Forsyth, E. R. | bioRxiv Science

bioRxiv2021

The META tool optimizes metagenomic analyses across sequencing platforms and classifiers.

A major challenge in the field of metagenomics is the selection of the correct combination of sequencing platform and downstream metagenomic analysis algorithm, or classifier. Here, we present the Metagenomic Evaluation Tool Analyzer (META), which produces simulated data and facilitates platform and algorithm selection for any given metagenomic use case. META-generated in silico read data are modular, scalable, and reflect user-defined community profiles, while the downstream analysis is done using a variety of metagenomic classifiers. Reported results include information on resource utilization, time-to-answer, and performance. Real-world data can also be analyzed using selected classifiers and results benchmarked against simulations. To test the utility of the META software, simulated data was compared to real-world viral and bacterial metagenomic samples run on four different sequencers and analyzed using 12 metagenomic classifiers. Lastly, we introduce META Score: a unified, quantitative value which rates an analytic classifiers ability to both identify and count taxa in a representative sample.

bioinformatics↗

bioRxiv2020

A Phased Canis lupus familiaris Labrador Retriever Reference Genome Utilizing High Molecular Weight DNA Extraction Methods and High Resolution Sequencing Technologies

Reference genome fidelity is critically important for genome wide association studies (GWAS), yet many are incomplete or too dissimilar from the study population. A typical whole genome sequencing approach implies short-read technologies resulting in fragmented assemblies with regions of ambiguity low complexity. Further information is lost by economic necessity when genotyping populations, as lower resolution technologies such as genotyping arrays are commonly utilized. Here we present a phased reference genome for Canis lupus familiaris utilizing high molecular weight sequencing technologies. We tested wet lab and bioinformatic approaches to demonstrate a minimum workflow to generate the 2.4 gigabase genome for a Labrador Retriever. The resulting de novo assembly required eight Oxford Nanopore R9.4 flowcells (~23X depth) and running a 10X Genomics library on the equivalent of one lane of an Illumina NovaSeq S1 flowcell (~88X depth), bringing the cost of generating a nearly complete reference genome to less than $10K. Mapping of publicly available short-read data from ten Labrador Retrievers against this breed-specific reference resulted in an average of approximately 1% more aligned reads compared to mapping against the current gold standard reference (CanFam3.1, p<0.001), indicating a more complete breed-specific reference. An average 15% reduction of variant calls was observed from the same mapped data, which increases the chance of identifying low effect size variants in a GWAS. We believe that by incorporating the cost to produce a full genome assembly into any large-scale canine genotyping study, an investigator can make an informed cost/benefit analysis regarding genotyping technology.

genomics↗