Seqanswers Leaderboard Ad

Collapse

Announcement

Collapse
No announcement yet.
X
 
  • Filter
  • Time
  • Show
Clear All
new posts

  • ExomeCNV: read.all.coverage problem

    The whole ExomeCNV pipeline works well using the demo data. Unfortunately, there are some errors when I read into the coverage returned by GATK (v2.1-13-g1706365, Compiled 2012/10/12 19:21:06) DepthOfCoverage.

    My data sets are deep whole-exome human normal and cancer genomes. I followed the instruction at https://secure.genome.ucla.edu/index...CNV_User_Guide. After GATK calculates the coverage, I used "read.all.coverage" to load the normal and tumor coverage information. However, I got the following message:
    > norm = read.all.coverage(prefix, suffix, chr.list, header=T)
    Warning messages:
    1: In `[<-.factor`(`*tmp*`, ri, value = c(109L, 200L, 98L, 26L, 29L, :
    invalid factor level, NAs generated
    2: In `[<-.factor`(`*tmp*`, ri, value = c(76L, 216L, 96L, 70L, 88L, :
    invalid factor level, NAs generated
    3: In `[<-.factor`(`*tmp*`, ri, value = c(12L, 53L, 77L, 76L, 74L, :
    invalid factor level, NAs generated
    4: In `[<-.factor`(`*tmp*`, ri, value = c(14L, 83L, 101L, 100L, 81L, :
    invalid factor level, NAs generated
    5: In `[<-.factor`(`*tmp*`, ri, value = c(28L, 46L, 4L, 10L, 60L, 47L, :
    invalid factor level, NAs generated
    6: In `[<-.factor`(`*tmp*`, ri, value = c(28L, 128L, 242L, 74L, 35L, :
    invalid factor level, NAs generated
    7: In `[<-.factor`(`*tmp*`, ri, value = c(1L, 86L, 70L, 18L, 65L, 131L, :
    invalid factor level, NAs generated
    8: In `[<-.factor`(`*tmp*`, ri, value = c(1L, 1L, 33L, 30L, 20L, 28L, :
    invalid factor level, NAs generated
    9: In `[<-.factor`(`*tmp*`, ri, value = c(88L, 68L, 73L, 39L, 54L, :
    invalid factor level, NAs generated

    Then I went to the coverage file and the format is as below.

    Target total_coverage average_coverage Sample_2N_total_cvg Sample_2N_mean_cvg Sample_2N_granular_Q1 Sample_2N_granular_median Sample_2N_granular_Q3 Sample_2N_%_above_15
    chr1:69090-70008 1212 1.32 1212 1.32 1 1 2 0.0

    Interestingly I found some lines include items '>500', which I suspect cause the reading problem:
    chr21:10969051-10969151 48274 477.96 48274 477.96 384 >500 >500 100.0
    chr21:10969985-10970085 53197 526.70 53197 526.70 487 >500 >500 100.0

    I wonder if I missed something or if anybody have any idea what happened to my data.

  • #2
    @mrfox

    Did you find a workaround for this? I am also facing issues, am using ExomeCNV for first time, the bamcoverage.sh does not work well as they need old version of samtools, so I tried gatk DepthofCoverage. But it gives single output files. Can you tell me how to take them as input since the input are done on chromosome wise, so do we have to separate gatk DepthofCoverage output in different chromosome files and then put them as input? If so then can you tell me how are you doing it? And then how did you tackle the problem of >500 which will not work in exomeCNV. I would appreciate some help

    Comment


    • #3
      I gave up finally and I used VarScan2. Sorry I am not able to help on ExomeCNV.

      Comment


      • #4
        I have used VarScan2 on my data but does not give any inferring results, I have however had some sort of success with Control-FREEC, now am trying other tools that are useful in calling somatic CNV for normal/tumor pair data. So I came across exomeCNV and want to try it but I am battling with it for over 3 days now.

        Comment

        Latest Articles

        Collapse

        • seqadmin
          Understanding Genetic Influence on Infectious Disease
          by seqadmin




          During the COVID-19 pandemic, scientists observed that while some individuals experienced severe illness when infected with SARS-CoV-2, others were barely affected. These disparities left researchers and clinicians wondering what causes the wide variations in response to viral infections and what role genetics plays.

          Jean-Laurent Casanova, M.D., Ph.D., Professor at Rockefeller University, is a leading expert in this crossover between genetics and infectious...
          09-09-2024, 10:59 AM
        • seqadmin
          Addressing Off-Target Effects in CRISPR Technologies
          by seqadmin






          The first FDA-approved CRISPR-based therapy marked the transition of therapeutic gene editing from a dream to reality1. CRISPR technologies have streamlined gene editing, and CRISPR screens have become an important approach for identifying genes involved in disease processes2. This technique introduces targeted mutations across numerous genes, enabling large-scale identification of gene functions, interactions, and pathways3. Identifying the full range...
          08-27-2024, 04:44 AM

        ad_right_rmr

        Collapse

        News

        Collapse

        Topics Statistics Last Post
        Started by seqadmin, 09-11-2024, 02:44 PM
        0 responses
        13 views
        0 likes
        Last Post seqadmin  
        Started by seqadmin, 09-06-2024, 08:02 AM
        0 responses
        146 views
        0 likes
        Last Post seqadmin  
        Started by seqadmin, 09-03-2024, 08:30 AM
        0 responses
        153 views
        0 likes
        Last Post seqadmin  
        Started by seqadmin, 08-27-2024, 04:40 AM
        0 responses
        163 views
        0 likes
        Last Post seqadmin  
        Working...
        X