Explore the vast range of titles available.

Galaxy provides an accessible platform where multi-step data analysis workflows integrating disparate software can be run, even by researchers with limited programming expertise.  Applications of such sophisticated workflows are many, including those which integrate software from different 'omic domains (e.g. genomics, proteomics, metabolomics). In these complex workflows, intermediate outputs are often generated as tabular text files, which must be transformed into customized formats which are compatible with the next software tools in the pipeline.  Consequently, many text manipulation steps are added to an already complex workflow, overly complicating the process and decreasing usability, especially for non-expert bench researchers focused on obtaining results.  In some cases, limitations to existing text manipulation are such that desired analyses can only be carried out using highly sophisticated processing steps beyond the reach of most users.  As a solution, we have developed the Query Tabular Galaxy tool, which leverages a SQLite database generated from tabular input data.  This database can be queried and manipulated to produce transformed and customized tabular outputs compatible with downstream processing steps.  Regular expressions can also be utilized for even more sophisticated manipulations, such as find and replace and other filtering actions.  Using several Galaxy-based multi-omic workflows as an example, we demonstrate how the Query Tabular tool dramatically streamlines and simplifies the creation of multi-step analyses, efficiently enabling complicated textual manipulations and processing.  This tool should find broad utility for users of the Galaxy platform seeking to develop and use sophisticated workflows involving text manipulation on tabular outputs.

Galaxy provides an accessible platform where multi-step data analysis workflows integrating disparate software can be run, even by researchers with limited programming expertise. Applications of such sophisticated workflows are many, including those which integrate software from different 'omic domains (e.g. genomics, proteomics, metabolomics). In these complex workflows, intermediate outputs are often generated as tabular text files, which must be transformed into customized formats which are compatible with the next software tools in the pipeline. Consequently, many text manipulation steps are added to an already complex workflow, overly complicating the process. In some cases, limitations to existing text manipulation are such that desired analyses can only be carried out using highly sophisticated processing steps beyond the reach of even advanced users and developers. For users with some SQL knowledge, these text operations could be combined into single, concise query on a relational database. As a solution, we have developed the Query Tabular Galaxy tool, which leverages a SQLite database generated from tabular input data. This database can be queried and manipulated to produce transformed and customized tabular outputs compatible with downstream processing steps. Regular expressions can also be utilized for even more sophisticated manipulations, such as find and replace and other filtering actions. Using several Galaxy-based multi-omic workflows as an example, we demonstrate how the Query Tabular tool dramatically streamlines and simplifies the creation of multi-step analyses, efficiently enabling complicated textual manipulations and processing. This tool should find broad utility for users of the Galaxy platform seeking to develop and use sophisticated workflows involving text manipulation on tabular outputs.

Down syndrome (DS) is the most common form of inherited intellectual disability caused by trisomy of chromosome 21, presenting with intellectual impairment, craniofacial abnormalities, cardiac defects, and gastrointestinal disorders. The Ts65Dn mouse model replicates many abnormalities of DS. We hypothesized that investigation of the cerebral cortex of fluoxetine-treated trisomic mice may provide proteomic signatures that identify therapeutic targets for DS. Subcellular fractionation of synaptosomes from cerebral cortices of age- and brain-area-matched samples from fluoxetine-treated vs. water-treated trisomic and euploid male mice were subjected to HPLC-tandem mass spectrometry. Analysis of the data revealed enrichment of trisomic risk genes that participate in regulation of synaptic vesicular traffic, pre-synaptic and post-synaptic development, and mitochondrial energy pathways during early brain development. Proteomic analysis of trisomic synaptic fractions revealed significant downregulation of proteins involved in synaptic vesicular traffic, including vesicular endocytosis (CLTA, CLTB, CLTC), synaptic assembly and maturation (EXOC1, EXOC3, EXOC8), anterograde axonal transport (EXOC1), neurotransmitter transport to PSD (SACM1L), endosomal-lysosomal acidification (ROGDI, DMXL2), and synaptic signaling (NRXN1, HIP1, ITSN1, YWHAG). Additionally, trisomic proteomes revealed upregulation of several trafficking proteins, involved in vesicular exocytosis (Rab5B), synapse elimination (UBE3A), scission of endocytosis (DBN1), transport of ER in dendritic spines (MYO5A), presynaptic activity-dependent bulk endocytosis (FMR1), and NMDA receptor activity (GRIN2A). Chronic fluoxetine treatment of Ts65Dn mice rescued synaptic vesicular abnormalities and prevented abnormal proteomic changes in adult Ts65Dn mice, pointing to therapeutic targets for potential treatment of DS.