Seqanswers Leaderboard Ad

Collapse

Announcement

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

  • Correcting a reference genome for Tophat

    Hi.

    I am currently mapping plant transcriptome reads from one cultivar to the genome of another culitvar of the same species. It has been suggested that to improve the mapping, it might be worthwhile to correct the reference genome for use with cultivar that my reads are from.

    I can see how this could improve the number of reads that map to the reference. It feels like it might be a bit circular though, as if I'm taking the transcriptome from one of my samples and using that as reference for itself (and the rest of my samples). Can anyone think of why this might be bad idea?

    At the moment I am using tophat and taking the resulting insertions.bed and deletions.bed, reading the files manually and then manually ineserting these into the reference genome. Is there a better way to do this?

    Cheers
    Ben.

  • #2
    Originally posted by tirohia View Post
    Hi.

    I am currently mapping plant transcriptome reads from one cultivar to the genome of another culitvar of the same species. It has been suggested that to improve the mapping, it might be worthwhile to correct the reference genome for use with cultivar that my reads are from.

    I can see how this could improve the number of reads that map to the reference. It feels like it might be a bit circular though, as if I'm taking the transcriptome from one of my samples and using that as reference for itself (and the rest of my samples). Can anyone think of why this might be bad idea?

    At the moment I am using tophat and taking the resulting insertions.bed and deletions.bed, reading the files manually and then manually ineserting these into the reference genome. Is there a better way to do this?

    Cheers
    Ben.
    If you want to improve mapping without bias, you need a new mapper, or a new assembly. So, either get a better mapper, or assemble reads from your sample denovo (which is very difficult), or map whole genome reads to the reference, then correct the reference by replacing the reference with any homozygous variation calls. This last method is the best as long as you have no reference bias. Which you will. But still, it could be the best if the ref-bias is minimal.

    Mapping to transcriptomes is already highly subjective, biased, and error-prone - no matter how robust the rest of your study may be. But mapping to a modified transcriptome based on RNA-seq data from a different strain should never get through peer-review.

    Therefore... you need whole-genome data. Exome, transcriptome, or whatever limited subset are not alone mathematically adequate in describing a genome - and thus, they cannot be used definitively when analyzing a related strain. If you just modify a transcriptome arbitrarily based on your reads, then remap, you will get indefensible data.

    To reduce bias to minimum, when cross-strain mapping, you should use the most sensitive and accurate mapper available. Using iterative stages of "map-changeReference-map-changeReference" will exponentially amplify bias.

    -Brian

    Comment

    Latest Articles

    Collapse

    • seqadmin
      Genetic Variation in Immunogenetics and Antibody Diversity
      by seqadmin



      The field of immunogenetics explores how genetic variations influence immune responses and susceptibility to disease. In a recent SEQanswers webinar, Oscar Rodriguez, Ph.D., Postdoctoral Researcher at the University of Louisville, and Ruben Martínez Barricarte, Ph.D., Assistant Professor of Medicine at Vanderbilt University, shared recent advancements in immunogenetics. This article discusses their research on genetic variation in antibody loci, antibody production processes,...
      11-06-2024, 07:24 PM
    • seqadmin
      Choosing Between NGS and qPCR
      by seqadmin



      Next-generation sequencing (NGS) and quantitative polymerase chain reaction (qPCR) are essential techniques for investigating the genome, transcriptome, and epigenome. In many cases, choosing the appropriate technique is straightforward, but in others, it can be more challenging to determine the most effective option. A simple distinction is that smaller, more focused projects are typically better suited for qPCR, while larger, more complex datasets benefit from NGS. However,...
      10-18-2024, 07:11 AM

    ad_right_rmr

    Collapse

    News

    Collapse

    Topics Statistics Last Post
    Started by seqadmin, 11-08-2024, 11:09 AM
    0 responses
    31 views
    0 likes
    Last Post seqadmin  
    Started by seqadmin, 11-08-2024, 06:13 AM
    0 responses
    26 views
    0 likes
    Last Post seqadmin  
    Started by seqadmin, 11-01-2024, 06:09 AM
    0 responses
    32 views
    0 likes
    Last Post seqadmin  
    Started by seqadmin, 10-30-2024, 05:31 AM
    0 responses
    22 views
    0 likes
    Last Post seqadmin  
    Working...
    X