PERFECTOS-APE - Predicting Regulatory Functional Effect of SNPs by Approximate P-value Estimation

The RAS/RAF/MEK/ERK pathway regulates certain cellular functions, including cell proliferation, differentiation, survival, and apoptosis. Dysregulation of this pathway leads to the occurrence and progression of cancers mainly by somatic mutations. This study aimed to assess if polymorphisms of the RAS/RAF/MEK/ERK pathway are associated with gastric cancer. A case-control study of 242 gastric cancer patients and 242 controls was performed to assess the association of 27 single nucleotide polymorphisms (SNPs) in the RAS/RAF/MEK/ERK pathway genes with gastric cancer. Analyses performed under the additive model (allele) showed four significantly associated SNPs: RAF1 rs3729931 (Odds ratio (OR) = 1.54, 95%, confidence interval (CI): 1.20–1.98, p-value = 7.95 × 10−4), HRAS rs45604736 (OR = 1.60, 95% CI: 1.16–2.22, p-value = 4.68 × 10−3), MAPK1 rs2283792 (OR = 1.45, 95% CI: 1.12–1.87, p-value = 4.91 × 10−3), and MAPK1 rs9610417 (OR = 0.60, 95% CI: 0.42–0.87, p-value = 6.64 × 10−3). Functional annotation suggested that those variants or their proxy variants may have a functional effect. In conclusion, this study suggests that RAF1 rs3729931, HRAS rs45604736, MAPK1 rs2283792, and MAPK1 rs9610417 are associated with gastric cancer.

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Outlier detection via localized p-value estimation

2009 47th Annual Allerton Conference on Communication, Control, and Computing (Allerton) ◽

10.1109/allerton.2009.5394501 ◽

2009 ◽

Cited By ~ 2

Author(s):

Manqi Zhao ◽

Venkatesh Saligrama

Keyword(s):

Outlier Detection ◽

P Value ◽

Value Estimation

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CIS: compound importance sampling method for protein-DNA binding site p-value estimation

Bioinformatics ◽

10.1093/bioinformatics/bti041 ◽

2004 ◽

Vol 21 (5) ◽

pp. 596-600 ◽

Cited By ~ 17

Author(s):

Y. Barash ◽

G. Elidan ◽

T. Kaplan ◽

N. Friedman

Keyword(s):

Dna Binding ◽

Binding Site ◽

Importance Sampling ◽

Sampling Method ◽

P Value ◽

Importance Sampling Method ◽

Dna Binding Site ◽

Value Estimation

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Assigning confidence to structural annotations from mass spectra with COSMIC

10.1101/2021.03.18.435634 ◽

2021 ◽

Author(s):

Martin A. Hoffmann ◽

Louis-Félix Nothias ◽

Marcus Ludwig ◽

Markus Fleischauer ◽

Emily C. Gentry ◽

...

Keyword(s):

In Silico ◽

Confidence Score ◽

Molecular Structures ◽

Support Vector ◽

P Value ◽

False Discovery Rates ◽

Biological Interpretation ◽

Value Estimation ◽

Structure Annotation ◽

Spectral Libraries

Untargeted metabolomics experiments rely on spectral libraries for structure annotation, but these libraries are vastly incomplete; in silico methods search in structure databases but cannot distinguish between correct and incorrect annotations. As biological interpretation relies on accurate structure annotations, the ability to assign confidence to such annotations is a key outstanding problem. We introduce the COSMIC workflow that combines structure database generation, in silico annotation, and a confidence score consisting of kernel density p-value estimation and a Support Vector Machine with enforced directionality of features. In evaluation, COSMIC annotates a substantial number of hits at small false discovery rates, and outperforms spectral library search for this purpose. To demonstrate that COSMIC can annotate structures never reported before, we annotated twelve novel bile acid conjugates; nine structures were confirmed by manual evaluation and two structures using synthetic standards. Second, we annotated and manually evaluated 315 molecular structures in human samples currently absent from the Human Metabolome Database. Third, we applied COSMIC to 17,400 experimental runs and annotated 1,715 structures with high confidence that were absent from spectral libraries.

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