Header Leaderboard Ad

Collapse

Minimum contig coverage to keep

Collapse

Announcement

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

  • Minimum contig coverage to keep

    Dear all,

    I am working on de novo assembly of bacterial genomes. Genomes were sequenced with Miseq 2x250 libs with an average coverage ~40X. Adapters were trimmed with cutadapt by the seq provider, then I performed quality trimming with BBduk and now de novo assembly with SPAdes 3.9.

    I ran some genomes with and without the cov-cutoff <auto> option and I've been comparing both assemblies.

    I've seen that for some genomes, the cov-cutoff option in auto mode still keeps some contigs (actually, I am looking at scaffolds) with low coverage (~1) whereas for other genomes the minimum is much higher (~14).

    Genomes will be compared using various tools, including ANI calculation. I'd rather not introduce bias by throwing away too much data from some genomes and not from others, or the contrary (keeping "junk" in some and not in other genomes).

    I BLASTed some of the low cov contigs to see if these are contaminations and I cannot tell if they are, because I they align to various sequences in the genus I am working on.

    So the question is basically:

    1. How SPAdes chooses the minimum coverage using the cov-cutoff <auto> option?

    2. Is there and what is the minimum contig coverage people suggest to keep/discard contigs?


    Thanks!

  • #2
    Are these single-cell libraries or isolate libraries? With isolate libraries there is no reason to expect highly variable coverage and contigs with much lower coverage than normal are most likely contaminants (or "junk"). If they align to the same genus then they are less likely to be contamination and more likely to be ... well, some other variety of thing that you don't want, like assembly from chimeric reads or some other artifact. With single-cell it's much more difficult to determine.

    Comment


    • #3
      These are isolate libraries.

      When I BLAST short contigs (<200bp) with high coverage (10'sx), these are often parts of ribosomal genes. When I BLAST contigs of length 200-~500bp, where most of low coverage contigs are, these are often pieces of different species in the genus or so isolates not identified to species level.

      So the question is then what is "coverage much lower than normal"? Should I stick to a standard cut-off for all genomes? What this cut-off usually is? Or the "auto" mode in SPAdes should be enough?

      Comment


      • #4
        "coverage much lower than normal" is on a per-library basis; you can't have a single cutoff value that's always appropriate. That said, while it's difficult to make strict rules... if a contig has coverage under 25% of the median coverage, in an isolate, it's probably best to discard it. Bear in mind, though, that bacterial isolates can have differential coverage by up to ~50% biased toward the origin of replication if you gathered the DNA during exponential growth phase.

        Comment

        Latest Articles

        Collapse

        • seqadmin
          A Brief Overview and Common Challenges in Single-cell Sequencing Analysis
          by seqadmin


          ​​​​​​The introduction of single-cell sequencing has advanced the ability to study cell-to-cell heterogeneity. Its use has improved our understanding of somatic mutations1, cell lineages2, cellular diversity and regulation3, and development in multicellular organisms4. Single-cell sequencing encompasses hundreds of techniques with different approaches to studying the genomes, transcriptomes, epigenomes, and other omics of individual cells. The analysis of single-cell sequencing data i...

          01-24-2023, 01:19 PM
        • seqadmin
          Introduction to Single-Cell Sequencing
          by seqadmin
          Single-cell sequencing is a technique used to investigate the genome, transcriptome, epigenome, and other omics of individual cells using high-throughput sequencing. This technology has provided many scientific breakthroughs and continues to be applied across many fields, including microbiology, oncology, immunology, neurobiology, precision medicine, and stem cell research.

          The advancement of single-cell sequencing began in 2009 when Tang et al. investigated the single-cell transcriptomes
          ...
          01-09-2023, 03:10 PM

        ad_right_rmr

        Collapse
        Working...
        X