Microarray-Based Quality Assessment as a Supporting Criterion for de novo Transcriptome Assembly Selection

Abstract

RNA-Sequencing and de novo assembly have enabled the analysis of species with non-available reference transcriptomes, although intrinsic features (biological and technical) induce errors in the reconstruction. A strategy to resolve these errors consists of varying assembling process parameters to generate multiple reconstructions. However, the best assembly selection remains a challenge. Quantitative metrics for quality assessment have been inconsistent when compared with pertinent references. In this paper, a criterion for supporting assembly selection based on mapping DNA microarray hybridized probes to assembly sets is proposed. Mouse and fruit fly RNA-Seq datasets were assembled with standard de novo procedures. Quality assessment was estimated using quantitative metrics and the proposed criterion. The assembly that best mapped to the available reference transcriptomes of these model species provided the highest quality assembly. The hybridized probes identified the best assemblies, whereas quantitative metrics remained inconsistent. For example, subtle probe mapping difference of 0.25 percent, but statistically significant (ANOVA, p < 0.05), enabled the assembly selection that led to identify 3,719 more contigs and led to 1,049 further mapped contigs to the mouse reference transcriptome. The microarray data availability for non-model species makes the proposed criterion suitable for quality assessment of multiple de novo assembly strategies.