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  • Noremac
    Junior Member
    • May 2011
    • 1

    De Novo Transcriptome Assembly QC

    Hello All,

    This is my first post, and I'm newish to assembly, so stick with me.

    I was curious as to what people out there are doing with regards to evaluating de novo transcriptome assemblies. I have tried read-remapping to assembled ests, pairing percentages (assuming you started with paired reads, lets say illumina), assembled est mapping to best references (to see how well your set represents whats out there), simple chimera searching by blasting against uniprot and looking for disjoint reference hits, frameshifts, and strand switches, and using coding and non-coding predictors (estscan, Infernal, Augustus) to determine what percentage of your set looks to be an est.

    Do these things make sense to anyone else?

    What if you don't have a well represented reference to compare against?

    What other things can I do?

    Lets say that the starting set is illumina pe reads of a reasonable length (54-100).

    Thank you for your time and commentary

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  • GATTACAT
    Reply to Nine Things a Sample Prep Scientist Thinks About Before Sequencing
    by GATTACAT
    Love this - good data definitely starts from good input, and poor input can only give relatively poor data. I particularly like the mention of Nanodrop/absorbance based methods for quantification. It's such a toss up if you'll get an accurate reading or what amounts to a randomly generated number, and a lot of library/sequencing related issues can be traced back to poor quant.
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  • SEQadmin2
    Nine Things a Sample Prep Scientist Thinks About Before Sequencing
    by SEQadmin2


    I’m not a sequencing expert. I’m a purification scientist who uses NGS to evaluate workflows my group develops. With this perspective, we think about the sample first and the NGS workflow second. The sequencer is an exceptionally honest reporter, but it can only report on what you give it, so whether you get clean, interpretable data from an NGS workflow is largely determined before you begin.

    Here are nine questions we think about, in roughly the order they matter, before...
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  • SEQadmin2
    From Collection to Sequencing: Why Sample Preparation and Preservation Define Sequencing Data
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    Data variability is still an issue in sequencing technologies despite the advances in reproducibility and accuracy of these platforms. But the problem does not originate in the sequencing itself, but in the previous steps, before the sample reaches the sequencer.


    The first step is collection, followed by preservation and sample preparation for analysis. Most scientists overlook those steps, but not being careful might just be skewing the experiment’s results.
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