title: An optimized protocol for generation and analysis of Ion Proton sequencing reads for RNA-Seq.
abstract: Previous studies compared running cost, time and other performance measures of popular sequencing platforms. However, comprehensive assessment of library construction and analysis protocols for Proton sequencing platform remains unexplored. Unlike Illumina sequencing platforms, Proton reads are heterogeneous in length and quality. When sequencing data from different platforms are combined, this can result in reads with various read length. Whether the performance of the commonly used software for handling such kind of data is satisfactory is unknown.
  
  
  
  By using universal human reference RNA as the initial material, RNaseIII and chemical fragmentation methods in library construction showed similar result in gene and junction discovery number and expression level estimated accuracy. In contrast, sequencing quality, read length and the choice of software affected mapping rate to a much larger extent. Unspliced aligner TMAP attained the highest mapping rate (97.27 % to genome, 86.46 % to transcriptome), though 47.83 % of mapped reads were clipped. Long reads could paradoxically reduce mapping in junctions. With reference annotation guide, the mapping rate of TopHat2 significantly increased from 75.79 to 92.09 %, especially for long (>150 bp) reads. Sailfish, a k-mer based gene expression quantifier attained highly consistent results with that of TaqMan array and highest sensitivity.
  
  
  
  We provided for the first time, the reference statistics of library preparation methods, gene detection and quantification and junction discovery for RNA-Seq by the Ion Proton platform. Chemical fragmentation performed equally well with the enzyme-based one. The optimal Ion Proton sequencing options and analysis software have been evaluated.
authors.affiliation: BGI-tech, BGI-Shenzhen, Shenzhen, 518083, Guangdong, China.
authors.forename: Yongxian
authors.initials: Y
authors.lastname: Yuan
authors.affiliation: BGI-tech, BGI-Wuhan, Wuhan, 430075, Hubei, China.
authors.forename: Huaiqian
authors.initials: H
authors.lastname: Xu
authors.affiliation: BGI-tech, BGI-Shenzhen, Shenzhen, 518083, Guangdong, China. yssun@hku.hk.|School of Public Health, The University of Hong Kong, Hong Kong, China. yssun@hku.hk.|Stanley Ho Centre for Emerging Infectious Diseases, The Chinese University of Hong Kong, Hong Kong, China. yssun@hku.hk.
authors.forename: Ross Ka-Kit
authors.initials: RK
authors.lastname: Leung
mesh_terms: D019295:Computational Biology / Q000379:methods; D020869:Gene Expression Profiling / Q000379:methods; D015723:Gene Library; D015894:Genome, Human; D023281:Genomics / Q000379:methods; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods*; D006801:Humans; D011522:Protons; D017423:Sequence Analysis, RNA; D059467:Transcriptome; D057188:Workflow
chemical_list: D011522:Protons
keywords: Ion Proton; RNA-Seq; Sequencing length; Sequencing quality; Transcriptome