DNA-mapping tool shows potential for identifying cancer progression
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Researchers at Roswell Park Comprehensive Cancer Center and University of Buffalo have developed a novel tool to analyze a DNA-mapping technology that identifies gene alterations responsible for cancer development and progression.
“The innovative computational method, known as HMMRATAC, outperforms all other methods used in the field to identify open and active chromatin because it takes advantage of the unique features of ATAC-seq — [which stands for] an assay for transposase-accessible chromatin sequencing — to identify chromatin structure more accurately,” Tao Liu, PhD, assistant professor of oncology in the department of biostatistics and bioinformatics at Roswell Park Comprehensive Cancer, said in a press release. “As HMMRATAC [Hidden Markov ModeleR for ATAC-seq] is a cross-platform and user-friendly algorithm dedicated to ATAC-seq, we envision it becoming the standard tool used for ATAC-seq data analysis.”
Liu spoke with Healio about the need for a tool such as this, how researchers conducted the study and the potential implications of the findings.
Question: What prompted the development of this tool?
Answer: The DNA mapping tool ATAC-seq has become a popular and standard genomic assay to identify open chromatin regions associated with gene regulations in the cell. Due to its low starting material requirement and simple protocol, ATAC-seq is especially useful for clinical research when the number of cells from a patient is limited. Although the number of published ATAC-seq studies has increased rapidly, the field is still using methods initially designed for ChIP-seq to analyze data. One of the most popular tools for analyzing ATAC-seq is MACS2 software, which was developed by me for transcription factor and histone mark ChIP-seq as a postdoc at Dana-Farber Cancer Institute. However, MACS2 is a general software tool, and it misses many characteristics in the ATAC-seq. A PhD student of mine, Evan D. Tarbell, PhD, who has since graduated, started a discussion about the need for a dedicated and specified algorithm to be developed. This project grew out of that conversation.
Q: How did you conduct the study?
A: We built a machine learning method, dubbed HMMRATAC, with an emphasis on the fact that the DNA fragments associated with well-positioned nucleosomes around open chromatin will be sequenced together with those originated from open regions alone. Current methodologies ignore this fact, so that either only reads from open chromatin are considered or all reads are considered without any differentiation. This new method separates a single ATAC-seq data set into different layers of information and integrates them to create a Hidden Markov Model (a machine learning approach) that identifies the chromatin structure at accessible genomic regions.
Q: What did you find?
A: Our tool outperforms all other computational methods in recapitulating open and active chromatin, with a large margin. Using HMMRATAC, one can have more accurate and sensitive predictions of DNA-accessible regions out of an ATAC-seq data set.
Q: What should clinicians take away from this?
A: We believe HMMRATAC will grant researchers the ability to better analyze ATAC-seq data sets to dig out more biological clues about the chromatin structures around accessible regions. With the advantages of ATAC-seq, such as the smaller amounts of cells required, and the proceeding of ATAC-seq into single-cell study, ATAC-seq will be an especially helpful tool for clinicians looking for clues about disease and drug response due to certain mutations in the nonprotein-coding DNA elements switching genes on and off.
Q: What is next for research on this?
A: We are building HMMRATAC software into the standard for ATAC-seq data analysis, replacing current methods based on ChIP-seq analysis. And we are currently building the method into single-cell ATAC-seq analysis. – by Jennifer Southall
Reference:
Tarbell ED and Liu T. Nucleic Acids Res. 2019;doi:10.1093/nar/gkz533.
For more information:
Tao Liu, PhD, can be reached at Roswell Park Comprehensive Cancer Center, Elm and Carlton Sts., Buffalo, NY 14263; email: tao.liu@roswellpark.org.
Disclosures: Liu reports no relevant financial disclosures.