Computational comparison of common event-based differential splicing tools: practical considerations for laboratory researchers

Abstract Background Computational tools analyzing RNA-sequencing data have boosted alternative splicing research by identifying and assessing differentially spliced genes. However, common alternative splicing analysis tools differ substantially in their statistical analyses and general performance. This report compares the computational performance (CPU utilization and RAM usage) of three event-level splicing tools; rMATS, MISO, and SUPPA2. Additionally, concordance between tool outputs was investigated. Results Log-linear relations were found between job times and dataset size in all splicing tools and all virtual machine (VM) configurations. MISO had the highest job times for all analyses, irrespective of VM size, while MISO analyses also exceeded maximum CPU utilization on all VM sizes. rMATS and SUPPA2 load averages were relatively low in both size and replicate comparisons, not nearing maximum CPU utilization in the VM simulating the lowest computational power (D2 VM). RAM usage in rMATS and SUPPA2 did not exceed 20% of maximum RAM in both size and replicate comparisons while MISO reached maximum RAM usage in D2 VM analyses for input size. Correlation coefficients of differential splicing analyses showed high correlation (β > 80%) between different tool outputs with the exception of comparisons of retained intron (RI) events between rMATS/MISO and rMATS/SUPPA2 (β < 60%). Conclusions Prior to RNA-seq analyses, users should consider job time, amount of replicates and splice event type of interest to determine the optimal alternative splicing tool. In general, rMATS is superior to both MISO and SUPPA2 in computational performance. Analysis outputs show high concordance between tools, with the exception of RI events.

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A survey of computational methods in transcriptome-wide alternative splicing analysis

BioMolecular Concepts ◽

10.1515/bmc-2014-0040 ◽

2015 ◽

Vol 6 (1) ◽

pp. 59-66 ◽

Cited By ~ 10

Author(s):

Jianbo Wang ◽

Zhenqing Ye ◽

Tim H.-M. Huang ◽

Huidong Shi ◽

Victor Jin

Keyword(s):

Alternative Splicing ◽

Experimental Validation ◽

Gene Diversity ◽

Exon Skipping ◽

Differential Splicing ◽

Sequencing Data ◽

Future Directions ◽

Specific Analysis ◽

Software Implementations ◽

Expected Outcomes

AbstractAlternative splicing is widely recognized for its roles in regulating genes and creating gene diversity. Consequently the identification and quantification of differentially spliced transcripts is pivotal for transcriptome analysis. Here, we review the currently available computational approaches for the analysis of RNA-sequencing data with a focus on exon-skipping events of alternative splicing and discuss the novelties as well as challenges faced to perform differential splicing analyses. In accordance with operational needs we have classified the software tools, which may be instrumental for a specific analysis based on the experimental objectives and expected outcomes. In addition, we also propose a framework for future directions by pinpointing more extensive experimental validation to assess the accuracy of the software predictions and improvements that would facilitate visualizations, data processing, and downstream analyses along with their associated software implementations.

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AStrap: identification of alternative splicing from transcript sequences without a reference genome

Bioinformatics ◽

10.1093/bioinformatics/bty1008 ◽

2018 ◽

Vol 35 (15) ◽

pp. 2654-2656 ◽

Cited By ~ 5

Author(s):

Guoli Ji ◽

Wenbin Ye ◽

Yaru Su ◽

Moliang Chen ◽

Guangzao Huang ◽

...

Keyword(s):

Machine Learning ◽

Alternative Splicing ◽

Single Molecule ◽

Reference Genome ◽

De Novo ◽

Supplementary Information ◽

Model Organisms ◽

Sequencing Data ◽

Extensive Evaluation ◽

Reference Genomes

Abstract Summary Alternative splicing (AS) is a well-established mechanism for increasing transcriptome and proteome diversity, however, detecting AS events and distinguishing among AS types in organisms without available reference genomes remains challenging. We developed a de novo approach called AStrap for AS analysis without using a reference genome. AStrap identifies AS events by extensive pair-wise alignments of transcript sequences and predicts AS types by a machine-learning model integrating more than 500 assembled features. We evaluated AStrap using collected AS events from reference genomes of rice and human as well as single-molecule real-time sequencing data from Amborella trichopoda. Results show that AStrap can identify much more AS events with comparable or higher accuracy than the competing method. AStrap also possesses a unique feature of predicting AS types, which achieves an overall accuracy of ∼0.87 for different species. Extensive evaluation of AStrap using different parameters, sample sizes and machine-learning models on different species also demonstrates the robustness and flexibility of AStrap. AStrap could be a valuable addition to the community for the study of AS in non-model organisms with limited genetic resources. Availability and implementation AStrap is available for download at https://github.com/BMILAB/AStrap. Supplementary information Supplementary data are available at Bioinformatics online.

