mutually virtulizating Windows, Linux, Mac OS

http://www.virtualbox.org/

创建windows，linux，mac 的虚拟机。

Workshops from UC riverside

http://facility.bioinformatics.ucr.edu/home/workshops

SOAPdenovo assembly quality assessment

http://biostar.stackexchange.com/questions/8308/soapdenovo-assembly-quality-assessment

Is there any tool for drawing a gene/protein diagram

http://biostar.stackexchange.com/questions/8314/is-there-any-tool-for-drawing-a-gene-protein-diagram

Resources for learning how to deal with next generation sequence

http://biostar.stackexchange.com/questions/8319/resources-for-learning-how-to-deal-with-next-generation-sequence

Quantitative genetic parameters for wild stream-living brown trout - using MCMCglmm

Quantitative genetic parameters for wild stream-living brown trout: heritability and parental effects

http://onlinelibrary.wiley.com/doi/10.1111/j.1420-9101.2010.02028.x/full

很显然，同样的方法可以用在松树里。

Adaptability depends on the presence of additive genetic variance for important traits. Yet few estimates of additive genetic variance and heritability are available for wild populations, particularly so for fishes. Here, we estimate heritability of length-at-age for wild-living brown trout (Salmo trutta), based on long-term mark-recapture data and pedigree reconstruction based on large-scale genotyping at 15 microsatellite loci. We also tested for the presence of maternal and paternal effects using a Bayesian version of the Animal model. Heritability varied between 0.16 and 0.31, with reasonable narrow confidence bands, and the total phenotypic variance increased with age. When introducing dam as an additional random effect (accounting for c. 7% of total phenotypic variance), the level of additive genetic variance and heritability decreased (0.12–0.21). Parental size (both for sires and for dams) positively influenced length-at-age for juvenile trout – either through direct parental effects or through genotype-environment correlations. Length-at-age is a complex trait reflecting the effects of a number of physiological, behavioural and ecological processes. Our data show that fitness-related traits such as length-at-age can retain high levels of additive genetic variance even when total phenotypic variance is high.

Bayesian Inference of Genetic Parameters Based on Conditional Decompositions of Multivariate Normal Distributions

http://www.genetics.org/content/185/2/645.abstract

It is widely recognized that the mixed linear model is an important tool for parameter estimation in the analysis of complex pedigrees, which includes both pedigree and genomic information, and where mutually dependent genetic factors are often assumed to follow multivariate normal distributions of high dimension. We have developed a Bayesian statistical method based on the decomposition of the multivariate normal prior distribution into products of conditional univariate distributions. This procedure permits computationally demanding genetic evaluations of complex pedigrees, within the user-friendly computer package WinBUGS. To demonstrate and evaluate the flexibility of the method, we analyzed two example pedigrees: a large noninbred pedigree of Scots pine (Pinus sylvestris L.) that includes additive and dominance polygenic relationships and a simulated pedigree where genomic relationships have been calculated on the basis of a dense marker map. The analysis showed that our method was fast and provided accurate estimates and that it should therefore be a helpful tool for estimating genetic parameters of complex pedigrees quickly and reliably. 

Codes for Multivariate Qst Fst analysis

http://www.isem.cnrs.fr/spip.php?article934

summary statistics in R

http://www.statmethods.net/stats/descriptives.html

Linkage Mapping and Comparative Genomics Using Next-Generation RAD Sequencing of a Non-Model Organism

http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0019315

Population Genomics of Parallel Adaptation in Threespine Stickleback using Sequenced RAD Tags

http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1000862

Metabolic network drawing

http://biostar.stackexchange.com/questions/8238/metabolic-network-drawing

Sequencing Technique Aids in Local De Novo Assembly; Yields Haplotype Information

http://www.genomeweb.com/sequencing/sequencing-technique-aids-local-de-novo-assembly-yields-haplotype-information

vocabulary for R functions

https://github.com/hadley/devtools/wiki/vocabulary

bioinformatic blogs

http://abhishek-tiwari.com/2009/02/30-blogs-about-bioinformatics-and.html

accessing MySQL from R

http://gettinggeneticsdone.blogspot.com/2011/05/accessing-databases-from-r-rstats-sql.html

Multiple Y-axis in an R plot

http://evolvingspaces.blogspot.com/2011/05/multiple-y-axis-in-r-plot.html

Best file system to share between Linux, Win & MacOS?

FAT32, should be the choice now. FAT32 (or even FAT16) is really fine unless you need single files >4GB or you need POSIX types of features.

NTFS has somewhat limited read/write support on OS X (using Fuse) and also on Linux, I think.

