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  • "nucleotide coverage" to genome feature coverage

    Hello,

    I am absolutely new to the area and am doing my self-education based on web resources. I try to establish a pipeline for bacterial RNA-seq and sRNA discovery based on Illumina NGS data. I primarily use R as my working language; this area seems to be at an early stage and Bioconductor packages are scattered. I've already written my own script to count genome coverage at a single-nucleotide level (leveraging functionality of ShortRead and Biostrings packages). I've also tried a conventional route from FASTQ to BAM files using Bowtie + samtools. As a result, here I am: start from FASTQ => Bowtie => SAMtools => BAM files; I also have my single-nucleotide coverage matrices which seem to match the information in the SAM/bam files.

    Here is a point where I suddenly stuck:

    Conversion of a "raw" genome coverage to coverage based on genomic features. I tried to read docs to GenomicRanges, IRanges etc packages but cannot see a clear path through them.

    What is your favorite way from BAM (using genome annotation .gff files) to a matrix of a feature-centric coverage ready to be used for differential expression tests? And again, the genome has nothing to do with Homo sapiens.

    Thanks a lot!

  • #2
    P.S. One part of the Galaxy pipeline does exactly what I need, via 2 of their functions:

    GFF2anno
    eQuant

    However, GFF2anno doesn't recognize GFF files for bacterial genomes (downloaded right from GenBank)!!!

    This conversion (from raw sequence coverage to genomic features-based coverage) is supposed to be a simple short step, and I believe dozens of tools should do this; the question is - what are they?

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    • #3
      What about the HTseq package for python (http://www-huber.embl.de/users/ander...verview.html)?

      The command htseq-count (http://www-huber.embl.de/users/ander...unt.html#count) seems to do what you need (if you have BAM you also have the SAM file). From the doc page:
      htseq-count [options] <sam_file> <gff_file>

      It's not R but it should do the job.

      Dario

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