Are you still storing testing scripts, test data and QA results in different places? Miqa allows you to manage all these on a single platform.
Track and manage different variations of your analysis pipeline—whether it's multiple versions, components, or parameter sets—with comprehensive QA logs and metadata.
Your QA metrics, or test assertions, can be packaged into reusable test blocks that can be applied across multiple datasets or pipelines. These test blocks can also be linked into test chains to create more comprehensive tests.
Each test run is meticulously tracked with a unique ID, execution timestamps, and linked to the specific analysis pipeline and testing workflow metadata.
Test results are clearly documented and linked to specific runs, allowing easy comparison between tests. Results include pass/fail outcomes, error logs, and key performance metrics for comprehensive pipeline insights.
Use well-characterized, standardized datasets to assess bioinformatic tool performance with high granularity. Select from our growing catalog of annotated test data, or upload your own data source.
Evaluate your analysis pipelines with a diverse range of test datasets, including bug data, development data, and real-world samples contributed by collaborators, to gain an accurate understanding of performance on realistic data.
Continuously curate data by integrating relevant metadata, such as cancer status, or analyzing uncharacterized datasets to uncover trends and assess performance against key metrics, like mutation rates or precision/recall. Work collaboratively to build a comprehensive repository of truth data over time, with dynamic evaluations conducted at every stage of the curation process.
Use the built-in Integrative Genomics Viewer (IGV) to examine each variant calls. Mark each variant call as true or false positive, add labels or comments and share with your team for final review all within the Curation module.
Achieve comprehensive coverage of edge cases and rare sample types with simulated sequencing data. Create spike-ins, subsamples, admixtures, and titrations from existing datasets, or generate fully synthetic data to enhance your testing framework.
