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The concept of gene co-expression networks was first introduced by Butte and Kohane in 1999 as relevance networks. [6] They gathered the measurement data of medical laboratory tests (e.g. hemoglobin level ) for a number of patients and they calculated the Pearson correlation between the results for each pair of tests and the pairs of tests which showed a correlation higher than a certain level ...
recursiveCorPlot Correlation based clustering for RNA-seq data (+ ggplot corrplot-like interface - R-package: recursiveCorPlot). [66] RNAeXpress Can be run with Java GUI or command line on Mac, Windows, and Linux. It can be configured to perform read counting, feature detection or GTF comparison on mapped rnaseq data.
RNA-Seq can also be used to determine exon/intron boundaries and verify or amend previously annotated 5' and 3' gene boundaries. Recent advances in RNA-Seq include single cell sequencing, bulk RNA sequencing, [6] 3' mRNA-sequencing, in situ sequencing of fixed tissue, and native RNA molecule sequencing with single-molecule real-time sequencing. [7]
WGCNA is widely used in neuroscientific applications, e.g. [19] [20] and for analyzing genomic data including microarray data, [21] single cell RNA-Seq data [22] [23] DNA methylation data, [24] miRNA data, peptide counts [25] and microbiota data (16S rRNA gene sequencing). [26] Other applications include brain imaging data, e.g. functional MRI ...
DESeq2 is a software package in the field of bioinformatics and computational biology for the statistical programming language R.It is primarily employed for the analysis of high-throughput RNA sequencing (RNA-seq) data to identify differentially expressed genes between different experimental conditions.
Pearson's correlation coefficient is the covariance of the two variables divided by the product of their standard deviations. The form of the definition involves a "product moment", that is, the mean (the first moment about the origin) of the product of the mean-adjusted random variables; hence the modifier product-moment in the name.
Molecular sequence and structure data of DNA, RNA, and proteins, gene expression profiles or microarray data, metabolic pathway data are some of the major types of data being analysed in bioinformatics. Among them sequence data is increasing at the exponential rate due to advent of next-generation sequencing technologies.
Ab Initio gene prediction is an intrinsic method based on gene content and signal detection. Because of the inherent expense and difficulty in obtaining extrinsic evidence for many genes, it is also necessary to resort to ab initio gene finding, in which the genomic DNA sequence alone is systematically searched for certain tell-tale signs of protein-coding genes.
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