Project description:MAGE-seq amplicon data from the paper RNA structural determinants of optimal codons revealed by MAGE-seq in Cell Systems 2016 by Kelsic, Chung, Cohen, Park, Wang & Kishony. Data contains read counts for PCR amplicons of the Escherichia coli gene infA: 1) Single codon mutants tiling along the entire gene, with timepoints from growth doublings in rich and minimal medias. 2) Codon pair mutants for positions at the beginning of the gene with timepoints for growth doublings in rich media. 3) Mutations in a hairpin of the 5' UTR for growth in rich media.
Project description:As part of the EcoToxChip project, 49 distinct exposure studies were conducted on three lab model species (Japanese quail, fathead minnow, African clawed frog) and three ecologically relevant species (double crested cormorant, rainbow trout, northern leopard frog), at multiple life stages (embryo, adult), exposed to eight chemicals of environmental concern (ethinyl estradiol-EE2, hexabromocyclododecane-HBCD, lead-Pb, selenomethionine-SeMe, 17β trenbolone-TB, chlorpyrifos-CPF, fluoxetine-FLX, and benzo [a] pyrene-BaP. Whole transcriptome analyses were conducted on these samples resulting in a rich RNA seq dataset covering various species, life stages and chemicals, which is one of the largest purposeful complications of RNA seq data within ecotoxicology. Recently, a unified bioinformatics platform of relevance to ecotoxicology, EcoOmicsAnalyst and ExpressAnalyst, was developed to facilitate RNA Seq analysis of non-model species lacking a reference transcriptome. The platform uses the Seq2Fun algorithm to map RNA-seq reads from eukaryotic species to an ortholog database comprised of protein sequences from >600 eukaryotic species (EcoOmicsDB) with a translated search. The availability of these tools presents a unique opportunity to examine the EcoToxChip RNA Seq dataset for cross species comparisons. This work shows the potential of the EcoOmicsAnalyst and Seq2Fun platform to facilitate fast and simple analysis of RNA Seq datasets from non-model organisms with unannotated genomes and conduct comparative transcriptomic analysis across various species and life stages for cross-species extrapolation.
Project description:Nitrate-reducing iron(II)-oxidizing bacteria are widespread in the environment contribute to nitrate removal and influence the fate of the greenhouse gases nitrous oxide and carbon dioxide. The autotrophic growth of nitrate-reducing iron(II)-oxidizing bacteria is rarely investigated and poorly understood. The most prominent model system for this type of studies is enrichment culture KS, which originates from a freshwater sediment in Bremen, Germany. To gain insights in the metabolism of nitrate reduction coupled to iron(II) oxidation under in the absence of organic carbon and oxygen limited conditions, we performed metagenomic, metatranscriptomic and metaproteomic analyses of culture KS. Raw sequencing data of 16S rRNA amplicon sequencing, shotgun metagenomics (short reads: Illumina; long reads: Oxford Nanopore Technologies), metagenome assembly, raw sequencing data of shotgun metatranscriptomes (2 conditions, triplicates) can be found at SRA in https://www.ncbi.nlm.nih.gov/bioproject/PRJNA682552. This dataset contains proteomics data for 2 conditions (heterotrophic and autotrophic growth conditions) in triplicates.