RNA-Seq Blog Transcriptome Research & Industry News
Efficient integration of heterogeneous and increasingly large single-cell RNA sequencing data poses a major challenge for analysis and, in...
Read More »Integration of millions of transcriptomes using batch-aware triplet neural networks
SuperFreq – detecting copy number alterations in RNA-Seq using
Calling copy number alterations (CNAs) from RNA sequencing (RNA-Seq) is challenging, because of the marked variability in coverage across genes and...
Read More »ppcseq – probabilistic outlier identification for RNA sequencing generalized linear models
Relative transcript abundance has proven to be a valuable tool for understanding the function of genes in biological systems. For the differential analysis...
Read More »Automated Isoform Diversity Detector (AIDD) – a pipeline for investigating transcriptome diversity of RNA-seq data
As the number of RNA-seq datasets that become available to explore transcriptome diversity increases, so does the need for easy-to-use comprehensive computational workflows. Many available...
Read More »EDGE – Ensemble dimensionality reduction and feature gene extraction for single-cell RNA-seq data
Single-cell RNA sequencing (scRNA-seq) technologies allow researchers to uncover the biological states of a single cell at high resolution. For computational efficiency and easy visualization...
Read More »The most commonly used methods for differential expression analysis may yield too many false positive results
RNA-seq studies are growing in size and popularity. Researchers from INSERM University of Bordeaux provide evidence that the most commonly used methods for differential expression analysis (DEA) may...
Read More »I-Impute – a self-consistent method to impute single cell RNA sequencing data
Single-cell RNA-sequencing (scRNA-seq) is becoming indispensable in the study of cell-specific transcriptomes. However, in scRNA-seq techniques, only a small fraction of the genes are captured due to “dropout” events. These dropout events require...
Read More »Information-theory-based benchmarking and feature selection algorithm improve cell type annotation and reproducibility of single cell RNA-seq data analysis pipelines
Single cell RNA sequencing (scRNA-seq), which promises to enable the quantitative study of biological processes at the single cell level, are now a routine part of experimental practice. One key computational challenge in analyzing scRNA-seq data is cell type annotation. ...
Read More »rssNMF – A robust semi-supervised NMF model for single cell RNA-seq data
Single-cell RNA-sequencing (scRNA-seq) technology is a powerful tool to study organism from a single cell perspective and explore the heterogeneity between cells. Clustering is a fundamental step in scRNA-seq data analysis and it is the key to understand...
Read More »GeTallele – a method for analysis of DNA and RNA allele frequency distributions
Variant allele frequencies (VAF) are an important measure of genetic variation that can be estimated at single-nucleotide variant (SNV) sites. RNA and...
Read More »