The 1,000 plants (1KP) project is an international multi-disciplinary consortium that has generated transcriptome data from over 1,000 plant species,

Objective: Bioinformatics publications typically include complex software workflows that are difficult to describe in a manuscript. We describe and demonstrate the use of interactive software notebooks to document and distribute bioinformatics research. We provide a user-friendly tool, BiocImageBuilder, that allows users to easily distribute their bioinformatics protocols through interactive notebooks uploaded to either a GitHub repository or a private server.Materials and methods: We present four different interactive Jupyter notebooks using R and Bioconductor workflows to infer differential gene expression, analyze cross-platform datasets, process RNA-seq data and KinomeScan data. These interactive notebooks are available on GitHub. The analytical results can be viewed in a browser. Most importantly, the software contents can be executed and modified. This is accomplished using Binder, which runs the notebook inside software containers, thus avoiding the need to install any software and ensuring reproducibility. All the notebooks were produced using custom files generated by BiocImageBuilder.Results: BiocImageBuilder facilitates the publication of workflows with a point-and-click user interface. We demonstrate that interactive notebooks can be used to disseminate a wide range of bioinformatics analyses. The use of software containers to mirror the original software environment ensures reproducibility of results. Parameters and code can be dynamically modified, allowing for robust verification of published results and encouraging rapid adoption of new methods.Conclusion: Given the increasing complexity of bioinformatics workflows, we anticipate that these interactive software notebooks will become as necessary for documenting software methods as traditional laboratory notebooks have been for documenting bench protocols, and as ubiquitous.

Function-as-a-Service (FaaS) platforms enable easy deployment and hosting of code known as serverless functions and have gained considerable traction among software developers. Recently, Amazon has offered ARM64 processors as an alternative to x86 for hosting serverless functions on AWS Lambda. To encourage developers to migrate code to ARM64, use of ARM64 processors has been incentivized with a 20% discount. In this paper, we investigate the implications of adopting ARM64 processors for hosting serverless functions. We deploy and benchmark 18 distinct serverless functions that stress a variety of system resources. Additionally, we scaled up the runtime for each of our functions by increasing the work they performed using 40 incremental steps to increase runtime from a few seconds to several minutes. We compared performance differences of x86 vs. ARM64 processors for our serverless function workoads and present our findings summarizing differences in: CPU utilization, function runtime, function runtime variation, and hosting costs. While only 7 of 18 functions ran faster on ARM64, 15 of 18 were less expensive thanks to the cloud provider discount. Performance variation on ARM64 processors was less than half compared to x86 processors potentially from the lack of hyperthreading and lower resource contention for ARM64 CPUs.

We present a novel method that yields 1100-fold computational speedup and allows biomedical scientists to interactively adjust alignment parameters in real time to iteratively improve final analytical results. Specifically, the alignment time for a 640 million read human RNA-sequencing dataset is reduced from 19 hours to 1 minute using serverless cloud computing. We provide a graphical interface for the accelerated workflows, thus making our methodology accessible to non-cloud experts.

Background: Long-read sequencing has great promise in enabling portable, rapid molecular-assisted cancer diagnoses. A key challenge in democratizing long-read sequencing technology in the biomedical and clinical community is the lack of graphical bioinformatics software tools which can efficiently process the raw nanopore reads, support graphical output and interactive visualizations for interpretations of results. Another obstacle is that high performance software tools for long-read sequencing data analyses often leverage graphics processing units (GPU), which is challenging and time-consuming to configure, especially on the cloud. Results: We present a graphical cloud-enabled workflow for fast, interactive analysis of nanopore sequencing data using GPUs. Users customize parameters, monitor execution and visualize results through an accessible graphical interface. The workflow and its components are completely containerized to ensure reproducibility and facilitate installation of the GPU-enabled software. We also provide an Amazon Machine Image (AMI) with all software and drivers pre-installed for GPU computing on the cloud. Most importantly, we demonstrate the potential of applying our software tools to reduce the turnaround time of cancer diagnostics by generating blood cancer (NB4, K562, ME1, 238 MV4;11) cell line Nanopore data using the Flongle adapter. We observe a 29x speedup and a 93x reduction in costs for the rate-limiting basecalling step in the analysis of blood cancer cell line data. Conclusions: Our interactive and efficient software tools will make analyses of Nanopore data using GPU and cloud computing accessible to biomedical and clinical scientists, thus facilitating the adoption of cost effective, fast, portable and real-time long-read sequencing.

We present the BioDepot-workflow-builder (Bwb), a software tool that allows users to create and execute reproducible bioinformatics workflows using a drag-and-drop interface. Graphical widgets represent Docker containers executing a modular task. Widgets are then graphically linked to build bioinformatics workflows that can be reproducibly deployed across different local and cloud platforms. Each widget contains a form-based user interface to facilitate parameter entry and a console to display intermediate results. Bwb provides tools for rapid customization of widgets, containers and workflows. Saved workflows can be shared using Bwb’s native format or exported as shell scripts.