Recent advances in RNA sequencing (RNA-Seq) technology have offered unprecedented scope and resolution for transcriptome analysis. However, precise quantification of mRNA abundance and identification of differentially expressed genes are complicated due to biological and technical variations in RNA-Seq data. Researchers ...
Read More »Methods for Joint Imaging and RNA-seq Data Analysis
Emerging integrative analysis of genomic and anatomical imaging data which has not been well developed, provides invaluable information for the holistic discovery of the genomic structure of disease and has the potential to open a new avenue for discovering novel ...
Read More »Comparison of Computational Methods for Identification of Allele-Specific Expression based on Next Generation Sequencing Data
Allele-specific expression (ASE) studies have wide-ranging implications for genome biology and medicine. Whole transcriptome RNA sequencing (RNA-Seq) has emerged as a genome-wide tool for identifying ASE, but suffers from mapping bias favoring reference alleles. Two categories of methods are adopted ...
Read More »MIRPIPE – quantification of microRNAs in niche model organisms
MicroRNAs represent an important class of small non-coding RNAs regulating gene expression in eukaryotes. Present algorithms typically rely on genomic data to identify miRNAs and require extensive installation procedures. Niche model organisms lacking genomic sequences cannot be analyzed by such ...
Read More »The correlation coefficient alone is not sufficient to assess equality among sample replicates
Reliability and reproducibility are key metrics for gene expression assays. This report assesses the utility of the correlation coefficient in the analysis of reproducibility and reliability of gene expression data. The correlation coefficient alone is not sufficient to assess equality ...
Read More »Correction of gene expression data: performance-dependency on inter-replicate and inter-treatment biases
This report investigates for the first time the potential inter-treatment bias source of cell number for gene expression studies. Cell-number bias can affect gene expression analysis when comparing samples with unequal total cellular RNA content or with different RNA extraction ...
Read More »A Comparative Study of Techniques for Differential Expression Analysis on RNA-Seq Data
Recent advances in next-generation sequencing technology allow high-throughput cDNA sequencing (RNA-Seq) to be widely applied in transcriptomic studies, in particular for detecting differentially expressed genes between groups. Many software packages have been developed for the identification of differentially expressed genes ...
Read More »shRNA-seq data analysis with edgeR
Pooled short hairpin RNA sequencing (shRNA-seq) screens are becoming increasingly popular in functional genomics research, and there is a need to establish optimal analysis tools to handle such data. This open-source shRNA processing pipeline in edgeR provides a complete analysis ...
Read More »Long Intergenic Non-Coding RNAs (LincRNAs) Identified by RNA-Seq
In an attempt to find breast cancer tissue and respective adjacent normal tissue were studied for the expression of lincRNAs by RNA-seq. Among the 538 lincRNAs studied, 124 lincRNAs were exclusively expressed in cancer adjacent tissues and 62 lincRNAs were ...
Read More »Corset – differential gene expression analysis for de novo assembled transcriptomes
Next generation sequencing has made it possible to perform differential gene expression studies in non-model organisms. For these studies, the need for a reference genome is circumvented by performing de novo assembly on the RNA-seq data. However, transcriptome assembly produces ...
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