A multiple genome alignment workflow shows the impact of repeat masking and parameter tuning on alignment of functional regions in plants

Abstract

Alignments of multiple genomes are a cornerstone of comparative genomics, but generating these alignments remains technically challenging and often impractical. We developed the msa_pipeline workflow (https://bitbucket.org/bucklerlab/msa_pipeline) based on the LAST aligner to allow practical and sensitive multiple alignment of diverged plant genomes with minimal user inputs. Our workflow only requires a set of genomes in FASTA format as input. The workflow outputs multiple alignments in MAF format, and includes utilities to help calculate genome-wide conservation scores. As high repeat content and genomic divergence are substantial challenges in plant genome alignment, we also explored the impact of different masking approaches and alignment parameters using genome assemblies of 33 grass species. Compared to conventional masking with RepeatMasker, a k-mer masking approach increased the alignment rate of CDS and non-coding functional regions by 25% and 14% respectively. We further found that default alignment parameters generally perform well, but parameter tuning can increase the alignment rate for non-coding functional regions by over 52% compared to default LAST settings. Finally, by increasing alignment sensitivity from the default baseline, parameter tuning can increase the number of non-coding sites that can be scored for conservation by over 76%.

AnchorWave: Sensitive alignment of genomes with high sequence diversity, extensive structural polymorphism, and whole-genome duplication

“…Based on the observation that long indels are generally derived from TE movement, while different mechanisms introduce shorter indel mutations ( 11 ), the two-piece affine gap cost strategy enables the alignment of short and long indels. The performance of other genome alignment implementations may improve by fine-tuning gap parameters ( 47 ), but, with the exception of minimap2, all other tested tools align long indels and short indels using the same gap penalty profile. This precludes parameter tuning as a solution to simultaneously optimize the alignment of long indels and short indels.…”