Effect of de novo transcriptome assembly on transcript quantification

Abstract

Correct quantification of transcript expression is essential to understand the functional elements in different physiological conditions. For the organisms without the reference transcriptome, de novo transcriptome assembly must be carried out prior to quantification. However, a large number of erroneous contigs produced by the assemblers might result in unreliable estimation. In this regard, this study investigates how assembly quality affects the performance of quantification based on de novo transcriptome assembly. We examined the over-extended and incomplete contigs, and demonstrated that assembly completeness has a strong impact on the estimation of contig abundance. Then we investigated the behavior of the quantifiers with respect to sequence ambiguity which might be originally presented in the transcriptome or accidentally produced by assemblers. The results suggested that the quantifiers often over-estimate the expression of family-collapse contigs and under-estimate the expression of duplicated contigs. For organisms without reference transcriptome, it remains challenging to detect the inaccurate estimation on family-collapse contigs. On the contrary, we observed that the situation of under-estimation on duplicated contigs can be warned through analyzing the read proportion of estimated abundance (RPEA) of contigs in the connected component inferenced by the quantifiers. In addition, we suggest that the estimated quantification results on the connected component level have better accuracy over sequence level quantification. The analytic results conducted in this study provides valuable insights for future development of transcriptome assembly and quantification.

Differential Gene Expression with an Emphasis on Floral Organ Size Differences in Natural and Synthetic Polyploids of Nicotiana tabacum (Solanaceae)

“…The de novo assemblies proved better than the assembled genomes with fewer fragmented contigs of candidate genes of organ size and flower color. Similar to other recent studies, there is a wide distribution of quality of assemblies based on the programs and methods used [137,138]; in our case, we saw that SPAdes outperformed Trinity in regards to completeness of BUSCO genes and number of contigs. Adding just over a million long-reads to the N. sylvestris transcriptome decreased the number of contigs by nearly 7000 and increased the N50 values of the assembly by approximately 500 bp while increasing the complete single copy BUSCO genes from 78.5% to 86.4% (Figure 3).…”

An improved de novo assembling and polishing of Solea senegalensis transcriptome shed light on retinoic acid signalling in larvae

“…2). www.nature.com/scientificreports/ www.nature.com/scientificreports/ But, even if de novo assembled transcriptomes are acceptable, bioinformatic tools for RNA-seq are not specifically designed for them due to the low annotation rates and biases in FDR corrections than are exacerbated by redundant transcripts 27 . This is why improved R scripts of DEGenes Hunter were developed in this study to analyse differential expression patterns using SOLSEv5.0 34 as reference.…”

“…Previous assembled transcriptomes in sole reported a high number of transcripts 7,8,24,25 that clearly exceeded the expected number of predicted genes as reported in closely related flatfish (about 21,000 protein-coding genes 1,3,26 ). Accurate transcript quantification for gene expression studies is hindered by over-represented transcriptomes 27 , resulting in biased transcript discovery, over-estimation of family-collapsed contigs, and under-estimation of redundant contigs 11,27 . Bioinformatic strategies to reduce artefacts and redundancy have been implemented in sole 7 , but the result was still far from being optimal, and further polishing to increase tissue representativity, transcript completeness and annotations is required.…”

The Peptide Venom Composition of the Fierce Stinging Ant Tetraponera aethiops (Formicidae: Pseudomyrmecinae)

“…However, for contigs containing the venom peptides open reading frames (ORF), the assembler often generates overextended contigs (see Table S1). Thus, the expression rate of the short venom peptides transcripts would be underestimated with TPM [51]. So we calculated the RPM value for each transcript of interest in two steps: (i) by dividing the number of aligned reads for each contig by the total number of million reads aligned for the sample, and (ii) by summing up the obtained values for each contig encoding the transcript when several contigs represent the same peptide.…”