Question: Agilent SureSelect design miss some target regions
gravatar for Amk
4.1 years ago by
Amk120 wrote:

Dear All,

I am designing probes using SureSelect DNA for targeted sequencing of a particular gene. But after designing using default settings I got the bed file with so many regions missed. Will it be a problem in picking up some important SNVs? I am new to this array designing, can any one give me the direction what should be the strategy dealing with these missed regions? Any suggestions will be helpful.

Thanks in advance.

genome sureselect agilent • 1.4k views
ADD COMMENTlink modified 4.1 years ago by Daniel Swan13k • written 4.1 years ago by Amk120
gravatar for Amitm
4.1 years ago by
Amitm1.9k wrote:


Depending on what is the criteria for SNV importance, you can go here -

Cosmic (for known cancer variants) -

dbNSFP (known non-syn. polymorphisms) -

[or you can go for complete dbSNP depending on what you deem appropriate]

and get the SNV/ SNP coordinates in a BED format. (You can convert Cosmic VCF into bed using bedops tool here)

One you have BED format of imp. SNVs, overlay them of the missed regions from Agilent Design and see what you are missing.

BEDs can be compared easily using again bedops utility or bedtools intersect.

ADD COMMENTlink modified 6 weeks ago by RamRS25k • written 4.1 years ago by Amitm1.9k
gravatar for Daniel Swan
4.1 years ago by
Daniel Swan13k
Aberdeen, UK
Daniel Swan13k wrote:

Baits are designed to work within the parameters of a hybridisation. Regions will be missed if they are high GC, high AT or known repeat/low complexity regions - a cursory glance at the Agilent exome bed file will show you in some genes tandem repeat exons are excluded from bait design.

You can - design custom baits to fill these regions, ignorning the hybridisation constraints that SureDesign places on you. Go to another vendor and tile across the region of interest, ignoring design constraints with their oligo selection.

Design variable length baits to the regions you're missing so that they maintain a similar Tm profile to the rest of the baits.

These kind of amelioration strategies may or may not work. Most bait design algorithms are designed to spit out baits that will most likely work in the environment they are intended. Without careful manual curation of a design, you cannot guarantee 100% coverage of your region, and even if you do manage to cover the area in baits it will be no guarantee that they will perform well. Baiting known repetitive regions is a great strategy for decreasing your on-target capture.

ADD COMMENTlink modified 4.1 years ago • written 4.1 years ago by Daniel Swan13k
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