title: Alternative splicing detection workflow needs a careful combination of sample prep and bioinformatics analysis.
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.
  
  
  CONCLUSIONS
  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.
authors.forename: Matteo
authors.initials: M
authors.lastname: Carrara
authors.forename: Josephine
authors.initials: J
authors.lastname: Lum
authors.forename: Francesca
authors.initials: F
authors.lastname: Cordero
authors.forename: Marco
authors.initials: M
authors.lastname: Beccuti
authors.forename: Michael
authors.initials: M
authors.lastname: Poidinger
authors.forename: Susanna
authors.initials: S
authors.lastname: Donatelli
authors.forename: Raffaele Adolfo
authors.initials: RA
authors.lastname: Calogero
authors.forename: Francesca
authors.initials: F
authors.lastname: Zolezzi
mesh_terms: D017398:Alternative Splicing / Q000235:genetics*; D019295:Computational Biology / Q000379:methods*; D005091:Exons / Q000235:genetics; D015723:Gene Library; D006801:Humans; D012313:RNA / Q000235:genetics; D012335:RNA, Ribosomal / Q000235:genetics / Q000378:metabolism; D017423:Sequence Analysis, RNA / Q000379:methods*; D057188:Workflow
chemical_list: D012335:RNA, Ribosomal; D012313:RNA