Identification of Genomic Features in the Classification of Loss- and Gain-of-Function Mutation

Jung, Seunghwan; Lee, Sejoon; Kim, Sangwoo; Nam, Hojung

doi:10.1145/2665970.2665977

Cited by 3 publications

(2 citation statements)

References 18 publications

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“…The 9,499 initial gene variants (excluding CNVs and fusions) extracted from all reports were mapped to 9,436 unique chromosomal coordinates, and 9,279 variants were annotated by VEP. 7,631 variants were classified as potentially deleterious and retained for further analysis based on satisfying at least one of the following criteria: CADD score > 15 [26]; mutations classified as stop gained, frameshift, start lost or stop lost, and splice variant changes, as these mutations are expected to result in truncated proteins [27,28]; or mutations with predicted consequences by MutPred. Variants that were not successfully mapped to a chromosomal location, not successfully annotated by VEP, or not deemed to be potentially deleterious were excluded from further analysis.…”

Section: Mining and Classifying Genomic Test Datamentioning

confidence: 99%

Pathway analysis of genomic pathology tests for prognostic cancer subtyping

Lyudovyk

Shen

Tatonetti

et al. 2019

Journal of Biomedical Informatics

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Genomic test results collected during the provision of medical care and stored in Electronic Health Record (EHR) systems represent an opportunity for clinical research into disease heterogeneity and clinical outcomes. In this paper, we evaluate the use of genomic test reports ordered for cancer patients in order to derive cancer subtypes and to identify biological pathways predictive of poor survival outcomes. A novel method is proposed to calculate patient similarity based on affected biological pathways rather than gene mutations. We demonstrate that this approach identifies subtypes of prognostic value and biological pathways linked to survival, with implications for precision treatment selection and a better understanding of the underlying disease. We also share lessons learned regarding the opportunities and challenges of secondary use of observational genomic data to conduct such research.

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Section: Mining and Classifying Genomic Test Datamentioning

confidence: 99%

Pathway analysis of genomic pathology tests for prognostic cancer subtyping

Lyudovyk

Shen

Tatonetti

et al. 2019

Journal of Biomedical Informatics

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“…For example, in a recent study, a computational method to predict missense GOF and LOF variants in voltage-gated sodium and calcium channels, SCNxA and CACNA1x family genes, has been presented [19]. In a separate study, 129 GOF mutations from 59 genes and 258 LOF mutations from 109 genes were analyzed and six features were proposed to be discriminant [20]. Additionally, a computational method, HMMvar-func, that predicts functional outcome of a mutation has been reported [21].…”

Section: Introductionmentioning

confidence: 99%

Identification of Discriminative Gene-level and Protein-level Features Associated with Gain-of-Function and Loss-of-Function Mutations

Bayrak

Jain

Stein

et al. 2021

Preprint

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Gain-of-function (GOF) and loss-of-function (LOF) mutations in the same gene may result in markedly different clinical phenotypes and hence require different therapeutic treatments. Identifying the functional consequences of mutations is an important step toward understanding disease mechanisms. While there are numerous computational tools (e.g., CADD, SIFT, PolyPhen-2) to predict the pathogenicity of a variant, there are currently no methods to predict whether a given genetic mutation results in a gene product with increased (gain-of-function; GOF) or diminished (loss-of-function; LOF) activity. Here, we investigated various gene- and protein-level features of GOF and LOF mutations. We generated the first extensive database of all currently known germline GOF and LOF pathogenic mutations by employing natural language processing (NLP) on the available abstracts in the Human Gene Mutation Database. Machine learning and statistical analyses of gene- and protein-level features associated with GOF and LOF mutations indicated significant differences. For example, GOF mutations were enriched in essential genes, autosomal dominant inheritance, protein binding and interaction domains, whereas LOF mutations were enriched in singleton genes, protein-truncating variants, and protein core regions. These results are consistent with the notion that mutations underlying recessive and dominant disorders have significantly different structural and functional properties. These findings could ultimately improve our understanding of how mutations affect gene/protein function thereby guiding future treatment options.

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