Challenges and advances for transcriptome assembly in non-model species

Abstract

Analyses of high-throughput transcriptome sequences of non-model organisms are based on two main approaches: de novo assembly and genome-guided assembly using mapping to assign reads prior to assembly. Given the limits of mapping reads to a reference when it is highly divergent, as is frequently the case for non-model species, we evaluate whether using blastn would outperform mapping methods for read assignment in such situations (>15% divergence). We demonstrate its high performance by using simulated reads of lengths corresponding to those generated by the most common sequencing platforms, and over a realistic range of genetic divergence (0% to 30% divergence). Here we focus on gene identification and not on resolving the whole set of transcripts (i.e. the complete transcriptome). For simulated datasets, the transcriptome-guided assembly based on blastn recovers 94.8% of genes irrespective of read length at 0% divergence; however, assignment rate of reads is negatively correlated with both increasing divergence level and reducing read lengths. Nevertheless, we still observe 92.6% of recovered genes at 30% divergence irrespective of read length. This analysis also produces a categorization of genes relative to their assignment, and suggests guidelines for data processing prior to analyses of comparative transcriptomics and gene expression to minimize potential inferential bias associated with incorrect transcript assignment. We also compare the performances of de novo assembly alone vs in combination with a transcriptome-guided assembly based on blastn both via simulation and empirically, using data from a cyprinid fish species and from an oak species. For any simulated scenario, the transcriptome-guided assembly using blastn outperforms the de novo approach alone, including when the divergence level is beyond the reach of traditional mapping methods. Combining de novo assembly and a related reference transcriptome for read assignment also addresses the bias/error in contigs caused by the dependence on a related reference alone. Empirical data corroborate these findings when assembling transcriptomes from the two non-model organisms: Parachondrostoma toxostoma (fish) and Quercus pubescens (plant). For the fish species, out of the 31,944 genes known from D. rerio, the guided and de novo assemblies recover respectively 20,605 and 20,032 genes but the performance of the guided assembly approach is much higher for both the contiguity and completeness metrics. For the oak, out of the 29,971 genes known from Vitis vinifera, the transcriptome-guided and de novo assemblies display similar performance, but the new guided approach detects 16,326 genes where the de novo assembly only detects 9,385 genes.

Identification of Glutathione Peroxidase (GPX) Gene Family in Rhodiola crenulata and Gene Expression Analysis under Stress Conditions

“…In recent years, RNA sequencing (RNA-seq) technology has been widely used to reveal the difference in gene transcriptional levels for many plants with the development of next generation sequencing [ 30 ]. Especially for no-model plants, these organisms were the absence of detailed genetic information or closest reference genome [ 31 ]. In order to avoid the problem of homogenetic amplification caused by non-whole genome sequencing, RNA sequencing (RNA-seq) is a quick way to obtain coding sequences, discover new genes by constructing the genomic library (cDNA) or the transcription database [ 32 ].…”

The Oyster River Protocol: a multi-assembler and kmer approach for de novo transcriptome assembly

“…Of note, consistent with all short-read assemblers ( Ungaro et al, 2017 ), the ORP assemblies may not accurately reflect the true isoform complexity. Specifically, because of the way that single representative transcripts are chosen from a cluster of related sequences, some transcriptional complexity may be lost.…”

“…In addition to reporting synthetic metrics related to assembly structure, reports individual metrics related to specific elements of assembly quality. One such metric estimates the rate of chimerism, a phenomenon which is known to be problematic in de novo assembly ( Ungaro et al, 2017 ; Singhal, 2013 ). Rates of chimerism are relatively constant between all assemblers, ranging from 10% for the assembly, to 12% for the assembly.…”

“…The ease with which these tools may be used to produce and characterize transcriptome assemblies belies the true complexity underlying the overall process ( Ungaro et al, 2017 ; Wang & Gribskov, 2017 ; Moreton, Izquierdo & Emes, 2015 ; Yang & Smith, 2013 ). Indeed, the subtle (and not so subtle) methodological challenges associated with transcriptome reconstruction may result in highly variable assembly quality.…”

De Novo Hepatic Transcriptome Assembly and Systems Level Analysis of Three Species of Dietary Fish, Sardinops sagax, Scomber japonicus, and Pleuronichthys verticalis

“…It is important to note that while overlapping genes may serve as the foundation to indicate relevant functions across species, trophic levels, and/or habitats, increased coverage provided extended details that may be useful in a targeted study, especially if the target environmental contaminants-affected transcript is not represented in low coverage data. Furthermore, additional optimization techniques of de novo assembly of non-model organisms may enhance transcriptomic information, supporting downstream analysis [ 63 , 64 ]. Future work will focus on evaluating the effect of different depths of sequencing combined with de novo assembly parameters (e.g., k -mer length) to determine their effect on discovery of contaminant-affected transcripts.…”