Coverage-versus-Length plots, a simple quality control step for de novo yeast genome sequence assemblies
Illumina sequencing has revolutionized yeast genomics, with prices for commercial draft genome sequencing now below $200. The popular SPAdes assembler makes it simple to generate a de novo genome assembly for any yeast species. However, whereas making genome assemblies has become routine, understanding what they contain is still challenging. Here, we show how graphing the information that SPAdes provides about the length and coverage of each scaffold can be used to investigate the nature of an assembly, and to diagnose possible problems. Scaffolds derived from mitochondrial DNA, ribosomal DNA, and yeast plasmids can be identified by their high coverage. Contaminating data, such as cross-contamination from other samples in a multiplex sequencing run, can be identified by its low coverage. Scaffolds derived from the bacteriophage PhiX174 and Lambda DNAs that are frequently used as molecular standards in Illumina protocols can also be detected. Assemblies of yeast genomes with high heterozygosity, such as interspecies hybrids, often contain two types of scaffold: regions of the genome where the two alleles assembled into two separate scaffolds and each has a coverage level C, and regions where the two alleles co-assembled (collapsed) into a single scaffold that has a coverage level 2C. Visualizing the data with Coverage-versus-Length (CVL) plots, which can be done using Microsoft Excel or Google Sheets, provides a simple method to understand the structure of a genome assembly and detect aberrant scaffolds or contigs. We provide a Python script that allows assemblies to be filtered to remove contaminants identified in CVL plots.\n\n100-word article summaryWe describe a simple new method, Coverage-versus-Length plots, for examining de novo genome sequence assemblies. These plots enable researchers to detect scaffolds that have unusually high or unusually low coverage, which allows contaminants, and scaffolds that come from atypical parts of the organisms DNA complement, to be detected. We show that contaminants are common in yeast genomes sequenced in multiplex Illumina runs. We provide instructions for making plots using Microsoft Excel or Google Sheets, and software for filtering assemblies to remove contaminants. Contaminants can be detected and removed, even without knowing their source.