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Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

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Meiotic crossover landscape within RAC1 disease resistance gene

ABSTRACT: To study recombination at the fine-scale, we used high-throughput sequencing of 300 to 1,000 crossovers within the RAC1 R gene hotspot. This revealed focused intragenic crossovers, overlapping exons encoding the TIR, NBS and LRR domains. To examine the role of recombination pathways, we repeated this experiment in recq4a recq4b, fancm and recq4a recq4b fancm mutants. Finally, in order to investigate how varying patterns of interhomolog divergence influence local patterns of crossover frequency, we repeated RAC1 pollen typing sequencing in different F1 hybrids.

INSTRUMENT(S): NextSeq 500

ORGANISM(S): Arabidopsis thaliana

SUBMITTER: Heidi Serra

PROVIDER: E-MTAB-6333 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Meiotic crossover hotspots and coldspots within Arabidopsis disease resistance genes

Project description:To study recombination at the fine-scale we used high-throughput sequencing to analyse >1,000 crossovers within the RAC1 R gene hotspot. This revealed focused intragenic crossovers, overlapping exons encoding the TIR, NBS and LRR domains (RAC1 pollen typing sequencing). To analyse chromatin structure we performed micrococcal nuclease digestion of wild type (Col-0) chromatin and gel purified the resulting ~150 bp mononucleosomal DNA band. This DNA was used to generate a library and paired-end sequencing performed (MNase-seq)

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Gene expression in somatotropic pituitary tumors

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Project description:Many environmental, genetic, and epigenetic factors are known to affect the frequency and positioning of meiotic crossovers (COs). Suppression of COs by large, cytologically visible inversions and translocations has long been recognized, but relatively little is known about how smaller structural variants (SVs) affect COs. To examine fine-scale determinants of the CO landscape, including SVs, we used a rapid, cost-effective method for high-throughput sequencing to generate a precise map of over 17,000 COs between the Col-0 and Ler accessions of Arabidopsis thaliana. COs were generally suppressed in regions with SVs, but this effect did not depend on the size of the variant region, and was only marginally affected by the variant type. CO suppression did not extend far beyond the SV borders, and CO rates were slightly elevated in the flanking regions. Disease resistance gene clusters, which often exist as SVs, exhibited high CO rates at some loci, but there was a tendency toward depressed CO rates at loci where large structural differences exist between the two parents. Our high-density map also revealed in fine detail how CO positioning relates to genetic (DNA motifs) and epigenetic (chromatin structure) features of the genome. We conclude that suppression of COs occurs over a narrow region spanning large and small-scale SVs, representing influence on the CO landscape in addition to sequence and epigenetic variation along chromosomes.

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EMpire: Deep isoform level integration of the transcriptome, translatome, and proteome

Project description:In this study, we make used of mRNA-seq and its ability to reliably quantify isoforms, integrating this data with ribosome profiling and LC-MS/MS, to assign ribosome footprints and peptides at the isoform level. We leverage the principle that most cell types, and even tissues, predominantly express a single principal isoform to set isoform-level mRNA-seq quantifications as priors to guide and improve allocation of footprints or peptides to isoforms. Through tightly integrated mRNAseq, ribosome footprinting and/or LC-MS/MS proteomics we demonstrate that a principal isoform can be identified in over 80% of gene products in homogenous HEK293 cell culture and over 70% of proteins detected in complex human brain tissue. Defining isoforms in experiments with matched RNA-seq and translatomic/proteomic data increases the functional relevance of such datasets and will further broaden our understanding of multi-level control of gene expression. In this PRIDE submission you will find the raw files for the HEK293 cell proteomics. Files for the human brain proteomics can be found at PXD005445. We have also uploaded a zip file that contains the input files for our HEK293 cell analysis, and the isoform level output files – there is a separate folder within the zip files for these. The data used to create the manuscript figures is in the Rdata file. Code for assigning peptides and footprints to isoforms can be found on Github here: https://github.com/rkitchen/EMpire

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2016-04-18 | PXD003271 | Pride

Microbial life in permafrost: predictions of microbial response to natural thaw using a multi-omics approach

Project description:Over 20% of Earth’s terrestrial surface is underlain by permafrost that represents one of the largest terrestrial carbon pools, with an estimated ~1700 Pg of carbon (C) contained in the upper 3 m of permafrost. Models estimate that C release from thawing permafrost might represent the largest new transfer of C from the biosphere to the atmosphere as the climate warms. Here we investigated microbial community phylogeny, genetic functional potential gene expression, and protein production patterns along a natural thaw gradient, including permafrost, the seasonally thawed active layer and nearby thawed thermokarst bog, using a combination of molecular “omics” approaches: metagenomics (MG), metatranscriptomics (MT) and metaproteomics (MP). Highlights from these analyses reveal energy yielding microbial processes and potential strategies for microbial survival in permafrost soils, and linkages between biogeochemical process rates and –omics measurements. The results provide new knowledge about microbial life and activity potential in permafrost, the potential importance of iron reduction as a survival strategy under frozen conditions in mineral soils, and the importance of methanogenesis following thaw. The multi-omics strategy demonstrated here enables better mechanistic understanding of the ecological strategies utilized by soil microbial communities in response to climate change. Associated metagenomics data available at the EBI Metagenomics portal under the accession number <a href="https://www.ebi.ac.uk/metagenomics/projects/SRP052575">SRP052575</a>.

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