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You could implement an approach like the one described in this paper: http://www.ploscompbiol.org/article/...l.pcbi.1000598 in order to estimate the background RPKM/FPKM and arrive at a cutoff. It's based on looking at false positive and false negative rates based on a set of known genes and a set of control regions for which there is no evidence that they are being expressed. -
FPKM/RPKM cut-off question
I have got FPKM and some genes have really low FPKM and I am going to do stat analysis for all the genes; my question is the analysis should be like microArray analysis and need some cut-off numbers? any suggestion for this?
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The COVID-19 pandemic highlighted the need for proactive pathogen surveillance systems. As ongoing threats like avian influenza and newly emerging infections continue to pose risks, researchers are working to improve how quickly and accurately pathogens can be identified and tracked. In a recent SEQanswers webinar, two experts discussed how next-generation sequencing (NGS) and machine learning are shaping efforts to monitor viral variation and trace the origins of infectious...03-24-2025, 11:48 AM -
This year’s Advances in Genome Biology and Technology (AGBT) General Meeting commemorated the 25th anniversary of the event at its original venue on Marco Island, Florida. While this year’s event didn’t include high-profile musical performances, the industry announcements and cutting-edge research still drew the attention of leading scientists.
The Headliner
The biggest announcement was Roche stepping back into the sequencing platform market. In the years since...03-03-2025, 01:39 PM
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