HotSpot3D web server: an integrated resource for mutation analysis in protein 3D structures

2020 ◽  
Vol 36 (12) ◽  
pp. 3944-3946 ◽  
Author(s):  
Shanyu Chen ◽  
Xiaoyu He ◽  
Ruilin Li ◽  
Xiaohong Duan ◽  
Beifang Niu
Keyword(s):  
Mutation Analysis ◽  
Web Server ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Supplementary Information ◽  
Supplementary Data ◽  
3D Structures ◽  
One Stop ◽  
Cancer Genome Atlas ◽  
Genome Atlas

Abstract Motivation HotSpot3D is a widely used software for identifying mutation hotspots on the 3D structures of proteins. To further assist users, we developed a new HotSpot3D web server to make this software more versatile, convenient and interactive. Results The HotSpot3D web server performs data pre-processing, clustering, visualization and log-viewing on one stop. Users can interactively explore each cluster and easily re-visualize the mutational clusters within browsers. We also provide a database that allows users to search and visualize proximal mutations from 33 cancers in the Cancer Genome Atlas. Availability and implementation http://niulab.scgrid.cn/HotSpot3D/. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals GMSimpute: a generalized two-step Lasso approach to impute missing values in label-free mass spectrum analysis

2019 ◽  
Vol 36 (1) ◽  
pp. 257-263 ◽  
Author(s):  
Qian Li ◽  
Kate Fisher ◽  
Wenjun Meng ◽  
Bin Fang ◽  
Eric Welsh ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Missing Values ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Supplementary Information ◽  
Label Free ◽  
Wide Range ◽  
Cancer Genome Atlas ◽  
Mean Square Errors ◽  
Genome Atlas

Abstract Motivation Missingness in label-free mass spectrometry is inherent to the technology. A computational approach to recover missing values in metabolomics and proteomics datasets is important. Most existing methods are designed under a particular assumption, either missing at random or under the detection limit. If the missing pattern deviates from the assumption, it may lead to biased results. Hence, we investigate the missing patterns in free mass spectrometry data and develop an omnibus approach GMSimpute, to allow effective imputation accommodating different missing patterns. Results Three proteomics datasets and one metabolomics dataset indicate missing values could be a mixture of abundance-dependent and abundance-independent missingness. We assess the performance of GMSimpute using simulated data (with a wide range of 80 missing patterns) and metabolomics data from the Cancer Genome Atlas breast cancer and clear cell renal cell carcinoma studies. Using Pearson correlation and normalized root mean square errors between the true and imputed abundance, we compare its performance to K-nearest neighbors’ type approaches, Random Forest, GSimp, a model-based method implemented in DanteR and minimum values. The results indicate GMSimpute provides higher accuracy in imputation and exhibits stable performance across different missing patterns. In addition, GMSimpute is able to identify the features in downstream differential expression analysis with high accuracy when applied to the Cancer Genome Atlas datasets. Availability and implementation GMSimpute is on CRAN: https://cran.r-project.org/web/packages/GMSimpute/index.html. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Exceptional Chemotherapy Response in Metastatic Colorectal Cancer Associated With Hyper-Indel–Hypermutated Cancer Genome and Comutation of POLD1 and MLH1

2017 ◽  
pp. 1-12
Author(s):  
Manish R. Sharma ◽  
James T. Auman ◽  
Nirali M. Patel ◽  
Juneko E. Grilley-Olson ◽  
Xiaobei Zhao ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Colon Cancer ◽  
Somatic Mutation ◽  
Mutation Rate ◽  
Germ Line ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Response To Chemotherapy ◽  
Cancer Genome Atlas ◽  
Genome Atlas

Purpose A 73-year-old woman with metastatic colon cancer experienced a complete response to chemotherapy with dose-intensified irinotecan that has been durable for 5 years. We sequenced her tumor and germ line DNA and looked for similar patterns in publicly available genomic data from patients with colorectal cancer. Patients and Methods Tumor DNA was obtained from a biopsy before therapy, and germ line DNA was obtained from blood. Tumor and germline DNA were sequenced using a commercial panel with approximately 250 genes. Whole-genome amplification and exome sequencing were performed for POLE and POLD1. A POLD1 mutation was confirmed by Sanger sequencing. The somatic mutation and clinical annotation data files from the colon (n = 461) and rectal (n = 171) adenocarcinoma data sets were downloaded from The Cancer Genome Atlas data portal and analyzed for patterns of mutations and clinical outcomes in patients with POLE- and/or POLD1-mutated tumors. Results The pattern of alterations included APC biallelic inactivation and microsatellite instability high (MSI-H) phenotype, with somatic inactivation of MLH1 and hypermutation (estimated mutation rate > 200 per megabase). The extremely high mutation rate led us to investigate additional mechanisms for hypermutation, including loss of function of POLE. POLE was unaltered, but a related gene not typically associated with somatic mutation in colon cancer, POLD1, had a somatic mutation c.2171G>A [p.Gly724Glu]. Additionally, we noted that the high mutation rate was largely composed of dinucleotide deletions. A similar pattern of hypermutation (dinucleotide deletions, POLD1 mutations, MSI-H) was found in tumors from The Cancer Genome Atlas. Conclusion POLD1 mutation with associated MSI-H and hyper-indel–hypermutated cancer genome characterizes a previously unrecognized variant of colon cancer that was found in this patient with an exceptional response to chemotherapy.

scholarly journals The construction and analysis of ceRNA networks in invasive breast cancer: a study based on The Cancer Genome Atlas

2018 ◽  
Vol Volume 11 ◽  
pp. 1-11 ◽  
Author(s):  
Chundi Gao ◽  
Huayao Li ◽  
Jing Zhuang ◽  
HongXiu Zhang ◽  
Kejia Wang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Invasive Breast Cancer ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Cancer Genome Atlas ◽  
Genome Atlas

scholarly journals Molecular Correlates of Metastasis by Systematic Pan-Cancer Analysis Across The Cancer Genome Atlas

2018 ◽  
Vol 17 (2) ◽  
pp. 476-487 ◽  
Author(s):  
Fengju Chen ◽  
Yiqun Zhang ◽  
Sooryanarayana Varambally ◽  
Chad J. Creighton
Keyword(s):  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Cancer Genome Atlas ◽  
Pan Cancer ◽  
Genome Atlas

scholarly journals Large-scale profiling of microRNAs for The Cancer Genome Atlas

2015 ◽  
Vol 44 (1) ◽  
pp. e3-e3 ◽  
Author(s):  
Andy Chu ◽  
Gordon Robertson ◽  
Denise Brooks ◽  
Andrew J. Mungall ◽  
Inanc Birol ◽  
...  
Keyword(s):  
Large Scale ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Cancer Genome Atlas ◽  
Genome Atlas
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