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Single-cell Differential Splicing Analysis Reveals High Heterogeneity of Liver Tumor-infiltrating T Cells

10.21203/rs.3.rs-123121/v1 ◽

2020 ◽

Author(s):

Shang Liu ◽

Biaofeng Zhou ◽

Liang Wu ◽

Yan Sun ◽

Jie Chen ◽

...

Keyword(s):

T Cells ◽

Alternative Splicing ◽

T Cell ◽

Single Cell ◽

Regulatory Factor ◽

Differential Splicing ◽

Cell Level ◽

Comprehensive Investigation ◽

Cancer Initiation ◽

Cell Groups

Abstract Recent advances in single-cell RNA sequencing (scRNA-seq) have improved our understanding of the association between tumor-infiltrating lymphocyte (TILs) heterogeneity and cancer initiation and progression. However, studies investigating alternative splicing (AS) as an important regulatory factor of heterogeneity remain limited. Here, we developed a new computational tool, DESJ-detection, which accurately detects differentially expressed splicing junctions (DESJs) between cell groups at the single-cell level. We analyzed 5,063 T cells of hepatocellular carcinoma (HCC) and identified 1,176 DESJs across 11 T cell subtypes. Interestingly, DESJs were enriched in UTRs, and have putative effects on heterogeneity. Cell subtypes with a similar function closely clustered together at the AS level. Meanwhile, we identified two novel cell states, pre-exhaustion and pre-activation with the isoform markers CD103-201 and ARHGAP15-205. In summary, we present a comprehensive investigation of alternative splicing differences, which provided novel insights into T cell heterogeneity and can be applied to other full-length scRNA-seq datasets.

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Splicing Express: a software suite for alternative splicing analysis using next-generation sequencing data

PeerJ ◽

10.7717/peerj.1419 ◽

2015 ◽

Vol 3 ◽

pp. e1419 ◽

Cited By ~ 6

Author(s):

Jose E. Kroll ◽

Jihoon Kim ◽

Lucila Ohno-Machado ◽

Sandro J. de Souza

Keyword(s):

Alternative Splicing ◽

Dna Sequencing ◽

Next Generation Sequencing Data ◽

Next Generation ◽

Sequencing Data ◽

Software Suite ◽

Next Generation Dna Sequencing ◽

Sequencing Technologies ◽

Body Map ◽

Sequencing Platforms

Motivation.Alternative splicing events (ASEs) are prevalent in the transcriptome of eukaryotic species and are known to influence many biological phenomena. The identification and quantification of these events are crucial for a better understanding of biological processes. Next-generation DNA sequencing technologies have allowed deep characterization of transcriptomes and made it possible to address these issues. ASEs analysis, however, represents a challenging task especially when many different samples need to be compared. Some popular tools for the analysis of ASEs are known to report thousands of events without annotations and/or graphical representations. A new tool for the identification and visualization of ASEs is here described, which can be used by biologists without a solid bioinformatics background.Results.A software suite namedSplicing Expresswas created to perform ASEs analysis from transcriptome sequencing data derived from next-generation DNA sequencing platforms. Its major goal is to serve the needs of biomedical researchers who do not have bioinformatics skills.Splicing Expressperforms automatic annotation of transcriptome data (GTF files) using gene coordinates available from the UCSC genome browser and allows the analysis of data from all available species. The identification of ASEs is done by a known algorithm previously implemented in another tool namedSplooce. As a final result,Splicing Expresscreates a set of HTML files composed of graphics and tables designed to describe the expression profile of ASEs among all analyzed samples. By using RNA-Seq data from the Illumina Human Body Map and the Rat Body Map, we show thatSplicing Expressis able to perform all tasks in a straightforward way, identifying well-known specific events.Availability and Implementation.Splicing Expressis written in Perl and is suitable to run only in UNIX-like systems. More details can be found at:http://www.bioinformatics-brazil.org/splicingexpress.

