Systematically evaluating interfaces for RNA-seq analysis from a life scientist perspective

Poplawski, Alicia; Marini, Fédérico; Hess, Moritz; Zeller, Tanja; Mazur, Johanna; Binder, Harald

doi:10.1093/bib/bbv036

Cited by 21 publications

(32 citation statements)

References 54 publications

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“…Despite the availability of various WMS, a recent review highlighted that these solutions have a fundamental limit: users have to switch to multiple GUIs to execute a complete NGS analysis (Poplawski et al, 2016). …”

Section: Workflow Management Systemsmentioning

confidence: 99%

Integrated Systems for NGS Data Management and Analysis: Open Issues and Available Solutions

et al. 2016

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Next-generation sequencing (NGS) technologies have deeply changed our understanding of cellular processes by delivering an astonishing amount of data at affordable prices; nowadays, many biology laboratories have already accumulated a large number of sequenced samples. However, managing and analyzing these data poses new challenges, which may easily be underestimated by research groups devoid of IT and quantitative skills. In this perspective, we identify five issues that should be carefully addressed by research groups approaching NGS technologies. In particular, the five key issues to be considered concern: (1) adopting a laboratory management system (LIMS) and safeguard the resulting raw data structure in downstream analyses; (2) monitoring the flow of the data and standardizing input and output directories and file names, even when multiple analysis protocols are used on the same data; (3) ensuring complete traceability of the analysis performed; (4) enabling non-experienced users to run analyses through a graphical user interface (GUI) acting as a front-end for the pipelines; (5) relying on standard metadata to annotate the datasets, and when possible using controlled vocabularies, ideally derived from biomedical ontologies. Finally, we discuss the currently available tools in the light of these issues, and we introduce HTS-flow, a new workflow management system conceived to address the concerns we raised. HTS-flow is able to retrieve information from a LIMS database, manages data analyses through a simple GUI, outputs data in standard locations and allows the complete traceability of datasets, accompanying metadata and analysis scripts.

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Section: Workflow Management Systemsmentioning

confidence: 99%

Integrated Systems for NGS Data Management and Analysis: Open Issues and Available Solutions

et al. 2016

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“…In our previous work (Poplawski et al, 2016), we reviewed a selection of interfaces for RNA-seq analysis from the perspective of a life scientist, defining criteria that cover many essential aspects of every software/framework, including e.g. installation, usability, flexibility, hardware requirements, and reproducibility.…”

Section: Introductionmentioning

confidence: 99%

ideal: an R/Bioconductor package for Interactive Differential Expression Analysis

Marini

Linke

Binder

2020

Preprint

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Background: RNA sequencing (RNA-seq) is an ever increasingly popular tool for transcriptome profiling. A key point to make the best use of the available data is to provide software tools that are easy to use but still provide flexibility and transparency in the adopted methods. Despite the availability of many packages focused on detecting differential expression, a method to streamline this type of bioinformatics analysis in a comprehensive, accessible, and reproducible way is lacking.Results: We developed the ideal software package, which serves as a web application for interactive and reproducible RNA-seq analysis, while producing a wealth of visualizations to facilitate data interpretation. ideal is implemented in R using the Shiny framework, and is fully integrated with the existing core structures of the Bioconductor project. Users can perform the essential steps of the differential expression analysis workflow in an assisted way, and generate a broad spectrum of publication-ready outputs, including diagnostic and summary visualizations in each module, all the way down to functional analysis. ideal also offers the possibility to seamlessly generate a full HTML report for storing and sharing results together with code for reproducibility.Conclusion: ideal is distributed as an R package in the Bioconductor project (http: //bioconductor.org/packages/ideal/), and provides a solution for performing interactive and reproducible analyses of summarized RNA-seq expression data, empowering researchers with many different profiles (life scientists, clinicians, but also experienced bioinformaticians) to make the ideal use of the data at hand.

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“…There are numerous software packages for the upstream transcriptional analysis as reviewed in Poplawski et al [11]. However, there is no other freely available open source software that provides the features stated above.…”

Section: Introductionmentioning

confidence: 99%

Transcriptome computational workbench (TCW): analysis of single and comparative transcriptomes

2019

Preprint

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De novo transcriptome sequencing and analysis provides a way for researchers of non-model organisms to explore the differences between various conditions and species. These experiments are expensive and produce large-scale data. The results are typically not definitive but will lead to new hypotheses to study. Therefore, it is important that the results be reproducible, extensible, queryable, and easily available to all members of the team. Towards this end, the Transcriptome Computational Workbench (TCW) is a software package to perform the fundamental computations for transcriptome analysis (singleTCW) and comparative analysis (multiTCW). It is a Java-based desktop application that uses MySQL. The input to singleTCW is sequence and optional count files; the computations are sequence similarity annotation, gene ontology assignment, open reading frame (ORF) finding using hit information and 5 th -order Markov models, and differential expression (DE). For DE analysis, TCW interfaces with an R script, where R scripts for edgeR and DEseq are provided, but the user can supply their own. TCW provides support for searching with the super-fast DIAMOND program against UniProt taxonomic databases, though the user can request BLAST and provide other databases to search against. The input to multiTCW is multiple singleTCW databases; the computations are homologous pair assignment, pairwise analysis (e.g. Ka/Ks) from codon-based alignments, clustering (bidirectional best hit, Closure, OrthoMCL, user-supplied), and cluster analysis and annotation. Both singleTCW and multiTCW provide a graphical interface for extensive query and display of the data. Example results are presented from three datasets: (i) a rhizome plant with de novo assembled contigs, (ii) a rhizome plant with gene models from a draft genome sequence, and (iii) a non-rhizome plant with gene models from a finished genome sequence. The two rhizome plants have replicate count data for rhizome, root, stem and leaf samples. The software is freely available at https://github.com/csoderlund/TCW.

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Systematically evaluating interfaces for RNA-seq analysis from a life scientist perspective

Cited by 21 publications

References 54 publications

Integrated Systems for NGS Data Management and Analysis: Open Issues and Available Solutions

Integrated Systems for NGS Data Management and Analysis: Open Issues and Available Solutions

ideal: an R/Bioconductor package for Interactive Differential Expression Analysis

Transcriptome computational workbench (TCW): analysis of single and comparative transcriptomes

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