X

Forgot your Password

If you have forgotten your password, please enter your account email below and we will reset your password and email you the new password.

X

Login to SciCrunch

X

Register an Account

Delete Saved Search

Are you sure you want to delete this saved search?

NO

NIF LinkOut Portal

FILTERS

Feature-based classifiers for somatic mutation detection in tumour-normal paired sequencing data.

Authors:
Ding J, Bashashati A, Roth A, Oloumi A, Tse K, Zeng T, Haffari G, Hirst M, Marra MA, Condon A, Aparicio S, Shah SP
Affiliation:
Journal:
Bioinformatics (Oxford, England)

Abstract

MOTIVATION: The study of cancer genomes now routinely involves using next-generation sequencing technology (NGS) to profile tumours for single nucleotide variant (SNV) somatic mutations. However, surprisingly few published bioinformatics methods exist for the specific purpose of identifying somatic mutations from NGS data and existing tools are often inaccurate, yielding intolerably high false prediction rates. As such, the computational problem of accurately inferring somatic mutations from paired tumour/normal NGS data remains an unsolved challenge. RESULTS: We present the comparison of four standard supervised machine learning algorithms for the purpose of somatic SNV prediction in tumour/normal NGS experiments. To evaluate these approaches (random forest, Bayesian additive regression tree, support vector machine and logistic regression), we constructed 106 features representing 3369 candidate somatic SNVs from 48 breast cancer genomes, originally predicted with naive methods and subsequently revalidated to establish ground truth labels. We trained the classifiers on this data (consisting of 1015 true somatic mutations and 2354 non-somatic mutation positions) and conducted a rigorous evaluation of these methods using a cross-validation framework and hold-out test NGS data from both exome capture and whole genome shotgun platforms. All learning algorithms employing predictive discriminative approaches with feature selection improved the predictive accuracy over standard approaches by statistically significant margins. In addition, using unsupervised clustering of the ground truth 'false positive' predictions, we noted several distinct classes and present evidence suggesting non-overlapping sources of technical artefacts illuminating important directions for future study. AVAILABILITY: Software called MutationSeq and datasets are available from http://compbio.bccrc.ca.

  1. Welcome

    Welcome to NIF. Explore available research resources: data, tools and materials, from across the web

  2. Community Resources

    Search for resources specially selected for NIF community

  3. More Resources

    Search across hundreds of additional biomedical databases

  4. Literature

    Search Pub Med abstracts and full text from PubMed Central

  5. Insert your Query

    Enter your search terms here and hit return. Search results for the selected tab will be returned.

  6. Join the Community

    Click here to login or register and join this community.

  7. Categories

    Narrow your search by selecting a category. For additional help in searching, view our tutorials.

  8. Query Info

    Displays the total number of search results. Provides additional information on search terms, e.g., automated query expansions, and any included categories or facets. Expansions, filters and facets can be removed by clicking on the X. Clicking on the + restores them.

  9. Search Results

    Displays individual records and a brief description. Click on the icons below each record to explore additional display options.

X