Sentence examples for datasets from different organisms from inspiring English sources

We analyzed various microarray datasets from different organisms and different microarray platforms (cDNA, Affymetrix).

The EuroDia database contains datasets from different organisms and different microarray platforms.

In this way, new RNA-seq datasets from different organisms are being deposited every month in public repositories.

We used a cross-species meta-analysis approach for the integration, at the gene level, of sixteen transcriptional datasets from different organisms (human, mouse, rat and yeast) and experimental platforms (Affymetrix, single and double channels spotted oligonucleotides).

By analyzing multiple ribosome profiling datasets from different organisms (including prokaryotes and eukaryotes), we report for the first time the organism-specific amino acids that significantly lead to ribosome stalling.

To ensure variety, we chose different types of datasets from different organisms: ChIP-Seq in Arabidopsis based on aborted microspores (AMS) pulldown, ChIP-Seq in yeast based on a nucleosome pulldown and four RNA-seq datasets from C2C12 mouse myoblasts where the samples have been prepared to have a mean fragment length of either 100 or 280.

Real-life datasets sampled from different organisms, including long-read datasets from Roche 454 sequencing platform and short-read datasets from Illumina sequencing platform, were used for comparative analysis of multiple aligners for computational performance evaluation.

It provides tools for annotating and simulating heterogeneous datasets, access to data from different organisms as a single data model, community sharing of resources including data and training material.

We have developed a dataset of protein-RNA complexes from different organisms with high sequence identity and identified the binding sites.

This paper presents a novel unsupervised algorithm to classify reads from different organisms in a metagenomic dataset, called BiMeta (i.e., A Binning algorithm for Metagenomic reads).

A plausible approach to improve the performance of such assemblers is to separate reads from different organisms present in a dataset before the assembly.