Assessing the quality of comparative genomics data and results with the cogeqc R/Bioconductor package

Abstract

Abstract Comparative genomics has become an indispensable part of modern biology due to the advancements in high‐throughput sequencing technologies and the accumulation of genomic data in public databases. However, the quality of genomic data and the choice of parameters used in software tools used for comparative genomics can greatly impact the accuracy of results. Here, we present cogeqc, an R/Bioconductor package that provides researchers with a toolkit to assess genome assembly and annotation quality, orthogroup inference, and synteny detection. The package offers context‐guided assessments of assembly and annotation statistics by comparing observed statistics to those of closely‐related species on NCBI. To assess orthogroup inference, cogeqc calculates a protein domain‐aware orthogroup score that aims at maximising the number of shared protein domains within the same orthogroup. The assessment of synteny detection consists in representing anchor gene pairs as a synteny network and analysing its graph properties, such as clustering coefficient, node count, and scale‐free topology fit. The application of cogeqc to real datasets allowed for an evaluation of multiple parameter combinations for orthogroup inference and synteny detection, providing researchers in need for comparative genomics with guidelines to aid in the selection of the most appropriate tools and parameters for their specific data. We demonstrate that the default parameters in orthogroup identification and synteny detection tools are not always the most suitable, highlighting the importance of performing assessments for each dataset. The assessment metrics provided by cogeqc will help researchers generate more accurate and reliable results.

Keywords

• evolutionary genomics
• gene family
• genome assembly
• quality control