EXT2 has drivers for both Windows and OS X, though, so if you do need large files or modern features, ext2fs is doable, although you will need to install drivers on every non-Linux box you use with the card.

OSX can read/write HFS, HFS+, FAT16, FAT32, and read NTFS.

如果你想创建一个Linux, Windows, Mac OS 都能识别／读写的硬盘分区。现在看来，FAT32 是个不二选择。但这个分区类型不支持单个文件大于 4Gb 的情况。不过，其它分区类型都不可行，据说有些新的分区格式似乎正在开发。

让U盘、移动硬盘在Mac和windows间都能识别／读写，且支持大文件，Linux不支持

     如果您的 U 盘、移动硬盘既要用于 PC 又要用于苹果电脑，Mac OS X 系统的 HFS＋ 和 Windows 的 NTFS 格式显然都不行……HFS+ 在 Windows 下不识别，NTFS 格式的 U 盘、移动硬盘插在苹果电脑上只能读不能写。格式化成 FAT32 显然可以支持两个系统，但单一文件大于 4GB 就歇菜。
    但苹果电脑 Mac OS X 10.6.5 系统做出了一项意义重大的升级：支持 exFAT 磁盘格式，格式化成 exFAT 格式的 U 盘、移动硬盘在 Windows PC 上和苹果电脑 Mac OS X 系统下均能读写，而且支持超过单个体积超过 4 GB 的大文件。

创建exFAT格式分区的方法，(1) 点击苹果电脑屏幕右上角的放大镜按钮，Sportlight 搜索“磁盘工具”，在磁盘工具侧边栏选择要格式化的 U 盘或移动硬盘分区，右侧选择“抹掉”标签，在格式下拉菜单里选择 ExFAT 即可。

(2) Windows 7 和 Vista 默认就支持 exFAT 格式，如果是 XP 系统，要下载 KB955704 补丁：http://www.microsoft.com/downloads/details.aspx?displaylang=zh-cn&FamilyID=1cbe3906-ddd1-4ca2-b727-c2dff5e30f61

http://www.mac52ipod.cn/post/use-u-disk-hdd-on-windows-pc-and-mac-os-x-4gb-exfat.php

unix - alias 命令快捷

给 unix 命令创建快捷，可以省去很多可能很繁琐的操作。比如，进入你的工作目录，每天都要做这样的操作很多次。如果你的目录很深，那么肯定费力。你可以通过创建快捷简化这个工作。

alias maowork="cd /mao/work/folder/"

This means you just type maowork into the terminal, you will be directed to the specific folder you pointed out in the former line.


aliases are not saved permanently if you do not explicitly tell the system to do so. You need to put the alias command line (the former command line) into the ".bash_profile" in your home directory, and source that file.

Automatic SSH/SCP Login without Password

It should be useful. I have not checked it.
 
http://waelchatila.com/2005/06/06/1118124232757.html

unix - processes 进程

Unix run processes in parallel is called multitasking.

# (1) check the running processes
ps

# give you more
ps -T

# (2) understanding the ps output
PID - process ID
TTY - terminal name
STAT - (S) sleep
            (O) running
            (R) wait to run on the processor
            (I) just be started
            (T) paused
            (Z) that is ending
TIME - amount of computing time the process has used
CMD - command used by the process

# (3) killing process
you need to know the process ID of which you would like to kill

kill PID

unix - wildcard 通配符

wildcard 很有价值，它通常用在查询上。下面是几个最为常用的wildcard （通配符）：

*         与任何存在和不存在的字符或字符串相配，protein*, protein, proteins, protein-sequence
?         与任何单一一个字符相配，seq?.txt, seq1.txt
[  ]      与包含在方括号中任意字符或字符界限相配，[A-Z]*,  anything with uppercase head
[!   ]    与上一条相反，[!a-zA-Z], anything but letters

unix one liner - delete a list of files

You may need to delete some files with a general pattern in their names. Here, the unix command:

find -name deletion_pattern.txt | xargs rm

you could substitute "deletion_pattern.txt" with a general pattern of your files needed to be delete.

