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| 1 | EpiFIT: functional interpretation of transcription factors based on combination of sequence and epigenetic information显示文摘Backgrounds Transcription factor is one of the most important regulators in the transcriptional process.Nevertheless,the functional interpretation of transcription factors is still a main challenge due to the poor performance of methods relating to regulatory regions to genes.Epigenetic information,such as chromatin accessibility,contains genome-wide knowledge about transcription regulation and thus may shed light on the functional interpretation of transcription factors.Methods:We propose EpiFIT(Epigenetic based Functional Interpretation of Transcription factors),a tool to infer functions of transcription factors from ChlP-seq data.Briefly,we adopt a variable distance rule to establish associations between regulatory regions and nearby genes.The associations are then filtered to ensure that the remaining regions and associated genes are co-open.Finally,GO enrichment is applied to all related genes and a ranking list of GO terms is provided as functional interpretation.Results:We first examined the chromatin openness correlation between regulatory regions and associated genes.The correlation can help EpiFIT purify regulatory region-gene associations.By evaluating EpiFIT on a set of real data,we demonstrated that EpiFIT outperforms other existing methods for precisely interpreting transcription factor functions.We further verify the efficiency of openness in interpretation and the ability of EpiFIT to build distal region-gene associations.Conclusion:EpiFIT is a powerful tool for interpreting the transcription factor functions.We believe EpiFIT will facilitate the functional interpretation of other regulatory elements,and thus open a new door to understanding the regulatory mechanism.Availability:The application is freely accessible at website:bioinfo.au.tsinghua.edu.cn/openness/EpiFIT/. | Shaoming Song Hongfei Cui Shengquan Chen Qiao Liu Rui Jiang | 2019 | Quantitative Biology2019，7，3： | 0 |
| 2 | The progress on the estimation of DNA methylation level and the detection of abnormal methylation显示文摘Background:DNA methylation is a key heritable epigenetic modification that plays a crucial role in transcriptional regulation and therefore a broad range of biological processes.The complex patterns of DNA methylation highlight the significance of the profiling the DNA methylation landscape.Results:In this review,the main high-throughput detection technologies are summarized,and then the three trends of computational estimation of DNA methylation levels were analyzed,especially the expanding of the methylation data with lower coverage.Furthermore,the detection methods of differential methylation patterns for sequencing and array data were presented.Conclusions:More and more research indicated the great importance of DNA methylation changes across different diseases,such as cancers.Although a lot of enormous progress has been made in understanding the role of DNA methylation,only few methylated genes or functional elements serve as clinically relevant cancer biomarkers.The bottleneck in DNA methylation advances has shifted from data generation to data analysis.Therefore,it is meaningful to develop machine learning models for computational estimation of methylation profiling and identify the potential biomarkers. | Shicai Fan Likun Wang Liang Liang Xiaohong Cao Jianxiong Tang Qi Tian | 2022 | Quantitative Biology2022，10，1： | 0 |
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