Conclusions

We covered the core krepp workflow: indexing, distance estimation, phylogenetic placement, and taxonomic placement using a small toy reference set.

Distance estimation (krepp dist) computes per-read Hamming distances to reference genomes, akin to the ratio of mismatches one would obtain through alignment, but sensitive to more divergent sequences. The default command produces a tab-separated table of read-to-reference mappings and maximum likelihood distances. Filtering (--filter) retains only statistically close hits; the --summarize flag aggregates results into operational genomic unit counts, which can be used for downstream analysis and sample comparison.

Phylogenetic placement (krepp place -t) finds placements of query reads on a given reference phylogeny, producing a .jplace file by default, a format that can be further processed by tools such as gappa, iTOL, and QIIME 2. Taxonomic placement (krepp place -l) uses a lineage file to assign reads to named clades. Both support tabular (--tabular) and summarised (--summarize) outputs, similar to distance estimation.

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Exercises with Web of Life

For further exercises, you can use the same type of commands with a slightly larger index we downloaded earlier. This is the tiny (i.e., heavily minimized and downsampled) version of the Web of Life index (>10,000 microbial genomes, see this page for the full versions) on the same query mixture:

Figure 6. Per-query mapping rate and mean distance to the best-matching reference, using the WoL index. With denser sampling and a more diverse representation, mapping rates are uniformly high and best-reference distances are consistently low, meaning that all these queries have relatively close matches in this index compared to the toy index we built.

Figure 7. Placement heat-tree on the WoL reference phylogeny (>10,000 leaves). Since the toy sample we generated has low complexity, only a few clades/branches are highlighted. Real and complex samples could result in more widely spread placements.