2011年11月18日星期五

The Patterns and Causes of Variation in Plant Nucleotide Substitution Rates

http://www.annualreviews.org/doi/abs/10.1146/annurev-ecolsys-102710-145119

The Genetics of Hybrid Incompatibilities

http://www.annualreviews.org/doi/abs/10.1146/annurev-genet-110410-132514

这篇综述给出了种间杂种不亲和的研究方法、可能机制。

2011年11月16日星期三

good intro for beamer

http://programming-r-pro-bro.blogspot.com/search/label/beamer

Slopegraphs - a useful tools

1. for introduction of slopegraphs
http://charliepark.org/slopegraphs/

2. for generating slopegraphs with R
http://www.jameskeirstead.ca/r/slopegraphs-in-r/

2011年11月8日星期二

outcrossing and selfing in plants - evolutionary and genomic consequence

Loss of Self‐Incompatibility and Its Evolutionary Consequences(pp. 93-104)
Boris Igic, Russell Lande, Joshua R. Kohn
DOI: 10.1086/523362
Stable URL: http://www.jstor.org/stable/10.1086/523362
Genomic Consequences of Outcrossing and Selfing in PlantsGenomic Consequences of Outcrossing and Selfing in Plants(pp. 105-118)
Stephen I. Wright, Rob W. Ness, John Paul Foxe, Spencer C. H. Barrett
DOI: 10.1086/523366
Stable URL: http://www.jstor.org/stable/10.1086/523366

2011年11月6日星期日

A funnel plot is a scatterplot of treatment effect against a measure of study size. It is used primarily as a visual aid to detecting bias or systematic heterogeneity. A symmetric inverted funnel shape arises from a ‘well-behaved’ data set, in which publication bias is unlikely. An asymmetric funnel indicates a relationship between treatment effect and study size.

A funnel plot is a useful graph designed to check the existence of publication bias in systematic reviews and meta-analyses. It assumes that the largest studies will be near the average, and small studies will be spread on both sides of the average. Variation from this assumption can indicate publication bias.

http://en.wikipedia.org/wiki/Funnel_plot

http://ouseful.files.wordpress.com/2011/10/cancerdatafunnelplot.png?w=600&h=419

2011年11月3日星期四

constrained elements in the human genome as revealed by mammalian alignments of 29 genomes

http://www.nature.com/nrg/journal/vaop/ncurrent/full/nrg3112.html

Lindblad-Toh, K. et al. A high-resolution map of human evolutionary constraint using 29 mammals. Nature 12 Oct 2011 (doi: 10.1038/nature10530)
- Article
Lin, M. F. et al. Locating protein-coding sequences under selection for additional, overlapping functions in 29 mammalian genomes. Genome Res. 12 Oct 2011 (doi: 10.1101/gr.108753.110)

2011年10月28日星期五

What do we need to know about speciation

http://www.sciencedirect.com/science/article/pii/S0169534711002618#sec2.1

Article Outline

Quantifying effects of environmental and geographical factors on patterns of genetic differentiation

The authors used a regression-based approach to simultaneously estimate the quantitative contributions of environmental adaptation and isolation by distance on genetic variation in Boechera stricta, a wild relative of Arabidopsis.

http://onlinelibrary.wiley.com/doi/10.1111/j.1365-294X.2011.05310.x/abstract

2011年10月27日星期四

Dated origin of Arabidopsis thaliana - 13 Mya

This paper gave much more robust dating for origin of Arabidopsis thaliana:

Dated molecular phylogenies indicate a Miocene origin for Arabidopsis thaliana

http://www.pnas.org/content/107/43/18724.long

We bring previously overlooked fossil evidence to bear on these questions and find the split between A. thaliana and Arabidopsis lyrata occurred about 13 Mya, and that Arabidopsis and the Brassica complex (broccoli, cabbage, canola) occurred about 43 Mya.

