Please use this identifier to cite or link to this item:
Title: Alternative splicing detection workflow needs a careful combination of sample prep and bioinformatics analysis
Authors: Carrara, M
Lum, J
Cordero, F
Beccuti, M
Poidinger, M 
Donatelli, S
Calogero, R.A
Zolezzi, F
Keywords: Bioinformatics
Alternative splicing
Benchmark datasets
Bioinformatics analysis
Combined effect
Sample preparation
Splice variants
Splicing variants
Statistical detection
ribosome RNA
alternative RNA splicing
gene library
sequence analysis
Alternative Splicing
Computational Biology
Gene Library
RNA, Ribosomal
Sequence Analysis, RNA
Issue Date: 2015
Citation: Carrara, M, Lum, J, Cordero, F, Beccuti, M, Poidinger, M, Donatelli, S, Calogero, R.A, Zolezzi, F (2015). Alternative splicing detection workflow needs a careful combination of sample prep and bioinformatics analysis. BMC Bioinformatics 16 : S2. ScholarBank@NUS Repository.
Rights: Attribution 4.0 International
Abstract: Background: RNA-Seq provides remarkable power in the area of biomarkers discovery and disease characterization. Two crucial steps that affect RNA-Seq experiment results are Library Sample Preparation (LSP) and Bioinformatics Analysis (BA). This work describes an evaluation of the combined effect of LSP methods and BA tools in the detection of splice variants. Results: Different LSPs (TruSeq unstranded/stranded, ScriptSeq, NuGEN) allowed the detection of a large common set of splice variants. However, each LSP also detected a small set of unique transcripts that are characterized by a low coverage and/or FPKM. This effect was particularly evident using the low input RNA NuGEN v2 protocol. A benchmark dataset, in which synthetic reads as well as reads generated from standard (Illumina TruSeq 100) and low input (NuGEN) LSPs were spiked-in was used to evaluate the effect of LSP on the statistical detection of alternative splicing events (AltDE). Statistical detection of AltDE was done using as prototypes for splice variant-quantification Cuffdiff2 and RSEM-EBSeq. As prototype for exon-level analysis DEXSeq was used. Exon-level analysis performed slightly better than splice variant-quantification approaches, although at most only 50% of the spiked-in transcripts was detected. The performances of both splice variant-quantification and exon-level analysis improved when raising the number of input reads. Conclusion: Data, derived from NuGEN v2, were not the ideal input for AltDE, especially when the exon-level approach was used. We observed that both splice variant-quantification and exon-level analysis performances were strongly dependent on the number of input reads. Moreover, the ribosomal RNA depletion protocol was less sensitive in detecting splicing variants, possibly due to the significant percentage of the reads mapping to non-coding transcripts. © 2015 Carrara et al.
Source Title: BMC Bioinformatics
ISSN: 14712105
DOI: 10.1186/1471-2105-16-S9-S2
Rights: Attribution 4.0 International
Appears in Collections:Elements
Staff Publications

Show full item record
Files in This Item:
File Description SizeFormatAccess SettingsVersion 
10_1186_1471-2105-16-S9-S2.pdf3.35 MBAdobe PDF




checked on Apr 7, 2021

Page view(s)

checked on Apr 8, 2021

Google ScholarTM



This item is licensed under a Creative Commons License Creative Commons