Unconfigured Ad

Collapse
X
 
  • Filter
  • Time
  • Show
Clear All
new posts
  • charlesli
    Junior Member
    • Mar 2013
    • 3

    How to quantify reads at a location?

    I have two bam files (H3K56ac ChIP and input) that I want to use to score regions (promoters) that I have defined in a bed file. I've been trying to use seqMINER to do this, but i'm pretty sure there is an easier method? Suggestions for a good tool to do this would be much appreciated. Thanks.
  • GenoMax
    Senior Member
    • Feb 2008
    • 7142

    #2
    Genomecov from Bedtools.

    Comment

    • shi
      Wei Shi
      • Feb 2010
      • 236

      #3
      Dear Charlesli,

      You may try the featureCounts function included in the bioconductor package Rsubread - http://bioconductor.org/packages/rel.../Rsubread.html

      Cheers,
      Wei

      Comment

      • AlexReynolds
        Member
        • Feb 2013
        • 45

        #4
        BEDOPS bedmap is useful for this task. This application takes elements that overlap other elements in UCSC BED files and applies operations to them (counting them, listing their IDs, calculating statistics from score column data, etc.).

        For this task, we can use bedmap --count to show the number of reads that overlap some regions of interest (promoters, distal elements, etc.).

        First, prepare your regions-of-interest with BEDOPS sort-bed:

        $ sort-bed unsortedRegions.bed > sortedRegions.bed

        This preparation step only has to be done once for any input to BEDOPS tools. This step allows BEDOPS tools to operate faster and with a lower memory profile than alternatives. Sorting only has to be done once, as BEDOPS tools export data in sorted order.

        Next, let's assume your reads are in BAM format. We can use BEDOPS bam2bed to convert this data to sorted BED, and we then pipe it to bedmap to apply the --count operation:

        $ bam2bed < reads.bam \
        | bedmap --echo --count sortedRegions.bed - \
        > answer.bed


        The file answer.bed contains results in the following format:

        [ region-1 ] | [ count of reads over region-1 ]
        [ region-2 ] | [ count of reads over region-2 ]
        ...
        [ region-N ] | [ count of reads over region-N ]


        If your regions are promoters, for example, then this result tells you how many reads are contained within each promoter's genomic coordinates, which you specify in sortedRegions.bed.

        The default overlap criterion is one base. In other words, one base of overlap between the read and region is sufficient for inclusion. You can adjust this overlap parameter with other options, if you need more stringency.

        Note that this result is a sorted BED file, as well, and can be piped to other BEDOPS tools (or other utilities which process BED data).
        Last edited by AlexReynolds; 04-29-2013, 03:18 PM.

        Comment

        • charlesli
          Junior Member
          • Mar 2013
          • 3

          #5
          bedops looks great. thanks for the detailed explanation. thanks to others for suggestions as well.

          Comment

          Latest Articles

          Collapse

          • 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
          • SEQadmin2
            Cancer Drug Resistance: The Lingering Barrier to Rising Survival
            by SEQadmin2



            Cancer survival rates have significantly increased in the last few decades in the United States, reaching a combined 70% 5-year survival rate by 2021. Behind this number, there are years of research to find new therapies, drug targets, and early detection methods. But there is one core challenge that keeps slowing down these advances, and it’s about drug resistance.

            There is no single reason why many patients don’t respond to treatment as expected. Cancer is...
            07-08-2026, 05:17 AM

          ad_right_rmr

          Collapse

          News

          Collapse

          Topics Statistics Last Post
          Started by SEQadmin2, 07-24-2026, 12:17 PM
          0 responses
          31 views
          0 reactions
          Last Post SEQadmin2  
          Started by SEQadmin2, 07-23-2026, 11:41 AM
          0 responses
          23 views
          0 reactions
          Last Post SEQadmin2  
          Started by SEQadmin2, 07-20-2026, 11:10 AM
          0 responses
          214 views
          0 reactions
          Last Post SEQadmin2  
          Started by SEQadmin2, 07-13-2026, 10:26 AM
          0 responses
          79 views
          0 reactions
          Last Post SEQadmin2  
          Working...