因此作者提出了DARTS,如下图所示:DARTS由两个成份组成,DNN(Deep neural network)基于外显子特异性的序列特征以及样本特异性的调控特征预测两个条件是不是差异可变剪切;BHT(Bayesian hypothesis testing)统计模型通过整合特定RNA-Seq数据的经验证据(由DNN预测得来)与可变剪切的先验概率推断差异可变剪切; 在训练的时候,首先...
(2012) Differential RNA sequencing (dRNA-Seq): deep- sequencing-based analysis of primary transcriptomes. In: Harbers,M. and Kahl,G. (eds) Tag-Based Next Generation Sequencing. Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim, Germany, pp. 109-121....
作者构建的DeepM6ASeq可以获取位置权重矩阵,从而转换成序列的motif,下图展示了部分motif结果。在阈值设置为0.05时,18,21,15个预测的motif分别对应到人类,鼠,斑马鱼已知motif。在这些识别到的motif中,Rbmx(即HNRNPG)是人和鼠中已知的m6A reader(能够识别m6A的蛋白质)。有趣的是,FMR1是人类中最近发现的m6A reader, De...
Here, we describe a differential RNA-seq (dRNA-seq) approach for the selective analysis of primary transcripts in the cell. The method is based on a differential exonuclease treatment of total RNA samples, which leads to depletion of processed RNAs, whereas primary transcripts get enriched in ...
BigRNA准确预测组织特异性RNA表达以及蛋白质和microRNA的结合位点 A. BigRNA训练示意图。BigRNA在70个人的基因组上进行了训练,预测了51个组织的总共2956个RNA-seq数据集,以及与RNA结合蛋白和microRNA位点相对应的693个数据集。B. 训练期间保留的基因的外显子区域的预测和测量的RNA-seq覆盖率之间的相关性分布(个体...
The aim of the present study was to investigate transcriptome differences in boar testis and liver tissues with divergent androstenone levels using RNA deep sequencing (RNA-Seq). The total number of reads produced for each testis and liver sample ranged from 13,221,550 to 33,206,723 and 12,...
RNA修饰是近几年一个热点,其中m6A 是一种普遍并且丰富的RNA修饰。在人类,老鼠和酵母中都有m6A的存在。检测m6A的实验方法主要是m6A-Seq 和MeRIP-Seq,但是这两种方法的分辨率低,新的技术miCLIP-Seq分辨率能够达到单碱基。今天介绍的DeepM6ASeq工具(https://github.com/rreybeyb/DeepM6ASeq)就是基于miCLIP-Seq实验数...
RNA-Seq data26,27as well as the method of normalization or statistical analysis has the strongest impact on performance28. Hence, benchmark studies showed that a combination of different methods in each step can be an efficient way to achieve more reliable results28. In the present study, to...
Quality control and preprocessing of single cell RNA-seq data Low-quality read pairs of single-cell RNA sequencing (scRNA-seq) data were filtered out if at least one end of the read pair met one of the following criteria: (1) ‘N’ bases account for ≥10% of the read length; (2) ...
图4 通过独立的乳腺癌和肝癌队列验证DeepProg亚型预测。HCC的RNA-Seq验证数据集:A LIRI(n = 230)和B GSE(n = 221);BRCA的验证数据集:C Patiwan(n = 159),D Metabric(n = 1981),E Anna(n = 249),和F Miller(n = 236)。 Fig. 5 (See legend on next page.) (See figure on previous page....