Unconfigured Ad

Collapse
X
 
  • Filter
  • Time
  • Show
Clear All
new posts
  • KDS
    Junior Member
    • May 2011
    • 4

    Advice on RNA-Seq Depth

    I am planning my first RNA-Seq experiment and wanted to make sure I was on the right track. I am going to be sequencing the transcriptome of 700 human lymphocyte samples on the Illumina HiSeq 2000 to look at expression differences, alternative splicing, and SNPs.

    After looking through numerous papers, it doesn't seem like there is a clear consensus on the level of coverage needed. It seems like the majority of papers recommend a minimum of 30X coverage. Does anyone have any input on going lower or higher than 30X coverage?

    Thanks for your help!
  • tonybolger
    Senior Member
    • Feb 2010
    • 156

    #2
    Originally posted by KDS View Post
    I am planning my first RNA-Seq experiment and wanted to make sure I was on the right track. I am going to be sequencing the transcriptome of 700 human lymphocyte samples on the Illumina HiSeq 2000 to look at expression differences, alternative splicing, and SNPs.
    I'm not sure you really want to use the same library / sequencing strategy for both differential expression and alternative splicing/SNP detection. For DE, you want the most reads possible, and you get diminishing returns above 40 or 50 bases, while for alternative splicing / SNP detection you want long reads, especially if you're expecting novel transcripts, where normalized libraries, paired end and strand specific sequencing begin to matter as well.

    Originally posted by KDS View Post
    After looking through numerous papers, it doesn't seem like there is a clear consensus on the level of coverage needed. It seems like the majority of papers recommend a minimum of 30X coverage. Does anyone have any input on going lower or higher than 30X coverage?
    How do you even calculate coverage of RNA-Seq? You'd typically have some transcripts with >1000x, and a long tail with very low coverage, and probably 1000s you miss entirely.

    Comment

    • woodydon
      Member
      • Jan 2010
      • 52

      #3
      You should calculate the sequencing depth of a gene or an exon. It doesn't make sense to have a number representing the overall sequencing depth of RNA-Seq. DNA and RNA-Seq are different.

      Besides, it is hard to believe that you will be sequencing 700 samples without knowing the differences of DNA-Seq and RNA-Seq.

      Finally, your budget determines how you do sequencing. ~$100 per sample or ~$10 per sample will have entirely different designs. The more you spend, the more information you get. It is that simple. Some people would say 5 ~ 10 million reads is enough for RNA-Seq. That is totally bull**** and nonsense.

      Don't bother the coverage illusion. Go find out how much money you are going to spend one a sample.

      Comment

      • puggie
        Member
        • Nov 2011
        • 52

        #4
        700 samples sounds like a lot, guess you'll be pooling many samples on the lanes unless you have good resources, one lane would give something around 400 million reads. Are you looking for anticipated highly differentially expressed genes from priori knowledge?

        Comment

        Latest Articles

        Collapse

        • SEQadmin2
          Nine Things a Sample Prep Scientist Thinks About Before Sequencing
          by SEQadmin2


          I’m not a sequencing expert. I’m a purification scientist who uses NGS to evaluate workflows my group develops. With this perspective, we think about the sample first and the NGS workflow second. The sequencer is an exceptionally honest reporter, but it can only report on what you give it, so whether you get clean, interpretable data from an NGS workflow is largely determined before you begin.

          Here are nine questions we think about, in roughly the order they matter, before...
          06-18-2026, 07:11 AM
        • SEQadmin2
          From Collection to Sequencing: Why Sample Preparation and Preservation Define Sequencing Data
          by SEQadmin2


          Data variability is still an issue in sequencing technologies despite the advances in reproducibility and accuracy of these platforms. But the problem does not originate in the sequencing itself, but in the previous steps, before the sample reaches the sequencer.


          The first step is collection, followed by preservation and sample preparation for analysis. Most scientists overlook those steps, but not being careful might just be skewing the experiment’s results.
          ...
          06-02-2026, 10:05 AM

        ad_right_rmr

        Collapse

        News

        Collapse

        Topics Statistics Last Post
        Started by SEQadmin2, 06-26-2026, 11:10 AM
        0 responses
        12 views
        0 reactions
        Last Post SEQadmin2  
        Started by SEQadmin2, 06-17-2026, 06:09 AM
        0 responses
        48 views
        0 reactions
        Last Post SEQadmin2  
        Started by SEQadmin2, 06-09-2026, 11:58 AM
        0 responses
        107 views
        0 reactions
        Last Post SEQadmin2  
        Started by SEQadmin2, 06-05-2026, 10:09 AM
        0 responses
        125 views
        0 reactions
        Last Post SEQadmin2  
        Working...