates the number and composition of pseudo-repeats. We have implemented our approach in ThorAxe, an efficient, versatile, and robust computational tool freely available at https://github.com/PhyloSofS-Team/thoraxe. The results are accessible and can be browsed interactively at http://www.lcqb.upmc.fr/ThorAxe.

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Dr.seq2: a quality control and analysis pipeline for parallel single cell transcriptome and epigenome data

10.1101/143271 ◽

2017 ◽

Author(s):

Chengchen Zhao ◽

Sheng’en Hu ◽

Xiao Huo ◽

Yong Zhang

Keyword(s):

Quality Control ◽

Single Cell ◽

Cell Heterogeneity ◽

Transcriptome Data ◽

Analysis Pipeline ◽

Link Type ◽

Data Application ◽

Heterogeneity Analysis ◽

Cell Transcriptome ◽

Single Cell Transcriptome

AbstractAn increasing number of single cell transcriptome and epigenome technologies, including single cell ATAC-seq (scATAC-seq), have been recently developed as powerful tools to analyze the features of many individual cells simultaneously. However, the methods and software were designed for one certain data type and only for single cell transcriptome data. A systematic approach for epigenome data and multiple types of transcriptome data is needed to control data quality and to perform cell-to-cell heterogeneity analysis on these ultra-high-dimensional transcriptome and epigenome datasets. Here we developed Dr.seq2, a Quality Control (QC) and analysis pipeline for multiple types of single cell transcriptome and epigenome data, including scATAC-seq and Drop-ChIP data. Application of this pipeline provides four groups of QC measurements and different analyses, including cell heterogeneity analysis. Dr.seq2 produced reliable results on published single cell transcriptome and epigenome datasets. Overall, Dr.seq2 is a systematic and comprehensive QC and analysis pipeline designed for parallel single cell transcriptome and epigenome data. Dr.seq2 is freely available at: http://www.tongji.edu.cn/~zhanglab/drseq2/ and https://github.com/ChengchenZhao/DrSeq2.

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SplicingFactory—splicing diversity analysis for transcriptome data

Bioinformatics ◽

10.1093/bioinformatics/btab648 ◽

2021 ◽

Author(s):

Benedek Dankó ◽

Péter Szikora ◽

Tamás Pór ◽

Alexa Szeifert ◽

Endre Sebestyén

Keyword(s):

Alternative Splicing ◽

R Package ◽

Supplementary Information ◽

Transcriptome Data ◽

Rna Seq ◽

Hematopoietic Stem ◽

Expression Levels ◽

Isoform Diversity ◽

Drug Treatments ◽

Gene Expression Levels

Abstract Motivation Alternative splicing contributes to the diversity of RNA found in biological samples. Current tools investigating patterns of alternative splicing check for coordinated changes in the expression or relative ratio of RNA isoforms where specific isoforms are up- or downregulated in a condition. However, the molecular process of splicing is stochastic and changes in RNA isoform diversity for a gene might arise between samples or conditions. A specific condition can be dominated by a single isoform, while multiple isoforms with similar expression levels can be present in a different condition. These changes might be the result of mutations, drug treatments or differences in the cellular or tissue environment. Here, we present a tool for the characterization and analysis of RNA isoform diversity using isoform level expression measurements. Results We developed an R package called SplicingFactory, to calculate various RNA isoform diversity metrics, and compare them across conditions. Using the package, we tested the effect of RNA-seq quantification tools, quantification uncertainty, gene expression levels, and isoform numbers on the isoform diversity calculation. We analyzed a set of CD34+ hematopoietic stem cells and myelodysplastic syndrome samples and found a set of genes whose isoform diversity change is associated with SF3B1 mutations. Availability and implementation The SplicingFactory package is freely available under the GPL-3.0 license from Bioconductor for the Windows, MacOS and Linux operating systems (https://www.bioconductor.org/packages/release/bioc/html/SplicingFactory.html). Supplementary information Supplementary data are available at Bioinformatics online.

