2019 Winners Announced for DCEG Informatics Tool Challenge
, by DCEG Staff
In May, DCEG Director Stephen J. Chanock, M.D., announced four winners of the 2019 DCEG Informatics Tool Challenge, a competitive funding program that supports innovative approaches to enhance epidemiological methods, data collection, analysis, and other research efforts of the Division using modern technology and informatics.
Proposals were evaluated for their novel approach to specific research needs, ability for the project to be completed within one year of initiation, and cost, not to exceed $20,000. Reviewers considered the utility to epidemiologic and genetic research as well as technical feasibility.
The NCI Center for Biomedical Informatics and Information Technology (CBIIT) is collaborating on three projects; researchers from the Washington University and Albert Einstein College of Medicine, as well as staff from the NCI Division of Cancer Control and Population Sciences (DCCPS), are collaborating on another.
MoCCA-SV: Modular Calling, Comparison, and Annotation of Structural Variants
Bari J. Ballew, Eric Dawson, Bin Zhu
MoCCA-SV: Modular Calling, Comparison, and Annotation of Structural Variants (SVs), is a flexible tool for analyzing next-generation sequencing data using multiple SV callers. This application will allow the user to select from a variety of SV calling tools, then will run each tool on the specified data. The framework will be highly modular. The pipeline will provide annotation of the resulting SV calls and it will perform cross-caller comparisons, allowing for straightforward filtering by consensus, possibly increasing precision in SV breakpoint identification by up to 260-fold (from ~0.15% to ~40%). Ultimately, MoCCA-SV is a platform-agnostic, open-source tool that will provide a single output file per sample or sample set (e.g. tumor/normal pair) with the union of SV calls across all selected callers that have been harmonized, annotated, and assessed for cross-caller concordance.
Life Years gained From Screening-CT (LYFS-CT): A webtool for individualized gain in life expectancy from undergoing lung screening
Life Years gained From Screening-CT (LYFS-CT) is a tool that calculates the individualized gain in life expectancy from lung screening. It should maximize the life years gained in a population, reduce the selection of smokers most likely to experience complications during screening, and should efficiently prevent more lung cancer deaths than USPSTF recommendations. Compared to our Lung Cancer Risk Assessment Tool (LCRAT), LYFS-CT selects fewer ever-smokers over age 70 and replaces them with more ever-smokers ages 50-70 who might benefit more from screening. To our knowledge, LYFS-CT is the first tool for individualized life gained for precision screening. We intend to integrate LYFS-CT into our existing tools and to develop an Excel tool for researchers. This will allow greater evaluation of LYFS-CT by CISNET and other researchers interested in this next step in precision screening, and by colleagues in DCCPS with regard to communicating individual life gained versus individual risk.
LDtrait: A webtool to investigate phenotype associations in linkage disequilibrium
LDtrait is a new, publicly available user-requested module to LDlink that will help genetic researchers to generate hyptheses and explore underlying mechanisms by enabling investigators to identify, easily and rapidly, previously published traits directly associated with or in linkage disequilibrium with a user-provided list of variants. LDtrait queries a daily updated database of the genome-wide association study (GWAS) catalog and uses existing LDlink infrastructure to calculate LD patterns and return results. As proposed, LDtrait will run on a Python Flask server and use the Amazon Web Service to host the service. LDtrait will use coding and web formatting that ensures cross platform compatibility on a variety of platforms. Our goal for LDtrait is to assist researchers in rapidly accessing traits associated with a list of input variants and help them in searching for biological insights into disease associations.
Integrative mutational signature portal (MsigPortal) for cancer genomics study
MsigPortal is a webtool that will visualize and explore all types of mutational signatures in both the Cancer Genome Atlas (TCGA) as well as user-uploaded datasets and analyze them integratively with other genomic and clinical data. Developed by CBIIT, under the direction of Dr. Tongwu Zhang, MsigPortal will
- run a benchmark for existing mutational signatures to evaluate different published methods
- allow users to visualize the mutational profiles and signatures in different styles at the sample level and explore the relationship of mutational signatures across samples and cancer types.
- will evaluate associations between mutational signatures and other available genomic features as well as other sample level-based variables.
- will automatically retrieve the genomic and clinical features from the cBio Cancer Genomics Portal for the samples for which we have derived mutation signatures. (for TCGA data)
- will allow users to derive mutational signatures, as well as analyze, and visualize them together with any kind of user-specified sample-based variables. (for custom, user-uploaded data)
- will facilitate comparison and combination of user data with TCGA samples to identify samples and cancer types based on the similarity of mutational signatures.