Unconfigured Ad

Collapse
X
 
  • Time
  • Show
Clear All
new posts
  • mikecz
    Junior Member
    • Jan 2012
    • 5

    #1

    Trinity transcript naming

    I don't know if I am missing something obvious but I can't find an explanation anywhere as to how Trinity RNA-seq names the transcripts it derives.

    I've been noticing that when I annotate with BLAST+ most of the transcripts that start with the same number (comp#_) are annotating as the same gene (isoforms?). They will then all have different ending numbers (_seq#) and some of them have different numbers int he middle (_c#_), for example (comp2335_c1_seq1, comp2335_c1_seq2, comp2335_c1_seq3).

    Are these names derived sequentially by inchworm (comp#) chrysalis (c#) then butterfly (seq#)?

    Can anyone verify whether these would be indeed considered isoforms of single genes within a comp# group or is there something else at work here?

    Thanks,
    -Mike
  • Pseudonym
    Research Engineer
    • Jun 2011
    • 12

    #2
    You're almost correct. The names are indeed based on the algorithm, but it's not quite as simple as the inchworm number, then the chrysalis number, then the butterfly number.

    This is the basic algorithm of Trinity, assuming that you've already built a de Bruijn assembly graph (which is identical to a (k+1)-mer count):
    1. Eagerly extract contigs from the de Bruijn graph. These contigs may or may not have any relationship to "real" transcripts, they're just whatever long contiguous paths happen to be found in the graph.
    2. Find reads which justify clustering/joining ("welding" in Trinity terminology) these contigs together. A set of contigs which are believed to belong together is called a "component".
    3. Align reads to components. For each read, decide which component it's most likely to belong to.
    4. For each component, treat the reads which map to that component as a separate assembly problem. This involves constructing a new (smaller) de Bruijn graph from only those reads which belong to a component.

    The output of Inchworm is the set of contigs. The output of Chrysalis is the set of components, plus the reads which are called as belonging to those components. The output of Butterfly is the called transcripts for each component.

    When Butterfly rebuilds a graph for each component and does cleanup, it sometimes finds that the resulting graph is disconnected. This is usually because the inital contigs discovered by Inchworm were not "real" contigs. Each connected component in the rebuilt graph is called a "subcomponent".

    So the "comp" is the component, "c" is the subcomponent, and "seq" is the extracted sequence from the subcomponent.

    Trinity does not reason in terms of genes, loci and alternative splicing events. It solves a graph problem, though of course the heuristics are tuned to the needs of biology. So while it's highly likely that all the isoforms of a given gene belong to the same subcomponent, you shouldn't assume that a subcomponent is a gene.
    Last edited by Pseudonym; 04-15-2012, 05:05 PM.
    sub f{($f)=@_;print"$f(q{$f});";}f(q{sub f{($f)=@_;print"$f(q{$f});";}f});

    Comment

    • nareshvasani
      Member
      • Apr 2013
      • 57

      #3
      Nice explaination Pseudonym...



      Originally posted by Pseudonym View Post
      You're almost correct. The names are indeed based on the algorithm, but it's not quite as simple as the inchworm number, then the chrysalis number, then the butterfly number.

      This is the basic algorithm of Trinity, assuming that you've already built a de Bruijn assembly graph (which is identical to a (k+1)-mer count):
      1. Eagerly extract contigs from the de Bruijn graph. These contigs may or may not have any relationship to "real" transcripts, they're just whatever long contiguous paths happen to be found in the graph.
      2. Find reads which justify clustering/joining ("welding" in Trinity terminology) these contigs together. A set of contigs which are believed to belong together is called a "component".
      3. Align reads to components. For each read, decide which component it's most likely to belong to.
      4. For each component, treat the reads which map to that component as a separate assembly problem. This involves constructing a new (smaller) de Bruijn graph from only those reads which belong to a component.

      The output of Inchworm is the set of contigs. The output of Chrysalis is the set of components, plus the reads which are called as belonging to those components. The output of Butterfly is the called transcripts for each component.

      When Butterfly rebuilds a graph for each component and does cleanup, it sometimes finds that the resulting graph is disconnected. This is usually because the inital contigs discovered by Inchworm were not "real" contigs. Each connected component in the rebuilt graph is called a "subcomponent".

      So the "comp" is the component, "c" is the subcomponent, and "seq" is the extracted sequence from the subcomponent.

      Trinity does not reason in terms of genes, loci and alternative splicing events. It solves a graph problem, though of course the heuristics are tuned to the needs of biology. So while it's highly likely that all the isoforms of a given gene belong to the same subcomponent, you shouldn't assume that a subcomponent is a gene.

      Comment

      Latest Articles

      Collapse

      • SEQadmin2
        Multiomic Liquid Biopsy: What DNA/RNA Panels Reveal That DNA Alone Can’t
        by SEQadmin2



        Tumors contain complex mixtures of cell populations that evolve in response to treatment and host-specific factors. Tissue biopsies provide only static snapshots of a tumor’s state. But what if we could follow that evolution over time using a minimally invasive technique?

        Liquid biopsies can detect tumor-derived material circulating in the bloodstream, providing a way to study treatment response, detect emerging resistance, and discover new biomarkers. Circulating
        ...
        Today, 09:55 AM
      • SEQadmin2
        New Genomics Technologies Take Aim at Long-Standing Limits
        by SEQadmin2


        Researchers using sequencing and genomics tools often have to make trade-offs. They can choose between speed or scale, short reads or long-range information, or targeted panels or a view of the whole transcriptome. New technologies that have been released this year are built to address those tough choices.

        We asked six companies the same four questions to learn about their latest products. The new technologies bring a lot to the table, including rethinking sequencing
        ...
        09-28-2026, 10:25 AM

      ad_right_rmr

      Collapse

      News

      Collapse

      Topics Statistics Last Post
      Started by SEQadmin2, Today, 05:39 AM
      0 responses
      10 views
      0 reactions
      Last Post SEQadmin2  
      Started by SEQadmin2, Yesterday, 09:56 AM
      0 responses
      12 views
      0 reactions
      Last Post SEQadmin2  
      Started by SEQadmin2, 10-09-2026, 11:14 AM
      0 responses
      26 views
      0 reactions
      Last Post SEQadmin2  
      Started by SEQadmin2, 09-29-2026, 09:51 AM
      0 responses
      77 views
      0 reactions
      Last Post SEQadmin2  
      Working...