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Has anyone been using the TruSeq RNA Access kit? We have been getting a snoRNA (SNORA63) soak up almost 10% of our reads from libraries we've generated with this kit. Seems to be present if we use high or low quality starting material. I'm attaching our Bioanalyzer traces, and you can clearly see there is an over-represented transcript present. I'm wondering if anyone else is having a similar issue? Any thoughts on whether this is due to insufficient post-capture washing? Thanks!
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Hi!
I'm having some similar trouble. is it just snora63? Any others affected? I have almost 90% TPM going to snoRNA for several of my preps. I'm concerned it may be an Illumina side error since it's happening in my UHR and HBR samples. -
We have seen several different snoRNA's soak up reads in our analysis. We have been seeing the data improve recently by pre-heating the wash buffer during the second wash following captures. I can't say with absolute certainty that this will fix your problems. I think part of this problem begins with sample quality/complexity. Are you using FFPE material?Comment
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Are you still experiencing a large proportion of your reads going to snoRNA with theTruSeq RNA Access kit? By what proportion does pre-heating the wash buffer during the second wash following captures reduce the snoRNA reads?Comment
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Unfortunately, I don't have any head to head comparison data to share. The snoRNAs are actually targeted by probes within the RNA Access oligo pool, so in many cases there is nothing you can do do get rid of them. We implemented the heated wash step because we figured it couldn't hurt. The workflow generates such high exonic mapping rates that we simply stopped worrying about the contaminating snoRNAs. At the end of the day, their presence wasn't dramatically affecting our work.
What alignment rates are you seeing for snoRNA's in your experiment?Comment
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We are just gearing up to run our first samples (fresh frozen) with the kit so it is good to hear that it generates high exonic mapping rates. Will let you know the outcome.Comment
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I think you'll find it to be a pretty solid workflow. You should be right around 90% exonic alignment rates with fresh frozen tissue. With FFPE material, we're in the 80-90% range. Best of luck!Comment
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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.07-01-2026, 11:43 AM
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