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Old 03-19-2010, 08:55 AM   #21
Xi Wang
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Quote:
Originally Posted by NGSfan View Post
I'm not clear on why converting the read coverage to a log scale would help understand distribution better. Simply visualizing coverage on a log scale will simply change the scale you're looking at, no?

Maybe I'm not understanding the advantage. Could you show me an example?
I did not have such data, so I can't show you examples.
But I noticed that the regions with high GC-content are less than the regions with average GC, as well as the low GC regions. That is to say there are more points in the figure around the average GC (x-axis). So it is more likely to have high coverage points in this part. This is what you saw in the figure. If you take log, the high-coverage points will decrease more than low-coverage do. This figure is more promising to reflect the nature of the relationship between read coverage and GC-content.
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Old 06-14-2010, 06:52 AM   #22
bryan haffer
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If you want to be able to enrich repetitive regions without bias, look at RainDance Technologies. Using their RainStorm approach, you can design PCR primers to capture 99% or greater of your target regions. The technology will also provide better uniformity allowing for less sequencing than SureSelect.

www.raindancetech.com
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Old 06-14-2010, 08:00 AM   #23
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Hi Bryan,

Thanks for the suggestion. The Rain Dance approach seems to really be the better approach to handle these biased regions, albeit on a smaller scale.

For example, it doesn't scale very well for say, 1000 genes like the SureSelect, or if you want the whole exome, for example.
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Old 06-14-2010, 09:17 AM   #24
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Originally Posted by bryan haffer View Post
If you want to be able to enrich repetitive regions without bias, look at RainDance Technologies. Using their RainStorm approach, you can design PCR primers to capture 99% or greater of your target regions. The technology will also provide better uniformity allowing for less sequencing than SureSelect.

www.raindancetech.com
In the interest of full disclosure. In the future please make your affiliations more clear.
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Old 03-17-2011, 04:15 PM   #25
DoubleA
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NGSfan,
Our lab has been using a custom SureSelect library to capture and sequence the extended HLA region (~8Mb). We have found that we get great coverage (>40X) over regions with <60% GC content while we get very poor coverage of regions with >60% GC content. I don't think that these results are out of the ordinary. I just came across the manuscript below...

http://www.ncbi.nlm.nih.gov/pubmed/21338519

They have shown that the PCR steps in the library constuction process create a huge bias against regions of high GC content. They have also shown how to resolve this problem. Check it out...

DoubleA

Last edited by DoubleA; 03-23-2011 at 10:08 AM.
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Old 06-23-2011, 07:11 AM   #26
Sam64
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DoubleA,
Thank you for your link, the manuscript is very interesting but it just refers to the Illumina Sureselect protocol.
Has somebody some experience for the Agilent sureselect protocol ? in particular for the enzyme Herculase II...Does this enzyme allow to restore the fragments with high GC percent (like the AccuPrime Taq HiFi used in the manuscript) ?
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Old 07-27-2011, 02:37 PM   #27
DoubleA
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Hello everyone,

I recently tried three different polymerases as well as different PCR conditions (increases in denaturation time) in an attempt to increase read coverage in regions with >60% GC content. I enriched 12 Illumina PE libraries with either AccuPrime, Phusion, or KAPA polymerase (4 with each polymerase). I should mention that I ligated the same adapter and incorporated a unique bar code for each library using the three primer enrichment approach (PE 1.0, PE 2.0, and a primer containing the bar code). Following library production, I pooled 4 libraries (all 4 created with the same polymerase) and performed a hybridization with a custom SureSelect bait library covering ~8Mb of the HLA region on human chromosome 6 (baits on ~3.8 Mb). Following hybridization and elution, I performed a final enrichment with primers covering the last 20bp of the 5' and 3' end of the Illumina libraries. All 3 pools (12 libraries) were mixed and run on a HiSeq lane for a single 40bp run. The coverage was of the target region was a bit low (10-50X) so we'll probably sequence these libraries in the future with a PE X 100bp run. I have attached graphs of read coverage vs %GC content (20bp window). As you can see, the coverage of the the GC rich regions is pretty similar for each polymerase and the increase in denaturation time per cycle did not help much either. Below is the % reads mapping to regions with GC content >60%. I thought some of you might be interested in these results.

Regards,
Double A

AccuPrime 15 second/cycle denaturation: 12.4%
AccuPrime 30 second/cycle denaturation: 12.9%
AccuPrime 45 second/cycle denaturation: 13.2%
AccuPrime 60 second/cycle denaturation: 14.4%

Phusion 15 second denaturation/cycle: 13.5%
Phusion 30 second denaturation/cycle: 16.4%
Phusion 45 second denaturation/cycle: 13.1%
Phusion 60 second denaturation/cycle: 12.6%

KAPA 15 second denaturation/cycle: 13.9%
KAPA 15 second denaturation/cycle: 10.7%
KAPA 15 second denaturation/cycle: 13.5%
KAPA 15 second denaturation/cycle: 11.6%
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Old 11-07-2011, 12:58 PM   #28
arvi8689
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Default coverage calc

Hey

I am trying to sequence the exome and the capture kit is 100MB

The sequencing core promised 120 million reads per lane and we are using paired end 100bp reads and our fragment size is 250 basepairs.

My calculation was I will get 120 million reads * 200= 240 million bases read

so coverage= 240 million bases/100MB= 240x coverage (average)

But some people say I will get a coverage of only 120x. What could be the reason? Or is the coverage actually 240x?
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Old 11-07-2011, 01:13 PM   #29
DoubleA
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Hi Arvi8689,

There are two things that will reduce your fold coverage with a exome capture experiment. First, at least 10% of your reads will be PCR duplicates and should be removed before alignment. Second, ~60-80% of your unique reads will be "on target". Therefore, it's likely that only 50% of your initial reads will be unique and map to your target.

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