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Amber的分子动力学模拟 (转贴）

amber进行MD的步骤如下：PDB文件需要去掉结晶水、氢和金属离子（非必须）。网上教程：http://enzyme.fbb.msu.ru/Tutorials/

金属酶的模拟：http://ambermd.org/tutorials/advanced/tutorial1_orig/ 蛋白和配体的模拟：

1：分子系统的准备

命令如下： tleap //进入leap source leaprc.ff99SB //加载蛋白力场 source leaprc.gaff //如果有小分子加载小分子力场 loadamberparams lig.frcmod //特殊情况还要加载其他特殊的力场参数 mol = loadpdb protein.pdb //定义mol变量并加载蛋白分子 bond mol.1.SG mol.6.SG //如果要二硫键。 check mol //检查蛋白 saveamberparm mol protein_vac.top protein_vac.crd //生成真空拓扑、坐标文件 solvatebox mol TIP3PBOX 10.0 //此为方形，八面体用solvateoct charge mol //检测系统电荷

R pheatmap

> library(caTools); > library(bitops); > library(grid); > data=read.csv(“/home/shenzy/Desktop/R/Bac.heatmap1.csv”) > data=read.csv(“/home/shenzy/Desktop/R/Bac.heatmap1.2.csv”) > View(data) > data=read.csv(“/home/shenzy/Desktop/R/Bac.heatmap1.2.csv”,sep=”\t”) > View(data) > row.names(data) <- data$X.OTU.ID; > View(data) > data_matrix<-data[,2:15] > View(data_matrix) > data_matrix<-data[,2:14] > View(data_matrix) > View(data) > data_matrix<-data[,1:14] > View(data_matrix) > library(pheatmap) > data_matrix[is.na(data_matrix)]<-1 > View(data_matrix) > data_log10<-log10(data_matrix) > View(data_log10) > data_log2<-log2(data_matrix) > View(data_log2) > pheatmap(data_log2,fontsize=9, fontsize_row=6) > pheatmap(data_log2, […]

graphlan a good tool for draw circle picture with tree in it

#!/bin/sh graphlan_annotate.py hmptree.xml hmptree.annot.xml –annot annot.txt graphlan.py hmptree.annot.xml hmptree.png –dpi 150 –size 14

0.9.5.tar

Batch download protein sequences from CMR (comprehensive microbial resource)

NCBI 有时批量下载的protein sequence会有不一致时，可以从以下资源数据库下载（eg, eth195)

http://cmr.jcvi.org/cgi-bin/CMR/shared/MakeFrontPages.cgi?page=batchdownload

Circos for comparative genomes

Circos: nucmer –prefix=refBAV1_qryVScds Dehalococcoides_BAV1.fasta Dehalococcoides_VS.cds.fasta show-tiling -i 80 -c refBAV1_qryVScds.delta > refBAV1_qryVScds.tiling awk -F ” ” ‘{print “chr1″ “\t” $1 “\t” $2}’ refBAV1_qryVScds.tiling > Dehalococcoidessp.VScds.gene_tableBAV1.txt

shenzy@shenzy-ubuntu:/winxp_disk2/shenzy/circos/circos-tutorials-0.62/tutorials/8/1$ perl ../../../../circos-0.62-1/bin/circos -conf circos.conf

shenzy@shenzy-ubuntu:/winxp_disk2/shenzy/circos/circos-tutorials-0.62/tutorials/8/1$ ll total 16001 -rwxrwxrwx 1 root root 528519 2012-12-27 16:11 circos1.png -rwxrwxrwx 1 root root 1065925 2012-12-27 14:33 circos2.png -rwxrwxrwx 1 root root 1230394 […]

How to measure codon usage bias

Codon adaptation index (CAI) is one of them. To examine the CAI value of a gene, a reference table of RSCU (relative synonymous codon usage) values for highly expressed genes is compiled.

