Unconfigured Ad

Collapse
X
 
  • Time
  • Show
Clear All
new posts
  • adaptivegenome
    Super Moderator
    • Nov 2009
    • 436

    #1

    454 and homopolymers

    I am planning to use CORTEX to assemble 454 data and I want to use the cut-homopolymer option but I am having trouble deciding at what size homopolymers are a problem for 454. Seems to me like it would somewhere in the 4-6 base range, however I would like to disrupt my reads as little as possible.

    Does anyone has some insight into this? Has anyone looked at error rates as a function of homopolymer length in 454?

    Thanks,
    David
  • Zam
    Member
    • Apr 2010
    • 51

    #2
    Hi there

    I wrote Cortex, so I know about that, but I have somewhat limited 454 experience.
    If you want to be systematic, you can

    1. load in your reads multiple times, and each time use a different homopolymer threshold, and use the --dump_filtered_readlen_distribution option to dump a file showing how it affects your read lengths. That tells you how much read-length you are throwing away

    2. Theres the issue of 454 homopolymer errors, and at what length they are prevalent. I can't help with that to be honest.

    If you are just making variant calls, as I was when I did this, I would use a limit of 3 and see how I go, but that's quite conservative.

    Comment

    • adaptivegenome
      Super Moderator
      • Nov 2009
      • 436

      #3
      Originally posted by Zam View Post
      Hi there

      I wrote Cortex, so I know about that, but I have somewhat limited 454 experience.
      If you want to be systematic, you can

      1. load in your reads multiple times, and each time use a different homopolymer threshold, and use the --dump_filtered_readlen_distribution option to dump a file showing how it affects your read lengths. That tells you how much read-length you are throwing away

      2. Theres the issue of 454 homopolymer errors, and at what length they are prevalent. I can't help with that to be honest.

      If you are just making variant calls, as I was when I did this, I would use a limit of 3 and see how I go, but that's quite conservative.
      I wrote a script to do #1 and if you set the threshold to 3 or 4, it seems like a lot of reads get fragmented beyond use. I was hoping to balance the threshold with the desire to keep as many reads as possible. It is hard to do this, however, without knowing at what length the error rate explodes for homopolymers. I suppose I could determine this but I am hoping someone on seqanswers has some experience here.

      Comment

      • Zam
        Member
        • Apr 2010
        • 51

        #4
        Fair enough - good luck!

        Comment

        Latest Articles

        Collapse

        • SEQadmin2
          Beyond CRISPR/Cas9: Understand, Choose, and Use the Right Genome Editing Tool
          by SEQadmin2



          CRISPR/Cas9 sparked the gene editing revolution for both research and therapeutics.1 But this system still showed severe issues that limited its applications. The most prominent were the heavy reliance on PAM sequences, delivery limitations, double-stranded breaks that prompt unintended edits and cell death, and editing inefficiency (both in targeting and in knock-in reliability).

          Despite this, “CRISPR helped turn genome editing from a specialized technique into
          ...
          07-31-2026, 11:01 AM
        • SEQadmin2
          Proteomic Platforms: How to Choose the Right Analytical Strategy to Improve Detection and Clinical Applications
          by SEQadmin2


          Proteomics platforms are evolving rapidly, with advances in mass spectrometry and affinity-based approaches expanding what researchers can detect and at what scale. As the field moves toward deeper proteome coverage and clinical applications, scientists face an increasingly complex landscape of tools. This article will explore how researchers are navigating these choices to find the right platform for their work.

          The systematic characterization of the human proteome has
          ...
          07-20-2026, 11:48 AM
        • SEQadmin2
          Advanced Sequencing Platforms Tackle Neuroscience’s Toughest Genomics Problems
          by SEQadmin2



          Genomics studies in neuroscience face a special challenge due to the brain’s complexity and scarcity of samples. Mapping changes in cell type and state using conventional next-generation sequencing methods remains challenging. Advances in technologies like single-cell sequencing, spatial transcriptomics, and long-read sequencing have opened the door to deeper studies of the brain and diseases like Alzheimer’s, amyotrophic lateral sclerosis (ALS), and schizophrenia.
          ...
          07-09-2026, 11:10 AM

        ad_right_rmr

        Collapse

        News

        Collapse

        Topics Statistics Last Post
        Started by SEQadmin2, Today, 07:41 AM
        0 responses
        8 views
        0 reactions
        Last Post SEQadmin2  
        Started by SEQadmin2, 08-03-2026, 10:13 AM
        0 responses
        21 views
        0 reactions
        Last Post SEQadmin2  
        Started by SEQadmin2, 07-31-2026, 02:55 AM
        0 responses
        35 views
        0 reactions
        Last Post SEQadmin2  
        Started by SEQadmin2, 07-24-2026, 12:17 PM
        0 responses
        25 views
        0 reactions
        Last Post SEQadmin2  
        Working...