Work

AnnoMate: Exploring and annotating integrated molecular data through custom interactive visualizations

Year: 2024

Type: article

Abstract: Summary Manual review is an integral part of any study. As the cost of data generation continues to decrease, the rapid rise in large-scale multi-omic studies calls for a modular, flexible framework to perform what is currently a tedious, error-prone process. We developed AnnoMate, a Python-based package built with Plotly Dash that creates interactive, highly customizable dashboards for reviewing and annotating data. Its object-oriented framework enables easy development and modification of custom dashboards for specific manual review tasks. We utilized this framework to implement "reviewer" dashboards for various tasks often performed in cancer genome sequencing studies.

Source: Patterns

DOI: 10.1016/j.patter.2024.101060

Authors: Claudia Chu, Conor Messer, Samantha Van Seters, Mendy Miller, Kristy Schlueter-Kuck, Gad Getz

Institutions: Broad Institute, Harvard University, Massachusetts General Hospital

Language: English

Topic: Data Visualization and Analytics

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $4,900

Funder: International Business Machines Corporation