Abstract
BackgroundShotgun metagenomes are often assembled prior to annotation of genes which biases the functional capacity of a community towards its most abundant members. For an unbiased assessment of community function, short reads need to be mapped directly to a gene or protein database. The ability to detect genes in short read sequences is dependent on pre- and post-sequencing decisions. The objective of the current study was to determine how library size selection, read length and format, protein database, e-value threshold, and sequencing depth impact gene-centric analysis of human fecal microbiomes when using DIAMOND, an alignment tool that is up to 20,000 times faster than BLASTX.ResultsUsing metagenomes simulated from a database of experimentally verified protein sequences, we find that read length, e-value threshold, and the choice of protein database dramatically impact detection of a known target, with best performance achieved with longer reads, stricter e-value thresholds, and a custom database. Using publicly available metagenomes, we evaluated library size selection, paired end read strategy, and sequencing depth. Longer read lengths were acheivable by merging paired ends when the sequencing library was size-selected to enable overlaps. When paired ends could not be merged, a congruent strategy in which both ends are independently mapped was acceptable. Sequencing depths of 5 million merged reads minimized the error of abundance estimates of specific target genes, including an antimicrobial resistance gene.ConclusionsShotgun metagenomes of DNA extracted from human fecal samples sequenced using the Illumina platform should be size-selected to enable merging of paired end reads and should be sequenced in the PE150 format with a minimum sequencing depth of 5 million merge-able reads to enable detection of specific target genes. Expecting the merged reads to be 180-250 bp in length, the appropriate e-value threshold for DIAMOND would then need to be more strict than the default. Accurate and interpretable results for specific hypotheses will be best obtained using small databases customized for the research question.
Highlights
Shotgun metagenomes are often assembled prior to annotation of genes which biases the functional capacity of a community towards its most abundant members
The 100 simulated metagenomes were mapped to a custom betagalactosidase database, the NCBI RefSeq database [13] or the SEED database [14] using DIAMOND with the “sensitive” flag and default e-value threshold
True positive rates were expressed as the proportion of reads known to originate from the target that were correctly identified as the target
Summary
Shotgun metagenomes are often assembled prior to annotation of genes which biases the functional capacity of a community towards its most abundant members. For an unbiased assessment of community function, short reads need to be mapped directly to a gene or protein database. One common way of functionally annotating a shotgun metagenome is to first assemble the reads into longer fragments of DNA called contigs, predict open reading frames in the contigs and map these ORFs to gene family databases. This is valuable because it can provide genomic context for individual genes. If the objective of the study is to assess the overall functional capacity of a community, it may not be necessary to put sequences in the context of their individual genomes
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