Challenges and strategies in transcriptome assembly and differential gene expression quantification. A comprehensive in silico assessment of RNA-seq experiments.

Transcriptome Shotgun Sequencing (RNA-seq) has been readily embraced by geneticists and molecular ecologists alike. As with all high-throughput technologies, it is critical to understand which analytic strategies are best suited and which parameters may bias the interpretation of the data. Here we use a comprehensive simulation approach to explore how various features of the transcriptome (complexity, degree of polymorphism π, alternative splicing), technological processing (sequencing error ε, library normalization) and bioinformatic workflow (de novo vs. mapping assembly, reference genome quality) impact transcriptome quality and inference of differential gene expression (DE). We find that transcriptome assembly and gene expression profiling (EdgeR vs. BaySeq software) works well even in the absence of a reference genome and is robust across a broad range of parameters. We advise against library normalization and in most situations advocate mapping assemblies to an annotated genome of a divergent sister clade, which generally outperformed de novo assembly (Trans-Abyss, Trinity, Soapdenovo-Trans). Transcriptome complexity (size, paralogs, alternative splicing isoforms) negatively affected the assembly and DE profiling, whereas the effects of sequencing error and polymorphism were almost negligible. Finally, we highlight the challenge of gene name assignment for de novo assemblies, the importance of mapping strategies and raise awareness of challenges associated with the quality of reference genomes. Overall, our results have significant practical and methodological implications and can provide guidance in the design and analysis of RNA-seq experiments, particularly for organisms where genomic background information is lacking.

Vijay N ，Poelstra JW ，Künstner A ，Wolf JB ... -  《-》

Comparative study of de novo assembly and genome-guided assembly strategies for transcriptome reconstruction based on RNA-Seq.

Transcriptome reconstruction is an important application of RNA-Seq, providing critical information for further analysis of transcriptome. Although RNA-Seq offers the potential to identify the whole picture of transcriptome, it still presents special challenges. To handle these difficulties and reconstruct transcriptome as completely as possible, current computational approaches mainly employ two strategies: de novo assembly and genome-guided assembly. In order to find the similarities and differences between them, we firstly chose five representative assemblers belonging to the two classes respectively, and then investigated and compared their algorithm features in theory and real performances in practice. We found that all the methods can be reduced to graph reduction problems, yet they have different conceptual and practical implementations, thus each assembly method has its specific advantages and disadvantages, performing worse than others in certain aspects while outperforming others in anther aspects at the same time. Finally we merged assemblies of the five assemblers and obtained a much better assembly. Additionally we evaluated an assembler using genome-guided de novo assembly approach, and achieved good performance. Based on these results, we suggest that to obtain a comprehensive set of recovered transcripts, it is better to use a combination of de novo assembly and genome-guided assembly.

Lu B ，Zeng Z ，Shi T 《-》

Comprehensive evaluation of de novo transcriptome assembly programs and their effects on differential gene expression analysis.

Wang S ，Gribskov M 《-》

Challenges and advances for transcriptome assembly in non-model species.

Ungaro A ，Pech N ，Martin JF ，McCairns RJS ，Mévy JP ，Chappaz R ，Gilles A ... -  《PLoS One》

Optimizing de novo transcriptome assembly from short-read RNA-Seq data: a comparative study.

With the fast advances in nextgen sequencing technology, high-throughput RNA sequencing has emerged as a powerful and cost-effective way for transcriptome study. De novo assembly of transcripts provides an important solution to transcriptome analysis for organisms with no reference genome. However, there lacked understanding on how the different variables affected assembly outcomes, and there was no consensus on how to approach an optimal solution by selecting software tool and suitable strategy based on the properties of RNA-Seq data. To reveal the performance of different programs for transcriptome assembly, this work analyzed some important factors, including k-mer values, genome complexity, coverage depth, directional reads, etc. Seven program conditions, four single k-mer assemblers (SK: SOAPdenovo, ABySS, Oases and Trinity) and three multiple k-mer methods (MK: SOAPdenovo-MK, trans-ABySS and Oases-MK) were tested. While small and large k-mer values performed better for reconstructing lowly and highly expressed transcripts, respectively, MK strategy worked well for almost all ranges of expression quintiles. Among SK tools, Trinity performed well across various conditions but took the longest running time. Oases consumed the most memory whereas SOAPdenovo required the shortest runtime but worked poorly to reconstruct full-length CDS. ABySS showed some good balance between resource usage and quality of assemblies. Our work compared the performance of publicly available transcriptome assemblers, and analyzed important factors affecting de novo assembly. Some practical guidelines for transcript reconstruction from short-read RNA-Seq data were proposed. De novo assembly of C. sinensis transcriptome was greatly improved using some optimized methods.

Zhao QY ，Wang Y ，Kong YM ，Luo D ，Li X ，Hao P ... -  《BMC BIOINFORMATICS》