Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

ARK: Aggregate of Reads by K-Means for Estimation of Bacterial Community Composition

ABSTRACT: ARK: Aggregate of Reads by K-Means for Estimation of Bacterial Community Composition

PROVIDER: PRJEB9828 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	ERR957639_1.fastq.gz	Fastqsanger.gz
	ERR957639_2.fastq.gz	Fastqsanger.gz
	ERR957640_1.fastq.gz	Fastqsanger.gz
	ERR957640_2.fastq.gz	Fastqsanger.gz
	ERR957641_1.fastq.gz	Fastqsanger.gz

Items per page:

1 - 5 of 58

Similar Datasets

ARK: Aggregation of Reads by K-Means for Estimation of Bacterial Community Composition.

Project description:MotivationEstimation of bacterial community composition from high-throughput sequenced 16S rRNA gene amplicons is a key task in microbial ecology. Since the sequence data from each sample typically consist of a large number of reads and are adversely impacted by different levels of biological and technical noise, accurate analysis of such large datasets is challenging.ResultsThere has been a recent surge of interest in using compressed sensing inspired and convex-optimization based methods to solve the estimation problem for bacterial community composition. These methods typically rely on summarizing the sequence data by frequencies of low-order k-mers and matching this information statistically with a taxonomically structured database. Here we show that the accuracy of the resulting community composition estimates can be substantially improved by aggregating the reads from a sample with an unsupervised machine learning approach prior to the estimation phase. The aggregation of reads is a pre-processing approach where we use a standard K-means clustering algorithm that partitions a large set of reads into subsets with reasonable computational cost to provide several vectors of first order statistics instead of only single statistical summarization in terms of k-mer frequencies. The output of the clustering is then processed further to obtain the final estimate for each sample. The resulting method is called Aggregation of Reads by K-means (ARK), and it is based on a statistical argument via mixture density formulation. ARK is found to improve the fidelity and robustness of several recently introduced methods, with only a modest increase in computational complexity.AvailabilityAn open source, platform-independent implementation of the method in the Julia programming language is freely available at https://github.com/dkoslicki/ARK. A Matlab implementation is available at http://www.ee.kth.se/ctsoftware.

| S-EPMC4619776 | biostudies-literature

Amphiuma means

Project description:Amphiuma means

| PRJNA1013506 | ENA

Soil Aggregate

Project description:Soil Aggregate Raw sequence reads

| PRJNA580368 | ENA

Cyanobacterial Aggregate

Project description:Cyanobacterial Aggregate Metagenome Raw Sequence

| PRJNA664872 | ENA

soil aggregate

Project description:soil aggregate Raw sequence reads

| PRJNA610098 | ENA

Cyanobacterial Aggregate

Project description:Cyanobacterial Aggregate Metagenome Raw sequence reads

| PRJNA664299 | ENA

Argiope (Orb weaver) aggregate glue protein composition

Project description:Using proteomics, we documented the glue composition in two congeners that live in different environments, Argiope argentata (dry southwest US) and A. trifasciata (humid southeast US). The viscoelastic protein cores of A. argentata droplets comprised a smaller portion of droplet volume than did those of A. trifasciata and, as humidity increased, incorporated a smaller percentage of absorbed water. Argiope argentata core protein was many times stiffer and tougher than A. trifasciata protein. Each species’ glue included ~30 aggregate-expressed proteins, most of which (24 and 23, respectively) were homologous between the two species. However, the relative contribution and number of gene family members of each homologous group differed. For instance, the aggregate spidroins (AgSp1 and AgSp2) accounted for nearly half of the detected glue composition in A. argentata, but only 38% in A. trifasciata. Additionally, AgSp1, which has highly negatively charged regions, was ~2X as abundant as the positively charged AgSp2 in A. argentata but ~3X as abundant in A. trifasciata. As another example, A. argentata glue included 11 members of a newly discovered cysteine-rich gene family, versus 5 in A. trifasciata. The ability to selectively express different glue protein genes and/or to extrude their products at different rates provides a faster mechanism to evolve material properties than sequence evolution alone.

2023-07-13 | PXD037612 | Pride

Cyanobacterial Aggregate

Project description:Cyanobacterial Aggregate 16S rRNA Raw sequence reads

| PRJNA664292 | ENA

Soil Aggregate Metagenome

Project description:Soil Aggregate Metagenome Raw sequence reads

| PRJNA847587 | ENA

Cyanobacterial Aggregate Metatranscriptome

Project description:Cyanobacterial Aggregate Metatranscriptome Raw sequence reads

| PRJNA664620 | ENA