![]() ![]() When I do rarefy, I would like to rarefy to equal number of frequencies (reads) across samples rather than rarefy to equal number of features (OTUs) across samples. I think we would say how many reads/seqs assign to each OTU in QIIME 1. However, you table is showing how many frequencies in each feature. The tutorial table means feature counts, which means how many features find in each sample. The is no break down table as you give to me.Ģ> Also, I don’t think your table is same as the tutorial table. Just simply concatenate F and R reads? (I found I have a lot of reads that I can’t join) B>for those reads can’t be joined, still add to the seqs.qza/rep-seqs.qza for downstream analyses? C>Can I choose not to join F and R reads. If the workflow only seek the joinable reads, it will discard a lot of reads? Is it possible to control this? For example, A>join whatever can join. Let’s if you do fungal ITS, where the region size varies. The merge here–Do you mean join the overlapped region of paired reads? For example, if you have 2X300bp data and there are 50bp overlapped, you could join them into ~550bp reads? If so, what does the workflow deal with non-joinable reads. There is some stats about merge in above results. After de-noising, I have a stats like this ( ) So, if the workflow order doesn’t matter, I suppose I don’t have to filter rep-seqs files, as you explained.Ģ> Since I am doing pair-ends Illumina sequencing, I use the tutorials of Atacama soils ( ). Later, I will rarefy the filtered feature table (this is what I want to do). If I have some taxa that I don’t want, I will filter them out directly. Well, I assign taxonomy first and check my feature table. My workflow is not exactly same as the tutorials.ġ> The tutorials calculate alpha and beta diversity before assign taxonomy. I will refresh the moving pictures tutorials, if I have further questions. The rep_seqs and raw req files are almost never used.įrequency in the feature table (ASV) = number of reads or not? Also, I don’t think I need to do any filtering for rep_seqs or raw seqs according to the tutorials. Eventually, I have an feature table that I am going to do some serious analyses and I would like to do equal-depth subsample and want to know the number of reads of the final feature table. I did multiple filtering (e.g., filter ASVs that I don’t want or low abundance ASV). As my experience, I run Dada workflow and build a feature table. If I want to rarefy to equal number of reads across samples, I should do it before DADA workflow? – Or rarefy by equal sequencing depth has been updated?Ĥ> I guess the point here is that is how can I track the sequence/persample after build a feature table. The only file that I can see this is after my reads/persample is after I multiplexer? However, after this step, I can’t see anything about reads/persample, even I run dada workflow. I don’t think the frequency in the feature table means the number of the reads? It’s just the feature counts.ģ> Where can I find my reads/persamle information after I build the feature table. I know qiime feature-table rarefy, but this seems to do different thing? I am really confused here. I want to something like thisĢ>Also, how does people normally rarefy feature table in Qiime 2 (just switch from qiime 1). For example, I have 3 samples and 100 unqiue ASV. Is this possible to to give me a summary something like QIIME 1 OTU table. 1> Hello, I use qiime feature-table summarize (default setting) to check my feature table (ASVs). ![]()
0 Comments
Leave a Reply. |