Without an input DNA you can not distinguish noises and real peaks in your sample. This is an important control step for ChIP seq. After sequencing, the distribution of the coverage in genome regions of the sample has to be compared with the distribution for input DNA. The results obtained from this comparison provides you the information of real peaks and false positives. Otherwise, you might take a noise as a real peak due to biases in your library.

One of the most important things after you selected your best possible Ab is a good strategy for sequencing and analysis. Sequencing and analysis is still a black box for most of us, mortal molecular biologists, who are familiar with what put (DNA) in and we get out (sequences), but know nothing about possible (almost unavoidable) biases introduced along the whole process. Apparently in NGS, the slightest technical bias in the the basic steps (i.e. linker ligation, amplification step, etc) could consequently introduce biases in the sequencing (that could also be platform-dependant) that will be reflected in the bioinformatic analysis. So, i guess that while these technologies are still evolving it is important in a ChIP-seq experiment to run a control using input DNA, that is a non-ChIP genomic DNA, so any introduced bias can be identified and be adjusted for. Good luck!

I think the comparison of the samples itself doesn't provide the information of the biases originated from sequencing run. For example; if you see same peak in all replicates in the same run you can't still conclude that it is a real peak not a noise because somehow some genome regions have higher coverage due to NGS system approaches. Thus, an input DNA can show you the bias in your run and you can ignore the noises which result from this bias during your analysis. Even if you sequence your samples in different sequencing runs you still need an input DNA because you might see same biases in different runs so you can use an input to distinguish the false positives.

I guess there are several options...but I found this in an article that I thought you may find interesting and makes lot of sense to understand this type of control "If you refer to the ChIP protocol the concept is actually explained therein. Input DNA is essentially the DNA purified by cell lysis and sonication. It is the same DNA that is run on a gel to test the efficiency of cell lysis and sonication".

And follows "In essence, if we were to PCR test for regions of previously characterized binding by our transcription factor of interest would we likely produce a band in this control? Yes, we would. Remember, it’s genomic DNA. All genomic loci are likely represented in the sample. What good is it then if positive control regions will be positive? Well, it’s all about enrichment. Yes, this sample will have our positive control loci - but - in much lower relative quantities. This is the essence of enrichment. Relative to our ChIP, Input DNA will have far fewer copies. We should be able to demonstrate a quantitative difference between our ChIP samples and our Input DNA samples (even if both are positive)." I hope this helps.

Input DNA is hihgly recommended. Most of the popular peak-calling software programs will be able to run the analysis without specifying an input sample, but the quality of the peak calling and your confidence in the results will be much higher if you include input.

In such analyzes, it is necessary to calculate the numerical value of the sequences from all samples. 'll Get best results if the numerical value calculated using the number of atoms in the sequences. (Amino acids, nucleotides, etc.). In this way you will get pictures of these mathematical patterns. This is followed by a mathematical analysis of the mathematical images of the samples. The mathematical analysis will give you the answer to the question that the binding of proteins to DNA. Mathematics is an exact science. These patterns have their own algorithms. And in this algorithm are clearly identified and characterized the binding sites.

Thank you very much to all of you for your help!

Pleasure ;-)

Laura Espana Serrano

The first step in this research is to express sequences in this way

Nucleotides t t g g c

Number of atoms 15 15 16 16 13

Position 1 2 3 4 5

Etc.

After that we need to decode the codes that are hidden in these sequences.

These sequences should be viewed as a mathematical problem. In this sense, we need to find the mathematical principles that connect these sequences. In fact, we need to decode the genetic codes. This can be done with a little creativity. I am ready to help with financial support. I give free advice.

Thanks!

whether or not input is highly correlated with experimental types. different TFs or histone modifications are different with controls. in my experience, if the ChIP-seq data is deep enough, the input could be neglected, although false positive will be increased or not significantly affected.