scholarly journals The Bioconductor channel in F1000Research

F1000Research ◽  
2015 ◽  
Vol 4 ◽  
pp. 217 ◽  
Author(s):  
Wolfgang Huber ◽  
Vincent J. Carey ◽  
Sean Davis ◽  
Kasper Daniel Hansen ◽  
Martin Morgan
Keyword(s):  
Genomic Data ◽  
Integrative Analysis ◽  
Omics Data ◽  
Data Types ◽  
Know How ◽  
Modelling Techniques ◽  
Task Oriented ◽  
Genome Scale ◽  
A Current ◽  
Scale Data

Bioconductor (bioconductor.org) is a rich source of software and know-how for the integrative analysis of genomic data. The Bioconductor channel in F1000Research provides a forum for task-oriented workflows that each cover a solution to a current, important problem in genome-scale data analysis from end to end, invoking resources from several packages by different authors, often combining multiple `omics data types, and demonstrating integrative analysis and modelling techniques.

scholarly journals The Bioconductor channel in F1000Research

F1000Research ◽  
2016 ◽  
Vol 4 ◽  
pp. 217 ◽  
Author(s):  
Wolfgang Huber ◽  
Vincent J. Carey ◽  
Sean Davis ◽  
Kasper Daniel Hansen ◽  
Martin Morgan
Keyword(s):  
Data Analysis ◽  
Genomic Data ◽  
Integrative Analysis ◽  
Know How ◽  
Software Packages ◽  
Bioinformatics Software ◽  
Task Oriented ◽  
Genome Scale ◽  
A Current ◽  
Scale Data

Bioconductor (bioconductor.org) is a rich source of software and know-how for the integrative analysis of genomic data. The Bioconductor channel in F1000Research provides a forum for task-oriented workflows that cover a solution to a current, important problem in genome-scale data analysis. It also hosts manuscripts describing software packages, package-based vignettes, teaching labs, benchmark studies, methodological reviews and bioinformatics software oriented perspective papers.

scholarly journals Approaches to Integrating Metabolomics and Multi-Omics Data: A Primer

2021 ◽  
Author(s):  
Takoua Jendoubi
Keyword(s):  
Integrative Analysis ◽  
Omics Data ◽  
Data Types ◽  
Analysis Pipeline ◽  
Metabolomics Data ◽  
Computational Tools ◽  
Complex Chemical Reactions ◽  
Living Organisms ◽  
Underlying Mechanisms ◽  
Omics Data Integration

Metabolomics deals with multiple and complex chemical reactions within living organisms and how these are influenced by external or internal perturbations. It lies at the heart of omics profiling technologies not only as the underlying biochemical layer that reflects information expressed by the genome, the transcriptome and the proteome, but also as the closest layer to the phenome. The combination of metabolomics data with the information available from genomics, transcriptomics, and proteomics offers unprecedented possibilities to enhance current understanding of biological functions, elucidate their underlying mechanisms and uncover hidden associations between omics variables. As a result, a vast array of computational tools have been developed to assist with integrative analysis of metabolomics data with different omics. Here, we review and propose five criteria – hypothesis, data types, strategies, study design and study focus – to classify statistical multi-omics data integration approaches into state-of-the-art classes under which all existing statistical methods fall. The purpose of this review is to look at various aspects that lead the choice of the statistical integrative analysis pipeline in terms of the different classes. We will draw a particular attention to metabolomics and genomics data to assist those new to this field in the choice of the integrative analysis pipeline.

scholarly journals Synergistic Effects of Different Levels of Genomic Data for the Staging of Lung Adenocarcinoma: An Illustrative Study

Genes ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 1872
Author(s):  
Yingxia Li ◽  
Ulrich Mansmann ◽  
Shangming Du ◽  
Roman Hornung
Keyword(s):  
Gene Expression ◽  
Lung Adenocarcinoma ◽  
Selection Process ◽  
Synergistic Effects ◽  
Multiple Kernel Learning ◽  
Genomic Data ◽  
Omics Data ◽  
Data Types ◽  
Effective Diagnosis ◽  
Ranking Technique

