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  • badribio
    Junior Member
    • Mar 2011
    • 2

    IP library vs Input library reads number

    Hi,
    I am newbies in this forum and this is my first post. I have recently performed H3K4me3 ChIP librairies preparation with using NEXTflex ChIP-Seq kit to sequencing 18 Immunoprecipitation products (IPs)and 6 inputs as negatives controls.
    This sequencing is achieved on 3 Illumina Rapid flow cells with PE 2X50 pb reads. 3 IPs and 1 input have been sequenced in the same lane onto HiSeq 2500 with the new version of HCS 2.2.38 software. After Casava demultiplexing, I have getting 80 to 100 million reads with IP librairies and 30-36 million reads for input librairies. I have used 5% PhiX as a balanced genome for this experience because I have get a good results last time when we used 1% PhiX with the old HCS version.
    Can you help me please to understand this result.

    Thanks a lot.

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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.
    Yesterday, 11:43 AM
  • 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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