TRForest: a novel random forest-based algorithm for tRNA-derived fragment target prediction
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA783302 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR17028931_1.fastq.gz | Fastqsanger.gz | |||
| SRR17028931_2.fastq.gz | Fastqsanger.gz | |||
| SRR17028932_1.fastq.gz | Fastqsanger.gz | |||
| SRR17028932_2.fastq.gz | Fastqsanger.gz | |||
| SRR17028933_1.fastq.gz | Fastqsanger.gz |
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Project description:Transcriptional enhancers play critical roles in regulation of gene expression, but their identification has remained a challenge. Recently, it was shown that enhancers in the mammalian genome are associated with characteristic histone modification patterns, which have been increasingly exploited for enhancer identification. However, only a limited number of histone modifications have previously been investigated for this purpose, leaving the questions answered whether there exist an optimal set of histone modifications that could improve the enhancer prediction. Here, we address this issue by exploring a rich dataset produced by the human Epigenome Roadmap Project. Specifically, we examined genome-wide profiles of 24 histone modifications in human embryonic stem cells and fibroblasts, and developed a Random-Forest based algorithm to integrate histone modification profiles for identification of enhancers.As a training set, we used histone modification profiles at genome-wide binding sites of p300 in the two cell types identified using ChIP-seq. We show that this algorithm not only leads to more accurate and precise prediction of enhancers than previous methods, but also helps identify an optimal set of three chromatin marks for enhancer prediction. ChIP-Seq Analysis of p300 in hESC H1 and IMR90 cells. Sequencing was done on the Illumina Genome Analyzer II platform for the H1 data and Illumina HiSeq for IMR90.Data was mapped to hg18 using Bowtie.
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Project description:ObjectiveAlzheimer's disease (AD) is the most common type of dementia and the prevalence rapidly increased as the elderly population increased worldwide. In the contemporary model of AD, it is regarded as a disease continuum involving preclinical stage to severe dementia. For accurate diagnosis and disease monitoring, objective index reflecting structural change of brain is needed to correctly assess a patient's severity of neurodegeneration independent from the patient's clinical symptoms. The main aim of this paper is to develop a random forest (RF) algorithm-based prediction model of AD using structural magnetic resonance imaging (MRI).MethodsWe evaluated diagnostic accuracy and performance of our RF based prediction model using newly developed brain segmentation method compared with the Freesurfer's which is a commonly used segmentation software.ResultsOur RF model showed high diagnostic accuracy for differentiating healthy controls from AD and mild cognitive impairment (MCI) using structural MRI, patient characteristics, and cognitive function (HC vs. AD 93.5%, AUC 0.99; HC vs. MCI 80.8%, AUC 0.88). Moreover, segmentation processing time of our algorithm (<5 minutes) was much shorter than of Freesurfer's (6-8 hours).ConclusionOur RF model might be an effective automatic brain segmentation tool which can be easily applied in real clinical practice.
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