摘要
De novo transcriptome assembly is an important approach in RNA-Seq data analysis and it can help us to reconstruct the transcriptome and investigate gene expression profiles without reference genome sequences.We carried out transcriptome assemblies with two RNA-Seq datasets generated from human brain and cell line,respectively.We then determined an efficient way to yield an optimal overall assembly using three different strategies.We first assembled brain and cell line transcriptome using a single k-mer length.Next we tested a range of values of k-mer length and coverage cutoff in assembling.Lastly,we combined the assembled contigs from a range of k values to generate a final assembly.By comparing these assembly results,we found that using only one k-mer value for assembly is not enough to generate good assembly results,but combining the contigs from different k-mer values could yield longer contigs and greatly improve the overall assembly.
De novo transcriptome assembly is an important approach in RNA-Seq data analysis and it can help us to reconstruct the tran- scriptome and investigate gene expression profiles without reference genome sequences. We carried out transcriptome assem- blies with two RNA-Seq datasets generated from human brain and cell line, respectively. We then determined an efficient way to yield an optimal overall assembly using three different strategies. We first assembled brain and cell line transcriptome using a single k-mer length. Next we tested a range of values of k-mer length and coverage cutoff in assembling. Lastly, we com- bined the assembled contigs from a range of k values to generate a final assembly. By comparing these assembly results, we found that using only one k-mer value for assembly is not enough to generate good assembly results, but combining the contigs from different k-mer values could yield longer contigs and greatly improve the overall assembly.
基金
supported by the National Basic Research Program of China (Grant Nos. 2010CB945401, 2007CB108800)
National Natural Science Foundation of China (Grant Nos. 30870575, 31071162,31000590)
the Science and Technology Commission of Shanghai Municipality (Grant No. 11DZ2260300)
