Unconfigured Ad

Collapse
X
 
  • Filter
  • Time
  • Show
Clear All
new posts
  • heytreeful
    Junior Member
    • Oct 2011
    • 6

    Alignment/transcriptome assembly/differential expression analysis with 40bp reads?

    Hi everyone,

    I am preparing to analyze some 40bp RNA-seq data generated by Illumina HiSeq from mouse samples. It seems that most RNA-seq tools nowadays are optimized for long sequence reads (>75bp) and paired-end reads.
    Can I still use TopHat and Cufflinks for these 40bp reads? Is it possible to perform transcriptome assembly with limited splice reads? Since the mouse genome is well annotated, can I get by without transcriptome assembly since the goal of our study is not to discover novel genes but just to detect differential expression of known genes? I am new to this; any suggestions/comments will be much appreciated! Thank you.
  • sqcrft
    Member
    • May 2012
    • 29

    #2
    Hi, fellow, is there any progress? I am stuck in the same dilemma.
    The read length is too short, and we need to identify some novel transcripts.

    Comment

    • heytreeful
      Junior Member
      • Oct 2011
      • 6

      #3
      hey, yup you can still use the conventional analyses suite for 40bp reads. There are still some splice reads present to allow for junction identification and transcript construction. Surely the resolution wasn't as great as longer read runs, but it was sufficient for our research.

      Comment

      • sqcrft
        Member
        • May 2012
        • 29

        #4
        thanks! That is really comforting.
        I had worried that the data and money might be wasted.

        Comment

        • heytreeful
          Junior Member
          • Oct 2011
          • 6

          #5
          it allowed us to detect presence of novel transcripts, but it didn't allow us to characterize them very well tho. Don't worry your data are not wasted

          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
          14 views
          0 reactions
          Last Post SEQadmin2  
          Started by SEQadmin2, 06-30-2026, 05:37 AM
          0 responses
          15 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...