Lung adenocarcinoma (LUAD) is a common and very lethal cancer. Accurate staging is a prerequisite for its effective diagnosis and treatment. Therefore, improving the accuracy of the stage prediction of LUAD patients is of great clinical relevance. Previous works have mainly focused on single genomic data information or a small number of different omics data types concurrently for generating predictive models. A few of them have considered multi-omics data from genome to proteome. We used a publicly available dataset to illustrate the potential of multi-omics data for stage prediction in LUAD. In particular, we investigated the roles of the specific omics data types in the prediction process. We used a self-developed method, Omics-MKL, for stage prediction that combines an existing feature ranking technique Minimum Redundancy and Maximum Relevance (mRMR), which avoids redundancy among the selected features, and multiple kernel learning (MKL), applying different kernels for different omics data types. Each of the considered omics data types individually provided useful prediction results. Moreover, using multi-omics data delivered notably better results than using single-omics data. Gene expression and methylation information seem to play vital roles in the staging of LUAD. The Omics-MKL method retained 70 features after the selection process. Of these, 21 (30%) were methylation features and 34 (48.57%) were gene expression features. Moreover, 18 (25.71%) of the selected features are known to be related to LUAD, and 29 (41.43%) to lung cancer in general. Using multi-omics data from genome to proteome for predicting the stage of LUAD seems promising because each omics data type may improve the accuracy of the predictions. Here, methylation and gene expression data may play particularly important roles.

scholarly journals PathwayMultiomics: An R Package for Efficient Integrative Analysis of Multi-Omics Datasets With Matched or Un-matched Samples

2021 ◽  
Vol 12 ◽  
Author(s):  
Gabriel J. Odom ◽  
Antonio Colaprico ◽  
Tiago C. Silva ◽  
X. Steven Chen ◽  
Lily Wang
Keyword(s):  
R Package ◽  
Integrative Analysis ◽  
Omics Data ◽  
Computationally Efficient ◽  
Data Types ◽  
Analysis Techniques ◽  
Genome Data ◽  
Analysis Strategies ◽  
Matched Samples ◽  
High Dimensional Datasets

Recent advances in technology have made multi-omics datasets increasingly available to researchers. To leverage the wealth of information in multi-omics data, a number of integrative analysis strategies have been proposed recently. However, effectively extracting biological insights from these large, complex datasets remains challenging. In particular, matched samples with multiple types of omics data measured on each sample are often required for multi-omics analysis tools, which can significantly reduce the sample size. Another challenge is that analysis techniques such as dimension reductions, which extract association signals in high dimensional datasets by estimating a few variables that explain most of the variations in the samples, are typically applied to whole-genome data, which can be computationally demanding. Here we present pathwayMultiomics, a pathway-based approach for integrative analysis of multi-omics data with categorical, continuous, or survival outcome variables. The input of pathwayMultiomics is pathway p-values for individual omics data types, which are then integrated using a novel statistic, the MiniMax statistic, to prioritize pathways dysregulated in multiple types of omics datasets. Importantly, pathwayMultiomics is computationally efficient and does not require matched samples in multi-omics data. We performed a comprehensive simulation study to show that pathwayMultiomics significantly outperformed currently available multi-omics tools with improved power and well-controlled false-positive rates. In addition, we also analyzed real multi-omics datasets to show that pathwayMultiomics was able to recover known biology by nominating biologically meaningful pathways in complex diseases such as Alzheimer’s disease.

scholarly journals Blockchain for genomics and healthcare: a literature review, current status, classification and open issues

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e12130
Author(s):  
Beyhan Adanur Dedeturk ◽  
Ahmet Soran ◽  
Burcu Bakir-Gungor
Keyword(s):  
Data Sharing ◽  
Genomic Data ◽  
Current Status ◽  
Omics Data ◽  
Data Types ◽  
Healthcare Applications ◽  
Advantages And Disadvantages ◽  
Blockchain Technology ◽  
Review Current ◽  
Open Issues

