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MRG: update more text to suggest sourmash tax / deprecate `sourmash…
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… lca` (#2784)

This PR further updates the command-line docs and the
classifying-signatures docs to deprecate LCA.

Fixes #2779
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ctb authored Sep 26, 2023
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72 changes: 35 additions & 37 deletions doc/classifying-signatures.md
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Expand Up @@ -35,41 +35,38 @@ analysis only.
See [the main sourmash tutorial](tutorial-basic.md#make-and-search-a-database-quickly)
for information on using `search` with and without `--containment`.

## Breaking down metagenomic samples with `gather` and `lca`
## Analyzing metagenomic samples with `gather`

Neither search option (similarity or containment) is effective when
comparing or searching with metagenomes, which typically have a
comparing or searching with metagenomes, which typically contain a
mixture of many different genomes. While you might use containment to
see if a query genome is present in one or more metagenomes, a common
question to ask is the reverse: **what genomes are in my metagenome?**

We have implemented two approaches in sourmash to do this.

<!-- CTB refactor this soon :) -->

One approach uses taxonomic information from e.g. GenBank to classify
individual k-mers, and then infers taxonomic distributions of
metagenome contents from the presence of these individual
k-mers. (This is the approach pioneered by
[Kraken](https://ccb.jhu.edu/software/kraken/) and used by many other tools.)
`sourmash lca` can be used to classify individual genome bins with
`classify`, or summarize metagenome taxonomy with `summarize`. The
[sourmash lca tutorial](tutorials-lca.md)
shows how to use the `lca classify` and `lca summarize` commands, and also
provides guidance on building your own database.

The other approach, `gather`, breaks a metagenome down into individual
genomes based on greedy partitioning. Essentially, it takes a query
metagenome and searches the database for the most highly contained
genome; it then subtracts that match from the metagenome, and repeats.
At the end it reports how much of the metagenome remains unknown. The
An alternative phrasing is this: **what reference genomes should I map
my metagenomic reads to?**

The main approach we provide in sourmash is `sourmash gather`. This
constructs the shortest possible list of reference genomes that cover
all of the known k-mers in a metagenome. We call this a *minimum
metagenome cover*.

From an algorithmic perspective, `gather` generates a minimum set
cover for a query metagenome, using the reference database you give
it. The minimum set cover is calculated using a greedy approximation
algorithm. Essentially, `gather` takes a query metagenome and
searches the database for the most highly contained genome; it then
subtracts that match from the metagenome, and repeats. At the end it
reports how much of the metagenome remains unknown. The
[basic sourmash tutorial](tutorial-basic.md#whats-in-my-metagenome)
has some sample output from using gather with GenBank. See Appendix A at
the bottom of this page for more technical details.
has some sample output from using gather with GenBank. See Appendix A
at the bottom of this page for more technical details.

Some benchmarking on CAMI suggests that `gather` is a very accurate
method for doing strain-level resolution of genomes. More on
that as we move forward!
The `gather` method is described in
[Lightweight compositional analysis of metagenomes with FracMinHash and minimum metagenome covers, Irber et al., 2022](https://www.biorxiv.org/content/10.1101/2022.01.11.475838v2).
Our benchmarking in that paper and also in
[Evaluation of taxonomic classification and profiling methods for long-read shotgun metagenomic sequencing datasets, Portik et al., 2022](https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-022-05103-0)
suggests that it is a very sensitive and specific method for
analyzing metagenomes.

## Taxonomic profiling with sourmash

Expand All @@ -95,13 +92,14 @@ create your own custom taxonomic ranks and even use them with private
databases of genomes to classify your own metagenomes.

The main disadvantage of sourmash's approach to taxonomy is that
sourmash doesn't classify individual metagenomic reads to either a genome
or a taxon. (Note that we're not sure
this can be done robustly in practice - neither short nor long reads typically
contain enough information to uniquely identify a single genome.) If you
want to do this, we suggest running `sourmash gather` first, and then
mapping the reads to the matching genomes; then you can use the mapping
to determine which read maps to which genome. This is the approach taken by
sourmash doesn't classify individual metagenomic reads to either a
genome or a taxon. (Note that we're not sure this can be done robustly
in practice - neither short nor long reads typically contain enough
information to uniquely identify a single genome, especially if there
are many genomes from the same species present in the database.) If
you want to do this, we suggest running `sourmash gather` first, and
then mapping the reads to the matching genomes; then you can determine
which read maps to which genome. This is the approach taken by
[the genome-grist pipeline](https://dib-lab.github.io/genome-grist/).

<!-- link to tutorials and examples -->
Expand All @@ -125,8 +123,8 @@ and appears to be both very accurate and very sensitive, unless you're
using Nanopore data or other data types that have a high sequencing
error rate.

It's important to note that taxonomy based on k-mers is very, very
specific and if you get a match, it's pretty reliable. On the
It's important to note that taxonomy based on multiple k-mers is very,
very specific and if you get a match, it's pretty reliable. On the
converse, however, k-mer identification is very brittle with respect
to evolutionary divergence, so if you don't get a match it may only
mean that the specific species or genus you're searching for isn't in
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7 changes: 3 additions & 4 deletions doc/command-line.md
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Expand Up @@ -373,10 +373,9 @@ collection itself.

Note:

Use `sourmash gather` to classify a metagenome against a collection of
genomes with no (or incomplete) taxonomic information. Use `sourmash
lca summarize` to classify a metagenome using a collection of genomes
with taxonomic information.
Use `sourmash gather` to analyze a metagenome against a collection of
genomes. Then use `sourmash tax metagenome` to integrate that collection
of genomes with taxonomic information.

#### Alternative search mode for low-memory (but slow) search: `--linear`

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