62 research outputs found
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
Single-molecule fluorescence multiplexing by multi-parameter spectroscopic detection of nanostructured FRET labels
Multiplexed, real-time fluorescence detection at the single-molecule level is
highly desirable to reveal the stoichiometry, dynamics, and interactions of
individual molecular species within complex systems. However, traditionally
fluorescence sensing is limited to 3-4 concurrently detected labels, due to low
signal-to-noise, high spectral overlap between labels, and the need to avoid
dissimilar dye chemistries. We have engineered a palette of several dozen
fluorescent labels, called FRETfluors, for spectroscopic multiplexing at the
single-molecule level. Each FRETfluor is a compact nanostructure formed from
the same three chemical building blocks (DNA, Cy3, and Cy5). The composition
and dye-dye geometries create a characteristic F\"orster Resonance Energy
Transfer (FRET) efficiency for each construct. In addition, we varied the local
DNA sequence and attachment chemistry to alter the Cy3 and Cy5 emission
properties and thereby shift the emission signatures of an entire series of
FRET constructs to new sectors of the multi-parameter detection space. Unique
spectroscopic emission of each FRETfluor is therefore conferred by a
combination of FRET and this site-specific tuning of individual fluorophore
photophysics. We show single-molecule identification of a set of 27 FRETfluors
in a sample mixture using a subset of constructs statistically selected to
minimize classification errors, measured using an Anti-Brownian ELectrokinetic
(ABEL) trap which provides precise multi-parameter spectroscopic measurements.
The ABEL trap also enables discrimination between FRETfluors attached to a
target (here: mRNA) and unbound FRETfluors, eliminating the need for washes or
removal of excess label by purification. We show single-molecule identification
of a set of 27 FRETfluors in a sample mixture using a subset of constructs
selected to minimize classification errors.Comment: 43 pages, 6 figures, 13 Supplementary figures, 3 Supplementary
tables, 5 Supplementary note
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
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Training Infrastructure as a Service
Background Hands-on training, whether in bioinformatics or other domains, often requires significant technical resources and knowledge to set up and run. Instructors must have access to powerful compute infrastructure that can support resource-intensive jobs running efficiently. Often this is achieved using a private server where there is no contention for the queue. However, this places a significant prerequisite knowledge or labor barrier for instructors, who must spend time coordinating deployment and management of compute resources. Furthermore, with the increase of virtual and hybrid teaching, where learners are located in separate physical locations, it is difficult to track student progress as efficiently as during in-person courses. Findings Originally developed by Galaxy Europe and the Gallantries project, together with the Galaxy community, we have created Training Infrastructure-as-a-Service (TIaaS), aimed at providing user-friendly training infrastructure to the global training community. TIaaS provides dedicated training resources for Galaxy-based courses and events. Event organizers register their course, after which trainees are transparently placed in a private queue on the compute infrastructure, which ensures jobs complete quickly, even when the main queue is experiencing high wait times. A built-in dashboard allows instructors to monitor student progress. Conclusions TIaaS provides a significant improvement for instructors and learners, as well as infrastructure administrators. The instructor dashboard makes remote events not only possible but also easy. Students experience continuity of learning, as all training happens on Galaxy, which they can continue to use after the event. In the past 60 months, 504 training events with over 24,000 learners have used this infrastructure for Galaxy training
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