RNA-Seq Blog Transcriptome Research & Industry News
Single-cell RNA-sequencing (scRNA-seq) has brought the study of the transcriptome to higher resolution and makes it possible for scientists to provide...
Read More »SC2P – two-phase differential expression analysis for single cell RNA-seq
Simultaneous transcriptomic profiling of different cell types
Transcriptomic changes induced in one cell type by another mediate many biological processes in the brain and elsewhere; however, achieving artifact-free physical separation of cell types to...
Read More »Integration of transcriptomic data and metabolic networks in cancer samples reveals highly significant prognostic power
Effective stratification of cancer patients on the basis of their molecular make-up is a key open challenge. Given the altered and heterogenous nature of cancer metabolism, we here propose to use the overall...
Read More »A robust methodology for assessing differential homeolog contributions to the transcriptomes of allopolyploids
Polyploidy has played a pivotal and recurring role in angiosperm evolution. Allotetraploids arise from hybridization between species and possess duplicated gene copies (homeologs) that serve redundant roles...
Read More »BGP – identifying gene-specific branching dynamics from single-cell data with a branching Gaussian process
High-throughput single-cell gene expression experiments can be used to uncover branching dynamics in cell populations undergoing differentiation through pseudotime methods. Researchers from...
Read More »Vex-seq – high-throughput identification of the impact of genetic variation on pre-mRNA splicing efficiency
Understanding the functional impact of genomic variants is a major goal of modern genetics and personalized medicine. Although many synonymous and non-coding variants act through altering the...
Read More »A mew machine learning-based framework for mapping uncertainty analysis in RNA-Seq read alignment and gene expression estimation
One of the main benefits of using modern RNA-Sequencing (RNA-Seq) technology is the more accurate gene expression estimations compared with previous generations of expression data, such as the microarray. However, numerous issues can result...
Read More »Using supervised learning methods for gene selection in RNA-Seq case-control studies
Whole transcriptome studies typically yield large amounts of data, with expression values for all genes or transcripts of the genome. The search for genes...
Read More »LncFinder – an integrated platform for long non-coding RNA identification
Discovering new long non-coding RNAs (lncRNAs) has been a fundamental step in lncRNA-related research. Nowadays, many machine learning-based tools have been developed for lncRNA identification. However, many methods predict lncRNAs using sequence-derived features alone, which tend to display unstable performances ...
Read More »LncPipe: A Nextflow-based pipeline for identification and analysis of long non-coding RNAs from RNA-Seq data
Recently, long noncoding RNA molecules (lncRNA) captured widespread attentions for its critical roles in diverse biological process and important implications in variety of human diseases and cancers...
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