Unconfigured Ad

Collapse
X
 
  • Filter
  • Time
  • Show
Clear All
new posts
  • jrss
    Junior Member
    • Jun 2010
    • 2

    how much sequence is needed to cover a mouse transcriptome

    Hi all, I am looking for a way to estimate the sequencing space needed to get a reasonable coverage of a transcriptome. Initially I am interested in rat and mouse, but if there is a rule of thumb or a place where I can check for this type of calculations.
    thanks a lot for any thoughts or advice

    regards

    jose
  • krobison
    Senior Member
    • Nov 2007
    • 734

    #2
    75M tags is what some vendors shoot for on a mammalian transcriptome (for example, on SOLiD4 this works out to 10 samples / slide). I've been meaning to look at the recent papers to try to work it out, though it can be a slog finding the numbers on a library-by-library basis.

    Comment

    • malachig
      Senior Member
      • Aug 2010
      • 117

      #3
      In my experience it depends a lot on what you consider 'reasonable' coverage. If you only want to profile gene level expression you don't need much. If you want to have a fair chance of detecting every isoform of every gene, you need at least an order of magnitude more reads. I have analyzed ~100 mouse and human transcriptomes using 'ALEXA-Seq'. When asked this question and forced to give a specific number I usually say that ~100 million paired reads is a good target. In practice it depends on many factors such as the mappability of your reads (which is highly dependent on the error rate at your center). I have also encountered some tissues that had extremely high expression of a small number of genes, and this consumed a lot of read depth. Other tissues did not suffer from this affect as much. You can see the outcome of analyzing some example mouse tissues sequenced to a depth of ~100 to ~250 million paired reads here:

      Provides a central portal to lists of top expressed, differentially expressed and differentially spliced features for all genes

      Comment

      Latest Articles

      Collapse

      • GATTACAT
        Reply to Nine Things a Sample Prep Scientist Thinks About Before Sequencing
        by GATTACAT
        Love this - good data definitely starts from good input, and poor input can only give relatively poor data. I particularly like the mention of Nanodrop/absorbance based methods for quantification. It's such a toss up if you'll get an accurate reading or what amounts to a randomly generated number, and a lot of library/sequencing related issues can be traced back to poor quant.
        07-01-2026, 11:43 AM
      • 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

      ad_right_rmr

      Collapse

      News

      Collapse

      Topics Statistics Last Post
      Started by SEQadmin2, 07-02-2026, 11:08 AM
      0 responses
      11 views
      0 reactions
      Last Post SEQadmin2  
      Started by SEQadmin2, 06-30-2026, 05:37 AM
      0 responses
      13 views
      0 reactions
      Last Post SEQadmin2  
      Started by SEQadmin2, 06-26-2026, 11:10 AM
      0 responses
      20 views
      0 reactions
      Last Post SEQadmin2  
      Started by SEQadmin2, 06-17-2026, 06:09 AM
      0 responses
      54 views
      0 reactions
      Last Post SEQadmin2  
      Working...