PMID- 28605406
OWN - NLM
STAT- MEDLINE
DCOM- 20180720
LR  - 20220316
IS  - 1367-4811 (Electronic)
IS  - 1367-4803 (Print)
IS  - 1367-4803 (Linking)
VI  - 33
IP  - 19
DP  - 2017 Oct 1
TI  - CloudNeo: a cloud pipeline for identifying patient-specific tumor neoantigens.
PG  - 3110-3112
LID - 10.1093/bioinformatics/btx375 [doi]
AB  - SUMMARY: We present CloudNeo, a cloud-based computational workflow for 
      identifying patient-specific 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. AVAILABILITY AND IMPLEMENTATION: 
      The CWL implementation is at: https://github.com/TheJacksonLaboratory/CloudNeo. 
      For users who have obtained licenses for all internal software, integrated 
      versions in CWL and on the Seven Bridges Cancer Genomics Cloud platform 
      (https://cgc.sbgenomics.com/, recommended version) can be obtained by contacting 
      the authors. CONTACT: jeff.chuang@jax.org. SUPPLEMENTARY INFORMATION: 
      Supplementary data are available at Bioinformatics online.
CI  - (c) The Author(s) 2017. Published by Oxford University Press.
FAU - Bais, Preeti
AU  - Bais P
AD  - The Jackson Laboratory for Genomic Medicine, Farmington, CT 06030, USA.
FAU - Namburi, Sandeep
AU  - Namburi S
AD  - The Jackson Laboratory for Genomic Medicine, Farmington, CT 06030, USA.
FAU - Gatti, Daniel M
AU  - Gatti DM
AD  - The Jackson Laboratory, Bar Harbor, ME 04609, USA.
FAU - Zhang, Xinyu
AU  - Zhang X
AD  - The Jackson Laboratory for Genomic Medicine, Farmington, CT 06030, USA.
FAU - Chuang, Jeffrey H
AU  - Chuang JH
AD  - The Jackson Laboratory for Genomic Medicine, Farmington, CT 06030, USA.
AD  - Department of Genetics and Genome Sciences, University of Connecticut Health, 
      Farmington, CT 06032, USA.
LA  - eng
GR  - P30 CA034196/CA/NCI NIH HHS/United States
GR  - R21 CA191848/CA/NCI NIH HHS/United States
PT  - Journal Article
PL  - England
TA  - Bioinformatics
JT  - Bioinformatics (Oxford, England)
JID - 9808944
RN  - 0 (Antigens, Neoplasm)
RN  - 0 (Peptides)
SB  - IM
MH  - Antigens, Neoplasm/chemistry/*genetics
MH  - Genomics
MH  - *High-Throughput Nucleotide Sequencing
MH  - Histocompatibility Testing
MH  - Humans
MH  - Mutation
MH  - Peptides/chemistry/genetics
MH  - *Software
MH  - Workflow
PMC - PMC5870764
EDAT- 2017/06/13 06:00
MHDA- 2018/07/22 06:00
PMCR- 2017/06/12
CRDT- 2017/06/13 06:00
PHST- 2016/11/14 00:00 [received]
PHST- 2017/06/07 00:00 [accepted]
PHST- 2017/06/13 06:00 [pubmed]
PHST- 2018/07/22 06:00 [medline]
PHST- 2017/06/13 06:00 [entrez]
PHST- 2017/06/12 00:00 [pmc-release]
AID - 3866879 [pii]
AID - btx375 [pii]
AID - 10.1093/bioinformatics/btx375 [doi]
PST - ppublish
SO  - Bioinformatics. 2017 Oct 1;33(19):3110-3112. doi: 10.1093/bioinformatics/btx375.