Will you critique/rate my mRNA seq alignment analysis?
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8.0 years ago
aswartz85 ▴ 20

What I'm trying to do is really straightforward - align ribosome profiling reads (only mRNA fragments protected from RNase degradation are sequenced) to the mouse transcriptome. I've completed this using the following:

I first ran my fastq file through FastQC. I noticed there was a lot of adapter contamination, so I ran my fastq through cutadapt/trim_galore. The output file appeared free of illumina adapters.

I then aligned to the transcriptome using Hisat2 w/ genome/transcriptome I downloaded from their website (GRCm38 genome_snp_tran files [I think this is what I want in order to align to the transcriptome?]). My command was as follows:

hisat2 -x [genome/transcriptome index] -U [single end read file].fastq -S [output file name].sam

samtools for SAM to sorted BAM conversion

Gene abundance using Cufflinks w/ command:

cufflinks -G [transcriptome annotation].gtf [input sorted bam]

Ultimately, the results looks okay. I did get a lot of unmapped reads (~30%) from Hisat2 alignment. This may have to do with the fact that the mice I'm using are not C57Bl6. Don't know if hisat2 genome/transcriptome build I'm using accounted for all snps.

Any suggestions?

RNA-Seq alignment • 2.0k views
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That's a reasonable enough plan. I've done something similar but used STAR instead, which produces a bit nicer results since it can soft-clip the alignments. I'm not the worlds biggest fan of cufflinks, but with a well annotated mouse genome it's probably OK. I should note that if possible it's really nice to combine a standard mRNAseq sample or two with your ribosomal profiling, mostly because it can make it easier to determine which transcripts are really the ones getting expressed to begin with.

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I actually DO have the mRNA sequencing data for this experiment, and not just ribosome footprinting. I actually asked a question here on how I can compare the 2 given that my mRNA seq data is reported in RNA counts and ribosome footprinting is reported in FPKM (how cufflinks outputs data). But you made a great argument, that one is not better than the other, but that mRNA seq can just be used to corroborate RF data.

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Sounds like a reasonable pipeline. That's indeed quite a lot of unmapped reads. Have you tried blasting some of them to find out where they are derived from?

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hi, for the reads that didn't align, you can attempt de novo using Trinity. Works well in reasonable time. Though probably 30% is quite a chunk but if your model has some genetic modification: like maybe carrying an oncogene insert with Cre modification, or maybe has been CRISPR exposed to edit a particular locus. In such scenario the affected genes express transcript structures carrying the vector backbone (antibiotic selection markers, viral promoters etc.) which fails to align. If such a scenario is your case then Trinity or any other de novo assembler can salvage affected reads.

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You may want to check the Mouse Genomes project for a reference genome for your specific strain if it's there. Might help with the unmapped reads.

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