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The new software package uses deep learning algorithms to analyze T cell receptor sequence data




Researchers at the Johns Hopkinskin Mel Cancer Center’s Bloomberg-Kimmel Cancer Immunotherapy Institute have developed Deep TCR, a software package that analyzes T-cell receptor (TCR) sequence data using deep learning algorithms. T cell receptors are found on the surface of immunity T cells..These receptors bind to specific ones antigen, Or protein. Found in abnormal cells such as cancer cells and cells infected with viruses and bacteria, T cells induce the affected cells to attack and destroy them.

“Deep TCR is open source software that can be used to answer questions in the study of infectious diseases, cancer immunology, and autoimmune diseases. Wherever the immune system plays a role through T cell receptors,” the lead research author said. John says. William Sidum, MD / Ph.D. A student at Johns Hopkins University School of Medicine and Biomedical Engineering, he works at the Bloomberg-Kimmel Cancer Immunotherapy Institute.

The study was published on March 11th Nature Communications..

Sidhom was inspired to develop software after attending a presentation on the use of deep learning in medicine at the 2017 American Association for Cancer Research conference. “I was doing research on T cell receptor sequencing and found that this was a good technique for better analysis of T cell sequencing data,” he says.

Deep learning is a type of artificial intelligence that roughly mimics the workings of the human brain from the perspective of pattern recognition. “Deep learning is a very flexible and powerful way to perform pattern recognition on all kinds of data. In this paper, we use deep learning to identify patterns in T cell receptor sequencing data.” Said Sidhom. The software searches for T cell receptors, but is similar to searching the Internet. “When someone searches the Internet for images of cats and dogs, the query applies an algorithm that looks at the features of the image, rather than looking for an image with a caption that labels the image as a cat or dog. Recognize patterns that identify images as cats or dogs. This is a deep learning. “

DeepTCR is a comprehensive deep learning framework that includes both unsupervised and supervised deep learning models that can be applied at the sequence and sample level. Sidhom’s unsupervised approach allows researchers to analyze data in exploratory ways that may not have known immune exposures, and a supervised approach allows researchers to leverage known exposures to train models. Can be improved. As a result, DeepTCR enables researchers to study the function of the T cell immune response in basic and clinical science by identifying patterns of receptors that give the function of T cells to recognize and kill pathological cells. He says.

One of the main challenges in analyzing TCR sequence data is to distinguish between meaningful sequence data and non-essential data. DeepTCR helps perform this analysis.

There are many sequences in someone’s immune repertoire. The immune response is very wide, as there are many pathogens that can infect someone. As a result, there is a sea of ​​noise in the immune response, only a portion of which is important for a particular infection at a particular time. I may show T cell responses to a thousand different viruses, but when the flu affects me, I just need to utilize a small subset of those T cells to fight the flu is. The main thing the algorithm can do is to isolate the appropriate T cells and match them to a particular response. “

John-William Sidhom, Principal Investigator

A software package that employs a type of deep learning architecture called a convolutional neural network provides users with the ability to find T cell sequence patterns associated with specific exposures such as influenza infections, cancers, and autoimmune diseases.

“Given a large amount of data, the algorithm can learn the rules for these TCR sequence patterns. For example, you may not know the rules for how your body responds to the flu, but you have enough data. With that, the software can learn those rules. Tell me what they are, “says Sidhom. “It is very suitable to identify complex patterns of very large immune repertoires to identify interaction partners between T cell receptors and their antigens.”


Journal reference:

Sidum, JW. , et al. (2021) DeepTCR is a deep learning framework for clarifying sequence concepts in the T cell repertoire. Nature Communications.


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