The tremendous boost in the next generation sequencing technologies and in the “omics” technologies resulted in the generation of hundreds of gigabytes of data per day. Nowadays, via integrating -omics data with other data types, such as imaging and electronic health record (EHR) data, panomics studies attempt to identify novel and potentially actionable biomarkers for personalized medicine applications. In this respect, for the accurate analysis of -omics data and EHR, there is a need to establish secure and robust pipelines that take the ethical aspects into consideration, regulate privacy and ownership issues, and data sharing. These days, blockchain technology has picked up significant attention in diverse fields, including genomics, since it offers a new solution for these problems from a different perspective. Blockchain is an immutable transaction ledger, which offers secure and distributed system without a central authority. Within the system, each transaction can be expressed with cryptographically signed blocks, and the verification of transactions is performed by the users of the network. In this review, firstly, we aim to highlight the challenges of EHR and genomic data sharing. Secondly, we attempt to answer “Why” or “Why not” the blockchain technology is suitable for genomics and healthcare applications in detail. Thirdly, we elucidate the general blockchain structure based on the Ethereum, which is a more suitable technology for the genomic data sharing platforms. Fourthly, we review current blockchain-based EHR and genomic data sharing platforms, evaluate the advantages and disadvantages of these applications, and classify these applications using different metrics. Finally, we conclude by discussing the open issues and introducing our suggestion on the topic. In summary, to facilitate the diagnosis, monitoring and therapy of diseases with the effective analysis of -omics data with other available data types, through this review, we put forward the possible implications of the blockchain technology to life sciences and healthcare.

scholarly journals MORONET: Multi-omics Integration via Graph Convolutional Networks for Biomedical Data Classification

2020 ◽  
Author(s):  
Tongxin Wang ◽  
Wei Shao ◽  
Zhi Huang ◽  
Haixu Tang ◽  
Jie Zhang ◽  
...  
Keyword(s):  
Data Classification ◽  
Integrative Analysis ◽  
Biomedical Data ◽  
Omics Data ◽  
Expression Data ◽  
Data Types ◽  
Convolutional Networks ◽  
Wide Range ◽  
Correlation Learning ◽  
Mirna Expression Data

ABSTRACTTo fully utilize the advances in omics technologies and achieve a more comprehensive understanding of human diseases, novel computational methods are required for integrative analysis for multiple types of omics data. We present a novel multi-omics integrative method named Multi-Omics gRaph cOnvolutional NETworks (MORONET) for biomedical classification. MORONET jointly explores omics-specific learning and cross-omics correlation learning for effective multi-omics data classification. We demonstrate that MORONET outperforms other state-of-the-art supervised multi-omics integrative analysis approaches from a wide range of biomedical classification applications using mRNA expression data, DNA methylation data, and miRNA expression data. Furthermore, MORONET is able to identify important biomarkers from different omics data types that are related with the investigated diseases.

Survey and comparative assessments of computational multi-omics integrative methods with multiple regulatory networks identifying distinct tumor compositions across pan-cancer data sets

2020 ◽  
Author(s):  
Zhuohui Wei ◽  
Yue Zhang ◽  
Wanlin Weng ◽  
Jiazhou Chen ◽  
Hongmin Cai
Keyword(s):  
Molecular Mechanisms ◽  
Genomic Data ◽  
Low Rank ◽  
Integrated Analysis ◽  
Data Sets ◽  
Omics Data ◽  
Data Types ◽  
Cancer Data ◽  
Cancer Types ◽  
Pan Cancer

Abstract The significance of pan-cancer categories has recently been recognized as widespread in cancer research. Pan-cancer categorizes a cancer based on its molecular pathology rather than an organ. The molecular similarities among multi-omics data found in different cancer types can play several roles in both biological processes and therapeutic developments. Therefore, an integrated analysis for various genomic data is frequently used to reveal novel genetic and molecular mechanisms. However, a variety of algorithms for multi-omics clustering have been proposed in different fields. The comparison of different computational clustering methods in pan-cancer analysis performance remains unclear. To increase the utilization of current integrative methods in pan-cancer analysis, we first provide an overview of five popular computational integrative tools: similarity network fusion, integrative clustering of multiple genomic data types (iCluster), cancer integration via multi-kernel learning (CIMLR), perturbation clustering for data integration and disease subtyping (PINS) and low-rank clustering (LRACluster). Then, a priori interactions in multi-omics data were incorporated to detect prominent molecular patterns in pan-cancer data sets. Finally, we present comparative assessments of these methods, with discussion over key issues in applying these algorithms. We found that all five methods can identify distinct tumor compositions. The pan-cancer samples can be reclassified into several groups by different proportions. Interestingly, each method can classify the tumors into categories that are different from original cancer types or subtypes, especially for ovarian serous cystadenocarcinoma (OV) and breast invasive carcinoma (BRCA) tumors. In addition, all clusters of the five computational methods show notable prognostic values. Furthermore, both the 9 recurrent differential genes and the 15 common pathway characteristics were identified across all the methods. The results and discussion can help the community select appropriate integrative tools according to different research tasks or aims in pan-cancer analysis.

