SIBER: systematic identification of bimodally expressed genes using RNAseq data
1Department of Bioinformatics and Computational Biology, The University of Texas M. D. Anderson Cancer Center, Houston, TX, USA.
Bioinformatics (Oxford, England)
|January 11, 2013
Summary
We developed SIBER, a new method for identifying bimodally expressed genes from RNA sequencing data. This approach is robust and powerful for analyzing gene expression patterns in next-generation sequencing.
Area of Science:
- Genomics
- Bioinformatics
- Computational Biology
Background:
- Bimodally expressed genes are crucial in cell differentiation, signaling, and disease.
- Existing methods for identifying bimodal genes are not suitable for next-generation sequencing (NGS) data.
- NGS technologies are increasingly replacing microarrays for gene expression analysis.
Purpose of the Study:
- To develop an effective method for identifying bimodally expressed genes from RNA sequencing (RNAseq) data.
- To evaluate and compare different statistical models for analyzing RNAseq count data in the context of bimodality detection.
- To present SIBER (systematic identification of bimodally expressed genes using RNAseq data) as a novel solution.
Main Methods:
- Evaluation of candidate statistical models for RNAseq count data, including the lognormal mixture model.
- Performance comparison of identified bimodal genes through simulation and real data analysis.
- Benchmarking SIBER against alternative methods like Profile Analysis using Clustering and Kurtosis (PACK) and Cancer Outlier Profile Analysis (COPA).
Main Results:
- The lognormal mixture model demonstrated superior performance in identifying bimodal genes, showing high power and robustness across various scenarios.
- SIBER effectively identifies bimodally expressed genes from RNAseq data.
- SIBER exhibits robustness, power, invariance to data scaling, no blind spots, and a sample-size-free interpretation.
Conclusions:
- SIBER provides a powerful and reliable method for identifying bimodally expressed genes in RNAseq data.
- The lognormal mixture model is recommended for analyzing RNAseq count data to detect gene bimodality.
- SIBER is a valuable tool for researchers studying gene expression patterns in various biological contexts.
Related Concept Videos
02:24Ribosome Profiling
Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
The technique helps...
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
The technique helps...
03:21RNA-seq
RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while microarray-based...
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while microarray-based...