Software for the Integration of Multiomics Experiments in Bioconductor

Ramos, Marcel; Schiffer, Lucas; Re, Angela; Azhar, Rimsha; Basunia, Azfar; Rodrı́guez, Carmen; Chan, Tiffany; Chapman, Phil; Davis, Sean; Gomez-Cabrero, David; Culhane, Aedín C.; Haibe‐Kains, Benjamin; Hansen, Kasper D.; Kodali, Hanish; Louis, Marie S.; Mer, Arvind Singh; Riester, Markus; Morgan, Martin; Carey, Vince; Waldron, Levi

doi:10.1158/0008-5472.can-17-0344

Cited by 87 publications

(88 citation statements)

References 11 publications

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“…All data handling tasks and functions in animalcules are based upon and work with the MultiAssayExperiment (MAE) data structure [21]. The MAE class is a standard data structure for multi-omics experiments with efficient data retrieval and manipulation methods that support the linkage of samples across multiple assays.…”

Section: Methodsmentioning

confidence: 99%

“…It ensures correct alignment of assays and subjects, and provides coordinated subsetting of samples and features. Additionally, it is easy to convert to or from a MAE object from the SummarizedExperiment class, which has been applied in many Bioconductor packages, enabling smooth interaction between other tools [21]. One important advantage of applying the MAE class in the microbiome research field is its extensible design supporting many multi-omics layers of data.…”

Section: Methodsmentioning

confidence: 99%

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animalcules: Interactive Microbiome Analytics and Visualization in R

Zhao

Federico

Faits

et al. 2020

Preprint

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18Background: Microbial communities that live in and on the human body play a vital role in 19 health and disease. Recent advances in sequencing technologies have enabled the study of 20 microbial communities at unprecedented resolution. However, these advances in data generation 21 have presented novel challenges to researchers attempting to analyze and visualize these data. 22Results: To address some of these challenges, we have developed animalcules, an easy-to-use 23 interactive microbiome analysis toolkit for 16S rRNA sequencing data, shotgun DNA 24 metagenomics data, and RNA-based metatranscriptomics profiling data. This toolkit combines 25Powerful interactive and dynamic figures generated by animalcules enable users to understand 29 their data and discover new insights. animalcules can be used as a standalone command-line R 30 package or users can explore their data with the accompanying interactive R Shiny interface. 31 Conclusions:We present animalcules, an R package for interactive microbiome analysis 32 through either an interactive interface facilitated by R Shiny or various command-line functions. 33It is the first microbiome analysis toolkit that supports the analysis of all 16S rRNA, DNA-based 34 shotgun metagenomics, and RNA-sequencing based metatranscriptomics datasets. animalcules 35 can be freely downloaded from GitHub at https://github.com/compbiomed/animalcules or 36 installed through Bioconductor at 37

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

animalcules: Interactive Microbiome Analytics and Visualization in R

Zhao

Federico

Faits

et al. 2020

Preprint

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“…In addition, recent advances in technology and protocols allow the simultaneous measurement of genetic, epigenetic, and transcriptomic information from the same cells [46][47][48][49][50][51][52]. The MultiAssayExperiment [53] package integrates heterogeneous data types that may be individually represented by SingleCellExperiment, DelayedArray, or other standard R/Bioconductor data structures.…”

Section: Figurementioning

confidence: 99%

Orchestrating Single-Cell Analysis with Bioconductor

Carey

Carpp

Lun

et al. 2019

Preprint

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Recent developments in experimental technologies such as single-cell RNA sequencing have enabled the profiling a high-dimensional number of genome-wide features in individual cells, inspiring the formation of large-scale data generation projects quantifying unprecedented levels of biological variation at the single-cell level. The data generated in such projects exhibits unique characteristics, including increased sparsity and scale, in terms of both the number of features and the number of samples. Due to these unique characteristics, specialized statistical methods are required along with fast and efficient software implementations in order to successfully derive biological insights. Bioconductor -an open-source, open-development software project based on the R programming language -has pioneered the analysis of such high-throughput, high-dimensional biological data, leveraging a rich history of software and methods development that has spanned the era of sequencing. Featuring state-of-the-art computational methods, standardized data infrastructure, and interactive data visualization tools that are all easily accessible as software packages, Bioconductor has made it possible for a diverse audience to analyze data derived from cutting-edge single-cell assays. Here, we present an overview of single-cell RNA sequencing analysis for prospective users and contributors, highlighting the contributions towards this effort made by Bioconductor. Figure 1: 10 years of Bioconductor in the high-throughput sequencing era. Bioconductor software packages associated with the analysis of sequencing technology were tracked by the total number of packages (left) and the number of distinct IPs (data recorded monthly) visiting their online documentation (right) over the course of ten years. Software packages were uniquely defined by their primary sequencing technology association, with examples of specific terms used for annotation below in parentheses. * Co-second authors. These authors (VJC, LNC, LG, ATLL, FM, KR, DR, CS, LW) contributed equally and are listed alphabetically. † Co-senior authors. These authors (RG, SCH) contributed equally.sparsity, due to biological fluctuations in the measured traits or limited sensitivity in quantifying small numbers of molecules [17][18][19]. In addition, data derived from single-cell assays have revealed more heterogeneity than previously seen [20][21][22][23][24][25][26][27]. This has led to the rapid development of statistical methods to address the increased sparsity and heterogeneity seen in this data [28][29][30][31]. The profound increase in the complexity of data measured at the single-cell level, along with the continued increases in the number of samples measured, have precipitated the need for fundamental changes in data access, management, and infrastructure to make data analyses scalable to empower scientific progress. Specifically, specialized statistical methods along with fast and memory-efficient software implementation are required to reap the full scientific potential of hig...

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“…The pipeline proposed in Figure 1 has been developed in R language, with the libraries (packages) downloaded from Bioconductor online repository [17]. Briefly, the selection of datasets is up to the user, but they can be downloaded inside the pipeline or recall from a local folder.…”

Section: Overview On the Pipelinementioning

confidence: 99%

Reusing microarray clinical data from a complex disease with bioinformatics tool

Prete

Facchiano

Lió

2018

Preprint

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Clinical bioinformatics, translational bioinformatics and personalised medicine are connected by the common task of analysing and integrating clinical data and results, in order to find important biomarkers related to pathologies and facilitate their prediction, diagnosis and treatment. New technologies provides the possibility to have more and more clinical data available in online databases. This data can be reused for studying complex disease from novel point of views. This work show how it is possible considering online microarray data from coeliac disease and some of its comorbidities, combining both the data and the results. The main goal is the extraction of common evidences among the selected pathologies, from genes to different kinds of functional annotation, showing which biological processes are more involved in these autoimmune disorders and quantifying the similarity between coeliac disease and its comorbidities. The pipeline of the work is developed in R language, and it is semi-automated. Methodologically, the advantage of this work is the possibility of performing the entire analysis starting from a different pathology; clinically, scientists can have the possibility of using data already published to highlight old and new evidences, with the possibility of improve the knowledge on a complex disease according to the availability of new microarray data.

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Software for the Integration of Multiomics Experiments in Bioconductor

Cited by 87 publications

References 11 publications

animalcules: Interactive Microbiome Analytics and Visualization in R

animalcules: Interactive Microbiome Analytics and Visualization in R

Orchestrating Single-Cell Analysis with Bioconductor

Reusing microarray clinical data from a complex disease with bioinformatics tool

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