Tag Archives: R package

Data from RNA-seq experiments provide us with many new possibilities to gain insights into biological and disease mechanisms of cellular functioning. However, the reproducibility and robustness of RNA-seq data analysis results is often unclear. This is in part attributed to ...

High-throughput single-cell technologies provide an unprecedented view into cellular heterogeneity, yet they pose new challenges in data analysis and interpretation. In this protocol, researchers from Stanford University describe the use of Spanning-tree Progression Analysis of Density-normalized Events (SPADE), a density-based ...

The use of high-throughput RNA sequencing to predict dynamic operon structures in prokaryotic genomes has recently gained popularity in bioinformatics. Researchers from the Finnish Institute of Occupational Health provide the R implementation of a novel method that uses transcriptomic features ...

Using R at the Bench: Step-by-Step Data Analytics for Biologists, published by Cold Spring Harbor Laboratory Press and available in spiral bound hardcover and e-book formats, is a convenient bench-side handbook for biologists, designed as a handy reference guide for ...

Cell differentiation processes are achieved through a continuum of hierarchical intermediate cell-states that might be captured by single-cell RNA seq. Existing computational approaches for the assessment of cell-state hierarchies from single-cell data might be formalized under a general framework composed ...

Statistical methods development for differential expression analysis of RNA sequencing (RNA-seq) requires software tools to assess accuracy and error rate control. Since true differential expression status is often unknown in experimental datasets, artificially-constructed datasets must be utilized, either by generating ...

Next-generation sequencing platforms for measuring digital expression such as RNA-Seq are displacing traditional microarray-based methods in biological experiments. The detection of differentially expressed genes between groups of biological conditions has led to the development of numerous bioinformatics tools, but so ...

RNA sequencing analysis methods are often derived by relying on hypothetical parametric models for read counts that are not likely to be precisely satisfied in practice. Methods are often tested by analyzing data that have been simulated according to the ...

QuasR is a package for the integrated analysis of high-throughput sequencing data in R, covering all steps from read preprocessing, alignment and quality control to quantification. QuasR supports different experiment types (including RNA-seq, ChIP-seq and Bis-seq) and analysis variants (e.g. ...

There is a critical need for standard approaches to assess, report and compare the technical performance of genome-scale differential gene expression experiments. Here, an international team led by researchers at the NIST assess technical performance with a proposed standard ‘dashboard’ ...