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irma-core

A tool to aid virus sequencing and accelerate IRMA.

https://github.com/cdcgov/irma-core

Science Score: 57.0%

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Keywords

bioinformatics cdc-influenza-division ncird-id
Last synced: 6 months ago · JSON representation ·

Repository

A tool to aid virus sequencing and accelerate IRMA.

Basic Info
Statistics
  • Stars: 8
  • Watchers: 8
  • Forks: 0
  • Open Issues: 0
  • Releases: 9
Topics
bioinformatics cdc-influenza-division ncird-id
Created about 1 year ago · Last pushed 6 months ago
Metadata Files
Readme Changelog License Citation

README.md

IRMA-core

IRMA-core is suite of subcommands for both stand-alone and integrated use with IRMA for select portions of the virus sequence assembly process. Processes integrated with IRMA are subject to more rapid internal revision and change while stand-alone processes will attempt to avoid breaking CLI changes where possible. More primitive bioinformatic functionality in IRMA-core can sometimes be pushed down to The Zoe Anthology library for general use cases (PRs welcomed).

This binary compiles using Rust nightly.

Processes and their usage

Standalone subcommands

| Process | Description | Usage | | --------- | ------------------------------------------------------------------------------------------------------- | -------------------------- | | trimmer | Used for removing adapters, barcodes, and primers among other things. Read the docs. | irma-core trimmer --help |

Integrated with IRMA

| Process | Description | Usage | | ------------ | ------------------------------------------------------------------------------------------------------------------ | ----------------------------- | | preprocess | Performs all-in-one FastQ quality control, trimming, and deflation to XFL and FASTA formats. Similar to trimmer. | irma-core preprocess --help | | merge-sam† | Merges Illumina paired-end reads with parsimonious error correction and detection | irma-core merge-sam --help | | xflate† | Deflates FastQ files to deduplicated Fasta files, or reinflates deduplicated Fasta files to FastQ files. | irma-core xflate --help | | num-procs | Provides the physical or logical cores of a CPU portably. | irma-core num-procs --help |

For compatibility notes between IRMA-core and IRMA, see the version matrix.

† May be combined into a future process, deprecated and removed.\ ø Deprecated, will be removed.

FAQ

How do I install IRMA-core?

The correct version of IRMA-core will ship with IRMA and is packaged in all releases since v1.1.0. IRMA-core became non-optional in IRMA v1.3 and later. You can also download IRMA-core for standalone use cases from the IRMA-core release page.

