scholarly journals Omics Pipe: A Computational Framework for Reproducible Multi-Omics Data Analysis

2014 ◽  
Author(s):  
Kathleen M Fisch ◽  
Tobias Meißner ◽  
Louis Gioia ◽  
Jean-Christophe Ducom ◽  
Tristan Carland ◽  
...  
Keyword(s):  
Data Analysis ◽  
High Performance ◽  
Best Practice ◽  
The Cancer Genome Atlas ◽  
Sequencing Analysis ◽  
Omics Data ◽  
Next Generation Sequencing Analysis ◽  
Cancer Genome Atlas ◽  
Computational Platform ◽  
Omics Data Analysis

Omics Pipe (https://bitbucket.org/sulab/omics_pipe) is a computational platform that automates multi-omics data analysis pipelines on high performance compute clusters and in the cloud. It supports best practice published pipelines for RNA-seq, miRNA-seq, Exome-seq, Whole Genome sequencing, ChIP-seq analyses and automatic processing of data from The Cancer Genome Atlas. Omics Pipe provides researchers with a tool for reproducible, open source and extensible next generation sequencing analysis.

scholarly journals An R package for divergence analysis of omics data

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0249002
Author(s):  
Wikum Dinalankara ◽  
Qian Ke ◽  
Donald Geman ◽  
Luigi Marchionni
Keyword(s):  
R Package ◽  
The Cancer Genome Atlas ◽  
High Dimensional ◽  
Omics Data ◽  
Ternary Code ◽  
Cancer Genome Atlas ◽  
Level Analysis ◽  
Data Analysis Methods ◽  
Genome Atlas ◽  
Omics Data Analysis

Given the ever-increasing amount of high-dimensional and complex omics data becoming available, it is increasingly important to discover simple but effective methods of analysis. Divergence analysis transforms each entry of a high-dimensional omics profile into a digitized (binary or ternary) code based on the deviation of the entry from a given baseline population. This is a novel framework that is significantly different from existing omics data analysis methods: it allows digitization of continuous omics data at the univariate or multivariate level, facilitates sample level analysis, and is applicable on many different omics platforms. The divergence package, available on the R platform through the Bioconductor repository collection, provides easy-to-use functions for carrying out this transformation. Here we demonstrate how to use the package with data from the Cancer Genome Atlas.

scholarly journals Mutation Profile of Aggressive Pheochromocytoma and Paraganglioma with Comparison of TCGA Data

Cancers ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 2389
Author(s):  
Yun Mi Choi ◽  
Jinyeong Lim ◽  
Min Ji Jeon ◽  
Yu-Mi Lee ◽  
Tae-Yon Sung ◽  
...  
Keyword(s):  
Somatic Mutations ◽  
The Cancer Genome Atlas ◽  
Sequencing Analysis ◽  
Poor Overall Survival ◽  
Sdhb Mutation ◽  
Targeted Next Generation Sequencing ◽  
Mutation Profile ◽  
Next Generation Sequencing Analysis ◽  
Number Variation ◽  
Frequent Mutations

In pheochromocytoma and paraganglioma (PPGL), germline or somatic mutations in one of the known susceptibility genes are identified in up to 60% patients. However, the peculiar genetic events that drive the aggressive behavior including metastasis in PPGL are poorly understood. We performed targeted next-generation sequencing analysis to characterize the mutation profile in fifteen aggressive PPGL patients and compared accessible data of aggressive PPGLs from The Cancer Genome Atlas (TCGA) with findings of our cohort. A total of 115 germline and 34 somatic variants were identified with a median 0.58 per megabase tumor mutation burden in our cohort. The most frequent mutation was SDHB germline mutation (27%) and the second frequent mutations were somatic mutations for SETD2, NF1, and HRAS (13%, respectively). Patients were subtyped into three categories based on the kind of mutated genes: pseudohypoxia (n = 5), kinase (n = 5), and unknown (n = 5) group. In copy number variation analysis, deletion of chromosome arm 1p harboring SDHB gene was the most frequently observed. In our cohort, SDHB mutation and pseudohypoxia subtype were significantly associated with poor overall survival. In conclusion, subtyping of mutation profile can be helpful in aggressive PPGL patients with heterogeneous prognosis to make relevant follow-up plan and achieve proper treatment.

