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  • pmiguel
    replied
    Originally posted by avilella View Post
    I haven't seen any RNA-seq reads of 400-500 bps in NCBI SRA, but I have seen the ones that are 260bp.
    That is interesting. I wonder why. Does the SRA have a maximum read length it allows? Maybe you have to dump Titanium reads into dbEST or dbGSS?

    By the way, I can assure you that Titanium read lengths really do tend to have a peak in the 400-500 base range--if all goes well.

    --
    Phillip

    Leave a comment:


  • avilella
    replied
    I haven't seen any RNA-seq reads of 400-500 bps in NCBI SRA, but I have seen the ones that are 260bp.

    Leave a comment:


  • pmiguel
    replied
    Originally posted by avilella View Post
    Has anyone got any info for the latest batch of 454 runs (~260bp)?
    I don't, but thought I should mention that Titanium chemistry reads have modal lengths in the 400-500 base range.

    --
    Phillip

    Leave a comment:


  • avilella
    replied
    Has anyone got any info for the latest batch of 454 runs (~260bp)?

    Originally posted by hlu View Post
    The paper is quite outdated. For one thing, the paper is about GS-20, which is not compatible with current FLX and Titanium platform.

    Titanium and FLX have different error profile than GS20, and much lower error rate than GS20.

    My understanding is that Titanium and FLX basecall software are not compatible with GS20 raw images.

    Leave a comment:


  • hlu
    replied
    Originally posted by bioinfosm View Post
    anyone with experience on the last 2 posts?
    joa_ds - did you make some observations that can be shared here?

    The paper is quite outdated. For one thing, the paper is about GS-20, which is not compatible with current FLX and Titanium platform.

    Titanium and FLX have different error profile than GS20, and much lower error rate than GS20.

    My understanding is that Titanium and FLX basecall software are not compatible with GS20 raw images.

    Leave a comment:


  • bioinfosm
    replied
    anyone with experience on the last 2 posts?
    joa_ds - did you make some observations that can be shared here?

    Leave a comment:


  • yannickwurm
    replied
    Hi y'all

    do you have any info on how the 454 basecalling software has improved?
    Ie if I have data thats a year old, should I get out the raw image files and rerun the basecalling using the latest software?

    Thanks & regards,

    yannick

    Leave a comment:


  • joa_ds
    replied
    hi, that paper is interesting indeed. I have it here on the desk, but it is quite outdated, I guess basecalling has already improved since then and it is not quite what i am looking for.

    If an error occurs, they describe the chance of being a homopolymer error or not.

    I am thinking about the other way aruond. Obeserve a variation, is it in a homopolymer? if so, what would be the chance of a random error in a homopolymer and use that data to say "false error or true error". I am trying my own approach to guess error rates, but any input is useful.

    I'll keep you updated...

    Leave a comment:


  • bioinfosm
    replied
    That is very interesting .. do keep us updated on what you observe.
    I have been looking at blat for 454 data as well, especially because gsMapper does not have a parameter for adjusting gap penalties.

    Originally posted by joa_ds View Post
    Hi 454 analysers,

    We are doing resequencing experiments here and are developing our own BLAT and a DB based mapping and SNP discovery pipeline.

    Finally we are able to detect SNVs, Isertions, Deletions and InDels. We have already validated the pipeline with random errors and known errors. But now the final part appears to be more tricky. The 1 million dollar question: is it a heterozygous or a homozygous variation.

    For example: 400x and 25% or 80% error rate, what do you do with that? In very long stretches of homopolymers, for example 10Cs. Chances are very big that you get 20% fake error of 1 or 2C extra.

    Well we are making a mathematical model to determine the cutoff frequencies of error rates at a certain coverage. The higher the coverage is the narrower the band becomes wherein a heterozygous error rate can be, but how narrow?

    I know the average error rate of 454 is around 1/1000, but that is not what i need, because Single nuc variations get filtered out in a very early stage (i filter everything that is <20%). The residual errors are either true variations or very frequent errors (such as homopolymers possibly?).

    Ok what i need is some kind of homopolymer error rate. I suppose it is linked to the length of the homopolymer, the longer it is, the more probable it is that random errors will occur. Is there a function known that gives the error rate ~ homopol length? I can calculate it myself, but i have some gut feeling this might not be this easy.

    Is anyone aware of a good article that describes different error rates of different types of errors? I have the article Pyrobayes: an improved base caller for SNP discovery in pyrosequences, but that only describes general error rates for substitution, deletion and insertion, not for homopolymer or normal.

    In the near future we will start with bisulfite treated amplicon sequencing, and with only 3 nucs, there will be even a bigger homopol error rate, and i would like to investigate/model some freqs of certain things upfront so that i can determine a useful coverage.

    Leave a comment:


  • Chema
    replied
    Perhaps this paper can help you:

    Leave a comment:


  • joa_ds
    started a topic 454 homopolymer error rate

    454 homopolymer error rate

    Hi 454 analysers,

    We are doing resequencing experiments here and are developing our own BLAT and a DB based mapping and SNP discovery pipeline.

    Finally we are able to detect SNVs, Isertions, Deletions and InDels. We have already validated the pipeline with random errors and known errors. But now the final part appears to be more tricky. The 1 million dollar question: is it a heterozygous or a homozygous variation.

    For example: 400x and 25% or 80% error rate, what do you do with that? In very long stretches of homopolymers, for example 10Cs. Chances are very big that you get 20% fake error of 1 or 2C extra.

    Well we are making a mathematical model to determine the cutoff frequencies of error rates at a certain coverage. The higher the coverage is the narrower the band becomes wherein a heterozygous error rate can be, but how narrow?

    I know the average error rate of 454 is around 1/1000, but that is not what i need, because Single nuc variations get filtered out in a very early stage (i filter everything that is <20%). The residual errors are either true variations or very frequent errors (such as homopolymers possibly?).

    Ok what i need is some kind of homopolymer error rate. I suppose it is linked to the length of the homopolymer, the longer it is, the more probable it is that random errors will occur. Is there a function known that gives the error rate ~ homopol length? I can calculate it myself, but i have some gut feeling this might not be this easy.

    Is anyone aware of a good article that describes different error rates of different types of errors? I have the article Pyrobayes: an improved base caller for SNP discovery in pyrosequences, but that only describes general error rates for substitution, deletion and insertion, not for homopolymer or normal.

    In the near future we will start with bisulfite treated amplicon sequencing, and with only 3 nucs, there will be even a bigger homopol error rate, and i would like to investigate/model some freqs of certain things upfront so that i can determine a useful coverage.

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