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In the paper mentioned, we used the ScaleData function in Seurat to regress out the number of reads, Rn45s abundance, and percent ribosomal gene transcripts. Ribosomal genes were found with the regular expression ^Rp[sl][[:digit:]]. tiss <- ScaleData(object = tiss, vars.to.regress = c("nReads", "percent.ribo","Rn45s")) Here's a fuller notebook, and we'...


5

We've found ribosomal RNA to be less of a problem with sequencing that depends on polyA, which suggests the issue might be in the library preparation, rather than the selection. Many polyA RNA library preparation methods involve amplification, rather than selection, which means that existing transcripts that are present in very high abundance (such as rRNA) ...


3

The rRNA genes in that dataset are Rn45s and Rn4.5s. BTW, you have gene counts, not transcript counts.


1

It's not so much that you have "intronic contamination" or "genomic contamination", rather you're not selecting explicitly for full-length mature transcripts with rRNA depletion. That is the most common cause for higher intronic read rates. There's nothing you can do about this post-hoc, just continue along. BTW, many lncRNA's are ...


1

SILVA is intended for metagenomic samples and thus does not contain the human ribosomal subunits. While the 5.8S subunit is in the ensembl, ncbi, and ucsc annotations, and there are numerous rRNA "biotype" (or "biomol") entries, the 5S, 18S, and 28S subunit sequences are absent. Even downloading rRNA entries in the rmsk table from ucsc will not return all ...


1

This is a copy-paste nearly verbatim of comments so that the question has an answer What you are trying to do is find what genes are in what homology cluster. This is common problem and there are many solution each with some issues. Uniprot90 is indeed a cluster of homologues but it is too limited. Whereas you require clusters that span all the domains (...


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