Soil and rhizosphere metagenomes
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA422917 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR6394720_1.fastq.gz | Fastqsanger.gz | |||
| SRR6394720_2.fastq.gz | Fastqsanger.gz | |||
| SRR6394730_1.fastq.gz | Fastqsanger.gz | |||
| SRR6394730_2.fastq.gz | Fastqsanger.gz | |||
| SRR6394271_1.fastq.gz | Fastqsanger.gz |
Items per page: 5 1 - 5 of 51 |
Similar Datasets
Project description:Background: The soil environment is responsible for sustaining most terrestrial plant life on earth, yet we know surprisingly little about the important functions carried out by diverse microbial communities in soil. Soil microbes that inhabit the channels of decaying root systems, the detritusphere, are likely to be essential for plant growth and health, as these channels are the preferred locations of new root growth. Understanding the microbial metagenome of the detritusphere and how it responds to agricultural management such as crop rotations and soil tillage will be vital for improving global food production. Methods: The rhizosphere soils of wheat and chickpea growing under + and - decaying root were collected for metagenomics sequencing. A gene catalogue was established by de novo assembling metagenomic sequencing. Genes abundance was compared between bulk soil and rhizosphere soils under different treatments. Conclusions: The study describes the diversity and functional capacity of a high-quality soil microbial metagenome. The results demonstrate the contribution of the microbiome from decaying root in determining the metagenome of developing root systems, which is fundamental to plant growth, since roots preferentially inhabit previous root channels. Modifications in root microbial function through soil management, can ultimately govern plant health, productivity and food security.
2020-03-06 | GSE146427 | GEO
Project description:Normalization of high-throughput small RNA sequencing (sRNA-Seq) data is required to compare sRNA levels across different samples. Commonly used relative normalization approaches can cause erroneous conclusions due to fluctuating small RNA populations between tissues. We developed a set of sRNA spike-in oligonucleotides (sRNA spike-ins) that enable absolute normalization of sRNA-Seq data across independent experiments, as well as the genome-wide estimation of sRNA:mRNA stoichiometries when used together with mRNA spike-in oligonucleotides.
2017-07-19 | GSE98553 | GEO
Project description:A highly complex set of 264 molecular spikes, based on 11 unique spike sequences spanning different lengths (570 to 3070 nts) and GC contents (40-60%) was designed. In order to be able to precisely evaluate quantification over different expression levels, transcript lengths and GC contents, barcodes of 7 nucleotides in 2-fold abundance steps were cloned into each spike sequence (12 steps in duplicates; 24 barcodes per sequence) creating a standard curve for each spike sequence. To determine the molecular abundance of each of the 264 molecular spike-ins (i.e., the ‘ground truth’), we performed an exhaustive sequencing across the spike barcodes and spUMIs and determined the total complexity in the pool to be 76 million unique molecules
2022-02-16 | E-MTAB-11448 | biostudies-arrayexpress
Project description:This data set contains 1376 mass spectrometry reads from root, rhizosphere and leaf sample of Populus Trichocarpa, as well as associated controls. This metabolomics data set was collected as part of a larger campaign which complements the metabolomics data with metagenome sequencing, transcriptomics, and soil measurement data.
2022-12-12 | MSV000090886 | MassIVE
Project description:The most widely-used method for detecting genome-wide protein-DNA interactions is chromatin immunoprecipitation on tiling microarrays, commonly known as ChIP-chip. Here, we conducted the first objective analysis of tiling array platforms and analysis algorithms in a simulated ChIP-chip experiment. Mixtures of human genomic DNA and "spike-ins" comprised of nearly 100 human sequences at various concentrations were hybridized to four tiling array platforms by eight independent groups. Blind to the number of spike-ins, their locations, and the range of concentrations, each group made predictions of the spike-in locations. All commercial tiling array platforms performed well, although each platform and analysis algorithm had distinct performance and cost characteristics. Simple sequence repeats and genome redundancy tend to result in false positives on oligonucleotide platforms. The spike-in DNA samples and the resulting array data presented here provide a stable benchmark against which future ChIP platforms, protocol improvements, and analysis methods can be evaluated. Keywords: chip-ChIP simulation For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODEDataReleasePolicyFinal2008.pdf
2007-12-01 | GSE9732 | GEO
Project description:Chromatin immunoprecipitation followed by next-generation sequencing (ChIP-seq) is a key technique for mapping the distribution and relative abundance of histone posttranslational modifications (PTMs) and chromatin-associated factors across genomes. There is a perceived challenge regarding the ability to quantitatively plot ChIP-seq data, and as such, approaches making use of exogenous additives, or “spike-ins” have recently been developed. Relying on the fact that the IP step of ChIP-seq is a competitive binding reaction, we present a quantitative framework for ChIP-seq analysis that circumvents the need to modify standard sample preparation pipelines with spike-in reagents. We also introduce a visualization technique that, when paired with our formal developments, produces a much more rich characterization of sequencing data.
2020-09-28 | GSE132906 | GEO
Project description:This study aims to assess the reliability of spike-in normalization for analyzing single-cell RNA sequencing data. This is done by performing mixture experiments where two different sets of spike-in RNA (ERCC and SIRV) are added separately to a single mouse cell (416B or trophoblast stem cells (TSCs)), followed by generation of sequencing libraries using a modified version of the Smart-seq2 protocol. The aim is to measure the variance of the log-ratio of the total counts between the two spike-in sets. This will quantify how precisely the spike-in RNA was added to each well. As a control, addition was also performed with a premixed solution of both spike-ins, to quantify the variability in the log-ratios due to the experimental protocol. The same data can also be used to measure the well-to-well variability in the differences in behaviour between the two spike-in sets. The data contain four batches of libraries (block), using different batches of cells that were processed and sequenced separately. Each batch contains libraries with all three types of spike-in addition (ERCC+SIRV, SIRV+ERCC or Premixed). The 416B cells also contain a CBFB-MYH11 oncogene, which is expressed in half of the cells (Induced) and silent in the other half (Control).
2017-05-31 | E-MTAB-5522 | biostudies-arrayexpress
Project description:Normalization of RNA sequencing (RNA-seq) data for gene expression comparison is essential to ensure accurate gene expression quantification. It has been argued that samples with large differences in global expression level cannot be properly normalized without spike-in control RNAs, however, spike-in controls are expensive and not yet widely used. Here, we presented a spike-in independent quantitative RNA sequencing (siqRNA-seq) method, which uses reads from genome DNA as an internal reference to quantify gene expression level. We showed that siqRNA-seq profiles gene expression as traditional RNA-seq, but allows to identify different expression genes between samples with distinct mRNA content. We also showed siqRNA-seq enable us to assess the copy number of mRNA per cell without counting cells and adding spike-ins. Thus, siqRNA-seq provides a convenient and versatile means to quantitatively profile the mRNA landscape in cells.
2024-04-08 | GSE223145 | GEO