Hello @wasade ,
thank you for your kind advice! Can I kindly ask you one more question? I now obtai several taxa names including numbers or capital letters, such as "g__Blautia_A_141781;s__Blautia_A_141781 faecis" or "p__Firmicutes_A;c__Clostridia_258483;o__Lachnospirales;f__Lachnospiraceae;g__Mediterraneibacter_A_155507;s__Mediterraneibacter_A_155507 faecis". So the questions are:
what do the numbers (141781, 258483,155507,..) mean?
what do the letters (Firmicutes _A, Mediterraneibacter _A,...) mean?
Good questions! The _A labels are directly from GTDB (see here for why). The _<number> is used to represent a distinct node in the phylogeny. In this case, "g__Blautia_A" is supported by more than one node, so we have to differentiate them to ensure the taxonomy label is unique. You can find the three Blautia clades in the Greengenes2 website if you'd like to explore the taxonomy directly.
Hello, I am also using qiime2 to analyze microbiome data for the first time, I am using V5-V7 region training classifier, I want to ask you if this problem is solved? Can you share your process at this step, thank you very much！
For V5-V7 data, I recommend using the non-v4-16s action which will perform closed reference OTU picking against the backbone. Or, you could perform naive Bayes classification using the full length model.
Hello @wasade - Very helpful information! I have a couple of questions. I have paired-end human stool data (processed with dada2) that is good quality through 250 not.
Do you know if there is any advantage to using the single-end versus paired-end data (i.e. the filter-features versus non-v4-16s approach)? And you mention trimming to 150nt (in the Deblur/Dada2 section) - is that a recommendation for filter-features and/or non-v4-16s? Thanks!
If the sequences were generated using 515F-806R EMP primers, then you could trim them to 150nt and filter-features. If you'd prefer to keep the full length, then you'd need to use non-v4-16s.
I'm unaware of literature that has independently benchmarked the various read stitching strategies. In my own analyses, I only use the fwd read from the EMP primers. Most of the taxonomic and phylogenetic signal is proximal to 515F as well, which is why studies like Yatsunenko et al 2012 Nature, which used 90 cycles if I recall correctly, still were quite exciting and compelling. In fact, quite a few of the analyses in the Thompson et al 2017 EMP paper were at 90nt too.
Hi Daniel, thank you for this resource! Can you provide a brief instruction on how to use this database outside of QIIME? For instance, I'd prefer to use Kraken2 and I have both 16s and shotgun sequencing. I presume I need the 16s sequence database, the whole-genome sequence database, and the shared taxonomy, but I can't immediately tell which files these correspond to since there are many files in the FTP repository with similar descriptions.
For shotgun, we recommend using the Woltka toolkit. The genome identifiers in the database are relative to the Web of Life version 2. It is possible Kraken2 will work although we haven't evaluated that. The exact commands we use are buried in here; as an alternative, I would encourage considering depositing data into Qiita as that resource will take care of the compute.
When using non-v4-16s, the plugin executes the q2-vsearch closed reference OTU picking pipeline against the backbone sequences. The features expressed in the resulting table will be a subset of the backbone. The exact code applied is here.
With 16S V4 ASVs, if using filter-features, the resulting features will be a subset of the V4 ASVs that have previously been placed into Greengenes2.
I'm not aware of a means to do that through QIIME 2 right now. The q2-vsearch's cluster_features_closed_reference action does not appear to return the UC mapping that describes the query / subject relationship. That mapping is necessary to determine what ASVs recruit to which backbone records.
@gregcaporaso I don't think there is a mechanism right now with q2-vsearch to obtain the mapping detail. Would that be valuable? If so I'll open an issue to track it
@wasade, you're right, there isn't right now. We have a type, FeatureMap, in q2-types-genomics intended for this type of mapping. We should move that over to q2-types so it's more generally accessible, and then use that for this purpose.
I am currently utilizing the "non-v4-16s" workflow and have encountered several challenges:
Unclassified Sequences: I've identified a number of sequences that remain unclassified within the workflow. Should there be any ID alterations for these sequences, how might I trace the unclassified species?
Feature Loss : I'm analyzing multiple regions and have observed a significant reduction in features, especially in regions like V1-V3 and V5-V7. While regions V3-V4 and V4 have a considerable number of classified ASVs/OTUs, they also appear to suffer from a loss of ASVs. The "non-v4-16s" workflow tends to classify at higher taxonomic levels. Could this be related to the feature loss I'm witnessing?
In terms of classifiers, would you advocate for the deployment of the full-length greengenes2 pre-constructed classifier when assessing features across diverse regions? Or would the extract-sequences approach with appropriate primers be more judicious? From the articule, I've discerned that the Naive Bayes classifier exhibits performance analogous to the phylogenetic classifier up to the genus level. Have you juxtaposed the classification performance of greengenes2 pre-constructed classifiers against other pre-constructed classifiers from others databases, such as the silva db?
I deeply appreciate your guidance on these matters.