Assembly-free rapid differential gene expression analysis in non-model organisms using DNA-protein alignment

Abstract

Background RNA-seq is being increasingly adopted for gene expression studies in a panoply of non-model organisms, with applications spanning the fields of agriculture, aquaculture, ecology, and environment. For organisms that lack a well-annotated reference genome or transcriptome, a conventional RNA-seq data analysis workflow requires constructing a de-novo transcriptome assembly and annotating it against a high-confidence protein database. The assembly serves as a reference for read mapping, and the annotation is necessary for functional analysis of genes found to be differentially expressed. However, assembly is computationally expensive. It is also prone to errors that impact expression analysis, especially since sequencing depth is typically much lower for expression studies than for transcript discovery. Results We propose a shortcut, in which we obtain counts for differential expression analysis by directly aligning RNA-seq reads to the high-confidence proteome that would have been otherwise used for annotation. By avoiding assembly, we drastically cut down computational costs – the running time on a typical dataset improves from the order of tens of hours to under half an hour, and the memory requirement is reduced from the order of tens of Gbytes to tens of Mbytes. We show through experiments on simulated and real data that our pipeline not only reduces computational costs, but has higher sensitivity and precision than a typical assembly-based pipeline. A Snakemake implementation of our workflow is available at: https://bitbucket.org/project_samar/samar. Conclusions The flip side of RNA-seq becoming accessible to even modestly resourced labs has been that the time, labor, and infrastructure cost of bioinformatics analysis has become a bottleneck. Assembly is one such resource-hungry process, and we show here that it can be avoided for quick and easy, yet more sensitive and precise, differential gene expression analysis in non-model organisms.

DNA-protein quasi-mapping for rapid differential gene expression analysis in non-model organisms

“…For benchmarking, we used an RNA-seq read dataset that simulates a typical small-sample-size and low-coverage whole-genome gene expression experiment from our prior work [3]. The dataset was generated using Polyester [14] from the protein-coding transcriptome (BDGP6.28) of D. melanogaster obtained from Ensembl Genes 101 and containing 28,692 transcripts of 13,320 genes.…”

“…We compared our mapping performance against several existing tools for DNA-protein alignment/mapping of large data: LAST [18] (version 1060), DIAMOND [19] (version 2.0.12), and Kaiju [20] (version 1.9.0). We selected LAST as it was the alignment tool used in SAMAR [3]. We selected DIAMOND because it is a widely used DNA-protein alignment tool for big sequence data, although mainly focusing on metagenomic data.…”

“…In our prior work, we proposed the novel pipeline SAMAR [3], which directly aligns RNA-seq reads to a protein database – the one that would have been after-all used by the assembly-based approach for functional analysis – to measure expression and for subsequent differential expression analysis. Another recently proposed software tool Seq2Fun [4] also uses nucleotide-to-protein alignment for gene abundance quantification and functional profiling of RNA-seq data.…”