How Can ChIP Seq Data Help Biologists In Their Research?

chip seq data researching

ChIP sequencing or ChIP-seq is a high throughput method that combines chromatin immunoprecipitation with next generation sequencing (NGS) techniques. The application of NGS to the chromatin immune-precipitation techniques allows research teams to explore the protein-DNA interactions inside the cell. 

In the case of human beings and other eukaryotic organisms, the chromatin structures and condensation levels vary according to the different stages of the cell cycle. Throughout the life of a cell, different proteins interact and bind to various sites of the chromatin. These play a significant role in the upregulation and downregulation of gene expression. Gene expression is related to disease states and several biological processes. 

The older version of ChIP – ChIP-ChIP used a hybridization array. This technique was thought to have introduced bias into the experiment by using a fixed set of array probes. On the other hand, ChIP-seq technology does not introduce similar biases. It can simultaneously sequence hundreds of precipitated protein-binding sites on the chromatin and find out which sites have proteins bound or unbound during a disease state or cell cycle stage for upregulation or downregulation of gene expression. 

How To Read ChIP Seq Data? 

ChIP-seq is one of the most powerful and accurate techniques for studying DNA-protein interactions or histone modifications across the entire genome. The latest ChIP-seq process generates data that is less complex than the data generated by other forms of parallel sequencing. However, the data set it creates is extensive, and it demands the use of powerful computational methods. that raises the question how to read ChIP seq data? 

Peak calling methods have been standardized over the last decade for the prediction of DNA-binding sites from the ChIP-sequence. One of the simplest and most commonly used methods is MACS – it models the ChIP-seq tag’s shift size and uses the empirical data for enhancing the spatial resolution of the supposed binding sites on the chromatin. 

Although NGS has advanced the sensitivity of ChIP-sequencing, the enormous data produced from the experiment generates new challenges in bioinformatics. Mapping of the resulting data done against a large genome requires pre-indexing structures and considerably large memory. Hence the used software needs to be memory efficient and lightning fast. 

BLAST was the answer to how to read ChIP seq data for almost three decade, but it has its caveats – sequencing errors and SNPs, resulting in mismatches. The new software technology addresses the old challenges in a new way – 

i. By considering gapped vs. ungapped alignment of sequences. 
ii. Solutions for treating non-unique reads. 
iii. Considering the quality value of each nucleotide base in the reads. 
iv. Determining a balance between speed and accuracy of the mapping process. 

While there are several software programs out there that allow reliable mapping and analysis of the ChIP-seq data, there are only a handful few that accommodate a variety of sequencing technologies and interests of application. 

Your team needs software that is not only reliable but also easy-to-use. It should provide replicable results and publication ready formats that holds its respect in the scientific community. 

What Insights Do The Analyses Of ChIP-Seq Data Provide? 

Here are the benefits of analysis of ChIP-seq data – 

Once researchers find the solution to how to read ChIP seq data problems, they will find that assessing the vast library of DNA sites that bind to different proteins of interest to be accurate and replicable. Chromatin immunoprecipitation isolates the regions of the DNA that interact with transcription factors and several other types of protein directly. The technique is used in conjunction with whole-genome sequencing databases to reveal the precise location of the interaction or binding sites. 

The massive volumes of data generated from the NGS of the immunoprecipitated chromatin-protein can be used by biologists to study the role of different proteins in the control of gene expressions during different time frames during cell division. ChIP-seq is applicable to almost all polymerases, transcription machinery, DNA modifications, protein modifications, and structural proteins. 

New Treatment For Breast ER-Dependent Cancer 

Hurtado et al. used ChIP-Seq to find a link between FoxA1 (pioneer factor) and breast cancer treatment. The experiment studied the reduced binding of the estrogen receptor (ER) after a knock-down of FoxA1. It proved that FoxA1 might have a critical role in ER-mediated transcription. It can serve as a therapeutic target in the treatment of estrogen-dependent breast cancer. 

The interactions between the chromatin and transcription factors are potential targets for effective therapeutics with minimum contraindications. The analysis of the data captured from ChIP-seq can contribute to the advances in the field of personalized medicine. 

Evolution Of Transcription Machinery
A research team led by Dominic Shmidt used ChIP-seq for studying the evolution of transcription factors and their binding sites on the chromatin. While a large number of transcription factors are usually conserved among related species, the team decided to focus on CEBPA and HNF4 in the liver tissues. They studied five diverse vertebrate species – humans, dogs, mouse, opossum, and chicken. 

It was one of the pioneering experiments that revealed the evolutionary dynamics that govern the binding of transcription factors to the different regulatory elements of a gene. This chemistry experiment would have been almost impossible with ChIP-ChIP techniques since it used five distinct samples from different species, and the process of designing probes would have been extremely complicated. 

An Insight Into The World Of Protein-DNA Interactions 

Today, further modification of the ChIP-seq techniques have led to the development of different methods of analysis, including the ChIP-seq for protein-RNA interaction in mammalian cells. Other methods include DNase-seq and FAIRE-seq. Both of these methods identify the regulatory regions within the genome. The advancements in the area of analysis and mapping software has answered more or less all questions in the vicinity of how to read ChIP seq data. 

These modifications not only allow the biology research teams to explore the distribution of transcription factors across the entire chromatin, but it also provides replicable information on the epigenetic modifications and biomarkers in the genome that might contribute to the development of specific phenotypes.

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