To compile and install IRMA-core yourself, first install rustup, and then:

```bash rustup toolchain install nightly git clone https://github.com/CDCgov/irma-core cd irma-core cargo +nightly b -r

Install here:

ls -l target/release/irma-core ```

For RHEL 8 compatible Linux distributions, you can also re-build IRMA-core using the latest builder image:

bash docker pull ghcr.io/your-org/irma-core/builder:latest

How should this application be cited?

One can cite the IRMA manuscript in general, but a software citation of IRMA-core specifically is also possible for stand-alone use cases. We include some citations for convenience in CITATION.bib).

Why does IRMA-core have a difference license than IRMA?

IRMA packages other binaries and that affects its license. IRMA-core is an indendent project from IRMA and so can use more flexible licensing.

Will IRMA-core replace IRMA?

No, IRMA contains data + code/binary + configuration that are appropriate for use in that repository. However, in the future it is possible IRMA-core could eliminate dependence on components and make IRMA easier to use and maintain.

What are some goals of the project?

In no particular order:

  • Retire technical debt and make the IRMA project more maintainable going forward.
  • Increase IRMA's portability to systems like Windows.
  • Eliminate unneeded dependencies and components.
  • Relax licensing for IRMA itself (see above).
  • Accelerate execution and/or reduce memory pressure within IRMA.
  • Ease the integration of modern features.
  • Provide stand-alone functionality outside of IRMA.

Notices

Contact Info

For direct correspondence on the project, feel free to contact: Samuel S. Shepard, Centers for Disease Control and Prevention or reach out to other contributors.

Public Domain Standard Notice

This repository constitutes a work of the United States Government and is not subject to domestic copyright protection under 17 USC § 105. This repository is in the public domain within the United States, and copyright and related rights in the work worldwide are waived through the CC0 1.0 Universal public domain dedication. All contributions to this repository will be released under the CC0 dedication. By submitting a pull request you are agreeing to comply with this waiver of copyright interest.

License Standard Notice

The repository utilizes code licensed under the terms of the Apache Software License and therefore is licensed under ASL v2 or later. This source code in this repository is free: you can redistribute it and/or modify it under the terms of the Apache Software License version 2, or (at your option) any later version. This source code in this repository is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the Apache Software License for more details. You should have received a copy of the Apache Software License along with this program. If not, see: http://www.apache.org/licenses/LICENSE-2.0.html. The source code forked from other open source projects will inherit its license.

Privacy Standard Notice

This repository contains only non-sensitive, publicly available data and information. All material and community participation is covered by the Disclaimer. For more information about CDC's privacy policy, please visit http://www.cdc.gov/other/privacy.html.

Contributing Standard Notice

Anyone is encouraged to contribute to the repository by forking and submitting a pull request. (If you are new to GitHub, you might start with a basic tutorial.) By contributing to this project, you grant a world-wide, royalty-free, perpetual, irrevocable, non-exclusive, transferable license to all users under the terms of the Apache Software License v2 or later.

All comments, messages, pull requests, and other submissions received through CDC including this GitHub page may be subject to applicable federal law, including but not limited to the Federal Records Act, and may be archived. Learn more at http://www.cdc.gov/other/privacy.html.

Records Management Standard Notice

This repository is not a source of government records, but is a copy to increase collaboration and collaborative potential. All government records will be published through the CDC web site.

Additional Standard Notices

Please refer to CDC's Template Repository for more information about contributing to this repository, public domain notices and disclaimers, and code of conduct.

Owner

  • Name: Centers for Disease Control and Prevention
  • Login: CDCgov
  • Kind: organization
  • Email: data@cdc.gov
  • Location: Atlanta, GA

CDC's collaborative software projects to protect America from health, safety, and security threats, both foreign and in the U.S.

Citation (CITATION.bib) 

@article{Shepard:2016aa,
  abstract      = {BACKGROUND: Deep sequencing makes it possible to observe low-frequency viral variants and sub-populations with greater accuracy and sensitivity than ever before. Existing platforms can be used to multiplex a large number of samples; however, analysis of the resulting data is complex and involves separating barcoded samples and various read manipulation processes ending in final assembly. Many assembly tools were designed with larger genomes and higher fidelity polymerases in mind and do not perform well with reads derived from highly variable viral genomes. Reference-based assemblers may leave gaps in viral assemblies while de novo assemblers may struggle to assemble unique genomes.
                   RESULTS: The IRMA (iterative refinement meta-assembler) pipeline solves the problem of viral variation by the iterative optimization of read gathering and assembly. As with all reference-based assembly, reads are included in assembly when they match consensus template sets; however, IRMA provides for on-the-fly reference editing, correction, and optional elongation without the need for additional reference selection. This increases both read depth and breadth. IRMA also focuses on quality control, error correction, indel reporting, variant calling and variant phasing. In fact, IRMA's ability to detect and phase minor variants is one of its most distinguishing features. We have built modules for influenza and ebolavirus. We demonstrate usage and provide calibration data from mixture experiments. Methods for variant calling, phasing, and error estimation/correction have been redesigned to meet the needs of viral genomic sequencing.
                   CONCLUSION: IRMA provides a robust next-generation sequencing assembly solution that is adapted to the needs and characteristics of viral genomes. The software solves issues related to the genetic diversity of viruses while providing customized variant calling, phasing, and quality control. IRMA is freely available for non-commercial use on Linux and Mac OS X and has been parallelized for high-throughput computing.},
  author        = {\textbf{Samuel S. Shepard} and Meno, Sarah and Bahl, Justin and Wilson, Malania M and Barnes, John and Neuhaus, Elizabeth},
  date-added    = {2016-09-12 16:56:21 +0000},
  date-modified = {2016-09-12 16:56:21 +0000},
  doi           = {10.1186/s12864-016-3030-6},
  journal       = {BMC Genomics},
  journal-full  = {BMC genomics},
  keywords      = {Deep sequencing; Ebola; High throughput; Influenza; NGS; Public health; Surveillance},
  pages         = {708},
  pmc           = {PMC5011931},
  pmid          = {27595578},
  pst           = {epublish},
  title         = {Viral deep sequencing needs an adaptive approach: IRMA, the iterative refinement meta-assembler},
  volume        = {17},
  year          = {2016},
  bdsk-url-1    = {http://dx.doi.org/10.1186/s12864-016-3030-6}
}

@software{IRMA-core,
  author  = {Shepard, Samuel S. AND "IRMA-core Contributors"},
  month   = {03},
  title   = {{IRMA-core: a tool for IRMA and to assist the sequencing of virus genomes}},
  url     = {https://github.com/CDCgov/irma-core},
  version = {0.3.0},
  year    = {2025}
}

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