Reproducible Bioinformatics Project: A community for reproducible bioinformatics analysis pipelines

Kulkarni, Neha; Alessandrì, Luca; Panero, Riccardo; Arigoni, Maddalena; Olivero, Martina; Cordero, Francesca; Beccuti, Marco; Calogero, Raffaele

doi:10.1101/239947

Cited by 21 publications

(22 citation statements)

References 29 publications

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“…Libraries with unique indexes were pooled and sequenced on a NextSeq 500 benchtop sequencer (Illumina) using NextSeq 500 High Output v2 kit and single-end 50bp sequencing chemistry. 4SeqGUI, docker4seq reproducible pipeline was used to process the miRNA-seq 57 . DESeq2 was used for differential expression analysis, using patient as a covariate.…”

Section: Mirna Sequencingmentioning

confidence: 99%

Translational control of cardiac fibrosis

Chothani

Schäfer

Adami

et al. 2018

Preprint

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BackgroundFibrosis is a common pathology in many cardiac disorders and is driven by the activation of resident fibroblasts.The global post-transcriptional mechanisms underlying fibroblast-to-myofibroblast conversion in the heart have not been explored. MethodsGenome-wide changes of RNA transcription and translation during human cardiac fibroblast activation were monitored with RNA sequencing and ribosome profiling. We then used miRNA-and RNA-binding protein-based analyses to identify translational regulators of fibrogenic genes. To reveal post-transcriptional mechanisms in the human fibrotic heart, we then integrated our findings with cardiac ribosome occupancy levels of 30 dilated cardiomyopathy patients. ResultsWe generated nucleotide-resolution translatome data during the TGFβ1-driven cellular transition of human cardiac fibroblasts to myofibroblasts. This identified dynamic changes of RNA transcription and translation at several time points during the fibrotic response, revealing transient and early-responder genes. Remarkably, about one-third of all changes in gene expression in activated fibroblasts are subject to translational regulation and dynamic variation in ribosome occupancy affects protein abundance independent of RNA levels. Targets of RNA-binding proteins were strongly enriched in post-transcriptionally regulated genes, suggesting genes such as MBNL2 can act as translational activators or repressors. Ribosome occupancy in the hearts of patients with dilated cardiomyopathy suggested an extensive post-transcriptional regulatory network underlying cardiac fibrosis. Key network hubs include RNA-binding proteins such as PUM2 and QKI that work in concert to regulate the translation of target transcripts in human diseased hearts. ConclusionsWe reveal widespread translational effects of TGFβ1 and define novel post-transcriptional events that control the fibroblast-to-myofibroblast transition. Regulatory networks that affect ribosome occupancy in fibroblasts are paralleled in human heart disease. Our findings show the central importance of translational control in fibrosis and highlight novel pathogenic mechanisms in heart failure.

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Section: Mirna Sequencingmentioning

confidence: 99%

Translational control of cardiac fibrosis

Chothani

Schäfer

Adami

et al. 2018

Preprint

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“…DolphinNext goes beyond providing a Nextflow graphical design environment and addresses many of the needs of high-throughput data processing: First, DolphinNext helps with reproducibility enabling the easy distribution and running of pipelines. In fact, reproducible data analysis requires making both the code and the parameters used in the analysis accessible to researchers [21][22][23][24]. DolphinNext allows users to package pipelines into portable containers [25,26] that can be run as stand-alone applications because they include the exact versions of all software dependencies that were tested and used.…”

Section: Introductionmentioning

confidence: 99%

DolphinNext: a distributed data processing platform for high throughput genomics

et al. 2020

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Background: The emergence of high throughput technologies that produce vast amounts of genomic data, such as next-generation sequencing (NGS) is transforming biological research. The dramatic increase in the volume of data, the variety and continuous change of data processing tools, algorithms and databases make analysis the main bottleneck for scientific discovery. The processing of high throughput datasets typically involves many different computational programs, each of which performs a specific step in a pipeline. Given the wide range of applications and organizational infrastructures, there is a great need for highly parallel, flexible, portable, and reproducible data processing frameworks. Several platforms currently exist for the design and execution of complex pipelines. Unfortunately, current platforms lack the necessary combination of parallelism, portability, flexibility and/or reproducibility that are required by the current research environment. To address these shortcomings, workflow frameworks that provide a platform to develop and share portable pipelines have recently arisen. We complement these new platforms by providing a graphical user interface to create, maintain, and execute complex pipelines. Such a platform will simplify robust and reproducible workflow creation for non-technical users as well as provide a robust platform to maintain pipelines for large organizations. Results: To simplify development, maintenance, and execution of complex pipelines we created DolphinNext. DolphinNext facilitates building and deployment of complex pipelines using a modular approach implemented in a graphical interface that relies on the powerful Nextflow workflow framework by providing 1. A drag and drop user interface that visualizes pipelines and allows users to create pipelines without familiarity in underlying programming languages. 2. Modules to execute and monitor pipelines in distributed computing environments such as highperformance clusters and/or cloud 3. Reproducible pipelines with version tracking and stand-alone versions that can be run independently. 4. Modular process design with process revisioning support to increase reusability and pipeline development efficiency. 5. Pipeline sharing with GitHub and automated testing 6. Extensive reports with Rmarkdown and shiny support for interactive data visualization and analysis. Conclusion: DolphinNext is a flexible, intuitive, web-based data processing and analysis platform that enables creating, deploying, sharing, and executing complex Nextflow pipelines with extensive revisioning and interactive reporting to enhance reproducible results.

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“…[12][13][14] . Several suggestions have been published to improve reproducibility in computational biology 12,15,16 and bioinformatics 17,18 .…”

Section: Introductionmentioning

confidence: 99%

Reproducibility in systems biology modelling

Tiwari

Kananathan

Roberts

et al. 2020

Preprint

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The reproducibility crisis has emerged as an important concern across many fields of science including life science, since many published results failed to reproduce. Systems biology modelling, which involves mathematical representation of biological processes to study complex system behaviour, was expected to be least affected by this crisis. While lack of reproducibility of experimental results and computational analysis could be a repercussion of several compounded factors, it was not fully understood why systems biology models with well defined mathematical expressions fail to reproduce and how prevalent it is. Hence, we systematically attempted to reproduce 455 kinetic models of biological processes published in peer-reviewed research articles from 152 journals; which is collectively a work of about 1400 scientists from 49 countries. Our investigation revealed that about half (49%) of the models could not be reproduced using the information provided in the published manuscripts. With further effort, an additional 12% of the models could be reproduced either by empirical correction or support from authors. The other 37% remained non-reproducible models due to missing parameter values, missing initial concentration, inconsistent model structure, or a combination of these factors. Among the corresponding authors of the non-reproducible model we contacted, less than 30% responded. Our analysis revealed that models published in journals across several fields of life science failed to reproduce, revealing a common problem in the peer-review process. Hence, we propose an 8-point reproducibility scorecard that can be used by authors, reviewers and journal editors to assess each model and address the reproducibility crisis.

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Reproducible Bioinformatics Project: A community for reproducible bioinformatics analysis pipelines

Cited by 21 publications

References 29 publications

Translational control of cardiac fibrosis

Translational control of cardiac fibrosis

DolphinNext: a distributed data processing platform for high throughput genomics

Reproducibility in systems biology modelling

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