PLINK/Seq - analysis of genomic variant data

A library for analysis of genomic variant data:

http://atgu.mgh.harvard.edu/plinkseq/overview.shtml

print out R vector object as sequence

# example code
x<-1:10
paste(x, sep="", collapse=",")

# output
# Note using paste() you can get different print of the vector object,
# Pls, compare these two

> paste(x, sep="", collapse=",") 

[1] "1,2,3,4,5,6,7,8,9,10"
> x 

[1]  1  2  3  4  5  6  7  8  9 10

##########################################
Rvector = function(vector) {
out = paste("c(",paste(x, sep="", collapse=","),")",sep="")
cat("\n",out,"\n","\n")
return(out)
}

Rmatrix = function(matrix) {
out = paste("matrix(", Rvector(as.matrix(mat)), "," ,nrow(mat), ",", ncol(mat), ")", sep="")
cat("\n",out,"\n","\n")
return(out)
} 

moving average - like sliding window calculation of mean

http://rforcancer.drupalgardens.com/content/ggheat-ggplot2-style-heatmap-function

ggheat - a ggplot2 style heatmap function

http://rforcancer.drupalgardens.com/content/ggheat-ggplot2-style-heatmap-function

gnmplot - between the biomaRt and ggplot2 packages

a new package that will create nice publication quality graphics of genome information. It's really an adaptor sitting between the biomaRt and ggplot2 packages

http://rforcancer.drupalgardens.com/content/gnmplot

Creating Repetitive Reports

http://learnr.wordpress.com/2009/09/09/brew-creating-repetitive-reports/

determine the number of cluster

http://blog.echen.me/2011/03/14/counting-clusters/

find the full for a specific file name

find the full for a specific file name in a folder,  this may be simple and useful.

# list the full path for a file name "mao_test.txt", in the current folder (path)
find -name 'mao_test.txt'

# list the full path for a file name "mao_test.txt", in the root folder (the computer)
find / -type f -name 'mao_test.txt'

Tools for visualizing overlap between GO terms

http://biostar.stackexchange.com/questions/1278/tools-for-visualizing-overlap-between-go-terms

Tool to generate proportional Venn diagrams

Here, you can find several tools of that.
http://biostar.stackexchange.com/questions/7736/tool-to-generate-proportional-venn-diagrams

VennDiagram: a package for the generation of highly-customizable Venn and Euler diagrams in R

http://www.biomedcentral.com/1471-2105/12/35 

 

 

Bayesian inference in ecology

Ellison, A.M. (2004) Bayesian inference in ecology. Ecology Letters, 7, 509–520.

Direct Link:

• Abstract
• Full Article (HTML)
• PDF(177K)
• References

a friend left words to my publication and me - thanks for their best wishes

 
很好！

凡是坚持的，
必能实现！

凡是美好的，
必会璀璨！

http://www.planta.cn/forum/viewtopic.php?t=25503

avoiding statistical problem - A protocol for data exploration to avoid common statistical problems

http://onlinelibrary.wiley.com/doi/10.1111/j.2041-210X.2009.00001.x/abstract

Summary

1. While teaching statistics to ecologists, the lead authors of this paper have noticed common statistical problems. If a random sample of their work (including scientific papers) produced before doing these courses were selected, half would probably contain violations of the underlying assumptions of the statistical techniques employed.

2.  Some violations have little impact on the results or ecological conclusions; yet others increase type I or type II errors, potentially resulting in wrong ecological conclusions. Most of these violations can be avoided by applying better data exploration. These problems are especially troublesome in applied ecology, where management and policy decisions are often at stake.

3.  Here, we provide a protocol for data exploration; discuss current tools to detect outliers, heterogeneity of variance, collinearity, dependence of observations, problems with interactions, double zeros in multivariate analysis, zero inflation in generalized linear modelling, and the correct type of relationships between dependent and independent variables; and provide advice on how to address these problems when they arise. We also address misconceptions about normality, and provide advice on data transformations.

4.  Data exploration avoids type I and type II errors, among other problems, thereby reducing the chance of making wrong ecological conclusions and poor recommendations. It is therefore essential for good quality management and policy based on statistical analyses.

plot network in R - ggplot2 and qgraph

tips of ggplot2:
 http://r-ecology.blogspot.com/2011/03/basic-ggplot2-network-graphs-ver2.html


qgraph:
https://sites.google.com/site/qgraphproject/home

Creating graphs where the nodes are images

你可以选取一个图片，作为R作图中的结点。想做出出色的图，这个话题有吸引力：

http://stackoverflow.com/questions/4975681/r-creating-graphs-where-the-nodes-are-images/4978111#4978111

correlation network

correlation network

 the theory:
http://en.wikipedia.org/wiki/Graph_%28mathematics%29

the blog:
http://www.investuotojas.eu/?p=464

knowledgeblog will change the academic publising process

knowledgeblog has put forward a blog-based academic publication strategy. That is very interesting and attractive. I would like to take part in.

