Scientific Computing and Data Management

Pipeliner provides a Nextflow-based framework for creating flexible and reproducible sequencing data processing pipelines, supporting bulk RNA-seq, scRNA-seq, and DGE data.

This paper introduces Arcana, a framework for creating adaptable neuroimaging workflows that can be reused across different computing environments and data sources.

The paper assesses the installability and archival stability of computational biology tools, finding many are inaccessible or hard to install, and recommends policy changes to improve these aspects.

GenPipes provides a scalable framework for genomic analyses, offering pre-built pipelines and flexibility for custom workflows.

BiocPkgTools provides a toolkit to access and analyze metadata from the Bioconductor package ecosystem, enabling downstream applications like data mining and graph analytics.

snakePipes provides a flexible, scalable pipeline for integrative epigenomic analysis across multiple assays, simplifying reproducibility and extending existing studies.

Tibanna automates bioinformatics pipeline execution on AWS using CWL, WDL, and Docker, supporting large-scale and variable computational needs.

SciPipe is a workflow library for managing complex and dynamic pipelines in bioinformatics, supporting machine learning tasks with features like branching and dynamic scheduling.

The paper presents tools for ensuring FAIRness in big biomedical data analysis through a case study on creating a transcription factor binding site atlas from ENCODE data.

CGAT-core is a Python framework for building scalable, reproducible computational biology workflows, demonstrated with an RNAseq analysis pipeline.

The paper develops CWL-metrics to analyze resource requirements of genomic workflows, enabling optimized cloud instance selection for cost and speed.

Pixel is a content management platform for multi-omics data that allows researchers to annotate, store, explore, and mine their results without computational analysis tools.

The paper introduces a microservices-based framework for scalable data analysis, validated in metabolomics, enabling dynamic scaling and integration of software tools.

The paper introduces a reproducible data analysis system for precision medicine, demonstrating its use in analyzing cancer genomic data.

PhenoMeNal provides a cloud-based platform for metabolomics data analysis, integrating tools and workflows to enable reproducible and scalable research.

BioJupies automates the creation of interactive Jupyter Notebooks for RNA-seq analysis, enabling users to generate reproducible reports from raw data to visualizations and differential expression analyses.

The paper presents reproducible genomics analysis pipelines using GNU Guix for RNA-seq, ChIP-seq, Bisulfite-seq, and single-cell RNA-seq, ensuring consistent results across different environments.

The paper introduces a reproducible bioinformatics project framework using Docker and R to ensure consistent and replicable analysis workflows across different systems.

bioSyntax provides syntax highlighting tools for computational biology data formats to improve readability and efficiency.

The paper demonstrates the use of Pachyderm for efficient and reproducible metabolomics workflows in bioinformatics.

The paper introduces a method to reduce cloud computing costs for bioinformatics by hot-starting software containers, avoiding repetitive initialization steps.

WAVES provides a web application for integrating bioinformatic tools through web pages, forms, and a RESTful API, with support for running computations on Galaxy instances.

SATurn is a modular bioinformatics framework for creating maintainable web and standalone applications, used to build a bioinformatics portal supporting diverse scientific research.

The paper attempts to reproduce a bioinformatics method, identifies challenges in reusability, and proposes solutions to improve reproducibility and research efficiency.

Bioconda provides a sustainable, comprehensive distribution of bioinformatics software using Conda for reproducible analysis across platforms.

YAMP provides a containerised workflow for analysing whole shotgun metagenomic data, ensuring reproducibility and ease of use across UNIX-like systems.

Tellurium Notebook enables reproducible and reusable dynamical models in systems biology by integrating community standards and literate coding practices.

The paper presents technologies that make computational reproducibility practical by integrating package management, containerization, and workflow systems.

SoS Notebook provides a web-based environment for multi-language data analysis, enabling seamless integration of different scripting languages in bioinformatics workflows.