does qiime downstream of feature-classifier need Evalue and bitscore?

Technical Support
1

I am wondering whether qiime steps after the classifier use the blastn 6 format Evalue and bitscore columns in any way (alignment quality filtering?!).
When it does not, it may be possible to substitute blastn/vsearch by minimap2 for long read assignments but minimap does not produce Evalues (bitscores) so if these are required, it does not qualify.
Thanks for your info

2

Good to see you again Stephane,

Let's open up the source code and see what that plugin is doing under the hood. :blue_book: -> :open_book:

It looks like after running vsearch and blast, the _consensus_assignments method is called.

That in turn calls _compute_consensus_annotations, which is further down the same file.

Here, I don't see any 'alignment quality filtering', but keep in mind that both vsearch and blast let you define filters to control what they consider as a hit.

def classify_consensus_vsearch(query: DNAFASTAFormat,
                               reference_reads: DNAFASTAFormat,
                               reference_taxonomy: pd.Series,
                               maxaccepts: int = 10,
                               perc_identity: float = 0.8,
                               query_cov: float = 0.8,
                               strand: str = 'both',
                               min_consensus: float = 0.51,
                               unassignable_label: str =
                               _get_default_unassignable_label(),
                               search_exact: bool = False,
                               top_hits_only: bool = False,
                               threads: str = 1) -> pd.DataFrame:

You can see that vsearch will not report a hit unless it's 80% similar and has 80% coverage. I think this is what you were asking about.

Keep in mind that vsearch does not report evalues either, so this should not conflict with minimap2.

Colin

P.S. I feel a little pedantic saying this, but e-values are different than bitscores. Bitscore are a weighted total of the matches and mismatches in the alignment. E-values are the chances of seeing a bitscore that good by chance alone in the database. E-values are probabilistic based on the size and complexity of the database and also the size and complexity of the query. I think this is why modern programs often don't report e-values.

1 Like
3

Thanks a lot Colin,

So when I succeed to derive a blastn 6 quasi format from the minimap2 PAF format, I may plug minimap into qiime provided I can somehow build a stringency filter to keep only long matches (80%) out of the mapping results. Well, this is still some way to go but I'll try.

4

PAF is a lot like blast6. Here's all the PAF columns:
https://lh3.github.io/minimap2/minimap2.html#10

I wanted to check in about your project a little more. I've found vsearch plenty fast, even at a very large scale. How many reads are you looking to assign taxonomy to? Have you reduced their number by dereplication, denoising, or high-identity clustering?

Colin