Name
Abstract | ||
|---|---|---|
We present CloudNeo, a cloud-based computational workflow for identifying patientspecific tumor neoantigens from next generation sequencing data. Tumor-specific mutant peptides can be detected by the immune system through their interactions with the human leukocyte antigen complex, and neoantigen presence has recently been shown to correlate with anti T-cell immunity and efficacy of checkpoint inhibitor therapy. However computing capabilities to identify neoantigens from genomic sequencing data are a limiting factor for understanding their role. This challenge has grown as cancer datasets become increasingly abundant, making them cumbersome to store and analyze on local servers. Our cloud-based pipeline provides scalable computation capabilities for neoantigen identification while eliminating the need to invest in local infrastructure for data transfer, storage or compute. The pipeline is a Common Workflow Language (CWL) implementation of human leukocyte antigen (HLA) typing using Polysolver or HLAminer combined with custom scripts for mutant peptide identification and NetMHCpan for neoantigen prediction. We have demonstrated the efficacy of these pipelines on Amazon cloud instances through the Seven Bridges Genomics implementation of the NCI Cancer Genomics Cloud, which provides graphical interfaces for running and editing, infrastructure for workflow sharing and version tracking, and access to TCGA data. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1093/bioinformatics/btx375 | BIOINFORMATICS |
Field | DocType | Volume |
Data mining,Computer science,Cloud computing | Journal | 33 |
Issue | ISSN | Citations |
19 | 1367-4803 | 0 |
PageRank | References | Authors |
0.34 | 2 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Preeti Bais | 1 | 0 | 0.34 |
| Sandeep Namburi | 2 | 0 | 0.68 |
| Daniel M. Gatti | 3 | 8 | 2.69 |
| Xinyu Zhang | 4 | 24 | 12.48 |
| Jeffrey H. Chuang | 5 | 0 | 1.35 |