What is good coverage on an RNA-seq experiment

[sdriscoll]

maybe this paper is useful for this discussion: http://genome.cshlp.org/content/earl....full.pdf+html. it would seem that depth results in detection of different types of RNAs. they also provide a very useful comparison of DE analysis (and of course introduce their own which appears to be less biased by sequencing depth and feature length plus has much lower FDR).

[sdriscoll]

fair enough!

so then can you tell me how many reads I should collect if i'm interested in genes expressed below the 5 FPKM level? is it worth it to spread a single sample across 8 lanes of a HiSeq 2000 run? those are the exact questions researchers want answers to. for example when you're proposing a grant for a sequencing based project and you have to think in terms of cost.

[pmiguel]

Okay, but what does "solid" mean? For the transcripts detected at all (or above some threshold) you get nearly the same count over replicates in the set? If that is what you are looking for, fine.

But there are lots of transcripts that are going to be present in an RNA sample that will not be detectable by sequencing 40 million reads. Could be they occur in only a small sub-population of the cells of a sample or are simply maintained at low levels in cells (or both).

I agree my answer was not helpful. But I was trying not to validate the question -- as I said, I think it is the wrong one to ask. Or at least it is too vague to be answered.

--
Phillip

[sdriscoll]

i thought the question was pretty straightforward and i don't think you helped much - no offense of course.

the question of how much sequencing depth is "good" is really an open ended question is all. maybe the best answer for now is "nobody knows". if we had some ultimate reference of expression values for genes that we could compare those calculated in our individual experiments to then maybe we could figure it out. as of now people can only compare fractions of their data to their full data and produce plots that are basically useless in-so-far as they don't help anyone decide how many reads they should be collecting for reliable results.

i know of the plots you're referring to from the cufflinks paper and they are similar to the plot from Wold's paper http://www.nature.com/nmeth/journal/...meth.1226.html where they only sequenced to 40 million reads. so their plot with 40 million reads looks like the plot from the cufflinks paper with 140 million reads. i've seen another paper where they sequenced to 1 billion reads and they, again, had the same plot. those plots are kind of senseless because they can't be used an as absolute reference for anyone else - they're only in terms of themselves. so you collect N reads, quantify expression, then re-quantify with 3/4, 1/2 and 1/4 of your reads and you find that the gene expression levels vary from their "final" value with all of your reads. i'm afraid you'll find the same thing no matter what your depth is. however there's no logic to only using fractions of your complete data set so why bother comparing the complete set to fractions of the set.

anyways, in my experience, having the coverage that LP_SEP23 says they have sounds like it will be a pretty solid dataset and i don't think they need to collect more reads per sample. they could and theoretically their expression values would be more reliable but down to what level - who knows.

[pmiguel]

Hi sdriscoll,
Not sure I agree with the conclusions you draw from the cufflinks paper. (Trapnell et al., Nature Biotechnology, 2010, 28:311)

If you accepted Trapnell et al.'s calling transcripts present at >15 "fragments per kilobase of transcript per million reads -- FPKM" as "moderately expressed", then 25 million reads/sample might get you where you need to go for your analysis. (Fig 4) Or not. Their example shows that at 20 million reads only around 50% of these "moderately expressed" messages will be estimated to within 15% a data set using 140 million reads.

If you are interested in genes that are expressed at level much lower than "moderately expressed" transcripts, then the answer would seem to be "no, not even close to enough."

The problem with questions like this one, is that the questioner does not seem to be trying to match an analysis method with what is likely or possibly going on in the pool of transcripts that compose the pool of RNAs present in the cells of a given sample. (And from this information derive some sort of information about what is actually going on in these samples.) Instead they seem to be asking "what is the standard practice of those deploying this methodology?" As if science is primarily a rules-based process and we are all part of some compliance-enforcing bureaucracy with no goals other than seeing that the "rules" are followed.

My take is that if you are so deep into a process that you have lost track of what it is you are ultimately trying to achieve, you need to step back and re-think what it is you are doing and why you are doing it.

--
Phillip

[sdriscoll]

You've got plenty of reads. If you check out the cufflinks paper you can see how much sequencing they did (re: a lot) and also they found that in their transcriptome analysis only about a third or a quarter of the reads were necessary. As they performed the analysis with more and more of the data they collected they found no new information. You've also got very good alignment percentages...better than I have ever seen in data from my lab. Two years ago we were doing differential expression analysis with 18 million 36 bp single end reads per sample and it worked fine.

if you're only interested in differential expression you could collect fewer reads per sample. for splicing i think the number of reads you've collected is good.

as far as the percentage of the mouse genome you data is aligning to - keep in mind that the genome is only about 5% genes (i think that's right). when people talk about fold coverage for an RNA-Seq experiment it's only relative to the parts of the genome that make RNA.

[LP_SEP23]

Hi,

I just got my first RNA-seq dataset alignment results today, and I am wondering what would be considered a "good run".
This is a pilot experiment, whose objective is to determine how many samples we can multiplex in one lane of Illumina HiSeq to get the information we want and the best trade off between cost, specificity and sensitivity.

This are mouse brain samples, I am getting 75 million read pairs, of which 85% map consistently (the fwd and rw read map to the same place) and uniquely. I get 97% of the reads mapped in total.
I am concerned about the coverage, I get about 10% of the bases in the mouse genome covered by unique mappers and 3% by non-unique mappers.
Is this typical? When people talk about 30X coverage of RNA-seq datasets, what is the coverage relative to? RefSeq mRNA?

Right now I am running 3 samples per lane, and want to know whether this coverage is enough for basic transcriptome analysis, whether I can multiplex more or if I should run less samples per lane

thanks for the help