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Nanopore long-read transcriptome data of fungal pathogen of chalkbrood disease, Ascosphaera apis

10.1101/2020.03.12.989863 ◽

2020 ◽

Cited By ~ 5

Author(s):

Yu Du ◽

Huazhi Chen ◽

Jie Wang ◽

Zhiwei Zhu ◽

Cuiling Xiong ◽

...

Keyword(s):

Alternative Splicing ◽

Fungal Pathogen ◽

Length Distribution ◽

Full Length ◽

List Type ◽

Transcriptome Data ◽

Ascosphaera Apis ◽

Oxford Nanopore ◽

Long Read ◽

Colony Productivity

ABSTRACTAscosphaera apis is a fungal pathogen that exclusively infects honeybee larvae, leading to chalkbrood disease, which damages the number of adult honeybees and colony productivity. In this article, A. apis mecylia and spores were respectively purified followed by Oxford Nanopore sequencing via PromethION platform. In total, 6,321,704 and 6,259,727 raw reads were generated from Aam and Aas, with a length distribution among 1 kb~10 kb. The quality (Q) scores of majority of raw reads were Q9 (Aam) and Q11 (Aas). Additionally, 5,669,436 and 6,233,159 clean reads were gained, among them 79.32% and 79.62% were identified as being full-length. The lengths of redundant reads-removed full-length transcripts were among 1 kb~8 kb and 1 kb~9 kb, and most abundant length for both was 1 kb. Furthermore, the length of redundant transcripts-removed clean reads was ranged from 1 kb~7 kb, with the largest group of 1 kb. The data reported here provides a beneficial genetic resource for improving genome and transcriptome annotations of A. apis and for exploring alternative splicing and polyadenylation of A. apis mRNAs.Value of the resultCurrent dataset enables better understanding of the complexity of A. apis transcriptome.The long-read transcriptome data can be used to identify of genes and transcripts associated with A. apis infection mechanism.The accessible data provides full-length transcripts for improving gene structure and functional annotation of A. apis transcriptome.This dataset could be utilized for investigation of alternative splicing and polyadenylation of A. apis mRNAs.

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Thousands of exon skipping events differentiate among splicing patterns in sixteen human tissues

F1000Research ◽

10.12688/f1000research.2-188.v2 ◽

2013 ◽

Vol 2 ◽

pp. 188 ◽

Cited By ~ 24

Author(s):

Liliana Florea ◽

Li Song ◽

Steven L Salzberg

Keyword(s):

Alternative Splicing ◽

Gene Diversity ◽

Exon Skipping ◽

Human Tissues ◽

Rna Seq ◽

Gene Splicing ◽

Link Type ◽

Normal Human ◽

Splicing Patterns

Alternative splicing is widely recognized for its roles in regulating genes and creating gene diversity. However, despite many efforts, the repertoire of gene splicing variation is still incompletely characterized, even in humans. Here we describe a new computational system, ASprofile, and its application to RNA-seq data from Illumina’s Human Body Map project (>2.5 billion reads). Using the system, we identified putative alternative splicing events in 16 different human tissues, which provide a dynamic picture of splicing variation across the tissues. We detected 26,989 potential exon skipping events representing differences in splicing patterns among the tissues. A large proportion of the events (>60%) were novel, involving new exons (~3000), new introns (~16000), or both. When tracing these events across the sixteen tissues, only a small number (4-7%) appeared to be differentially expressed (‘switched’) between two tissues, while 30-45% showed little variation, and the remaining 50-65% were not present in one or both tissues compared. Novel exon skipping events appeared to be slightly less variable than known events, but were more tissue-specific. Our study represents the first effort to build a comprehensive catalog of alternative splicing in normal human tissues from RNA-seq data, while providing insights into the role of alternative splicing in shaping tissue transcriptome differences. The catalog of events and the ASprofile software are freely available from the Zenodo repository(http://zenodo.org/record/7068; doi:10.5281/zenodo.7068) and from our web site http://ccb.jhu.edu/software/ASprofile.

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