Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Motivation: Identification 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.#R##N##R##N#Results:  present SIBER (systematic identification of bimodally expressed genes using RNAseq data) for effectively identifying bimodally expressed genes from next-generation RNAseq data.  evaluate several candidate methods for modelling RNAseq count data and compare their performance in identifying bimodal genes through both simulation and real data analysis.  show that the lognormal mixture model performs best in terms of por and robustness under various scenarios.  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, porful, invariant to shifting and scaling, has no blind spots and has a sample-size-free interpretation.#R##N##R##N#Availability: The R package SIBER is available at the bsite http://bioinformatics.mdanderson.org/main/OOMPA:Overview.#R##N##R##N#Contact: gro.nosrednadm@sebmoock#R##N##R##N#Supplementary information: Supplementary data are available at Bioinformatics online.
['Pan Tong', 'Yong Chen', 'Xiao Su', 'Kevin R. Coombes']
I am writing a research paper on a idea SIBER: systematic identification of bimodally expressed genes using RNAseq data.
[{"from": "human", "value": "I am writing a research paper on a idea SIBER: systematic identification of bimodally expressed genes using RNAseq data."}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Motivation: Identification 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.#R##N##R##N#Results:  present SIBER (systematic identification of bimodally expressed genes using RNAseq data) for effectively identifying bimodally expressed genes from next-generation RNAseq data.  evaluate several candidate methods for modelling RNAseq count data and compare their performance in identifying bimodal genes through both simulation and real data analysis.  show that the lognormal mixture model performs best in terms of por and robustness under various scenarios.  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, porful, invariant to shifting and scaling, has no blind spots and has a sample-size-free interpretation.#R##N##R##N#Availability: The R package SIBER is available at the bsite http://bioinformatics.mdanderson.org/main/OOMPA:Overview.#R##N##R##N#Contact: gro.nosrednadm@sebmoock#R##N##R##N#Supplementary information: Supplementary data are available at Bioinformatics online."}]