But, it looks it is just on the its beginning. No real case/example there. 

http://knowledgeblog.org/

generating animation in R

generating animation in R

http://eigensomething.blogspot.com/2011/01/video-from-kaggle-traffic-prediction.html
http://blog.revolutionanalytics.com/2009/06/animate-r-graphics-with-flash.html
http://yihui.name/en/2009/06/creating-tag-cloud-using-r-and-flash-javascript-swfobject/

Flash Tip: Embedding Your SWF in a Web Page

http://animation.about.com/od/flashanimationtutorials/qt/embedswfwebpage.htm

detecting population structure by PCA

(1) several papers:

A genealogical interpretation of principal components analysis 

http://www.ncbi.nlm.nih.gov/sites/entrez/19834557?dopt=Abstract&holding=f1000,f1000m,isrctn

Genome-wide patterns of population structure and admixture in West Africans and African Americans

http://www.ncbi.nlm.nih.gov/sites/entrez/20080753?dopt=Abstract&holding=f1000,f1000m,isrctn

Analysis of population structure: a unifying framework and novel methods based on sparse factor analysis

http://www.ncbi.nlm.nih.gov/sites/entrez/20862358?dopt=Abstract&holding=f1000,f1000m,isrctn

(2) adegent

- a review of applications of multivariate analyses to genetic markers data:

Jombart T, Pontier D, Dufour AB. (2009) Heredity 102: 330-341. doi:10.1038/hdy.2008.130. [link to the journal's pdf - free abstract] Genetic markers in the playground of multivariate analysis.

- the paper presenting the spatial principal component analysis (sPCA, function spca), global and local tests (global.rtest and local.rtest):
Jombart T, Devillard S, Dufour AB, Pontier D (2008) Revealing cryptic spatial patterns in genetic variability by a new multivariate method.  Heredity 101: 92-103. doi: 10.1038/hdy.2008.34 [link on the journal's website - free abstract]

- the paper presenting the SeqTrack algorithm (seqTrack), and simulations of genealoies of haplotypes (haploGen): 

Jombart T, Eggo RM, Dodd PJ, Balloux F (2010) Reconstructing disease outbreaks from genetic data: a graph approach. Heredity. Doi: 10.1038/hdy.2010.78 

- the paper introducing the Discriminant Analysis of Principal Components (DAPC, functions find.clusters and dapc): 

Jombart T , Devillard S and Balloux F (2010) Discriminant analysis of principal components: a new method for the analysis of genetically structured populations. BMC Genetics 11:94. doi:10.1186/1471-2156-11-94 [free pdf] [free html version] [evaluation by Laurent Excoffier on F1000]

 

Environmental factors predict adaptive phenotypic differentiation within and between two wild andean tomatoes

the article
Environmental factors predict adaptive phenotypic differentiation within and between two wild andean tomatoes

http://onlinelibrary.wiley.com/doi/10.1111/j.1558-5646.2008.00332.x/full

the lab
http://sites.bio.indiana.edu/~moylelab/publications.html

two books of introductory bayesian statistics

As recommended by this blogger.
http://telliott99.blogspot.com/search/label/bayes

(1) a book by Dennis Lindley entitled Understanding Uncertainty

(2) further understanding of Bayesian methods is William Bolstad, Introduction to Bayesian Statistics.

generate invalid variable names

http://4dpiecharts.com/

用单引号扩起来，你可以创建一个R的无效变量名。记住，它也是有用的。

Rcpp and compiler - speed up R in R-2.13.0

see this for introduction

http://dirk.eddelbuettel.com/blog/2011/04/12/#the_new_r_compiler_package

about updating of R-2.13.0

http://yihui.name/cn/2011/04/r-updated-to-2-13-0/#more-1970

testing for different rates of continuous trait evolution - r8s and Brownie

O'Meara, B.C., C. Ané, M.J. Sanderson, and P.C. Wainwright. 2006. Testing for different rates of continuous trait evolution using likelihood. Evolution 60(5): 922-933.
http://www.brianomeara.info/publications

Rates of phenotypic evolution have changed throughout the history of life, producing variation in levels
of morphological, functional, and ecological diversity among groups. Testing for the presence of these rate shifts is
a key component of evaluating hypotheses about what causes them. In this paper, general predictions regarding changes
in phenotypic diversity as a function of evolutionary history and rates are developed, and tests are derived to evaluate
rate changes. Simulations show that these tests are more powerful than existing tests using standardized contrasts.
The new approaches are distributed in an application called Brownie and in r8s

UNICODE characters

The Adobe Symbol Encoding

http://www.stat.auckland.ac.nz/~paul/R/CM/AdobeSym.html

Drop unused factor levels

When you subset a data.frame object, you will face the problem of drop unused factor levels, this blog give us guides on that.

http://quantitative-ecology.blogspot.com/2008/02/drop-unused-factor-levels.html