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  • Kmer content

    Hello,

    I have recently got back my Illumina RNA sequencing dataset and I have used Fastqc software to check its quality. I want to know what is Kmer content and what is its significance?

    Thank you
    Subuhi

  • #2
    Hola

    A k-mer is a motif (or a small word) of length k observed more than once in a genomic or sequenced sequence. The order of the kmer is defined by its word size.

    Examples for 2, 3, and 4

    for repeats

    acacacacacac.. (this is "AC" dinucleotides)


    gacgacgacgacgac (this is "GAC" trinucleotides)

    for spaced occurrences

    tttccGAGGaaggcgtagcgacgacGAGGaagcctca ( this is "GAGG" tetrads)


    The content is the number of times the kmer occurs in the sequence and the distribution is related with the enrichment of a genomic sequence based on a particular kmer.

    Taking into account that you can search for kmers of any size (the concept can be extended to larger words) the significances are diverse, searching and masking of repeats and mobile elements, preprocessing of fastqs, denovo assembling etc etc.

    This is a very short explanation it is just the basic but it can helps you to check papers related with software and pipeline for seraching repeats mobile elements de novo using kmers or also and of course papers and manuals for software oriented to de novo assembling etc.

    Best
    Carlos

    Comment


    • #3
      Originally posted by cllorens View Post
      Hola

      A k-mer is a motif (or a small word) of length k observed more than once in a genomic or sequenced sequence. The order of the kmer is defined by its word size.

      Examples for 2, 3, and 4

      for repeats

      acacacacacac.. (this is "AC" dinucleotides)


      gacgacgacgacgac (this is "GAC" trinucleotides)

      for spaced occurrences

      tttccGAGGaaggcgtagcgacgacGAGGaagcctca ( this is "GAGG" tetrads)


      The content is the number of times the kmer occurs in the sequence and the distribution is related with the enrichment of a genomic sequence based on a particular kmer.

      Taking into account that you can search for kmers of any size (the concept can be extended to larger words) the significances are diverse, searching and masking of repeats and mobile elements, preprocessing of fastqs, denovo assembling etc etc.

      This is a very short explanation it is just the basic but it can helps you to check papers related with software and pipeline for seraching repeats mobile elements de novo using kmers or also and of course papers and manuals for software oriented to de novo assembling etc.

      Best
      Carlos
      Hi,Carlos

      I have used SOAPdenovo, and the minimum length of its contig is the value of Kmer. I also used Cortex, in its result file there is the following string: lst_kmer:ATATTTTCTTACATGTTCCAAGGGT. I want to had a deeper understanding of Kmer.

      I am a beginner. Thanks for your help.

      Comment


      • #4
        Hi there

        1. Kmers are just words (chunks of sequence) of length k.
        2. The current version of Cortex contains some unnecessary stuff in the output.
        This text
        lst_kmer:ATATTTTCTTACATGTTCCAAGGGT

        just tells you the last kmer in the contig. "lst" stands for last.
        fst_kmer is the first kmer. It was once useful, but is not any more, and I have just removed it from Cortex - when I make the next release, it will be gone.

        Sorry for this, I've been meaning to remove it for a while, it just confuses new users.

        Comment


        • #5
          Hi Zhang

          In addition of Zam comments (it is like that Zam says k-mers are words of a particular size that you can find repeated in a genome with a particular frequency that depends of their size), perhaps I attach some references on distinct topics using K-mers for you to read them if you want to get deeper.


          The production of genome sequences has led to another important advance in their annotation, which is closely linked to the exact determination of their content in terms of repeats, among which are transposable elements (TEs). The evolutionary implications and the presence of coding regions in some …


          The Tallymer software was effective in a variety of applications to aid genome annotation in maize, despite limitations imposed by the relatively low coverage of sequence available. For more information on the software, see http://www.zbh.uni-hamburg.de/Tallymer.


          Hope you to enjoy them
          Carlos

          Comment


          • #6
            There is goes another interesting reference i forget to attach in the post above.

            Comment


            • #7
              Originally posted by Zam View Post
              Hi there

              1. Kmers are just words (chunks of sequence) of length k.
              2. The current version of Cortex contains some unnecessary stuff in the output.
              This text
              lst_kmer:ATATTTTCTTACATGTTCCAAGGGT

              just tells you the last kmer in the contig. "lst" stands for last.
              fst_kmer is the first kmer. It was once useful, but is not any more, and I have just removed it from Cortex - when I make the next release, it will be gone.

              Sorry for this, I've been meaning to remove it for a while, it just confuses new users.
              Thanks, Zam

              So, what is the meaning of "fst_r:GT fst_f:G" and "lst_r:A lst_f:AT"? I thought "r" stood for reverse and "f" stood for forward, am I right?

              If I want to get a consensus assembly from a set of reads possibly in SV structure, guess I should use Cortex_con? or Cortex_var? I don't understand the the fundamental difference between the two.

              And, If I run different Kmers, which result is better? "length" or "average_coverage"?

              Thank you for your answer!
              Last edited by xlzhang; 03-04-2012, 07:55 PM.

              Comment


              • #8
                Thanks, Carlos.

                Comment


                • #9
                  Hi xlzhang

                  "fst_r:GT fst_f:G" and "lst_r:A lst_f:AT"

                  This describes the edges going in/out of the contig at the first/last nodes.
                  The first node has G and T edges going out in the reverse complement direction, and a G forwards. The last node has A and T going out forwards and A in the reverse. I don't think you need to pay attention to this though for most uses.

                  As for cortex_con versus cortex_var - the fundamental difference is one of goal. Con is for making a consensus/haploid assembly of a single whole genome - it delas with one sample. Var is for assembling polymorphism, in one or many samples. If you have a set of reads which you know are precisely the reads for an alternate haplotype/SV, then you have effectively reduced your problem to a haploid one, and I would try cortex_con (or any standard assembler of your choice, depends a bit on the size of your region). If you have a set of reads from a structurally variant region, from a sample which might be heterozygous, I would try cortex_var. There is a Cortex_var google group where you could post more detailed questions if you like

                  best wishes

                  Zam

                  Comment


                  • #10
                    Originally posted by Zam View Post
                    Hi xlzhang

                    "fst_r:GT fst_f:G" and "lst_r:A lst_f:AT"

                    This describes the edges going in/out of the contig at the first/last nodes.
                    The first node has G and T edges going out in the reverse complement direction, and a G forwards. The last node has A and T going out forwards and A in the reverse. I don't think you need to pay attention to this though for most uses.

                    As for cortex_con versus cortex_var - the fundamental difference is one of goal. Con is for making a consensus/haploid assembly of a single whole genome - it delas with one sample. Var is for assembling polymorphism, in one or many samples. If you have a set of reads which you know are precisely the reads for an alternate haplotype/SV, then you have effectively reduced your problem to a haploid one, and I would try cortex_con (or any standard assembler of your choice, depends a bit on the size of your region). If you have a set of reads from a structurally variant region, from a sample which might be heterozygous, I would try cortex_var. There is a Cortex_var google group where you could post more detailed questions if you like

                    best wishes

                    Zam
                    You've given me a lot of help. Thank you.

                    Comment

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