Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

RNA seq analysis of murine whole colon using Takara SMART-Seq Stranded kit. Libraries were sequenced on an Illumina NovaSeq 6000 to an approximate sequencing depth of 60 million total reads

ABSTRACT: The intestinal microbiota fundamentally influences the development of a normal intestinal physiology and education and functioning of the mucosal immune system. The goal of this study is to analyze how the transcriptional profile of the murine colon can be influence by colonization of gnotobiotic mice with specific bacterial strains .

ORGANISM(S): Mus musculus

PROVIDER: GSE181502 | GEO | 2021/08/09

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Similar Datasets

Fourteen commensals restore immunocompetence in adult germfree mice via maturation of the intestinal vascular system

Project description:The intestinal microbiota influences the development of a normal intestinal physiology, education and functioning of the mucosal immune system. The goal of this study is to analyze how the transcriptional profile of the colonic endothelial cells is influence by colonization of gnotobiotic mice with specific bacterial strains . The goal of this study is to analyze how the transcriptional profile of the colonic endothelial cells is influence by colonization of gnotobiotic mice with specific bacterial strains .

2022-10-05 | GSE180907 | GEO

RNA seq analysis of murine whole colon using Takara SMART-Seq Stranded kit. Libraries were sequenced on an Illumina NovaSeq 6000 to an approximate sequencing depth of 60 million total reads

Project description:RNA seq analysis of murine whole colon using Takara SMART-Seq Stranded kit. Libraries were sequenced on an Illumina NovaSeq 6000 to an approximate sequencing depth of 60 million total reads

| PRJNA752275 | ENA

Phylogenetic Heatmaps Highlight Composition Biases in Sequenced Reads.

Project description:Due to advancements in sequencing technology, sequence data production is no longer a constraint in the field of microbiology and has made it possible to study uncultured microbes or whole environments using metagenomics. However, these new technologies introduce different biases in metagenomic sequencing, affecting the nucleotide distribution of resulting sequence reads. Here, we illustrate such biases using two methods. One is based on phylogenetic heatmaps (PGHMs), a novel approach for compact visualization of sequence composition differences between two groups of sequences containing the same phylogenetic groups. This method is well suited for finding noise and biases when comparing metagenomics samples. We apply PGHMs to detect noise and bias in the data produced with different DNA extraction protocols, different sequencing platforms and different experimental frameworks. In parallel, we use principal component analysis displaying different clustering of sequences from each sample to support our findings and illustrate the utility of PGHMs. We considered contributions of the read length and GC-content variation and observed that in most cases biases were generally due to the GC-content of the reads.

| S-EPMC5374381 | biostudies-literature

Sniper: improved SNP discovery by multiply mapping deep sequenced reads.

Project description:SNP (single nucleotide polymorphism) discovery using next-generation sequencing data remains difficult primarily because of redundant genomic regions, such as interspersed repetitive elements and paralogous genes, present in all eukaryotic genomes. To address this problem, we developed Sniper, a novel multi-locus Bayesian probabilistic model and a computationally efficient algorithm that explicitly incorporates sequence reads that map to multiple genomic loci. Our model fully accounts for sequencing error, template bias, and multi-locus SNP combinations, maintaining high sensitivity and specificity under a broad range of conditions. An implementation of Sniper is freely available at http://kim.bio.upenn.edu/software/sniper.shtml.

| S-EPMC3218843 | biostudies-literature

Local sequence and sequencing depth dependent accuracy of RNA-seq reads.

Project description:Many biases and spurious effects are inherent in RNA-seq technology, resulting in a non-uniform distribution of sequencing read counts for each base position in a gene. Therefore, a base-level strategy is required to model the non-uniformity. Also, the properties of sequencing read counts can be leveraged to achieve a more precise estimation of the mean and variance of measurement.In this study, we aimed to unveil the effects on RNA-seq accuracy from multiple factors and develop accurate modeling of RNA-seq reads in comparison. We found that the overdispersion rate decreased when sequencing depth increased on the base level. Moreover, the influence of local sequence(s) on the overdispersion rate was notable but no longer significant after adjusting the effect from sequencing depth. Based on these findings, we propose a desirable beta-binomial model with a dynamic overdispersion rate on the base-level proportion of sequencing read counts from two samples.The current study provides thorough insights into the impact of overdispersion at the position level and especially into its relationship with sequencing depth, local sequence, and preparation protocol. These properties of RNA-seq will aid in improvement of the quality control procedure and development of statistical methods for RNA-seq downstream analyses.

| S-EPMC5550947 | biostudies-other

Accurate local-ancestry inference in exome-sequenced admixed individuals via off-target sequence reads.

