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ImmuneDB

Analyzes vast amounts of IGHV (immunoglobulin heavy-chain variable) sequences and exploring the resulting data. ImmuneDB can take as input raw FASTA/FASTQ data, identify genes, determine clones, construct lineages, as well as provide information such as selection pressure and mutation analysis. It uses an industry leading database, MySQL, to provide fast analysis and avoid the complexities of using error prone flat-files. After analysis, resulting data can then be easily visualized, queried, and exported through its web-based interface.

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ImmuneDB versioning

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ImmuneDB classification

ImmuneDB specifications

Software type:
Framework/Library
Restrictions to use:
None
Output data:
A web interface to ImmuneDB is provided, allowing users to interactively explore data after analysis with the pipeline. Most data including raw sequences, metadata, mutation analysis, and clonal assignments can be easily downloaded in a variety of formats including FASTA, FASTQ, and CSV allowing further processing with external tools.
Programming languages:
Python
Computer skills:
Advanced
Source code URL:
https://github.com/arosenfeld/immunedb
Interface:
Command line interface
Input data:
ImmuneDB takes as input raw FASTA or FASTQ files with full or partial IGHV sequences, optionally pre-processed by pRESTO.
Operating system:
Unix/Linux
Database management system:
MySQL
Stability:
Stable
Maintained:
Yes

ImmuneDB support

Maintainer

  • Uri Hershberg <>

Additional information

A free trial is available on the site.

Credits

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Publications

Institution(s)

School of Biomedical Engineering, Science and Health Systems, Drexel University, Philadelphia, PA, USA; Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Department of Microbiology and Immunology, College of Medicine, Drexel University, Philadelphia, PA, USA

Funding source(s)

This work was supported by the National Institute of Allergy and Infectious Diseases of the National Institutes of Health under Award Number P01AI106697 and by NIH P30-CA016520.

Link to literature

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