Routine methods for assaying steady-state mRNA levels such as RNA-seq and micro-arrays are commonly used as readouts to study the role of transcription factors (TFs) in gene expression regulation. However, cellular RNA levels do not solely depend on activity of TFs and subsequent transcription by RNA polymerase II (Pol II), but are also affected by RNA turnover rate. Here, we demonstrate that i...

Background. Single-cell RNA-Seq is a new technique that can measure gene expression levels in individual cells. We would like to use single-cell RNA-seq data to learn genetic regulatory networks. This is a natural task for causal-model structurelearning algorithms, which aim to learn the causal relationships between the measured variables. Causal algorithms perform poorly in high dimensions unl...

High-density genetic maps are essential for high resolution mapping of quantitative traits. Here, we present a new genetic map for an Arabidopsis Bayreuth × Shahdara recombinant inbred line (RIL) population, built on RNA-seq data. RNA-seq analysis on 160 RILs of this population identified 30,049 single-nucleotide polymorphisms (SNPs) covering the whole genome. Based on a 100-kbp window SNP binn...

MOTIVATION RNA-seq has been widely used in transcriptome analysis to effectively measure gene expression levels. Although sequencing costs are rapidly decreasing, almost 70% of all the human RNA-seq samples in the gene expression omnibus do not have biological replicates and more unreplicated RNA-seq data were published than replicated RNA-seq data in 2011. Despite the large amount of single re...

MOTIVATION RNA-seq co-expression analysis is in its infancy and reasonable practices remain poorly defined. We assessed a variety of RNA-seq expression data to determine factors affecting functional connectivity and topology in co-expression networks. RESULTS We examine RNA-seq co-expression data generated from 1970 RNA-seq samples using a Guilt-By-Association framework, in which genes are as...

Motivation In T-cell lymphoma, malignant T cells arising from a founding clone share an identical T cell receptor (TCR) and can be identified by the over-representation of this TCR relative to TCRs from the patient's repertoire of normal T cells. Here, we demonstrate that TCR information extracted from RNA-seq data can provide a higher resolution view of peripheral T cell lymphomas (PTCLs) than...

MOTIVATION Gene finding in eukaryotic genomes is notoriously difficult to automate. The task is to design a work flow with a minimal set of tools that would reach state-of-the-art performance across a wide range of species. GeneMark-ET is a gene prediction tool that incorporates RNA-Seq data into unsupervised training and subsequently generates ab initio gene predictions. AUGUSTUS is a gene fin...

Next-generation sequencing (NGS) technologies-based transcriptomic profiling method often called RNA-seq has been widely used to study global gene expression, alternative exon usage, new exon discovery, novel transcriptional isoforms and genomic sequence variations. However, this technique also poses many biological and informatics challenges to extracting meaningful biological information. The...

In recent years, RNA sequencing (RNA-seq) has become a very widely used technology for profiling gene expression. One of the most common aims of RNA-seq profiling is to identify genes or molecular pathways that are differentially expressed (DE) between two or more biological conditions. This article demonstrates a computational workflow for the detection of DE genes and pathways from RNA-seq da...

MOTIVATION High-throughput RNA sequencing (RNA-seq) is now the standard method to determine differential gene expression. Identifying differentially expressed genes crucially depends on estimates of read-count variability. These estimates are typically based on statistical models such as the negative binomial distribution, which is employed by the tools edgeR, DESeq and cuffdiff. Until now, the...