Seqanswers Leaderboard Ad

Collapse

Announcement

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

  • SNP coverage using Pile up From RSamtools

    Hi, I am using pile up from R Package, ‘Rsamtools’. I could not get the correct coverage for the SNP positions I am looking for in my BAM files. The issue is that I do not have the strand information (+ or -) and keep missing some of the reads in my output. Is there a way to resolve this issue?

    Here are the parameters I am using for Pile up:
    Code:
    summary[1:5,]
    [1,] "chr1"  "115258748" "115258748"   
    [2,] "chr1"  "115258748" "115258748"    
    [3,] "chr1"  "115258747" "115258747"    
    [4,] "chr1"  "115258747" "115258747"    
    [5,] "chr1"  "115258747" "115258747"    
    
    data <- summary ### summary could be a subset of a.indel - . a.indel[wanted,core.ann.gr)
    data.gr <- GRanges(seqnames =data[,"chr"],ranges = IRanges(start=as.numeric(data[,"start"]),end=as.numeric(data[,"end"])))
    
    which <-   data.gr
    which
    # GRanges object with 103 ranges and 0 metadata columns:
    #   seqnames                 ranges strand
    # <Rle>              <IRanges>  <Rle>
    #   [1]     chr1 [115258748, 115258748]      
    #   [2]     chr1 [115258748, 115258748]      
    #   [3]     chr1 [115258747, 115258747]      
    #   [4]     chr1 [115258747, 115258747]      
    
    params <-ScanBamParam(which=which,flag=scanBamFlag(isUnmappedQuery=FALSE,isDuplicate=FALSE,isNotPassingQualityControls=FALSE),simpleCigar = FALSE,reverseComplement = FALSE,what=c("qname","flag","rname","seq","strand","pos","qwidth","cigar","qual","mapq") )  ### NOTE isValidVendorRead=FALSE shoudl be TRUE
    
    
    param.pile <- PileupParam(max_depth=2500, min_base_quality=0, min_mapq=0,min_nucleotide_depth=1, min_minor_allele_depth=0,distinguish_strands=TRUE, distinguish_nucleotides=TRUE,ignore_query_Ns=TRUE, include_deletions=TRUE,cycle_bins=numeric() )
    #minimum base quality 20
    Last edited by MAPK; 11-05-2015, 11:45 PM.

Latest Articles

Collapse

  • seqadmin
    The Impact of AI in Genomic Medicine
    by seqadmin



    Artificial intelligence (AI) has evolved from a futuristic vision to a mainstream technology, highlighted by the introduction of tools like OpenAI's ChatGPT and Google's Gemini. In recent years, AI has become increasingly integrated into the field of genomics. This integration has enabled new scientific discoveries while simultaneously raising important ethical questions1. Interviews with two researchers at the center of this intersection provide insightful perspectives into...
    02-26-2024, 02:07 PM
  • seqadmin
    Multiomics Techniques Advancing Disease Research
    by seqadmin


    New and advanced multiomics tools and technologies have opened new avenues of research and markedly enhanced various disciplines such as disease research and precision medicine1. The practice of merging diverse data from various ‘omes increasingly provides a more holistic understanding of biological systems. As Maddison Masaeli, Co-Founder and CEO at Deepcell, aptly noted, “You can't explain biology in its complex form with one modality.”

    A major leap in the field has
    ...
    02-08-2024, 06:33 AM

ad_right_rmr

Collapse

News

Collapse

Topics Statistics Last Post
Started by seqadmin, 02-28-2024, 06:12 AM
0 responses
27 views
0 likes
Last Post seqadmin  
Started by seqadmin, 02-23-2024, 04:11 PM
0 responses
74 views
0 likes
Last Post seqadmin  
Started by seqadmin, 02-21-2024, 08:52 AM
0 responses
82 views
0 likes
Last Post seqadmin  
Started by seqadmin, 02-20-2024, 08:57 AM
0 responses
69 views
0 likes
Last Post seqadmin  
Working...
X