--- title: "fq2bam" publisher: "nvidia" type: "endpoint" updated: "2025-07-20T16:32:55.149Z" description: "Generate BAM output given one or more pairs of FASTQ files, by running BWA-MEM & GATK best practices." canonical: "https://build.nvidia.com/nvidia/parabricks-fq2bam" --- # Algorithm Overview ## Description The Parabricks **fq2bam** tool is used to generate Binary Alignment Map (BAM)/Compressed Reference Oriented Alignment Map (CRAM) output using BWA-MEM and GATK best practices given pairs of pair-ended FASTQ files as input. This algorithm is ready for commercial use. fq2bam performs the following steps: - BWA-MEM alignment - Co-ordinate sorting - Mark duplicates - BQSR BWA-MEM is a fast, accurate algorithm for mapping DNA sequence reads to a reference genome, performing local alignment and producing alignment for different parts of the query sequence. It is the default algorithm in Burrows-Wheeler Aligner (BWA) for reads that are longer than 70bp and is designed for high-throughput sequencing technologies such as Illumina and Pacific Biosciences. Note that this is currently a minimal implementation of the full fq2bam tool. It accepts multiple pairs of FASTQ files but does not accept single-ended FASTQ files, nor does it accept known sites files or interval files. It does not currently produce a BQSR report, a duplicate metrics report or QC metrics. ## Terms of use By using this software or model, you are agreeing to the NVIDIA Parabricks [Terms of Use](https://docs.nvidia.com/clara/parabricks/latest/documentation/eula.html) ## References(s) See the documentation for the [Parabricks fq2bam tool](https://docs.nvidia.com/clara/parabricks/latest/documentation/tooldocs/man_fq2bam.html#man-fq2bam). ## Input **Input Type(s):** Indices (Text, Binary)
**Input Format(s):** Tarball
**Input Parameters:** One Dimensional (1D)
**Other Properties Related to Input:** Text for FASTA file and associated indices; Binary for GZIP compressed FASTQ
## Output **Output Type(s):** Binary (Alignment, Index of the Alignment), Text (File Locations & Signing Identifiers, Chromosomes)
**Output Format:** BAM, BAM Index (BAI), .Txt, .Txt
**Output Parameters:** 1D
**Other Properties Related to Output:** BAM: file location
## Software Integration **Supported Hardware Microarchitecture Platform(s):** Any NVIDIA GPU that supports CUDA architecture 70, 75, 80, 86, 89 or 90 and has at least 24GB of GPU RAM. * Ampere
* Hopper
* Lovelace
* Turing
* Volta

**Supported Operating System(s):**
fq2bam runs inside a Docker container and is compatible with any operating system that supports Docker containers. ## Model Version: * V4.2.1-1
**Engine:** [Triton and PyTriton](https://developer.nvidia.com/triton-inference-server) ## Ethical Considerations: NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse. For more detailed information on ethical considerations for this model, please see the Model Card++ Explainability, Bias, Safety & Security, and Privacy Subcards [Insert Link to Model Card++ here]. Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/). ## Bias Field | Response :---------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------- Participation considerations from adversely impacted groups ([protected classes](https://www.senate.ca.gov/content/protected-classes)) in model design and testing: | Not Applicable (N/A) Measures taken to mitigate against unwanted bias: | N/A ## Explainability Field | Response :------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------- Intended Applications & Domains: | Identifying genomic locations of sequencing reads Types: | Genomic Sequencing Read Alignment Intended Users: | This model is intended for developers or researchers who want to study the human genome sequence. Output: | Binary (Alignment, Index of the Alignment), Text (File Locations & Signing Identifiers, Chromosomes) Describe how the model works: | Generates Binary Alignment Map (BAM)/Compressed Reference Oriented Alignment Map (CRAM)s using pairs of alignments. Name the adversely impacted groups this has been tested to deliver comparable outcomes regardless of: | None Technical Limitations: | Model may struggle with longer sequences. Verified to have met prescribed quality standards: | Yes Performance Metrics: | Functional Equivalence; Runtime Potential Known Risks: | Algorithm may provide nonexistence alignments. Licensing: | [The Parabricks End User License Agreement](https://docs.nvidia.com/clara/parabricks/latest/documentation/eula.html) ## Privacy Field | Response :----------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------- Generatable or reverse engineerable personally-identifiable information (PII)? | None Was consent obtained for any PII used? | Not Applicable (N/A) Protected class data used to create this model? | None How often is dataset reviewed? | N/A Is a mechanism in place to honor data subject right of access or deletion of personal data? | N/A If PII collected for the development of the model, was it collected directly by NVIDIA? | N/A If PII collected for the development of the model by NVIDIA, do you maintain or have access to disclosures made to data subjects? | N/A If PII collected for the development of this AI model, was it minimized to only what was required? | N/A Is there provenance for all datasets used in training? | N/A Does data labeling (annotation, metadata) comply with privacy laws? | N/A Is data compliant with data subject requests for data correction or removal, if such a request was made? | N/A ## Safety & Security Field | Response :---------------------------------------------------|:---------------------------------- Model Application(s): | Alignment of Sequencing Read Data Describe the life-critical impacts (if present). | Should not be used for life-critical use cases per [The Parabricks End User License Agreement](https://docs.nvidia.com/clara/parabricks/latest/documentation/eula.html) Use Case Restriction(s): | Abide by [The Parabricks End User License Agreement](https://docs.nvidia.com/clara/parabricks/latest/documentation/eula.html) Model and Dataset Restriction(s): | The Principle of least privilege (PoLP) is applied limiting access for dataset generation and model development. Restrictions enforce dataset access during training, and dataset license constraints adhered to.