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Additional file 1 of Machine learning predicts nucleosome binding modes of transcription factors

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数据链接：

https://figshare.com/articles/dataset/Additional_file_1_of_Machine_learning_predicts_nucleosome_binding_modes_of_transcription_factors/14344378 数据链接链接失效反馈

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资源简介：

Additional file 1. Supplementary Tables S1 to S13.

应用场景：

创建时间：

2021-03-30

序列基序分析

转录因子预测

Rank of the “correct” motif in the output of various programs. “Target” refers to data sets defined in [3]. “Ref.” gives the accession number in the JASPAR or miRbase database, or the target consensus

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Classification results on the H1-hESC test data set.

基序发现

转录因子预测

We display the sensitivity (for specificity of 99%) on the independent test data set . Here we pick the optimal model from the cross validation experiment (green) and compare it with the PWM model (bl

NIAID Data Ecosystem60

Mnase-Seq study of Arabidopsis thaliana mutant chr5

植物表观遗传学

核小体定位

Comparative study of nuclesome occupancy in Arabidopsis thaliana mutant chr5 and wild type strain. Mnase digest of chromatin from wt and chr5 mutants, library construction of protected DNA, Illumina S

NIAID Data Ecosystem50

The correlation coefficients between four nucleotides and the nucleosome occupancy across five organisms.

核小体定位

DNA序列特征关联

The correlation coefficients between four nucleotides and the nucleosome occupancy across five organisms.

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Nucleosomal signatures impose nucleosome positioning in coding and non-coding sequences in the genome. Nucleosomal signatures impose nucleosome positioning in coding and non-coding sequences in the genome

核小体定位

染色质序列决定

In the yeast genome, a large proportion of nucleosomes occupy well-defined positions. While the contribution of chromatin remodelers and DNA binding proteins to maintain this organization is well esta

NIAID Data Ecosystem60

Additional file 1 of Machine learning predicts nucleosome binding modes of transcription factors

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