转录组的组装Stingtie和Cufflinks Posted: 十月 18, 2017 Under: Transcriptomics By Kai no Comments 首先这两款软件都是用于基于参考基因组的转录组组装,当然也可用于转录本的定量.前者于2016年的 protocol上发表的转录组流程HISAT, StringTie and Ballgown后被广泛使用,后者则是老牌的RNA分析软件了.在算法上来说Stringtie使用的是流神经网络算法,Cufflinks则是吝啬算法:
Transcriptome assembly and differential expression analysis for RNA-Seq. Cufflinks assembles transcripts, estimates their abundances, and tests for differential expression and regulation in RNA-Seq samples. It accepts aligned RNA-Seq reads and assemb
Cole Trapnell said: there are three strategies: 1) merge bams and assemble in a single run of Cufflinks2) assemble each bam and cuffcompare them to get a combined.gtf3) assemble each bam and cuffmerge them to get a merged.gtf All three options work a
featureCounts真的很厉害. 常见的参数(没什么好说的,毕竟是固定的): -a -o input_file1 -F -t -g -Q -T 关键是以下几个参数怎么设置: -f # Perform read counting at feature level -O # Assign reads to all their overlapping meta-features -M # Multi-mapping reads will also be counted. --primary #
文献名:Quantitative mass spectrometry to interrogate proteomic heterogeneity in metastatic lung adenocarcinoma and validate a novel somatic mutation CDK12-G879V(利用定量质谱技术探究转移性肺腺瘤的蛋白质组异质性及验证新体细胞突变CDK12-G879V) 期刊名:Mol Cell Proteomics 发表时间:(2019年4月) IF:5.23
大家好,本周分享的是发表在MCP上的一篇关于鸟枪蛋白质组学中的错误率的文章,题目是Integrated identification and quantification error probabilities for shotgun proteomics,作者是瑞典皇家理工学院的Matthew The 和Lukas Kall. 鸟枪法蛋白质组学中蛋白的无标定量存在诸多误差.蛋白的鉴定就是其中的一个来源.然而人们经常忽略某些错误源,且在后续步骤中认为过滤得到的列表是完全正确的,这两个错误的累加很
