How to Use FASTQ to VCF
The FASTQ to VCF tool in Subio Platform detects genomic variants, such as SNPs and indels, from RNA-Seq FASTQ files. The tool is designed for human RNA-Seq data and runs the entire pipeline, from FASTQ preprocessing to variant calling.
The pipeline is based on the GATK RNA-Seq short variant discovery workflow . However, it uses HISAT2 instead of STAR for read alignment to reduce memory usage.
This tool supports only human RNA-Seq data. It cannot be used with data from other species.
1. Set Up the Environment
Before using FASTQ to VCF for the first time, you need to configure Java, GATK, Python, and reference data, in addition to the tools required for FASTQ preprocessing and read alignment. Follow the appropriate guide for your operating system:
The reference genome, HISAT2 index, GTF file, dbSNP, and known indel reference files must all be compatible with the same reference genome assembly. See the setup guides for details.
2. Run FASTQ to VCF
Once the environment is configured, open the FASTQ to VCF tool, select the FASTQ files you want to analyze, specify the required settings, and start processing.
The pipeline performs FASTQ preprocessing, read alignment to the reference genome, and variant calling with GATK in sequence. The results are saved as VCF files containing the detected variants.
The following video demonstrates the basic workflow of the FASTQ to VCF tool.
3. Pause, Stop, and Resume Processing
FASTQ to VCF supports pausing and stopping processing, as well as resuming a run using previously saved results.
The tool validates previous results for each sample and processing step, reuses completed steps, and resumes processing from the appropriate step. This means that even if processing is interrupted or an error occurs, you do not need to rerun the entire pipeline from the beginning.
The tool also supports automatic retries after failures and retrying with reduced thread counts or fewer concurrent processes.
4. Check the Execution Log if Processing Fails
If an error occurs while processing FASTQ files, check the temporary folder created inside the folder containing the FASTQ files.
The run.log file in this temporary folder
records information about the tools and analysis settings used,
processing progress, and any errors encountered.
If the log is gzip-compressed as run.log.gz,
decompress it before examining its contents.
You can also provide run.log to an AI assistant
such as ChatGPT and ask it to help identify possible causes
of the error and suggest solutions.
This may provide useful clues for troubleshooting.
5. Important Considerations for Variant Calling from RNA-Seq
Because RNA-Seq analyzes reads originating from expressed genes, it cannot comprehensively detect variants across the entire genome. Variants may be missed in genes with low expression or in regions with insufficient read coverage.
Alignment errors near splice junctions and RNA editing can also affect variant calling results. When interpreting detected variants, consider read quality and coverage as well.
If variants detected from RNA-Seq data will be used to draw critical research conclusions, consider confirming them using another method, such as DNA sequencing.