Unconfigured Ad

Collapse
X
 
  • Filter
  • Time
  • Show
Clear All
new posts
  • EMeyer
    Junior Member
    • Jul 2008
    • 7

    Interpretation of Euler-SR assembly (454 transcriptome)

    Hi all,

    We've recently completed a run of 454 sequences from the transcriptome of a reef-building coral, and I am in the process of attempting de novo assembly. Since it seems Euler-SR does better on this type of data than Newbler or CAP3, I am trying this software. But Im finding the output kind of hard to interpret.

    Does anyone know of a good set of instructions for using this software? Anything specifically geared toward transcriptome, rather than genome, assembly would be especially useful.

    On a more detailed note, does anyone know the following regarding the output of Euler-SR?

    1. Is it possible to view singletons, and if so, where is that file?
    2. Is there any output that describes the mapping of reads to contigs? (as, for example, was easily parsed from CAP3 output).
    3. On what basis are reads excluded prior to assembly (e.g., for my test set of 2500 reads only 2477 were included at the beginning of assembly). And how can this behavior be adjusted if needed?

    Thank for your any advice you might have,
    -Eli
  • sklages
    Senior Member
    • May 2008
    • 628

    #2
    It might be interesting for you to have a look at MIRA for such kind of assemblies.


    Output alignment formats are CAF/gap4 and ACE.
    It performs well on EST data (at least for what I have seen).

    Cheers,
    Sven

    Comment

    • EMeyer
      Junior Member
      • Jul 2008
      • 7

      #3
      Thank you, I am impressed with the level of documentation for that package! I will give it a shot.

      I didnt see a publication of the method itself. Do you know if there is one in preparation / in review?

      Comment

      • sklages
        Senior Member
        • May 2008
        • 628

        #4
        There are two papers describing the package,

        mira,
        Chevreux, B., Wetter, T. and Suhai, S. (1999): Genome Sequence Assembly Using Trace Signals and Additional Sequence Information. Computer Science and Biology: Proceedings of the German Conference on Bioinformatics (GCB) 99, pp. 45-56.

        miraEST,
        Chevreux, B., Pfisterer, T., Drescher, B., Driesel, A. J., Müller, W. E., Wetter, T. and Suhai, S. (2004): Using the miraEST Assembler for Reliable and Automated mRNA Transcript Assembly and SNP Detection in Sequenced ESTs. Genome Research, 14(6)

        I don't know if there is a publication in progress describing the forthcoming version 3 and/or the Illumina/SOLiD/454 support.

        Comment

        • EMeyer
          Junior Member
          • Jul 2008
          • 7

          #5
          Thanks again. Its performing well on my dataset, giving a slightly larger average contig size than Newbler and a distribution of contigs that is both narrower and more normal. The big drawback I see at this point is that it gives me ~20% more contigs... this may be a good thing or a bad thing!

          I'm still calculating the N50 and coverage for this assembly, but so far so good. One note, though -- on my dataset Mira performs very similarly to CAP3. (Mira is slightly better than CAP3 as judged by higher mean contig size and lower contig number).

          Does that sound reasonable or does it sound like I'm doing something wrong?

          Comment

          Latest Articles

          Collapse

          • SEQadmin2
            Cancer Drug Resistance: The Lingering Barrier to Rising Survival
            by SEQadmin2



            Cancer survival rates have significantly increased in the last few decades in the United States, reaching a combined 70% 5-year survival rate by 2021. Behind this number, there are years of research to find new therapies, drug targets, and early detection methods. But there is one core challenge that keeps slowing down these advances, and it’s about drug resistance.

            There is no single reason why many patients don’t respond to treatment as expected. Cancer is...
            Yesterday, 05:17 AM
          • 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, Yesterday, 10:08 AM
          0 responses
          6 views
          0 reactions
          Last Post SEQadmin2  
          Started by SEQadmin2, 07-07-2026, 11:05 AM
          0 responses
          8 views
          0 reactions
          Last Post SEQadmin2  
          Started by SEQadmin2, 07-02-2026, 11:08 AM
          0 responses
          31 views
          0 reactions
          Last Post SEQadmin2  
          Started by SEQadmin2, 06-30-2026, 05:37 AM
          0 responses
          29 views
          0 reactions
          Last Post SEQadmin2  
          Working...