Integrated multi‐omics data analyses for exploring the co‐occurring and mutually exclusive gene alteration events in colorectal cancer

2020 ◽  
Author(s):  
Yuan Zhou ◽  
Xiaoqing Cheng ◽  
Fenglan Zhang ◽  
Qingqing Chen ◽  
Xinyu Chen ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Omics Data ◽  
Gene Alteration ◽  
Data Analyses

scholarly journals ASAP 2020 update: an open, scalable and interactive web-based portal for (single-cell) omics analyses

2020 ◽  
Vol 48 (W1) ◽  
pp. W403-W414
Author(s):  
Fabrice P A David ◽  
Maria Litovchenko ◽  
Bart Deplancke ◽  
Vincent Gardeux
Keyword(s):  
Big Data ◽  
Single Cell ◽  
Single Cell Analysis ◽  
Omics Data ◽  
Cell Analysis ◽  
Rna Seq ◽  
Analysis Pipeline ◽  
Web Based ◽  
Data Analyses ◽  
The Web

Abstract Single-cell omics enables researchers to dissect biological systems at a resolution that was unthinkable just 10 years ago. However, this analytical revolution also triggered new demands in ‘big data’ management, forcing researchers to stay up to speed with increasingly complex analytical processes and rapidly evolving methods. To render these processes and approaches more accessible, we developed the web-based, collaborative portal ASAP (Automated Single-cell Analysis Portal). Our primary goal is thereby to democratize single-cell omics data analyses (scRNA-seq and more recently scATAC-seq). By taking advantage of a Docker system to enhance reproducibility, and novel bioinformatics approaches that were recently developed for improving scalability, ASAP meets challenging requirements set by recent cell atlasing efforts such as the Human (HCA) and Fly (FCA) Cell Atlas Projects. Specifically, ASAP can now handle datasets containing millions of cells, integrating intuitive tools that allow researchers to collaborate on the same project synchronously. ASAP tools are versioned, and researchers can create unique access IDs for storing complete analyses that can be reproduced or completed by others. Finally, ASAP does not require any installation and provides a full and modular single-cell RNA-seq analysis pipeline. ASAP is freely available at https://asap.epfl.ch.

scholarly journals Empowering biologists with multi-omics data: colorectal cancer as a paradigm

2014 ◽  
Vol 31 (9) ◽  
pp. 1436-1443 ◽  
Author(s):  
Jing Zhu ◽  
Zhiao Shi ◽  
Jing Wang ◽  
Bing Zhang
Keyword(s):  
Colorectal Cancer ◽  
Omics Data

scholarly journals Microbiome-Mediated Immune Signaling in Inflammatory Bowel Disease and Colorectal Cancer: Support From Meta-omics Data

2021 ◽  
Vol 9 ◽  
Author(s):  
Molly Pratt ◽  
Jessica D. Forbes ◽  
Natalie C. Knox ◽  
Charles N. Bernstein ◽  
Gary Van Domselaar
Keyword(s):  
Colorectal Cancer ◽  
Gut Microbiome ◽  
Intestinal Inflammation ◽  
Bile Acid Metabolism ◽  
Omics Data ◽  
Chronic Colitis ◽  
Immune Signaling ◽  
Cancer Support ◽  
Inflammatory Bowel ◽  
Chronic Intestinal Inflammation

Chronic intestinal inflammation and microbial dysbiosis are hallmarks of colorectal cancer (CRC) and inflammatory bowel diseases (IBD), such as Crohn’s disease and ulcerative colitis. However, the mechanistic relationship between gut dysbiosis and disease has not yet been fully characterized. Although the “trigger” of intestinal inflammation remains unknown, a wealth of evidence supports the role of the gut microbiome as a mutualistic pseudo-organ that significantly influences intestinal homeostasis and is capable of regulating host immunity. In recent years, culture-independent methods for assessing microbial communities as a whole (termed meta-omics) have grown beyond taxonomic identification and genome characterization (metagenomics) into new fields of research that collectively expand our knowledge of microbiomes. Metatranscriptomics, metaproteomics, and metabolomics are meta-omics techniques that aim to describe and quantify the functional activity of the gut microbiome. Uncovering microbial metabolic contributions in the context of IBD and CRC using these approaches provides insight into how the metabolic microenvironment of the GI tract shapes microbial community structure and how the microbiome, in turn, influences the surrounding ecosystem. Immunological studies in germ-free and wild-type mice have described several host-microbiome interactions that may play a role in autoinflammation. Chronic colitis is a precursor to CRC, and changes in the gut microbiome may be an important link triggering the neoplastic process in chronic colitis. In this review, we describe several microbiome-mediated mechanisms of host immune signaling, such as short-chain fatty acid (SCFA) and bile acid metabolism, inflammasome activation, and cytokine regulation in the context of IBD and CRC, and discuss the supporting role for these mechanisms by meta-omics data.

scholarly journals Constructing Tumor Immune Microenvironment and Identifying the Immune-Related Prognostic Signatures in Colorectal Cancer Using Multi-omics Data

