10/31/14
Fri, 10/31/2014 at 7:09 AM
Customer: Do you have any experience in analyzing chip seq data for histone modifications?
Fri, 10/31/2014 at 9:17 AM
AccuraScience LB: Yes, processing and analysis of ChIP-seq data - for either transcription factors or histone modifications, as well as that for a cluster of similar experiments such as FAIRE-seq, DNase-seq, MNase-seq, and CHIA-PET - are all considered as "routine analysis" or Track 1 analysis, meaning we have had so much experience with them that we consider them as routine . Would you like to describe your project a little more, to see if we might be able to inject some ideas or providing some suggestions?
Fri, 10/31/2014 at 9:37 AM
Customer: Our major concern at the moment is to check if our completed ChIP-seq experiment has worked. We want to make sure that our data has a good quality (for example ENCODE quality metrics like RSC, NSC, NRF etc). Our bioinformatician has been using MACs and SPP.
Fri, 10/31/2014 at 12:27 PM
AccuraScience LB: We've had a lot of experience processing "broad" and "mixed" types of ChIP-seq data, but frankly, this is the first time a research has asked about detailed questions such as those on NRF and RSC. In our previous projects, we would check for depth coverage, control samples, and QC of the sequencing data (with FastQC) to ensure that basic ENCODE guidelines are followed, then would go ahead with the peak calling. ENCODE's guidelines for "broad" and "mixed" types of ChIP-seq experiments/analysis are in fact not exactly complete. Criteria for depth coverage and controls are well specified, but those for NRF are only "provisional" in nature.
MACS and SPP are both OK to use for "broad" and "mixed" ChIP-seq data, but it is important to increase the "bandwidth" and relax the "peak cutoff" parameter value for these two types of data. In particular, for "mixed" type, there needs to be a trial-and-error type tweaking of these parameters. Besides MACS and SPP, some recent tools were developed specifically for peak calling of "broad" and "mixed" data, Some of them are SICER, Zinba, and PeakRanger.
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Note: LB stands for Lead Bioinformatician. An AccuraScience LB is a senior bioinformatics expert and leader of an AccuraScience data analysis team.
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