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TSNAD specifications

Information


Unique identifier OMICS_19236
Name TSNAD
Software type Application/Script
Interface Graphical user interface
Restrictions to use None
Operating system Unix/Linux
Computer skills Medium
Stability Stable
Maintained Yes

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Maintainers


  • person_outline Zhixi Su
  • person_outline Shuqing Chen

Publication for TSNAD

TSNAD citations

 (2)
library_books

Population level distribution and putative immunogenicity of cancer neoepitopes

2018
BMC Cancer
PMCID: 5899330
PMID: 29653567
DOI: 10.1186/s12885-018-4325-6

[…] variation, these are the criteria applied by all computational tools for neoepitope prediction from tumor genomic sequencing data, including epi-seq [], epitoolkit [], pvac-seq [], integrate-neo [], tsnad [], mupexi [], and cloudneo []., our central assertion is that the immunogenicity of a neoepitope is directly related to its novelty: that is, the extent to which it or a closely matching […]

library_books

Immunopharmacogenomics towards personalized cancer immunotherapy targeting neoantigens

2018
Cancer Sci
PMCID: 5834780
PMID: 29288513
DOI: 10.1111/cas.13498

[…] to accurately predict the interaction between neoantigens and immune cells. there are currently several publicly available neoantigen prediction pipelines, including pvac‐seq, integrate‐neo, tsnad. pvac‐seq combines the tumor mutation and expression data to predict neoantigens by invoking netmhc 3.4; integrate‐neo was designed to predict neoantigens from fusion genes based […]


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TSNAD institution(s)
Zhejiang Provincial Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China; College of Computer Science and Technology, Zhejiang University, Hangzhou, China; Department of Genetics, Development and Cell Biology, Program of Bioinformatics and Computational Biology, Iowa State University, Ames, IA, USA; State Key Laboratory of Genetic Engineering and MOE Key Laboratory of Contemporary Anthropology, School of Life Sciences, Fudan University, Shanghai, China
TSNAD funding source(s)
Supported by grants from the National Natural Science Foundation of China (31501021 and 81430081), the Zhejiang Provincial Natural Sciences Foundation of China (LY15C060001), the Fundamental Research Funds for the Central Universities and the State Key Laboratory of Genetic Engineering at Fudan University.

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