Project description:Estimates of the ancestry of specific chromosomal regions in admixed individuals are useful for studies of human evolutionary history and for genetic association studies. Previously, this ancestry inference relied on high-quality genotypes from genome-wide association study (GWAS) arrays. These high-quality genotypes are not always available when samples are exome sequenced, and exome sequencing is the strategy of choice for many ongoing genetic studies. Here we show that off-target reads generated during exome-sequencing experiments can be combined with on-target reads to accurately estimate the ancestry of each chromosomal segment in an admixed individual. To reconstruct local ancestry, our method SEQMIX models aligned bases directly instead of relying on hard genotype calls. We evaluate the accuracy of our method through simulations and analysis of samples sequenced by the 1000 Genomes Project and the NHLBI Grand Opportunity Exome Sequencing Project. In African Americans, we show that local-ancestry estimates derived by our method are very similar to those derived with Illumina's Omni 2.5M genotyping array and much improved in relation to estimates that use only exome genotypes and ignore off-target sequencing reads. Software implementing this method, SEQMIX, can be applied to analysis of human population history or used for genetic association studies in admixed individuals.

| S-EPMC3824118 | biostudies-literature

RepARK--de novo creation of repeat libraries from whole-genome NGS reads.

Project description:Generation of repeat libraries is a critical step for analysis of complex genomes. In the era of next-generation sequencing (NGS), such libraries are usually produced using a whole-genome shotgun (WGS) derived reference sequence whose completeness greatly influences the quality of derived repeat libraries. We describe here a de novo repeat assembly method--RepARK (Repetitive motif detection by Assembly of Repetitive K-mers)--which avoids potential biases by using abundant k-mers of NGS WGS reads without requiring a reference genome. For validation, repeat consensuses derived from simulated and real Drosophila melanogaster NGS WGS reads were compared to repeat libraries generated by four established methods. RepARK is orders of magnitude faster than the other methods and generates libraries that are: (i) composed almost entirely of repetitive motifs, (ii) more comprehensive and (iii) almost completely annotated by TEclass. Additionally, we show that the RepARK method is applicable to complex genomes like human and can even serve as a diagnostic tool to identify repetitive sequences contaminating NGS datasets.

| S-EPMC4027187 | biostudies-literature

Transcriptional Modulation of Genes by Gut Microbiota in Germfree and Conventional Mice

Project description:To characterize the effect of microbiota on global gene expression in the distal small intestine during postnatal gut development we employed mouse models with experimental colonization by intestinal microbiota. Using microarray analysis to assess global gene expression in ileal mucosa at the critical stage of intestinal development /maturation associated with weaning, and asking how expression is affected by microbial colonization In the study presented here, preweaned and postweaned GF, SPF mouse small intestinal total RNAs were used. Also, 3-week-old gnotobiotic mouse as well as GF mouse small intestinal RNAs were used.

2013-03-02 | E-GEOD-44788 | biostudies-arrayexpress

Differential gene expression in colon organoids isolated from germ-free, E.coli strains Nissle 1917 and O6K13 monoassociated, and conventionally reared mice

Project description:Commensal microbiota contribute to gut homeostasis and influence gene expression. Intestinal organoid culture closely represent intestinal epithelium and retain intestinal stem cells and dynamic recovery capabilities as well as all major cell types of the intestinal epithelium. We established organoid culture using colon crypts isolated from germ-free (GF), and gnotobiotic mice monocolonized either with the E.coli strain O6K13 (O) or Nissle 1917 strain (N). The expression profiles of these organoids were compared to the organoid culture isolated from conventionally reared (CR) mice in order to disclose genes differentially expressed in response to the change in the intestinal microflora composition.

2019-09-13 | E-MTAB-7723 | biostudies-arrayexpress

IM-TORNADO: a tool for comparison of 16S reads from paired-end libraries.

Project description:Motivation16S rDNA hypervariable tag sequencing has become the de facto method for accessing microbial diversity. Illumina paired-end sequencing, which produces two separate reads for each DNA fragment, has become the platform of choice for this application. However, when the two reads do not overlap, existing computational pipelines analyze data from read separately and underutilize the information contained in the paired-end reads.ResultsWe created a workflow known as Illinois Mayo Taxon Organization from RNA Dataset Operations (IM-TORNADO) for processing non-overlapping reads while retaining maximal information content. Using synthetic mock datasets, we show that the use of both reads produced answers with greater correlation to those from full length 16S rDNA when looking at taxonomy, phylogeny, and beta-diversity.Availability and implementationIM-TORNADO is freely available at http://sourceforge.net/projects/imtornado and produces BIOM format output for cross compatibility with other pipelines such as QIIME, mothur, and phyloseq.

| S-EPMC4266640 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data