Algorithm¶

The MOAlmanac algorithm (moalmanac) is a clinical interpretation algorithm that annotates and evaluates whole-exome and transcriptome data from individual patient samples, using the database as its knowledge base. It can:

• Identify mutations and genomic features related to therapeutic sensitivity and resistance, and of prognostic relevance.
• Annotate somatic and germline variants against several datasources.
• Sort and evaluate somatic single nucleotide variants, insertions and deletions, copy number alterations and fusions by clinical and biological relevance.
• Integrate data types to see which genes are altered in both the somatic and germline setting.
• Extract and evaluate germline variants relevant to adult and hereditary cancers.
• Identify overlap between somatic variants observed in DNA and RNA, or in any other validation sequencing.
• Identify variants that may be related to microsatellite stability.
• Calculate coding mutational burden and compare a patient to TCGA.
• Identify genomic features that may be related to one another.
• Create portable web-based actionability reports summarizing clinically relevant findings.

Running it¶

The algorithm is available from its GitHub repository, as a Docker image, and as a Terra workflow. Running it from GitHub requires building some of its datasources, which the Docker image already contains. See the repository's README for installation and usage.

Inputs and outputs¶

• Description of inputs: every required and optional argument, with the expected file formats and columns.
• Description of outputs: every file the algorithm produces, and the columns they share.

Both pages are copied from the algorithm repository's docs/ folder.