RNA-Seq Data Analysis for Differential Gene Expression Using HISAT2-StringTie-Ballgown Pipeline.

Comparison of transcriptome for candidate gene discovery has become an important tool for biologists. While such studies lack the degree of resolution one gets from well-designed forward or reverse genetic studies, nevertheless, this has been a method of choice for giving coarse insight into the underlying biological processes or mechanisms. This was further accelerated with the availability of sequencing technologies. While many pipelines are available for RNA-seq data analysis, the protocol discussed here will guide the first-time users for conducting routine RNA-seq analysis using whole genome sequence as reference.

Thakur V 《-》

Plant Transcriptome Analysis with HISAT-StringTie-Ballgown and TopHat-Cufflinks Pipelines.

The development of next-generation sequencing technology has led to a burst of data in a single assay. Management of a large dataset requires high demands on bioinformatic skills and computing resources. Here we present two popular pipelines for RNA-seq data analysis, using open-source software tools HISAT-StringTie-Ballgown and TopHat-Cufflinks. To meet the need of plant scientist, we describe in detail how to perform such comprehensive analysis beginning with raw RNA-seq reads and available reference genome. It allows biologists to align short reads to a reference genome, measure the transcript abundance, and analyze gene differential expression under two or more conditions. We also discuss other RNA-seq tools that are comparable or alternative to this protocol.

Rao X 《-》

SPARTA: Simple Program for Automated reference-based bacterial RNA-seq Transcriptome Analysis.

Johnson BK ，Scholz MB ，Teal TK ，Abramovitch RB ... -  《BMC BIOINFORMATICS》

A comparison of transcriptome analysis methods with reference genome.

The application of RNA-seq technology has become more extensive and the number of analysis procedures available has increased over the past years. Selecting an appropriate workflow has become an important issue for researchers in the field. In our study, six popular analytical procedures/pipeline were compared using four RNA-seq datasets from mouse, human, rat, and macaque, respectively. The gene expression value, fold change of gene expression, and statistical significance were evaluated to compare the similarities and differences among the six procedures. qRT-PCR was performed to validate the differentially expressed genes (DEGs) from all six procedures. Cufflinks-Cuffdiff demands the highest computing resources and Kallisto-Sleuth demands the least. Gene expression values, fold change, p and q values of differential expression (DE) analysis are highly correlated among procedures using HTseq for quantification. For genes with medium expression abundance, the expression values determined using the different procedures were similar. Major differences in expression values come from genes with particularly high or low expression levels. HISAT2-StringTie-Ballgown is more sensitive to genes with low expression levels, while Kallisto-Sleuth may only be useful to evaluate genes with medium to high abundance. When the same thresholds for fold change and p value are chosen in DE analysis, StringTie-Ballgown produce the least number of DEGs, while HTseq-DESeq2, -edgeR or -limma generally produces more DEGs. The performance of Cufflinks-Cuffdiff and Kallisto-Sleuth varies in different datasets. For DEGs with medium expression levels, the biological verification rates were similar among all procedures. Results are highly correlated among RNA-seq analysis procedures using HTseq for quantification. Difference in gene expression values mainly come from genes with particularly high or low expression levels. Moreover, biological validation rates of DEGs from all six procedures were similar for genes with medium expression levels. Investigators can choose analytical procedures according to their available computer resources, or whether genes of high or low expression levels are of interest. If computer resources are abundant, one can utilize multiple procedures to obtain the intersection of results to get the most reliable DEGs, or to obtain a combination of results to get a more comprehensive DE profile for transcriptomes.

Liu X ，Zhao J ，Xue L ，Zhao T ，Ding W ，Han Y ，Ye H ... -  《BMC GENOMICS》

Short-Read RNA-Seq.

Hu R ，Islam MN ，Varghese RS ，Ressom HW ... -  《-》