The Roslin Institute forms part of the Royal (Dick) School of Veterinary Studies, University of Edinburgh. This project was supported by the Biotechnology and Biological Sciences Research Council (BBSRC; BB/S006680/1, BB/R015023/1), including institute strategic program grant BBS/E/D/30002276. R.H.S. is supported by an EASTBIO studentship funded by BBSRC (BB/M010996/1). A.W.W. and the Rowett Institute receive core financial support from the Scottish Government Rural and Environmental Sciences and Analytical Services (SG-RESAS). We would like to thank all of those who were involved in creating and publicly sharing both the Hungate Collection data and the RUG data.Peer reviewedPostprin

Glendinning, Laura

Smith, Rebecca Louise

Walker, Alan

Watson, Mick

Animal Microbiome

English

PubMed

Additional file 1: Table S1. Classification rate of reads for six reference databases at various taxonomic levels. Classification rate refers to whether the read was classified, or unclassified, regardless of accuracy. Each row denotes the six databases used to classify reads with Kraken2. The “Overall” column refers to the percentage of reads that were classified or unclassified by Kraken2 regardless of taxonomic level. Subsequent columns refer to the percentage of reads that were classified or unclassified by Kraken2 at various taxonomic levels as shown in the column headers. Table S2. Classification status of reads compared to the ground truth for the six reference databases at various taxonomic levels. The databases and detailed classification status are shown in the first column. Subsequent columns contain the percentage of reads at that taxonomic level, which had been classified by the database and had the particular classification status outlined in the first column. “Correct” and “incorrect” refer to reads that were classified correctly or incorrectly by Kraken2 using the respective database. “Truth unknown” refers to the reads that originate from genomes that do not have an assigned family or genus. “Unclassified at any level” refers to reads that were not classified to any taxonomic level. “Unclassified at this level” refers to reads that were classified at other taxonomic levels, but not the level being examined in a given column. Fig. S1 The frequency of genera and species in the ground truth data, and in the classification results for each reference database. The total frequency is shown in the top two graphs, the middle graphs show the frequency of false positives occurring, and the bottom two graphs show the frequency of false negatives. Fig. S2 Scatterplots show the comparison between the simulated metagenomic data (ground truth, x-axis) and classified reads (y-axis) when classified using the HunRUG (A) and RefHunRUG (B) reference databases. Data are plotted as a percentage of classified reads for the classified data, and a percentage of simulated reads for the ground-truth data. The data were transformed by log10. A y=x line (shown in red) was added to demonstrate how data points would appear on the graph if the number of ground-truth and classified reads were the same. A linear regression was added (shown in blue) and used to calculate the R2 statistic. The R2 statistic is shown (C) for each reference database at the Phylum, Family, Genus and Species levels

Rebecca H. Smith (14139034)

Laura Glendinning (823276)

Alan W. Walker (7153784)

Mick Watson (533792)

FigShare

Additional file 1 of Investigating the impact of database choice on the accuracy of metagenomic read classification for the rumen microbiome

The Roslin Institute forms part of the Royal (Dick) School of Veterinary Studies, University of Edinburgh. This project was supported by the Biotechnology and Biological Sciences Research Council (BBSRC; BB/S006680/1, BB/R015023/1), including institute strategic program grant BBS/E/D/30002276. R.H.S. is supported by an EASTBIO studentship funded by BBSRC (BB/M010996/1). A.W.W. and the Rowett Institute receive core financial support from the Scottish Government Rural and Environmental Sciences and Analytical Services (SG-RESAS). We would like to thank all of those who were involved in creating and publicly sharing both the Hungate Collection data and the RUG data.Peer reviewe

Aberdeen University Research

Investigating the impact of database choice on the accuracy of metagenomic read classification for the rumen microbiome

Microbiome analysis is quickly moving towards high-throughput methods such as metagenomic sequencing. Accurate taxonomic classification of metagenomic data relies on reference sequence databases, and their associated taxonomy. However, for understudied environments such as the rumen microbiome many sequences will be derived from novel or uncultured microbes that are not present in reference databases. As a result, taxonomic classification of metagenomic data from understudied environments may be inaccurate. To assess the accuracy of taxonomic read classification, this study classified metagenomic data that had been simulated from cultured rumen microbial genomes from the Hungate collection. To assess the impact of reference databases on the accuracy of taxonomic classification, the data was classified with Kraken 2 using several reference databases. We found that the choice and composition of reference database significantly impacted on taxonomic classification results, and accuracy. In particular, NCBI RefSeq proved to be a poor choice of database. Our results indicate that inaccurate read classification is likely to be a significant problem, affecting all studies that use insufficient reference databases. We observed that adding cultured reference genomes from the rumen to the reference database greatly improved classification rate and accuracy. We also demonstrated that metagenome-assembled genomes (MAGs) have the potential to further enhance classification accuracy by representing uncultivated microbes, sequences of which would otherwise be unclassified or incorrectly classified. However, classification accuracy was strongly dependent on the taxonomic labels assigned to these MAGs. We therefore highlight the importance of accurate reference taxonomic information and suggest that, with formal taxonomic lineages, MAGs have the potential to improve classification rate and accuracy, particularly in environments such as the rumen that are understudied or contain many novel genomes. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s42523-022-00207-7

Smith, Rebecca H.

Walker, Alan W.

PubMed Central

https://aura.abdn.ac.uk/bitstream/2164/19264/1/Smith_etal_AM_Investigating_the_Impact_AAM.pdf

Investigating the impact of database choice on the accuracy of metagenomic read classification for the rumen microbiome

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Aberdeen University Research

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