Unconfigured Ad

Collapse
X
 
  • Time
  • Show
Clear All
new posts
  • Patrice
    Member
    • Sep 2016
    • 16

    #1

    Trimmomatic: Quality Trimming read loss -

    Hello community,

    I plan to do a reference assisted genome assembly for a yeast genome.
    I have some questions regarding the trimming prior the assembly:

    # Setup:
    Illumnia MiSeq / Paired end sequencing / coverage around 80x

    # Case:
    Intended pipeline:
    raw reads --> trimming --> Spades or Velvet --> Contiguator

    I ran FastQC on the raw reads (see attached files, forward reads shown):
    FastQC does not detect adapters.
    Since the quality drops in the end of the reads, so I decided to use trimmomatic for quality trimming.

    I needed to apply a strict quality cutoff in order to obtain better FastQC results (see attachements). I used following parameters in trimmomatic:
    Code:
    SLIDINGWINDOW:4:30 HEADCROP:11 MINLEN:40
    However, I recognized a huge loss of reads after the trimming :

    Raw reads: 9322401
    trimmed reads: 7320129
    -> loss of 21 %

    # Questions:
    1. Since FastQC did not detect illumina adapters in the raw reads, would you still run a adapter removing step (eg AdapterRemoval or trimmomatic [ILLUMINACLIP])?

    2. What would you suggest regarding the quality trimming? Some people suggest not to run any trimming at all. Should I go down with the cutoff values in order to keep more reads?

    Thank you and best regards
    Attached Files
  • Patrice
    Member
    • Sep 2016
    • 16

    #2
    I calculated the Coverage:

    After trimming
    Coverage = (7320129 reads * 130 mean reads)/ 20.500000 bp = 46,8x

    So I suppose that is still fine even after trimming

    Comment

    • GenoMax
      Senior Member
      • Feb 2008
      • 7142

      #3
      It would be wise to scan (and trim as needed) the data for presence of extraneous sequences. Since you are going to de novo assembly, you don't want any other sequence that does not belong to your genome in there.

      You could trim at Q20 to begin with and see what you get. If you feel you have plenty of coverage (which you seem to think) then you could go with the ultra-stringent Q30 trim.

      Comment

      • Gorgon_VZ
        Member
        • Oct 2016
        • 10

        #4
        Having a look on your FastQC figures I recognised, that even before your trimming approach you have a huge number of short reads around 90bp (I guess the first figure shows the data before trimming and the second one afterwards!?)! Are you sure that your data is rawdata? Regarding the adapters you should also check if your adapter is present in the FastQC "adapter_list.txt" file. If this is not the case, you can provide a custom file via --adapters option. Looking at the kmers is also a good indicator of possible artificial sequence contamination!

        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
          ...
          Yesterday, 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, Yesterday, 02:55 AM
        0 responses
        8 views
        0 reactions
        Last Post SEQadmin2  
        Started by SEQadmin2, 07-24-2026, 12:17 PM
        0 responses
        12 views
        0 reactions
        Last Post SEQadmin2  
        Started by SEQadmin2, 07-23-2026, 11:41 AM
        0 responses
        12 views
        0 reactions
        Last Post SEQadmin2  
        Started by SEQadmin2, 07-20-2026, 11:10 AM
        0 responses
        24 views
        0 reactions
        Last Post SEQadmin2  
        Working...