正态检验的用途－似乎不大－要小心

1. Normality tests don't do what most think they do. Shapiro's test, Anderson Darling, and others are null hypothesis tests AGAINST the the assumption of normality. These should not be used to determine whether to use normal theory statistical procedures. In fact they are of virtually no value to the data analyst. Under what conditions are we interested in rejecting the null hypothesis that the data are normally distributed? I have never come across a situation where a normal test is the right thing to do. When the sample size is small, even big departures from normality are not detected, and when your sample size is large, even the smallest deviation from normality will lead to a rejected null.

http://stackoverflow.com/questions/7781798/seeing-if-data-is-normally-distributed-in-r/

2. I, personally, have never come across a situation where a normal test is the right thing to do. The problem is that when the sample size is small, even big departures from normality are not detected, and when your sample size is large, even the smallest deviation from normality will lead to a rejected null.

http://blog.fellstat.com/

HyPhy - Hypothesis testing using phylogenies

1. HyPhy is a scriptable package that can fit statistical evolutionary models to alignment of homologous sequences using Maximum likelihood ²⁾, estimate various parameters that have biological meaning, for example branch lengths, substitution rates, dN/dS ratios, recombination breakpoints, and test hypotheses about how sequences in the alignment have evolved. HyPhy focuses on inference about the evolutionary process. Even though it can do limited alignment and phylogenetic reconstruction, much better specialized programs exist for these purposes.
Here are some of the applications that HyPhy is often used for:

Positive and negative selection detection
Recombination analysis
Detecting co-evolving residues
Genomic and multiple-gene evolutionary inference
Molecular clock and relative rate tests
Nucleotide, protein and codon model selection
As a likelihood analysis engine for other software and web services
One-off analyses: tasks that no other package does out of the box and are not worth writing a specialized program for

http://www.datam0nk3y.org/hyphy/doku.php

2. Some of the most popular HyPhy functions (recombination, positive selection detection, etc) are implemented in a web-server hosted at http://www.datamonkey.org

Which codon sites are under diversifying positive or negative selection?: Three different codon-based maximum likelihood methods, SLAC, FEL and REL, can be used estimate the dN/dS (also known as Ka/Ks or ω) ratio at every codon in the alignment. An exhaustive discussion of each approach can be found in the methodology paper. All methods can also take recombination into account. This is done by screening the sequences for recombination breakpoints, identifying non-recombinant regions and allowing each to have its own phylogentic tree.
Is there evidence of selection in my alignment?: The PARRIS method, developed by Konrad Scheffler and colleagues, extends traditional codon-based likelihood ratio tests to detect if a proportion of sites in the alignment evolve with dN/dS>1. The method takes recombination and synonymous rate variation into account.
What is the evolutionary fingerprint of a gene?: The ESD method, described in a recent paper, fits a versatile general discrete bivariate model of site-by-site selective force variation to partition all sites into selective classes, and obtains an approximate posterior distribution of this partititoning. The resulting "noisy" distribution of selective regimes is the evolutionary fingerprint of a gene. The EVF (evolutionary fingerprinting) module implements this procedure, and can also infer which individual sites appear to be positively selected while accounting for parameter estimation error (analogous to the BEB methodology of the PAML package).
Which codon sites are under positive or negative selection at the population level?: The codon-based maximum likelihood IFEL method can investigate whether sequences sampled from a population (e.g. viral sequences from different hosts) have been subject to selective pressure at the population level (i.e. along internal branches). A discussion of the method and its application can be found here
Did selective pressure vary along lineages, i.e. over time?: The codon-based genetic algorithm GABranch method can automatically partition all branches of the phylogeny describing non-recombinant data into groups according to dN/dS. Robust multi-model inference is used to collate results from all models examined during the run to provide confidence intervals on dN/dS for each branch and guard against model misspecification and overfitting (method details).
How about episodic diversifying selection (branch-site methods)? Using the modeling framework, which allows the efficient estimations with models which permit dN/dS variation along both sites and lineages, Datamonkey implements two tests geared towards finding lineages and sites subject to episodic diversifying selection (EDS).: The Branch-site REL method, identifies those branches where a proportion of sites evolves under EDS. If you are primarily interested in finding which lineages (but don't care about which sites) have experienced EDS, use this method. Alternatively, if you are interested in sites (but don't care about which lineages) subject to EDS, then the MEME method is appropriate.
What about different types of selection?: Protein sequences can be screened for evidence of directional using the DEPS method, described here, useful when one wants to detect convergent evolution or selective sweeps. For coding sequences, the TOGGLE model, developed by Wayne Delport and colleagues, can detect selection-driven changes that result in amino-acid toggling. A canonical example of this can be found in immune-driven evolution of HIV-1 (escape and reversion).
Which evolutionary model should I use for my data?: For each type of data, nucleotide, amino-acid and codon, Datamonkey implements separate model selection procedures. An exhaustive search is performed for all possible (Markov, time-reversible) models of nucleotide evolution. For protein data, a collection of published empirical models are fitted to the alignment and the best one is selected using AIC_c. Finally, for coding data, a sophisticated genetic-algorithm procedure described in our recent paper is used to examine thousands of potential models and report the best one and various metrics based on the set of credible models - this feature is implemented in the CMS module.
Did any sites co-evolve?: A Bayesian graphical model is deduced from reconstructed substitutions at each branch/site combination to infer conditional evolutionary dependancies of sites in the alignments, i.e. whether a site is more or less likely to experience a non-synonymous substitution at a branch when certain other sites do (or do not) experience non-synonymous substitutions at the same branch. The SPIDERMONKEY method was introduced in the evolutionary context in our paper on the evolution of the phenotypically important and highly variable V3 loop of the envelope glycoprotein in HIV-1.
Has recombination acted upon sequences in an alignment?: Recombination leaves an imprint on sequence alignments: different segments of the alignment may be described by different phylogenetic trees, called phylogenetic discordance. Datamonkey.org implementes two methods: SBP, suitable for answering the question "Is there evidence of recombination in the alignment?", and GARD, that attempts to find all the recombination breakpoints. Both method are described in this paper. The output of GARD is accepted by most other analyses, and because recombination can mislead phylogenetic analysis that do not account for it, we strongly urge that recombination testing be done on any alignment that is going to be analyzed for positive selection.You can also submit a collection of HIV-1 sequences for recombination screening by a specialized recombination detection algorithm SCUEAL described in this paper.
What were the ancestral sequences?: The ASR module implements three different approaches to reconstructing ancestral sequences: joint, marginal and sampled - see this paper for a description and original methodology attribution, from simple or partitioned alignments.