scholarly journals Approaches to Integrating Metabolomics and Multi-Omics Data: A Primer

Metabolites ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 184
Author(s):  
Takoua Jendoubi
Keyword(s):  
Integrative Analysis ◽  
Omics Data ◽  
Data Types ◽  
Analysis Pipeline ◽  
Metabolomics Data ◽  
Computational Tools ◽  
Complex Chemical Reactions ◽  
Living Organisms ◽  
Underlying Mechanisms ◽  
Omics Data Integration

Metabolomics deals with multiple and complex chemical reactions within living organisms and how these are influenced by external or internal perturbations. It lies at the heart of omics profiling technologies not only as the underlying biochemical layer that reflects information expressed by the genome, the transcriptome and the proteome, but also as the closest layer to the phenome. The combination of metabolomics data with the information available from genomics, transcriptomics, and proteomics offers unprecedented possibilities to enhance current understanding of biological functions, elucidate their underlying mechanisms and uncover hidden associations between omics variables. As a result, a vast array of computational tools have been developed to assist with integrative analysis of metabolomics data with different omics. Here, we review and propose five criteria—hypothesis, data types, strategies, study design and study focus— to classify statistical multi-omics data integration approaches into state-of-the-art classes under which all existing statistical methods fall. The purpose of this review is to look at various aspects that lead the choice of the statistical integrative analysis pipeline in terms of the different classes. We will draw particular attention to metabolomics and genomics data to assist those new to this field in the choice of the integrative analysis pipeline.

scholarly journals Untangling the early diversification of eukaryotes: a phylogenomic study of the evolutionary origins of Centrohelida, Haptophyta and Cryptista

2016 ◽  
Vol 283 (1823) ◽  
pp. 20152802 ◽  
Author(s):  
Fabien Burki ◽  
Maia Kaplan ◽  
Denis V. Tikhonenkov ◽  
Vasily Zlatogursky ◽  
Bui Quang Minh ◽  
...  
Keyword(s):  
Genomic Data ◽  
High Quality ◽  
Plastid Evolution ◽  
Strong Affinity ◽  
Evolutionary Origins ◽  
Microbial Eukaryotes ◽  
Early Diversification ◽  
Genome Scale ◽  
Phylogenomic Study ◽  
Scale Data

Assembling the global eukaryotic tree of life has long been a major effort of Biology. In recent years, pushed by the new availability of genome-scale data for microbial eukaryotes, it has become possible to revisit many evolutionary enigmas. However, some of the most ancient nodes, which are essential for inferring a stable tree, have remained highly controversial. Among other reasons, the lack of adequate genomic datasets for key taxa has prevented the robust reconstruction of early diversification events. In this context, the centrohelid heliozoans are particularly relevant for reconstructing the tree of eukaryotes because they represent one of the last substantial groups that was missing large and diverse genomic data. Here, we filled this gap by sequencing high-quality transcriptomes for four centrohelid lineages, each corresponding to a different family. Combining these new data with a broad eukaryotic sampling, we produced a gene-rich taxon-rich phylogenomic dataset that enabled us to refine the structure of the tree. Specifically, we show that (i) centrohelids relate to haptophytes, confirming Haptista; (ii) Haptista relates to SAR; (iii) Cryptista share strong affinity with Archaeplastida; and (iv) Haptista + SAR is sister to Cryptista + Archaeplastida. The implications of this topology are discussed in the broader context of plastid evolution.

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