Trinity : How To Co-Assemble Different Samples (Tumor And Healthy)
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9.7 years ago


I'm using Trinity to assemble RNAseq data (2X76bp) from an non-model species without available genome. I'm studying tumor samples. For this, I've three control samples (healthy cells) and 9 tumor samples. To perform differential expression analysis (with DESeq or edgeR per example) after the assembly, I've to co-assemble all the samples at once (so concatenate all the fastq files in one big fastq file and perform trinity on it)

Is that right ? Even if the tumor cell can have a totally different transcriptome (genetic modification, etc...)

Thanks a lot for your advices.

trinity assembly transcriptome • 4.6k views
Entering edit mode
9.7 years ago
Rt ▴ 90

Your understanding is correct! It should be more clear on Trinity documentation.

Here is Brian J. Haas's explanation:

The general idea is to combine all your rna-seq data and generate one assembly. Then, to align the reads from the different samples separately to the Trinity assemblies, computing abundance estimates based on each read set. Finally, you do the differential expression analysis to identify those Trinity assemblies that are of interest. This is all outlined in the downstream analysis section of the documentation. If there's still some confusion about this, let's continue to work through it.

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When you combine samples, do you suggest limiting the number of reads for each sample? I had read one of Matthew MacManes' papers awhile back suggesting 40 million reads was a good number for Trinity assemblies of most metazoans.

However, I have 28 samples across 3 treatments that I want to use for DESeq and so, if I follow the advice above, that's 28x40 million reads = 1.1 billion reads going into a Trinity assembler... how is it suggested to combine these samples in situations like this?


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