Impact of short-read sequencing on the misassembly of a plant genome

Abstract

Background Availability of plant genome sequences has led to significant advances. However, with few exceptions, the great majority of existing genome assemblies are derived from short read sequencing technologies with highly uneven read coverages indicative of sequencing and assembly issues that could significantly impact any downstream analysis of plant genomes. In tomato for example, 0.6% (5.1 Mb) and 9.7% (79.6 Mb) of short-read based assembly had significantly higher and lower coverage compared to background, respectively. Results To understand what the causes may be for such uneven coverage, we first established machine learning models capable of predicting genomic regions with variable coverages and found that high coverage regions tend to have higher simple sequence repeat and tandem gene densities compared to background regions. To determine if the high coverage regions were misassembled, we examined a recently available tomato long-read based assembly and found that 27.8% (1.41 Mb) of high coverage regions were potentially misassembled of duplicate sequences, compared to 1.4% in background regions. In addition, using a predictive model that can distinguish correctly and incorrectly assembled high coverage regions, we found that misassembled, high coverage regions tend to be flanked by simple sequence repeats, pseudogenes, and transposon elements. Conclusions Our study provides insights on the causes of variable coverage regions and a quantitative assessment of factors contributing to plant genome misassembly when using short reads.

High quality long-read genomes produced from single MinION flow cells clarify polyploid and demographic histories of critically endangered ash species (Fraxinus: Oleaceae)

“…Repetitive elements made up nearly 60% of all three Fraxinus genome assemblies (Table S3), while they made up about only about 45% of the F. pennsylvanica reference assembly, which was based on Illumina short reads. Since short-read assemblies have the propensity to collapse duplicated sequences, this difference was likely due to better read-through of repetitive DNA on long ONT single molecules [24]. The largest group of retroelements discovered were Ty1/Copia and Gypsy/DIRS LTR elements, each comprising about 15% of all repetitive elements characterized.…”

“…The assemblies from this study were also closer to genome sizes estimated by ow cytometry [22], with the reference-guided assemblies being over 100 MB smaller than estimated genome sizes. Again, these disparities may be partly due to better repeat detection in long-read genome assemblies, which likely contain fewer collapses of identical or near-identical sequences [24].…”

Depth and evenness of sequence coverage are associated with assembly quality, genome structure, and choice of sequencing platform in archived plastid genomes

“…Alternatively, the observed differences may represent a methodological artifact. For example, repeat regions are known to affect the bioinformatic process of mapping sequence reads to a reference genome (Schbath et al, 2012; Thankaswamy-Kosalai et al, 2017; Wang et al, 2021). In Bowtie2, for example, reads are mapped to a reference genome based on a mapping score that is identical across exact repeats, but pseudo-random numbers are used to break ties between repeat locations (Langmead and Salzberg, 2012).…”

“…For example, equality in length and sequence of the IRs of a plastid genome can be used as an indicator of its overall assembly quality (Gruenstaeudl and Jenke, 2020; Zheng et al, 2020), as IR equality is maintained naturally and rarely deviated from (Goulding et al, 1996; Ruhlman et al, 2017). Third, researchers can measure the depth of sequence coverage of a genome assembly (Sims et al, 2014), with both the average sequencing depth and the evenness of coverage indicative of assembly quality (Chen et al, 2013; Wang et al, 2021). Average sequencing depth can assist in identifying the overall adequacy of sequence coverage, whereas the precise distribution of sequencing depth can help to identify regions of inadequate coverage.…”

“…Gu et al (2016), for example, employed sequencing evenness as an indicator to refine the assembly of the plastid genome of crape myrtle ( Lagerstroemia fauriei ). Similarly, Wang et al (2021) assessed sequencing evenness in the nuclear genome assembly of a tomato cultivar to infer the factors contributing to genome mis-assembly. Several metrics to measure sequencing evenness exist, with the standard deviation ( σ ) around the mean value of the normalized sequence coverage representing a commonly used metric (Oexle, 2016).…”