3. One functionality from HyPhy:

A random effects branch-site model for detecting episodic diversifying selection

http://mbe.oxfordjournals.org/content/early/2011/06/11/molbev.msr125.abstract

2011年10月26日星期三

synonymous/nonsynonymous mutation rate (Ks/Kn)

(1) Ks saturation (Ks > 1):

http://biostar.stackexchange.com/questions/13501/ks-saturation-after-a-pairwise-comparison-analysis-using-codeml

http://sysbio.oxfordjournals.org/content/57/3/367.full
It has been widely accepted that synonymous substitutions may saturate quickly as sequences diverge (e.g., Maynard Smith and Smith, 1996). The analysis of mammal and yeast data reveals that synonymous substitutions are very important and should not be ignored.

(2) intraspecific

(2.1) if you have intraspecific polymorphism data, you should be calculating pi or theta (not Ka or Ks) for synonymous and non-synonymous sites.

http://biostar.stackexchange.com/questions/12153/ka-ks-ratio-to-detect-selection-nonsynonmous-to-synonymous-in-dnasp

(2.2) Within populations or species, there are suggestions that ka/ks may not be a good measure of selective constraints because the selection and ka/ks ratio doesn't follow a monotonic function. See http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1000304 from the Plotkin lab. If I were to use ka/ks here, I'll try codon-based approach implemented in HyPhy to see site and lineage specific dN/dS ratio. Also, I think it might be a better idea to explore population genetics based approach, like Tajima's D, to test the null hypothesis of neutral evolution.
To answer your direct questions, yes, you input all coding sequences, and you do get average values from all pairwise comparisons. But I wonder whether you'll see any significant values because they should be all highly similar.

http://biostar.stackexchange.com/questions/12174/ka-ks-ratio-for-within-a-gene-very-confused

(3) new models on mutation and selection

(3.1) Statistical Comparison of Nucleotide, Amino Acid, and Codon Substitution Models for Evolutionary Analysis of Protein-Coding Sequences

http://sysbio.oxfordjournals.org/content/early/2009/06/29/sysbio.syp015.full

(3.2) Mutation-selection models of coding sequence evolution with site-heterogeneous amino acid fitness profiles
http://www.pnas.org/content/107/10/4629.full

