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
    Essential Discoveries and Tools in Epitranscriptomics
    by seqadmin


    The field of epigenetics has traditionally concentrated more on DNA and how changes like methylation and phosphorylation of histones impact gene expression and regulation. However, our increased understanding of RNA modifications and their importance in cellular processes has led to a rise in epitranscriptomics research. “Epitranscriptomics brings together the concepts of epigenetics and gene expression,” explained Adrien Leger, PhD, Principal Research Scientist on Modified Bases...
    Today, 07:01 AM
  • seqadmin
    Current Approaches to Protein Sequencing
    by seqadmin


    Proteins are often described as the workhorses of the cell, and identifying their sequences is key to understanding their role in biological processes and disease. Currently, the most common technique used to determine protein sequences is mass spectrometry. While still a valuable tool, mass spectrometry faces several limitations and requires a highly experienced scientist familiar with the equipment to operate it. Additionally, other proteomic methods, like affinity assays, are constrained...
    04-04-2024, 04:25 PM

ad_right_rmr

Collapse

News

Collapse

Topics Statistics Last Post
Started by seqadmin, 04-11-2024, 12:08 PM
0 responses
37 views
0 likes
Last Post seqadmin  
Started by seqadmin, 04-10-2024, 10:19 PM
0 responses
41 views
0 likes
Last Post seqadmin  
Started by seqadmin, 04-10-2024, 09:21 AM
0 responses
35 views
0 likes
Last Post seqadmin  
Started by seqadmin, 04-04-2024, 09:00 AM
0 responses
54 views
0 likes
Last Post seqadmin  
Working...
X