[captainentropy]

Did you (elly) run your libraries on a bionalayzer? If your library is mostly amplified adapters then what you observe is exactly what you would expect. A bioanalyzer trace would very clearly indicate this with a strong peak in the 124bp range.

And if you have access to an instrument like a Pippin Prep or a LabChip XT then you should use that. It will pretty much eliminate adapter contamination prior to the PCR step (assuming you aren't size selecting below say 150bp).

[Hamid]

Hi Elly,

Shearing to a specific size range does not necessarily correspond to efficient IP. Often people expose their chromatin to too much energy which provides great shearing results at the expense of epitope integrity. I would suggest that you carry out a western of your sheared material with the same antibody you are using for your IP. It is a cheaper and an effective method of QC for ChIP. We routinely do that in our lab, and the results correlates very well with our IP. You can also carry out qPCR with epitope specific primers.

Thank you

Hamid

[rmetz]

Did you by chance use salmon sperm or another DNA as a block or carrier during the chIP?

(We've had 2 separate groups who did just this. Leads to very low % alignment. Uggg! On the other hand were up to 100million salmon reads if anyone wants to take a go at a denovo assembly.)

Has anyone else had this problem?

[smallcreek]

Hi guys,
I am happy to read your valuable discussion here. We recently had problem that the color balance of the sequencing is completely off, which caused majority of image pannel failed. On the WFA, every parameter looks fine except P2_Gain is too low (lower than 8). Is that color balance off is caused by the concatamerization of adaptors? We did not know this trick and did not dilute the adaptors while our ChIP DNA concentration is very low. If the P2_gain is a result of adaptor concatamerization, we can use it to titrate the adaptor dilution based on the concentration of ChIP Seq samples from our customers.

Thanks in advance

Thanks in advance.

Smallcreek

[Nix]

Did you by chance use salmon sperm or another DNA as a block or carrier during the chIP?

(We've had 2 separate groups who did just this. Leads to very low % alignment. Uggg! On the other hand were up to 100million salmon reads if anyone wants to take a go at a denovo assembly.)

[Susanne]

Chipper, replying to your post #11 in this thread, how are you able to tell whether or not ssDNA is contaminating the sample?

[kmay]

Chipper,

I am not sure wheter I understand your question right. The amplification in the wet lab amplifies everything, including unspecific noise. One should expect for later analysis that higher copy number tags get up more quickly than unique signals and downstram analysis for perfect and unique matches only should eliminate this. We always at the first step look at perfect and unique maches only. However, there is a lot of "noise" (unspecific bound DNA or otherwise carried over oligos) which matches pefect and uniquely, too.

We don´t do te wet lab, we do only analyses. And with the many different data sets we saw so far, we found that amplification seems to be the next crucial step after ab-specificity.

If you are interested, I could bring in our specialist for that.

Klaus

[Chipper]

Klaus,

did not see your answer to elly before. Is the amplification a problem in your data even if you do unique positions only for the uniquely aligned reads?

[Chipper]

Hi again,
I guess Klaus is reffering to the fact that of all aligned reads only a small percentage occur in peaks of significant enrichment. You will always have some genomic background and due to the large genome size this will generate a high number of radomly aligned sequences even if you have a good enrichment ratio.

What beads and blocking did you use for ChIP, is there any possible contaminats like ssDNA?

[kmay]

elly,

sorry for having been mis-understandable!

I am talking about two steps.
1st: mapping of the reads to the genome
2nd: clustering of the mapped reads from step 1 into regions (clusters) of enriched read density.

We have only example data for a DNAse-seq experiment online, but they might be helpful in explaining the difference.

Step1 statistics
Step 2 statistics with arbitrary variation of tag density per bp-window, to demonstrate effects of such. Usually we calculate significance of tag density based on a poisson distribution

If step 1 delivers only 5% something is terribly wrong.
Did you try other mapping algorithms than Eland? Do mappings with increasing relaxation criteria:
1 point mutation,2..,3... indel1, 2, 3... and see how mapped tag numbers behave.

The 5% I´ve been talking about, correspond to the number of mapped tags falling into clusters of enriched density from step2.
The "noisyness" in this stage largely depends on specificity of the antibody. There is always a lot of unspecific binding carried over. Another major effect has the experimental set-up. Whether and how you do a control for subtraction.
Unspecific ab or just input control. To our experience the latter shows better results. Last but not least, be very careful with amplifications. Noise rather quickly gets up to signal levels.

5% vs. th rest 95%: well, i am afraid there is no clear-cut statistical method to decide at the end about the success. It requires some human brain to look at the raw data and clusters in the genome annotation (we do this in ElDorado). You can sign up for free for two weeks and inspect the open chromatin data, or go here to see the DGE results from our Science paper. Go down to "user data" and choose which data you want to see.

Statistics comes into play again at the next step: see which TF-binding sites are over-represented in the clusters (hopefully the one you IPed), whether they are part of a complex model or they are phylogenetically conserved.

Hope this helps!

Cheers

Klaus

[elly]

Hi kmay,

I'm so sorry that I didn't reply to my thread.
I was so busy to set up another application.

I'm sure that my referernce genome sequence was correct.

Could you please explain why ChIP-seq usually generate noisy data?
And how 4~5% aligned data can be used for further analysis?
What is the rest 95~96% of reads?

I haven't solved this problem yet and still seeking solution.

Your suggestion and explaination would be so helpful for me.
Thank you in advance.

elly.

[kmay]

This low alignment rate puzzles me. elly never replied to this thread
My first guess was what apfejes took into account: was it the right reference genome? Otherwise the experiment went terribly wrong.

chIP-seq usually generates rather noisy data. But the noise is in the aligned tags and delivers often rates of 4% to 5% of aligned tags falling into clusters. Here amplification plays a crucial role. One step too many generates quite some headache. However, 4% is enough to still get good results at the end.

Klaus

[sblake]

aha! makes sense, tks

[apfejes]

Hi sblake,

I was told the people in the lab run the gel with a blue dye that migrates at along with fragments of a particular fragment size. They use this as a guide to indicate the approximate position to excise - I understand it took a bit of practice to get the technique right, but once you have it down, it's not too bad.

Off hand, i don't know which dye they use, but I certainly remember the blue dyes from my (long ago) days of running gels. I'd hate to try this on a really small gel, but on a longer one, I don't see this being a problem.

[sblake]

apfejes - in regards to your contamination issue of the ladder with a sample on the chip-seq size selection gel, how do evaluate what to excise without a ladder? tks