Manipulation of FASTQ data with Galaxy

Summary: Here, we describe a tool suite that functions on all of the commonly known FASTQ format variants and provides a pipeline for manipulating next generation sequencing data taken from a sequencing machine all the way through the quality filtering steps

Integrating diverse databases into an unified analysis framework: a Galaxy approach

Recent technological advances have lead to the ability to generate large amounts of data for model and non-model organisms. Whereas, in the past, there have been a relatively small number of central repositories that serve genomic data, an increasing number of distinct specialized data repositories and resources have been established. Here, we describe a generic approach that provides for the integration of a diverse spectrum of data resources into a unified analysis framework, Galaxy (http://usegalaxy.org). This approach allows the simplified coupling of external data resources with the data analysis tools available to Galaxy users, while leveraging the native data mining facilities of the external data resources

The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2018 update

Galaxy (homepage: https://galaxyproject.org, main public server: https://usegalaxy.org) is a web-based scientific analysis platform used by tens of thousands of scientists across the world to analyze large biomedical datasets such as those found in genomics, proteomics, metabolomics and imaging. Started in 2005, Galaxy continues to focus on three key challenges of data-driven biomedical science: making analyses accessible to all researchers, ensuring analyses are completely reproducible, and making it simple to communicate analyses so that they can be reused and extended. During the last two years, the Galaxy team and the open-source community around Galaxy have made substantial improvements to Galaxy's core framework, user interface, tools, and training materials. Framework and user interface improvements now enable Galaxy to be used for analyzing tens of thousands of datasets, and >5500 tools are now available from the Galaxy ToolShed. The Galaxy community has led an effort to create numerous high-quality tutorials focused on common types of genomic analyses. The Galaxy developer and user communities continue to grow and be integral to Galaxy's development. The number of Galaxy public servers, developers contributing to the Galaxy framework and its tools, and users of the main Galaxy server have all increased substantially

Classification and Performance Evaluation of Instruction Buffering Techniques

The speed disparity between processor and memory subsystems has been bridged in many existing large-scale scientific computers and microprocessors with the help of instruction buffers or instruction caches. In this paper we classify these buffers into traditional instruction buffers, conventional instruction caches and prefetch queues, detail their prominent features, and evaluate the performance of buffers in several existing systems, using trace driven simulation. We compare these schemes with a recently proposed queue-based instruction cache memory. An implementation independent performance metric is proposed for the various organizations and used for the evaluations. We analyze the simulation results and discuss the effect of various parameters such as prefetch threshold, bus width and buffer size on performance

No more business as usual: Agile and effective responses to emerging pathogen threats require open data and open analytics

The current state of much of the Wuhan pneumonia virus (severe acute respiratory syndrome coronavirus 2 [SARS-CoV-2]) research shows a regrettable lack of data sharing and considerable analytical obfuscation. This impedes global research cooperation, which is essential for tackling public health emergencies and requires unimpeded access to data, analysis tools, and computational infrastructure. Here, we show that community efforts in developing open analytical software tools over the past 10 years, combined with national investments into scientific computational infrastructure, can overcome these deficiencies and provide an accessible platform for tackling global health emergencies in an open and transparent manner. Specifically, we use all SARS-CoV-2 genomic data available in the public domain so far to (1) underscore the importance of access to raw data and (2) demonstrate that existing community efforts in curation and deployment of biomedical software can reliably support rapid, reproducible research during global health crises. All our analyses are fully documented at https://github.com/galaxyproject/SARS-CoV-2