A software call CodonW, you can download it from: http://codonw.sourceforge.net/. There is also a PhD thesis associated to it.

shenzy@shenzy-ubuntu:~/Downloads/CondonW/codonW$ codonw input.dat -all_indices […]

Using prcomp/princomp for PCA in R （二）

###############################

PCA ############################### install.packages(“vegan”) library(vegan)

> STpcoa<-read.table(file=”bactera_16s_final.subsample.phylip.tre1.weighted.phylip.pcoa.axes”, header=T,row.names=1) > STpcoa axis1 axis2 axis3 axis4 Cellulose -0.020878 -0.234601 0.167454 0 Foodwaste -0.234592 0.221741 0.085802 0 Sludge 0.368882 0.100725 -0.010570 0 Xylan -0.113413 -0.087865 -0.242686 0 >pl.STpcoa<-princomp(STpcoa) > summary(pl.STpcoa) Importance of components: Comp.1 Comp.2 Comp.3 Comp.4 Standard deviation 0.2260563 0.1746944 0.1536319 0 Proportion of Variance 0.4856521 0.2900347 […]

Using prcomp/princomp for PCA in R （一）

Difference between prcomp and princomp:

‘princomp’ can only be used with more units than variables”

prcomp是基于SVD分解（svd()函数，princomp是基于特征向量eigen()函数)

Good video source:

http://www.youtube.com/watch?v=oZ2nfIPdvjY

http://www.youtube.com/watch?v=I5GxNzKLIoU&feature=relmfu

http://www.planta.cn/forum/viewtopic.php?t=16754&highlight=%D3%EF%D1%D4

###########################################

以下所有代码包括练习数据，都可在R平台上直接运行。

#主成分分析和主成分回归主成分分析的思想是Pearson 1901年提出的，Hotelling 1933进一步发展在R中，进行主成分分析用到princomp() 函数

用法 princomp(x, cor = FALSE, scores = TRUE, covmat = NULL, subset = rep(TRUE, nrow(as.matrix(x))), …)

# 分析用数据 # cor 是否用样本的协方差矩阵作主成分分析 prcomp() 二 summary()函数三 […]

4sample CA RDA analysis

> gtsdata_test=read.table(“gtsdata.txt”, header=T) > gtsenv=read.table(“gtsenv.txt”, header=T) > gtsdata_data_t<-t(gtsdata_data) > decorana(gtsdata_data_t)

Call: decorana(veg = gtsdata_data_t)

Detrended correspondence analysis with 26 segments. Rescaling of axes with 4 iterations.

DCA1 DCA2 DCA3 DCA4 Eigenvalues 0.8634 0.4834 0.23788 0 Decorana values 0.8721 0.3793 0.07223 0 Axis lengths 5.3292 2.1115 1.80907 0

> gts.ca=cca(gtsdata_data_t) > gts.ca Call: cca(X = […]

基于Vegan 软件包的生态学数据排序分析学习

“基于Vegan 软件包的生态学数据排序分析赖江山米湘成 (中国科学院植物研究所植被与环境变化国家重点实验室，北京 100093) 摘要：群落学数据一般是多维数据，例如物种属性或环境因子的属性。多元统计分析是群落生态学常用的分析方法，排序（ordination）是多元统计最常用的方法之一。CANOCO是广泛使用的排序软件，但缺点是商业软件价格不菲，版本更新速度也很慢。近年来，R语言以其灵活、开放、易于掌握、免费等诸多优点，在生态学和生物多样性研究领域迅速赢得广大研究人员的青睐。R语言中的外在软件包“Vegan”是专门用于群落生态学分析的工具。Vegan能够提供所有基本的排序方法，同时具有生成精美排序图的功能，版本更新很快。我们认为Vegan包完全可以取代CANOCO，成为今后排序分析的首选统计工具。本文首先简述排序的原理和类型，然后介绍Vegan的基本信息和下载安装过程，最后以古田山24公顷样地内随机抽取40个20m×20m的样方为例，展示Vegan包内各种常用排序方法（PCA,RDA,CA和CCA）和排序图生成过程，希望能为R的初学者尽快熟悉并利用Vegan包进行排序分析提供参考。

gtsdata

gtsenv.txt

赖江山.pdf

> setwd(“/winxp_disk2/shenzy/R/Vegan”) > gtsdata=read.table(“gtsdata.txt”, header=T) > gtsenv=read.table(“gtsenv.txt”, header=T) > install.packages(“vegan”) Installing package(s) into ‘/home/shenzy/R/x86_64-pc-linux-gnu-library/2.15’ (as ‘lib’ is unspecified) 试开URL’http://cran.csiro.au/src/contrib/vegan_2.0-4.tar.gz’ Content type ‘application/x-gzip’ length 1576584 bytes (1.5 Mb) 打开了URL ================================================== downloaded 1.5 Mb * installing *source* package ‘vegan’ … ** 成功将‘vegan’程序包解包并MD5和检查 ** libs gfortran -fpic -O3 […]

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