Browser compatibility
| OS | Version | Chrome | Firefox |
| Linux | Ubuntu 18.04 | 78.0 | 70.0.1 |
| Windows | 10 | 78.0 | 70.0.1 |
1.The detailed example
vcf file: mutect_call.vcf The vcf file should be obtained from either MuTect or MuTect2.
2. The meaning of each parameter in the results
| Annotation | HLA | Peptide | Binding score | Immunogenic score | Rank |
|---|---|---|---|---|---|
| RYR2_V1476L_8 | HLA-A01:03 | LGDEKGKLHE | 0.9684 | 0.545 | 0 |
| RYR2_V1476L_8 | HLA-A01:03 | LGDEKGKLHES | 0.9987 | 0.5566 | 0 |
| SLC9B1_I279M_6 | HLA-A01:03 | FSSGGMLN | 0.9883 | 0.5324 | 0 |
| SLC9B1_I279M_6 | HLA-A01:03 | FSSGGMLNN | 0.4751 | 0.5275 | 0 |
| POMGNT2_L526M_5 | HLA-A01:03 | YEVWMQEQ | 0.9516 | 0.5423 | 1 |
| SLC9B1_I279M_6 | HLA-A01:03 | FSSGGMLNNA | 0.8862 | 0.5214 | 1 |
| SLC9B1_I279M_7 | HLA-A01:03 | VFSSGGMLN | 0.0034 | 0.5061 | 1 |
| TMCC1_AA180-181A_3 | HLA-A01:03 | AAACLPGEEGT | 0.9607 | 0.5417 | 1 |
| SLC9B1_I279M_7 | HLA-A01:03 | VFSSGGMLNN | 0.6364 | 0.5065 | 2 |
| SYT3_P544L_4 | HLA-A01:03 | AADLHGREHWA | 0.8917 | 0.5011 | 2 |
In the predicted result, all the HLA-peptide pairs with immunogenicity score less than 0.5 was discarded and not shown in the result table.
Annotation: normally, the content in this column includes gene name, mutation position in protein and the position of mutated amino acid in peptide.
Binding score: the score predicted by binding model of DeepHLApan.
Immunogenicity score : the score predicted by immunogenicity model of DeepHLApan. 0.5 is the threshold to select the predicted immunogenic pHLA.
Rank : the HLA-peptide pairs were ranked according to their predicted binding score across different length of peptide.
3.Contact
E-mail: zhanzhou@zju.edu.cn
Address: College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang
Citation:
1. Zhou Z#, Lyu X#, Wu J, Yang X, Wu S, Zhou J, Gu X, Su Z*, Chen S*. TSNAD: an integrated software for cancer somatic mutation and tumour-specific neoantigen detection. R Soc Open Sci. 2017, 4: 170050. https://doi.org/10.1098/rsos.170050
2. Wu J, Wang W, Zhang J, Zhou B, Zhao W, Su Z, Gu X, Wu J, Zhou Z*, Chen S*. DeepHLApan: A deep learning approach for neoantigen prediction considering both HLA-peptide binding and immunogenicity. Front Immunol, 2019, 10: 2559. https://doi.org/10.3389/fimmu.2019.02559
3. Zhou Z#,*, Wu J#, Ren J, Chen W, Zhao W, Gu X, Chi Y, He Q, Yang B, Wu J*, Chen S*. TSNAD v2.0: A one-stop software solution for tumor-specific neoantigen detection. Comput Struct Biotechnol J, 2021, 19:4510-4516. https://doi.org/10.1016/j.csbj.2021.08.016