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Exploiting single-cell RNA sequencing data to link alternative splicing and cancer heterogeneity: A computational approach

The International Journal of Biochemistry & Cell Biology ◽

10.1016/j.biocel.2018.12.015 ◽

2019 ◽

Vol 108 ◽

pp. 51-60 ◽

Cited By ~ 1

Author(s):

Ichcha Manipur ◽

Ilaria Granata ◽

Mario Rosario Guarracino

Keyword(s):

Alternative Splicing ◽

Single Cell ◽

Rna Sequencing ◽

Computational Approach ◽

Sequencing Data ◽

Cancer Heterogeneity ◽

Single Cell Rna Sequencing

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rMAPS2: an update of the RNA map analysis and plotting server for alternative splicing regulation

Nucleic Acids Research ◽

10.1093/nar/gkaa237 ◽

2020 ◽

Vol 48 (W1) ◽

pp. W300-W306 ◽

Cited By ~ 2

Author(s):

Jae Y Hwang ◽

Sungbo Jung ◽

Tae L Kook ◽

Eric C Rouchka ◽

Jinwoong Bok ◽

...

Keyword(s):

Alternative Splicing ◽

High Throughput ◽

Splice Site ◽

High Throughput Sequencing ◽

Rna Binding ◽

Rna Binding Protein ◽

Sequencing Data ◽

High Throughput Sequencing Data ◽

Retained Intron ◽

Map Analysis

Abstract The rMAPS2 (RNA Map Analysis and Plotting Server 2) web server, freely available at http://rmaps.cecsresearch.org/, has provided the high-throughput sequencing data research community with curated tools for the identification of RNA binding protein sites. rMAPS2 analyzes differential alternative splicing or CLIP peak data obtained from high-throughput sequencing data analysis tools like MISO, rMATS, Piranha, PIPE-CLIP and PARalyzer, and then, graphically displays enriched RNA-binding protein target sites. The initial release of rMAPS focused only on the most common alternative splicing event, skipped exon or exon skipping. However, there was a high demand for the analysis of other major types of alternative splicing events, especially for retained intron events since this is the most common type of alternative splicing in plants, such as Arabidopsis thaliana. Here, we expanded the implementation of rMAPS2 to facilitate analyses for all five major types of alternative splicing events: skipped exon, mutually exclusive exons, alternative 5′ splice site, alternative 3′ splice site and retained intron. In addition, by employing multi-threading, rMAPS2 has vastly improved the user experience with significant reductions in running time, ∼3.5 min for the analysis of all five major alternative splicing types at once.

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Exploration of predictive and prognostic alternative splicing signatures in lung adenocarcinoma using machine learning methods

Journal of Translational Medicine ◽

10.1186/s12967-020-02635-y ◽

2020 ◽

Vol 18 (1) ◽

Author(s):

Qidong Cai ◽

Boxue He ◽

Pengfei Zhang ◽

Zhenyu Zhao ◽

Xiong Peng ◽

...

Keyword(s):

Machine Learning ◽

Alternative Splicing ◽

Lung Adenocarcinoma ◽

Prognostic Model ◽

Cox Regression ◽

Machine Learning Algorithms ◽

The Cancer Genome Atlas ◽

Sequencing Data ◽

Learning Methods ◽

Machine Learning Methods

Abstract Background Alternative splicing (AS) plays critical roles in generating protein diversity and complexity. Dysregulation of AS underlies the initiation and progression of tumors. Machine learning approaches have emerged as efficient tools to identify promising biomarkers. It is meaningful to explore pivotal AS events (ASEs) to deepen understanding and improve prognostic assessments of lung adenocarcinoma (LUAD) via machine learning algorithms. Method RNA sequencing data and AS data were extracted from The Cancer Genome Atlas (TCGA) database and TCGA SpliceSeq database. Using several machine learning methods, we identified 24 pairs of LUAD-related ASEs implicated in splicing switches and a random forest-based classifiers for identifying lymph node metastasis (LNM) consisting of 12 ASEs. Furthermore, we identified key prognosis-related ASEs and established a 16-ASE-based prognostic model to predict overall survival for LUAD patients using Cox regression model, random survival forest analysis, and forward selection model. Bioinformatics analyses were also applied to identify underlying mechanisms and associated upstream splicing factors (SFs). Results Each pair of ASEs was spliced from the same parent gene, and exhibited perfect inverse intrapair correlation (correlation coefficient = − 1). The 12-ASE-based classifier showed robust ability to evaluate LNM status of LUAD patients with the area under the receiver operating characteristic (ROC) curve (AUC) more than 0.7 in fivefold cross-validation. The prognostic model performed well at 1, 3, 5, and 10 years in both the training cohort and internal test cohort. Univariate and multivariate Cox regression indicated the prognostic model could be used as an independent prognostic factor for patients with LUAD. Further analysis revealed correlations between the prognostic model and American Joint Committee on Cancer stage, T stage, N stage, and living status. The splicing network constructed of survival-related SFs and ASEs depicts regulatory relationships between them. Conclusion In summary, our study provides insight into LUAD researches and managements based on these AS biomarkers.

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