Closed arakawa7 closed 2 years ago
ay-lab
commented
2 years ago Have you looked at QCs of your data and/or plotted heatmaps to see if you are able to visually identify specific loop patterns? Also the read depth is ok (not great) but maybe you would want to try 10kb or 20kb resolution to see if things change
arakawa7
commented
2 years ago Thanks for your reply. I will try to do FitHiChIP again with the current data with a different resolution.
Is there anything else to do with QCs in HiChIP besides checking the quality of the library and the shape of the peaks, I am new to Hi-C and HiChIP experiments and don't know how to do QCs.
2022年5月12日(木) 0:29 ay-lab @.***>:
Have you looked at QCs of your data and/or plotted heatmaps to see if you are able to visually identify specific loop patterns? Also the read depth is ok (not great) but maybe you would want to try 10kb or 20kb resolution to see if things change
— Reply to this email directly, view it on GitHub https://github.com/ay-lab/FitHiChIP/issues/76#issuecomment-1123932455, or unsubscribe https://github.com/notifications/unsubscribe-auth/ASMJPLOMBNAMNW5FP5ENG63VJPG5ZANCNFSM5VUAIJHA . You are receiving this because you authored the thread.Message ID: @.***>
ay-lab
commented
2 years ago HiCPro already generates a lot of statistics and plots that can be used for QC. For Hi-C data, you can take a look at this paper from ENCODE, some ideas apply to HiChIP data as well: https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1658-7
arakawa7
commented
2 years ago Thank you for your kind attention. I will review the data generated by HiCPRO again and check the paper from ENCODE as I was not aware of it. I will use FitHiChIP again after QC confirmation.
2022年5月12日(木) 13:27 ay-lab @.***>:
HiCPro already generates a lot of statistics and plots that can be used for QC. For Hi-C data, you can take a look at this paper from ENCODE, some ideas apply to HiChIP data as well: https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1658-7
— Reply to this email directly, view it on GitHub https://github.com/ay-lab/FitHiChIP/issues/76#issuecomment-1124517560, or unsubscribe https://github.com/notifications/unsubscribe-auth/ASMJPLMWSAZ3A2THAYORMLLVJSCEBANCNFSM5VUAIJHA . You are receiving this because you authored the thread.Message ID: @.***>
HiChIP with MNase was performed on mouse cells, followed by analysis with HiCPRO and FitHiChIP. Valid Read Pairs 80M PE reads Config file settings for FithiChIP IntType=3 BINSIZE=5000 LowDistThr=20000 UppDistThr=2000000 UseP2PBackgrnd=0 BiasType=1 MergeInt=1 QVALUE=0.01
FitHiChIP was performed using ChIP data from ENCODE. Number of interacting (non zero contacts) bin pairs ( all to all ) 11191560 Number of interactions considered 561607 Total number of significant interactions 0 What are the reasons for significant interactions 0?
Also, if I use the inferred ChIP data from the HiChIP data Number of interacting (non zero contacts) bin pairs ( all to all ) 10194721 Number of interactions considered 20004 Total number of significant interactions 45 Total number of significant interactions ( FDR 0.01 ) after merging adjacent loops 16 The number of interactions considered is very small. What could be the problem?