TransPi-a comprehensive TRanscriptome ANalysiS PIpeline for de novo transcriptome assembly

被引:21
作者
Rivera-Vicens, Ramon E. [1 ]
Garcia-Escudero, Catalina A. [1 ,2 ]
Conci, Nicola [1 ]
Eitel, Michael [1 ]
Woerheide, Gert [1 ,3 ,4 ]
机构
[1] Ludwig Maximilians Univ Munchen, Dept Earth & Environm Sci Paleontol & Geobiol, Munich, Germany
[2] Ludwig Maximilians Univ Munchen, Fac Biol, Grad Sch Evolut Ecol & Systemat, Planegg Martinsried, Germany
[3] Ludwig Maximilians Univ Munchen, GeoBlo Ctr, Munich, Germany
[4] SNSB Bayer Staatssammlung Palaontol & Geol, Munich, Germany
基金
欧盟地平线“2020”;
关键词
annotation; assembly; de novo; Nextflow; nonmodel; pipeline; RNA-Seq; transcriptome; QUALITY ASSESSMENT; GENERATION; RECONSTRUCTION; ANNOTATION; ALIGNMENT;
D O I
10.1111/1755-0998.13593
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
The use of RNA sequencing (RNA-Seq) data and the generation of de novo transcriptome assemblies have been pivotal for studies in ecology and evolution. This is especially true for nonmodel organisms, where no genome information is available. In such organisms, studies of differential gene expression, DNA enrichment bait design and phylogenetics can all be accomplished with de novo transcriptome assemblies. Multiple tools are available for transcriptome assembly, but no single tool can provide the best assembly for all data sets. Therefore, a multi-assembler approach, followed by a reduction step, is often sought to generate an improved representation of the assembly. To reduce errors in these complex analyses while at the same time attaining reproducibility and scalability, automated workflows have been essential in the analysis of RNA-Seq data. However, most of these tools are designed for species where genome data are used as reference for the assembly process, limiting their use in nonmodel organisms. We present TransPi, a comprehensive pipeline for de novo transcriptome assembly, with minimum user input but without losing the ability of a thorough analysis. A combination of different model organisms, k-mer sets, read lengths and read quantities was used for assessing the tool. Furthermore, a total of 49 nonmodel organisms, spanning different phyla, were also analysed. Compared to approaches using single assemblers only, TransPi produces higher BUSCO completeness percentages, and a concurrent significant reduction in duplication rates. TransPi is easy to configure and can be deployed seamlessly using Conda, Docker and Singularity.
引用
收藏
页码:2070 / 2086
页数:17
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