The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2020 update

Jalili, Vahid; Afgan, Enis; Gu, Qiang; Clements, D. L.; Blankenberg, Daniel; Goecks, Jeremy; Taylor, James; Nekrutenko, Anton

doi:10.1093/nar/gkaa554

Cited by 111 publications

(112 citation statements)

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“…Raw sequencing data were uploaded to usegalaxy.org (Jalili et al, 2020). For analysis of amplicon from cDNA and exon 2 from genomic DNA, paired-end reads were joined with fastq-join tool, with the maximum percentage difference between matching segments set at 20% and the minimum length of matching segments set at 10.…”

Section: Methodsmentioning

confidence: 99%

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Signaling amplitude molds the Ras mutation tropism of urethane

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2021

Preprint

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RAS genes are commonly mutated in cancers yet despite many possible mutations, cancers have a ‘tropism’ towards a specific subset. As driver mutations, these patterns ostensibly originate from normal cells. High oncogenic RAS activity causes oncogenic stress and different oncogenic mutations can impart different levels of activity. Here we show that changing rare codons to common in the murine Kras gene to increase translation shifts tumors induced by the carcinogen urethane from arising from canonical Q61 to biochemically less active G12Kras driver mutations, despite the carcinogen still being biased towards generating Q61 mutations. Loss of p53 to blunt oncogenic stress partially reversed this effect, restoring Q61 mutations. Finally, transcriptional analysis revealed similar signaling amongst tumors driven by different mutations and Kras alleles. These finding suggest that the RAS mutation tropism of urethane is largely product of selection in normal cells for mutations promoting proliferation without causing oncogenic stress

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Section: Methodsmentioning

confidence: 99%

“…Raw data were uploaded Galaxy Cloudman (Jalili et al, 2020). Read 1 and read 2 were joined via PEAR pair-end read merger (Zhang et al, 2014b).…”

Section: Analysis Of Mds Datamentioning

confidence: 99%

Signaling amplitude molds the Ras mutation tropism of urethane

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2021

Preprint

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“…The Account Details tab allows you to change your public username for sharing lists with other users. It also provides options to generate an API key for programmatic access and to enter a URL to enable sending query outputs to a Galaxy instance (Jalili et al, 2020).…”

Section: Myminementioning

confidence: 99%

MaizeMine: A Data Mining Warehouse for the Maize Genetics and Genomics Database

et al. 2020

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MaizeMine is the data mining resource of the Maize Genetics and Genome Database (MaizeGDB; http://maizemine.maizegdb.org). It enables researchers to create and export customized annotation datasets that can be merged with their own research data for use in downstream analyses. MaizeMine uses the InterMine data warehousing system to integrate genomic sequences and gene annotations from the Zea mays B73 RefGen_v3 and B73 RefGen_v4 genome assemblies, Gene Ontology annotations, single nucleotide polymorphisms, protein annotations, homologs, pathways, and precomputed gene expression levels based on RNA-seq data from the Z. mays B73 Gene Expression Atlas. MaizeMine also provides database cross references between genes of alternative gene sets from Gramene and NCBI RefSeq. MaizeMine includes several search tools, including a keyword search, built-in template queries with intuitive search menus, and a QueryBuilder tool for creating custom queries. The Genomic Regions search tool executes queries based on lists of genome coordinates, and supports both the B73 RefGen_v3 and B73 RefGen_v4 assemblies. The List tool allows you to upload identifiers to create custom lists, perform set operations such as unions and intersections, and execute template queries with lists. When used with gene identifiers, the List tool automatically provides gene set enrichment for Gene Ontology (GO) and pathways, with a choice of statistical parameters and background gene sets. With the ability to save query outputs as lists that can be input to new queries, MaizeMine provides limitless possibilities for data integration and meta-analysis.

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“…The resulting alignment was used to build a Maximum-Likelihood phylogenetic tree using MEGA X with 100 bootstraps which was visualized and annotated using iTOL v5 (Kumar et al, 2018, Letunic & Bork, 2019. XRE-cupin homolog identification was performed by Reciprocal Best Blast Hit (RBBH) analysis (Cock et al, 2015) on the European Galaxy server (Jalili et al, 2020) using protein sequences from P. aeruginosa PAO1 against all protein sequences from the other 502 genomes.…”

Section: Plasmids and Genetic Manipulations Plasmids And Primers Arementioning

confidence: 99%

Transcription inhibitors with XRE DNA-binding and cupin signal-sensing domains drive metabolic diversification in Pseudomonas

Trouillon

Ragno

Simon

et al. 2020

Preprint

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Transcription factors (TFs) are instrumental in the bacterial response to new environmental conditions. They can act as direct signal sensors and subsequently induce changes in gene expression leading to physiological adaptation. Here, by combining RNA-seq and DAP-seq, we studied a family of eight TFs in Pseudomonas aeruginosa. This family, encompassing TFs with XRE-like DNA-binding and cupin signal-sensing domains, includes the metabolic regulators ErfA, PsdR and PauR and five so far unstudied TFs. The genome-wide delineation of their regulons identified 39 regulatory interactions with genes mostly involved in metabolism. We found that the XRE-cupin TFs are inhibitors of their neighboring genes, forming local, functional units encoding proteins with functions in condition-specific metabolic pathways. The phylogenetic analysis of this family of regulators across the Pseudomonas genus revealed a wide diversity of such metabolic regulatory modules and identified species with potentially higher metabolic versatility. Numerous uncharacterized XRE-cupin TFs were found near metabolism-related genes, illustrating the need of further systematic characterization of transcriptional regulatory networks in order to better understand the mechanisms of bacterial adaptation to new environments.IMPORTANCEBacteria of the Pseudomonas genus, including the major human pathogen P. aeruginosa, are known for their complex regulatory networks and high number of transcription factors, which contribute to their impressive adaptive ability. However, even in the most studied species, most of the regulators are still uncharacterized. With the recent advances in high-throughput sequencing methods, it is now possible to fill this knowledge gap and help understanding how bacteria adapt and thrive in new environments. By leveraging these methods, we provide an example of a comprehensive analysis of an entire family of transcription factors and bring new insights into metabolic and regulatory adaptation in the Pseudomonas genus.

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The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2020 update

Cited by 111 publications

References 0 publications

Signaling amplitude molds the Ras mutation tropism of urethane

Signaling amplitude molds the Ras mutation tropism of urethane

MaizeMine: A Data Mining Warehouse for the Maize Genetics and Genomics Database

Transcription inhibitors with XRE DNA-binding and cupin signal-sensing domains drive metabolic diversification in Pseudomonas

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