Comparative analysis of de novo assemblers for variation discovery in personal genomes

Abstract

Current variant discovery approaches often rely on an initial read mapping to the reference sequence. Their effectiveness is limited by the presence of gaps, potential misassemblies, regions of duplicates with a high-sequence similarity and regions of high-sequence divergence in the reference. Also, mapping-based approaches are less sensitive to large INDELs and complex variations and provide little phase information in personal genomes. A few de novo assemblers have been developed to identify variants through direct variant calling from the assembly graph, micro-assembly and whole-genome assembly, but mainly for whole-genome sequencing (WGS) data. We developed SGVar, a de novo assembly workflow for haplotype-based variant discovery from whole-exome sequencing (WES) data. Using simulated human exome data, we compared SGVar with five variation-aware de novo assemblers and with BWA-MEM together with three haplotype- or local de novo assembly-based callers. SGVar outperforms the other assemblers in sensitivity and tolerance of sequencing errors. We recapitulated the findings on whole-genome and exome data from a Utah residents with Northern and Western European ancestry (CEU) trio, showing that SGVar had high sensitivity both in the highly divergent human leukocyte antigen (HLA) region and in non-HLA regions of chromosome 6. In particular, SGVar is robust to sequencing error, k-mer selection, divergence level and coverage depth. Unlike mapping-based approaches, SGVar is capable of resolving long-range phase and identifying large INDELs from WES, more prominently from WGS. We conclude that SGVar represents an ideal platform for WES-based variant discovery in highly divergent regions and across the whole genome.

Structural variant calling: the long and the short of it

“…Cortex uses a colored de Bruijn graph (see Table 2 for definition) to simultaneously infer SVs and complex combinations of SNVs, indels, and rearrangements [31]. SGVar [32] is a more recent string graph-based (see Table 2 for definition) de novo assembly pipeline based on the SGA assembler [75] that also uses short-read sequencing data. SGVar uses a stringent read preprocessing based on the read length and read quality.…”

“…It requires a perfect match to merge reads or sequences, which improves the assembly quality. Using both simulated and real data (chromosome six of the human genome), SGVar has been shown to outperform other methods, such as Cortex, for insertion and deletion identification [32].…”

“…Theoretically, all forms of structural variants can be identified given a fully contiguous and complete de novo assembly. The main strength of de novo assembly-based approaches compared to other approaches lies in detecting larger insertions (3+ kbp) [34, 32]. One major challenge is the lack of haplotype representation.…”

A Comparison of Performance for Different SARS-Cov-2 Sequencing Protocols

“…Their effectiveness is limited by the presence of gaps, potential misassemblies, regions of duplicates with a high-sequence similarity and regions of high-sequence divergence in the reference. Also, mapping-based approaches are less sensitive to large INDELs and complex variations" (1) "We document that 18.6% of SNP genotype calls in HLA genes are incorrect and that allele frequencies are estimated with an error greater than ±0.1 at approximately 25% of the SNPs in HLA genes. We found a bias toward overestimation of reference allele frequency for the 1000G data, indicating mapping bias is an important cause of error in frequency estimation in this dataset."…”

On Variant Discovery in Genomes of Fungal Plant Pathogens

“…(McKenna et al, 2010;Rimmer et al, 2014). Conversely, recovery of large-scale structural variants is typically achieved by first assembling individual genomes, which are then combined into a whole genome alignment (WGA) (Tian et al, 2018). The WGA enables the accurate location of large indels (typically larger than 3 kb) (Nattestad and Schatz, 2016;Tian et al, 2018).…”