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phylosift's Issues

Easy way to calculate KR distances among samples with guppy

guppy can calculate the Kantorovich Rubenstein distance among a pair of pplacer placement files. These distances are useful for assessing how similar two microbial communities are to each other. amphora2 should have a way to calculate these distances among a group of samples using the protein marker families.

Calculate edge PCA in amphora2

Based on KR distances among samples, one can calculate an edge principle components analysis which, among other things, can highlight which groups of organisms contribute the greatest difference between microbial communities. amphora2 should have an easy way to invoke guppy to do this for a group of samples.

join paired reads in concatenate output

when a single long read or two reads in a paired-end read hit multiple markers, they are not treated as a single sequence in the concatenate alignment produced. This needlessly discards valuable linkage information.

"recursive" processing of reads in well-sampled parts of the tree

Phylogenetic placement of reads that are very close to a reference sequence would be more accurately placed using their DNA sequence. We should identify these reads/sequences on the basis of rapsearch/blast output and flag them for DNA analysis instead of protein analysis.

Related to this, we will need the updating script to divide up the database into subsets of taxa that are similar enough for nucleotide analysis. Leaving them lumped together has been tried and results in poor quality inference -- the phylogenetic models get confused by the extensive diversity at any particular site. An alternative would be codon analysis, but no known read placement tool supports this.

PD pruning during marker database update

Currently the marker databases include sequences from all genomes. Many of these are 100% identical to each other and don't offer any additional phylogenetic resolution. Inclusion of these sequences makes the marker gene database large (currently > 1GB). PD pruning could be used to include a subset of sequences that are most informative.

design and construct test datasets

ideally there would be a script to generate test datasets from genome sequence data.
classic approach is to take reads from isolate genomes and mix them in known abundances.
need to get isolate genome reads.
design microbial communities using them.
aaron has some of these scripts already.

also would be good to construct in-vitro simulations.

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