LIEA RNA-seq – Low-cost, Low-bias and Low-input RNA-seq with High Experimental Verifiability

Low-input RNA-seq is powerful to represent the gene expression profiles with limited number of cells, especially when single-cell variations are not the aim. However, pre-amplification-based and molecule index-based library construction methods boost bias or require higher throughput. Here researchers at Jinan University demonstrate a simple, low-cost, low-bias and low-input RNA-seq with ion torrent semiconductor sequencing (LIEA RNA-seq). They also developed highly accurate and error-tolerant spliced mapping algorithm FANSe2splice to accurately map the single-ended reads to the reference genome with better experimental verifiability than the previous spliced mappers. Combining the experimental and computational advancements, our solution is comparable with the bulk mRNA-seq in quantification, reliably detects splice junctions and minimizes the bias with much less mappable reads.

Low-input and low-bias ion torrent mRNA-seq

rna-seq

(A) Principle of the ion-torrent LIEA method and comparison to the smart-seq (pre-amplification) strategy. (B) Read length distribution of ion-torrent low-input mRNA-seq. (C) Comparison of the total read count, mapped read count and number of genes quantified by ion torrent LIEA method, smart-seq2 and Illumina bulk RNA-seq, for HBE cell line. (D) Error rate distribution of two platforms. An error is defined as a mismatch or indel nucleotide when aligned to the reference sequence. (E) Overlap of quantified genes of the three methods. (F) Comparison of the gene expression quantification using two low-input methods against Illumina bulk mRNA-seq, all for HBE cells. Ion torrent LIEA method was carried out twice as biological replicates. (G) Comparison of the gene expression quantification using LIEA method with 200 cells as starting material and Illumina bulk mRNA-seq.

Availability – FANSe2Splice can be freely downloaded at http://bioinformatics.jnu.edu.cn/software/fanse2splice.

Mai Z, Xiao C, Jin J, Zhang G. (2017) Low-cost, Low-bias and Low-input RNA-seq with High Experimental Verifiability based on Semiconductor Sequencing. Sci Rep 7(1):1053. [article]

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