BioMicroCenter:Sequencing Quality Control: Difference between revisions

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Much effort and time has been spent on optimizing loading concentrations and accounting for variations in various library prep techniques to generate optimal cluster densities on the HiSeq. The following plot shows the optimal cluster range we aim for in each experiment to efficiently generate reads with a high pass filter percentage:
Much effort and time has been spent on optimizing loading concentrations and accounting for variations in various library prep techniques to generate optimal cluster densities on the HiSeq. The following plot shows the optimal cluster range we aim for in each experiment to efficiently generate reads with a high pass filter percentage:


[[Image:HiSeqStats.png|thumb|right|HiSeq QC]]
<br>[[Image:HiSeqStats.png|thumb|right|HiSeq QC]] </br>


== Possible Sample QC Techniques ==
== Possible Sample QC Techniques ==

Revision as of 11:02, 6 April 2011

HOME -- SEQUENCING -- LIBRARY PREP -- HIGH-THROUGHPUT -- COMPUTING -- OTHER TECHNOLOGY

Why is QC Important?

It is very important to have a reliable measurement of the amount of starting material so that the sample can be prepared for hybridization and amplification on the flow cell. The ideal sample is a library with 10nM of successfully ligated DNA. A 1.3ng/ul, 200bp sample is approximately 10nM.

As of March 2011 the optimum cluster range is from about 250,000 to 350,000 per tile on the GAIIx and 600,000-800,000 on the HiSeq. It is crucial that we have accurate concentrations on hand to prevent under- and over-clustering. If a sample is too concentrated or the fragment size is too variable the sequencers will not be able to distinguish between clusters properly, resulting in the loss of reads. If the sample is too dilute the optimum number of reads per lane will not be achieved. Having reliable concentration measurements allows us to optimize the number of reads per lane and maximize the quality of data produced.

For results generated with our choice of QC methods, please view the following poster:

Much effort and time has been spent on optimizing loading concentrations and accounting for variations in various library prep techniques to generate optimal cluster densities on the HiSeq. The following plot shows the optimal cluster range we aim for in each experiment to efficiently generate reads with a high pass filter percentage:


HiSeq QC


Possible Sample QC Techniques

  • NanoDrop ND-1000- The NanoDrop is one of the most commonly used tools to measure the concentration of DNA in solution. The NanoDrop has a detection limit of about 5ng/ul. Unfortunately, due to noise at the lower detection limit, samples speced on the Nanodrop have not shown reliable results on the Solexa sequencer. We do not recommend using the NanoDrop as the primary method of determining concentration for samples on the Sequencer.
  • 2100 BioAnalyzer - The Bioanalyzer produces data similar to that of gel electrophoresis, although it requires much less sample input (1uL) and provides quantification data in addition to valuable distribution information. Due to the amount of error introduced in low concentration or widely distributed samples the Bioanalyzer is not recommended as the primary method of determining concentration for samples that are less than 20ng/ul.
  • PicoGreen - PicoGreen is a fluorescent dye that binds specifically to dsDNA and allows for quantification. PicoGreen can be measured in a few different ways, the Boyer lab uses a photospectrometer and Invitrogen states that the Qubit can also be used. More information can be found at: http://probes.invitrogen.com/media/pis/mp07581.pdf
  • RT-PCR, SYBERgreen assay - This assay uses primers that are specific for the adapters used during the ligation step of sample preparation. This allows for the amount of DNA that will actually bind to the flowcell to be quantified. The RT-PCR assay is recommended in addition to the techniques explained above and provides additional and more precise concentration information.

Flow Cell QC

SYBR Green Cluster Visualization- This protocol allows for the visualization of clusters on a flowcell after amplification and before it is put on the Genome Analyzer. It is especially helpful to ensure proper amplification has occurred if there has been a clog or an error on the cluster station or if an older Cluster Generation kit that may be expired is being used.