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Integrated analysis and annotation for T-cell receptor sequences using TCRosetta
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AbstractBackgroundT cells and T cell receptors (TCRs) are essential components of the adaptive immune system. TCRs, on the surface of T cells, are responsible for recognizing and binding antigen peptide-MHC complex and play vital roles in T-cell immunology. Characterization of TCR repertoire offers a promising and high informative source for understanding the functions of T cells in immune responses and immunotherapies. Many researchers are now interested in TCR repertoire study; however, there are rare online servers for TCR analysis, especially no servers for TCR annotation and advanced analyses.ResultsWe developed TCRosetta, a comprehensive online server integrating the analytical methods for TCR repertoire/sequences analysis and visualization. TCRosetta combines general features analysis, large-scale sequence clustering, network construction, TCR-peptide binding prediction, generation probability calculation, and k-mer motif analysis for robust TCR sequence analysis, making TCR data analysis as simple as possible and allowing users to concentrate on research rather than coding. In addition, TCRosetta could annotate disease information for TRB CDR3 sequences by batch searching similar sequences in manually curated disease-related TCR database. The TCRosetta server accepts multiple input data formats and can analyze ∼20000 TCR sequences in less than three minutes.ConclusionsTCRosetta is the most comprehensive web server to date for TCR sequences/repertoires analysis and it is freely available athttp://bioinfo.life.hust.edu.cn/TCRosetta/. It can be applied to help discover novel biomarkers for disease diagnosis and identify cancer-associated TCR sequences.WHAT IS ALERADY KNOWN ON THIS TOPICT cell receptor repertoires are largely untapped resource than can be used for predicting immune responses to different exposures including viral infections and tumor neoantigens. The downstream analysis of TCR repertoire is often performed by different tools requiring diverse operating environments and expertise. There is no webserver for comprehensive TCR repertoire analysis including general and advanced analysis.WHAT THIS STUDY ADDSWe developed TCRosetta, a comprehensive platform for analyzing T-cell repertoire which combines nearly all TCR analysis methods. It supports different kinds of the format of input including most mainstream TCR extraction tools or amino acid sequences. It could analyze the features of TCR repertoire and display them in interactive graphs and is the first platform with a batch search and TCR annotation function.HOW THIS STUDY MIGHT AFFECT RESEARCH, PACTICE OR POLICYTCRosetta can be applied to discover novel biomarkers to predict response in immunotherapy such TCR repertoire diversity and clonality. It also can identify cancer-associated TCR sequences by clustering biochemically similar CDR3 sequences. It can make TCR repertoire analysis as effortless as possible and help users focus on research instead of coding.
Title: Integrated analysis and annotation for T-cell receptor sequences using TCRosetta
Description:
AbstractBackgroundT cells and T cell receptors (TCRs) are essential components of the adaptive immune system.
TCRs, on the surface of T cells, are responsible for recognizing and binding antigen peptide-MHC complex and play vital roles in T-cell immunology.
Characterization of TCR repertoire offers a promising and high informative source for understanding the functions of T cells in immune responses and immunotherapies.
Many researchers are now interested in TCR repertoire study; however, there are rare online servers for TCR analysis, especially no servers for TCR annotation and advanced analyses.
ResultsWe developed TCRosetta, a comprehensive online server integrating the analytical methods for TCR repertoire/sequences analysis and visualization.
TCRosetta combines general features analysis, large-scale sequence clustering, network construction, TCR-peptide binding prediction, generation probability calculation, and k-mer motif analysis for robust TCR sequence analysis, making TCR data analysis as simple as possible and allowing users to concentrate on research rather than coding.
In addition, TCRosetta could annotate disease information for TRB CDR3 sequences by batch searching similar sequences in manually curated disease-related TCR database.
The TCRosetta server accepts multiple input data formats and can analyze ∼20000 TCR sequences in less than three minutes.
ConclusionsTCRosetta is the most comprehensive web server to date for TCR sequences/repertoires analysis and it is freely available athttp://bioinfo.
life.
hust.
edu.
cn/TCRosetta/.
It can be applied to help discover novel biomarkers for disease diagnosis and identify cancer-associated TCR sequences.
WHAT IS ALERADY KNOWN ON THIS TOPICT cell receptor repertoires are largely untapped resource than can be used for predicting immune responses to different exposures including viral infections and tumor neoantigens.
The downstream analysis of TCR repertoire is often performed by different tools requiring diverse operating environments and expertise.
There is no webserver for comprehensive TCR repertoire analysis including general and advanced analysis.
WHAT THIS STUDY ADDSWe developed TCRosetta, a comprehensive platform for analyzing T-cell repertoire which combines nearly all TCR analysis methods.
It supports different kinds of the format of input including most mainstream TCR extraction tools or amino acid sequences.
It could analyze the features of TCR repertoire and display them in interactive graphs and is the first platform with a batch search and TCR annotation function.
HOW THIS STUDY MIGHT AFFECT RESEARCH, PACTICE OR POLICYTCRosetta can be applied to discover novel biomarkers to predict response in immunotherapy such TCR repertoire diversity and clonality.
It also can identify cancer-associated TCR sequences by clustering biochemically similar CDR3 sequences.
It can make TCR repertoire analysis as effortless as possible and help users focus on research instead of coding.
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