2021 ◽  
Author(s):  
Shuai Zhang ◽  
Jiali Lv ◽  
Bingbing Fan ◽  
Zhe Fan ◽  
Chunxia Li ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Cross Validation ◽  
Cox Regression ◽  
Risk Group ◽  
The Cancer Genome Atlas ◽  
Omics Data ◽  
Epigenetic Alterations ◽  
Lasso Regression ◽  
Immune Microenvironment ◽  
Tumor Immune Microenvironment

ABSTRACTBackgroundThe tumor immune microenvironment (TIME) plays a key role in occurrence, progression and prognosis of colorectal cancer (CRC). However, the genetic and epigenetic alterations and potential mechanisms in the TIME of CRC are still unclear.MethodsWe investigated the immune-related differences in three types of genetic or epigenetic alterations (gene expression, somatic mutation, and DNA methylation) and considered the potential roles that these alterations have in the immune response and the immune-related biological processes by analyzing the multi-omics data from The Cancer Genome Atlas (TCGA) portal. Additionally, a four-step method based on LASSO regression and Cox regression was used to construct the prognostic prediction model. Cross validation was performed to validate the model.ResultsA total of 1,745 differentially expressed genes, 178 differentially mutated genes and 1,961 differentially methylation probes were identified between the high-immunity group and the low-immunity group. We retained 15 genetic and epigenetic variables after using LASSO regression and Cox regression. For the prognostic predictions on the TCGA profiles, the performance of the model with 1-year, 3-year, and 5-year areas under the curve (AUCs) equal to 0.861, 0.797, and 0.875. Finally, the overall risk score model was constructed based on genetic, epigenetic, demographic and clinical characteristics, which comprised 18 variables and achieved a high degree of accuracy on the 1-year (AUC = 0.865), 3-year (AUC = 0.839), and 5-year (AUC = 0.914) survival predictions. Kaplan-Meier survival analysis demonstrated that the overall survival of the high-risk group was significantly poorer compared with the low-risk group. Prognostic nomogram, calibration plot and cross validation showed excellent predictive performance.ConclusionsOur study provides a new clue to explore the TIME of CRC patients in genetic and epigenetic aspects. Meanwhile, the prognostic model also has clinical prognostic value and may provide new indicators for the treatment of CRC patients.

scholarly journals Multi-omics Data Analyses Construct TME and Identify the Immune-Related Prognosis Signatures in Human LUAD

2020 ◽  
Vol 21 ◽  
pp. 860-873 ◽  
Author(s):  
Yuwei Zhang ◽  
Minglei Yang ◽  
Derry Minyao Ng ◽  
Maria Haleem ◽  
Tianfei Yi ◽  
...  
Keyword(s):  
Omics Data ◽  
Data Analyses

scholarly journals Integrative omics data analyses of repeated dose toxicity of valproic acid in vitro reveal new mechanisms of steatosis induction

Toxicology ◽  
2018 ◽  
Vol 393 ◽  
pp. 160-170 ◽  
Author(s):  
Simone G.J. van Breda ◽  
Sandra M.H. Claessen ◽  
Marcel van Herwijnen ◽  
Daniël H.J. Theunissen ◽  
Danyel G.J. Jennen ◽  
...  
Keyword(s):  
Valproic Acid ◽  
Repeated Dose ◽  
Omics Data ◽  
Data Analyses ◽  
Repeated Dose Toxicity ◽  
Integrative Omics

scholarly journals crcTRP: A Translational Research Platform for Colorectal Cancer

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Ning Deng ◽  
Ling Zheng ◽  
Fang Liu ◽  
Li Wang ◽  
Huilong Duan
Keyword(s):  
Colorectal Cancer ◽  
Translational Research ◽  
Clinical Data ◽  
Genomic Data ◽  
Basic Research ◽  
Information Collection ◽  
Omics Data ◽  
Research Platform ◽  
Epidemiology Data ◽  
Er Model

Colorectal cancer is a leading cause of cancer mortality in both developed and developing countries. Transforming basic research results into clinical practice is one of the key tasks of translational research, which will greatly improve the diagnosis and treatments of colorectal cancer. In this paper, a translational research platform for colorectal cancer, named crcTRP, is introduced. crcTRP serves the colorectal cancer translational research by providing various types of biomedical information related with colorectal cancer to the community. The information, including clinical data, epidemiology data, individual omics data, and public omics data, was collected through a multisource biomedical information collection solution and then integrated in a clinic-omics database, which was constructed with EAV-ER model for flexibility and efficiency. A preliminary exploration of conducting translational research on crcTRP was implemented and worked out a set of clinic-genomic relations, linking clinical data with genomic data. These relations have also been applied to crcTRP to make it more conductive for cancer translational research.

scholarly journals Systematically Prioritizing Functional Differentially Methylated Regions (fDMRs) by Integrating Multi-omics Data in Colorectal Cancer

2015 ◽  
Vol 5 (1) ◽  
Author(s):  
Huihui Fan ◽  
Hongying Zhao ◽  
Lin Pang ◽  
Ling Liu ◽  
Guanxiong Zhang ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Omics Data ◽  
Differentially Methylated Regions
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