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Building Salmon and STAR index using rat GRCr8 reference genome

Good day, I am doing analysis of my RNA-Seq data using Salmon for quantification and STAR for alignment. Both tools require building a reference index. The GRCr8 rat reference genome files are from ensembl: https://www.ensembl.org/Rattus_norvegicus/Info/Index

For Salmon, I followed the advice of building a decoy-aware index by combining the cdna library (https://ftp.ensembl.org/pub/release-115/fasta/rattus_norvegicus/cdna/#:~:text=Rattus_norvegicus.GRCr8.cdna.all.fa.gz) with the genome primary assembly. My problem is with the genome primary assembly file -- the available GRCr8 primary assembly files are separated per chromosome (https://ftp.ensembl.org/pub/release-115/fasta/rattus_norvegicus/dna/).

Hence, I concatenated the per-chromosome primary assembly files (non-chromosomal, chromosome 1-20, MT, X, Y) into a single primary assembly file by simple cat command. Then I built the decoy-aware index following the instructions here: https://combine-lab.github.io/alevin-tutorial/2019/selective-alignment/

I also built my STAR index using my concatenated primary assembly file.

However, there is a huge discrepancy in the aligned reads: only ~33% in Salmon and ~90% in STAR.

I'd like to ask if anyone has experience building an index from the GRCr8 rat reference genome and what are the expected results for aligned reads in Salmon and STAR. Would the alternative options be the best:

Salmon:

  1. Use only the cdna library as the index (not decoy-aware)
  2. Use the toplevel file for the genome reference in building a decoy-aware index, instead of concatenating the per-chromosome primary assembly files.

STAR:

  1. Use the toplevel file for the genome reference, instead of concatenating the per-chromosome primary assembly files.

Thanks in advance

star index salmon grcr8

1 answer

Salmon reports with towards the transcriptome, even when using a genome decoy. STAR reports genome alignment percentages. You probably have compromised library quality. Use featureCounts to check assignment of STAR to the transcriptome. That's the interesting comparison with salmon. Methods should not widely disagree.

Thank you for your answer. In STAR, I get 40% aligned to genes (exons) - should this be equivalent to the salmon report? Are these numbers expected for rat rna-seq or rRNA depleted libraries?

So 40% is much closer to 33% you are getting with salmon. Examine your STAR alignments in a browser such as IGV. You will have reads aligning outside known genes. As ATpoint said, this is likely reflective of the quality of your libraries.

The 40% vs 33% sounds reasonable. The tools are not interchangible so one would not expect perfect overlap. I have no experience in rat, nor rRNA depletion, but generally in RNA-seq it is a bit low, but maybe it's just fine. Do downstream analysis and see whether results make sense.

Thank you all for inputs! I'll proceed with the downstream analysis now.

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