ArticleBioinformatics (Oxford, England)2013
SIBER: systematic identification of bimodally expressed genes using RNAseq data.
Article in Bioinformatics (Oxford, England), 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Bimodality in pan-cancer proteomics reveals new opportunities for biomarker discovery.Genome biology · 2026Article
- Complementary ROC-Derived Indices for Screening Improper Expression Profiles in RNA-Seq Differential Expression Analysis.Balkan medical journal · 2026Article
- Bimodal Genomic Approach Predicting Semaphorin 7A (SEMA7A) as Prognostic Biomarker in Adrenocortical Carcinoma.Cancers · 2025Article
- Gene expression variability in long-term survivors of childhood cancer and cancer-free controls in response to ionizing irradiation.Molecular medicine (Cambridge, Mass.) · 2023Article
- Neurocan expression associates with better survival and viral positivity in Merkel cell carcinoma.PloS one · 2023Article
- Modulating gene regulation function by chemically controlled transcription factor clustering.Nature communications · 2022Article
- Genome-wide cross-cancer analysis illustrates the critical role of bimodal miRNA in patient survival and drug responses to PI3K inhibitors.PLoS computational biology · 2022Article
- Single-cell analyses reveal increased intratumoral heterogeneity after the onset of therapy resistance in small-cell lung cancer.Nature cancer · 2020Article
- Single Cell Gene Co-Expression Network Reveals FECH/CROT Signature as a Prognostic Marker.Cells · 2019Article
- HNF1B Loss Exacerbates the Development of Chromophobe Renal Cell Carcinomas.Cancer research · 2017Article
- CHK1 Inhibition in Small-Cell Lung Cancer Produces Single-Agent Activity in Biomarker-Defined Disease Subsets and Combination Activity with Cisplatin or Olaparib.Cancer research · 2017Article
- Complex Sources of Variation in Tissue Expression Data: Analysis of the GTEx Lung Transcriptome.American journal of human genetics · 2016Article
- In-silico interaction-resolution pathway activity quantification and application to identifying cancer subtypes.BMC medical informatics and decision making · 2016Article
- Selecting Reliable mRNA Expression Measurements Across Platforms Improves Downstream Analysis.Cancer informatics · 2016Article
- SigFuge: single gene clustering of RNA-seq reveals differential isoform usage among cancer samples.Nucleic acids research · 2014Article
- The most informative spacing test effectively discovers biologically relevant outliers or multiple modes in expression.Bioinformatics (Oxford, England) · 2014Article
- Multiplatform single-sample estimates of transcriptional activation.Proceedings of the National Academy of Sciences of the United States of America · 2013Article
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4 authors.
Funding
Abstract
motivationIdentification of bimodally expressed genes is an important task, as genes with bimodal expression play important roles in cell differentiation, signalling and disease progression. Several useful algorithms have been developed to identify bimodal genes from microarray data. Currently, no method can deal with data from next-generation sequencing, which is emerging as a replacement technology for microarrays.
resultsWe present SIBER (systematic identification of bimodally expressed genes using RNAseq data) for effectively identifying bimodally expressed genes from next-generation RNAseq data. We evaluate several candidate methods for modelling RNAseq count data and compare their performance in identifying bimodal genes through both simulation and real data analysis. We show that the lognormal mixture model performs best in terms of power and robustness under various scenarios. We also compare our method with alternative approaches, including profile analysis using clustering and kurtosis (PACK) and cancer outlier profile analysis (COPA). Our method is robust, powerful, invariant to shifting and scaling, has no blind spots and has a sample-size-free interpretation. AVAILABILITY: The R package SIBER is available at the website http://bioinformatics.mdanderson.org/main/OOMPA:Overview.
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