Using machine learning approaches for multi-omics data analysis: A review

2021 ◽  
Vol 49 ◽  
pp. 107739
Author(s):  
Parminder S. Reel ◽  
Smarti Reel ◽  
Ewan Pearson ◽  
Emanuele Trucco ◽  
Emily Jefferson
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Omics Data ◽  
Learning Approaches ◽  
Omics Data Analysis

scholarly journals Computational prediction of inter-species relationships through omics data analysis and machine learning

2018 ◽  
Vol 19 (S14) ◽  
Author(s):  
Diogo Manuel Carvalho Leite ◽  
Xavier Brochet ◽  
Grégory Resch ◽  
Yok-Ai Que ◽  
Aitana Neves ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Computational Prediction ◽  
Omics Data ◽  
Species Relationships ◽  
Omics Data Analysis

scholarly journals From data to knowledge: The future of multi-omics data analysis for the rhizosphere

Rhizosphere ◽  
2017 ◽  
Vol 3 ◽  
pp. 222-229 ◽  
Author(s):  
Richard Allen White ◽  
Mark I. Borkum ◽  
Albert Rivas-Ubach ◽  
Aivett Bilbao ◽  
Jason P. Wendler ◽  
...  
Keyword(s):  
Data Analysis ◽  
Omics Data ◽  
The Future ◽  
Omics Data Analysis

scholarly journals Expanding the Perseus Software for Omics Data Analysis With Custom Plugins

2020 ◽  
Vol 71 (1) ◽  
Author(s):  
Sung‐Huan Yu ◽  
Daniela Ferretti ◽  
Julia P. Schessner ◽  
Jan Daniel Rudolph ◽  
Georg H. H. Borner ◽  
...  
Keyword(s):  
Data Analysis ◽  
Omics Data ◽  
Omics Data Analysis

scholarly journals An R Package for Divergence Analysis of Omics Data

2019 ◽  
Author(s):  
Wikum Dinalankara ◽  
Qian Ke ◽  
Donald Geman ◽  
Luigi Marchionni
Keyword(s):  
High Throughput Sequencing ◽  
R Package ◽  
The Cancer Genome Atlas ◽  
High Dimensional ◽  
Omics Data ◽  
Sequencing Data ◽  
High Throughput Sequencing Data ◽  
Ternary Code ◽  
Cancer Genome Atlas ◽  
Level Analysis

AbstractGiven the ever-increasing amount of high-dimensional and complex omics data becoming available, it is increasingly important to discover simple but effective methods of analysis. Divergence analysis transforms each entry of a high-dimensional omics profile into a digitized (binary or ternary) code based on the deviation of the entry from a given baseline population. This is a novel framework that is significantly different from existing omics data analysis methods: it allows digitization of continuous omics data at the univariate or multivariate level, facilitates sample level analysis, and is applicable on many different omics platforms. The divergence package, available on the R platform through the Bioconductor repository collection, provides easy-to-use functions for carrying out this transformation. Here we demonstrate how to use the package with sample high throughput sequencing data from the Cancer Genome Atlas.

scholarly journals Novel cancer subtyping method based on patient-specific gene regulatory network

2021 ◽  
Author(s):  
Mai Adachi Nakazawa ◽  
Yoshinori Tamada ◽  
Yoshihisa Tanaka ◽  
Marie Ikeguchi ◽  
Kako Higashihara ◽  
...  
Keyword(s):  
Gene Networks ◽  
Regulatory Networks ◽  
The Cancer Genome Atlas ◽  
Patient Specific ◽  
Specific Gene ◽  
Omics Data ◽  
Cancer Subtypes ◽  
Molecular Systems ◽  
Molecular Features ◽  
Cancer Genome Atlas

The identification of cancer subtypes is important for the understanding of tumor heterogeneity. In recent years, numerous computational methods have been proposed for this problem based on the multi-omics data of patients. It is widely accepted that different cancer subtypes are induced by different molecular regulatory networks. However, only a few incorporate the differences between their molecular systems into the classification processes. In this study, we present a novel method to classify cancer subtypes based on patient-specific molecular systems. Our method quantifies patient-specific gene networks, which are estimated from their transcriptome data. By clustering their quantified networks, our method allows for cancer subtyping, taking into consideration the differences in the molecular systems of patients. Comprehensive analyses of The Cancer Genome Atlas (TCGA) datasets applied to our method confirmed that they were able to identify more clinically meaningful cancer subtypes than the existing subtypes and found that the identified subtypes comprised different molecular features. Our findings show that the proposed method, based on a simple classification using the patient-specific molecular systems, can identify cancer subtypes even with single omics data, which cannot otherwise be captured by existing methods using multi-omics data.

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