MetRef, an Auto-updatable Database for Reference/representative Genomes and their Complexity Metrics

Introduction The use of genomic metrics to evaluate biological complexity is an important area of research in evolutionary biology; however, this area of study has been hindered by the need for high levels of computation and the lack of a uniform method of performing genomic complexity analyses. This research presents an auto-updating web-based application called MetRef (Metrics of Reference Genomes) that provides an interface to standardize and display genomic complexity analyses. Methods MetRef uses two key metrics: Genomic Signature (GS), which uses the normalized frequency of k-mers, and Biobit (BB), which is a combination of both the entropic and anti-entropic components. In order to reduce the computational overhead associated with k-mer counting, MetRef includes the specialized tool KMC 3. Additionally, the MetRef application utilizes a multi-layered architectural framework that incorporates Django, PostgreSQL, and Celery/Redis for managing high-quality assemblies from the NCBI RefSeq and Taxonomy databases. Results Using KMC 3 resulted in a reduction in processing time for the human genome from 36 hours to 5 minutes. Users can select comparative studies and receive automatic statistical reports that include Pearson correlation matrices, Principal Component Analysis (PCA), and ANOVA testing, with color-blind-friendly palettes. Discussion The advantages of MetRef to address two major limitations in genomic complexity research (computational efficiency and lack of uniform procedures) lie within its use of a special-purpose C++ counting engine (KMC 3) for calculating genetic complexities, and through the unification of calculation pipelines. As such, the MetRef removes inconsistencies generated from varying treatments of non-ACGT bases; therefore, all genetic complexities calculated with the same metrics can be compared directly across the entire tree of life. Conclusion In addition to these technical advantages, MetRef establishes a solid, scalable base for evolutionary genomics through an architecturally stable structure using Django and Celery / Redis. Furthermore, the system reduces the researcher's workload, providing them with automatically generated statistical reports and previously calculated, versioned data; thus enabling researchers to easily design and perform additional large-scale comparative studies of genetic complexities.