(4) The Population Genetics of dN/dS

http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1000304

material resources for Arabidopsis analysis

拟南芥研究的生物物质资源：

1、这是所有资源类别的列表
http://www.arabidopsis.org/servlets/Order?state=catalog

2、拟南芥天然群体取样个体列表
http://www.arabidopsis.org/abrc/catalog/natural_accession_1.html

visualizing your interests on pathway

These two questions in BioStar would be helpful, if you want to visualize what you are interested in on pathways.

http://biostar.stackexchange.com/questions/13361/overlaying-gene-expression-results-on-metabolic-pathways

http://biostar.stackexchange.com/questions/4813/tools-for-visualizing-significantly-altered-pathways-and-related-genes-in-a-pathw

2011年10月24日星期一

turtorials of PCA with R

http://gettinggeneticsdone.blogspot.com/2010/05/tutorial-principle-components-analysis.html

http://programming-r-pro-bro.blogspot.com/2011/10/principal-component-analysis-use.html

http://programming-r-pro-bro.blogspot.com/2011/10/principal-component-analysis-use_23.html

2011年10月23日星期日

genetic/genomic bases of local adaptation

A Map of Local Adaptation in Arabidopsis thaliana

http://www.sciencemag.org/content/334/6052/86.full

http://www.sciencemag.org/content/334/6052/49.full

2011年10月21日星期五

minicourse of R and Latex

http://scc.stat.ucla.edu/mini-courses/materials-from-past-mini-courses/

Winter 2010 Mini-Course Materials

Winter 2010 Schedule

Week 1
Date	Topic	Stats 285 Track	Presenter	Materials
January 4	Organizational Meeting for Statistics 285	n/a	Mahtash Esfandiari
January 6	Basic Statistics and Introduction to Graphics in R	R Graphics	Brigid Brett-Esborn
Week 2
January 11	LaTeX I: Writing a Paper or Thesis in LaTeX	LaTeX	David Diez
January 13	LaTeX II: Bibliographies, Style and Math in LaTeX	LaTeX	David Diez
Week 3
January 20	LaTeX III: Sweave – Embedding R in LaTeX	LaTeX	Colin Rundel
Week 4
January 25	Intermediate Graphics in R	R Graphics	Irina Kukuyeva
January 27	Advanced Graphics in R	R Graphics	Ryan Rosario
Week 5
February 1	Introductory Statistics in R	R Stats	Mine Cetinkaya
February 3	LaTeX IV: Academic Talks and Presentations in LaTeX	LaTeX	Mine Cetinkaya
Week 6
February 8	Intermediate R	R Stats	Denise Ferrari
February 10	Linear Regression	R Stats	Tiffany Himmel
Week 7
February 17	Nonlinear Regression	R Stats	Tiffany Himmel
Week 8
February 22	Survival Analysis in R	R Stats	Mine Cetinkaya
February 24	Spatial Statistics in R	R Stats	David Diez
Week 9
March 1	Time Series in R	R Stats	Irina Kukyeva
March 3	Advanced Topics in R	R Stats	TBA

Visualizing 3d data by plotting quartiles separately - good idea

Here, the author presented a nice way to present 3d data - by plotting quartiles separately.
http://gossetsstudent.wordpress.com/2010/07/30/visualizing-3d-data-plotting-quartiles-separately/

Visualizing genomes: techniques and challenges

http://www.nature.com/nmeth/journal/v7/n3s/full/nmeth.1422.html

2011年10月20日星期四

comparative genomics tools - alignment tools

Custom Alignment

We have developed several comparative genomics servers that calculate pairwise and multiple alignments of user-submitted sequences of any length and perform associated conservation analysis.
The user-friendly visualization technique implemented in our servers allows a biomedical researcher to analyze megabases of sequence alignments with underlying sequence annotation, predicted transcription factor binding sites, and evolutionary relationships.

mVISTA
Align and compare your sequences from multiple species

Submit Instructions Download About Cite
rVISTA
(regulatory VISTA) combines transcription factor binding sites database search with a comparative sequence analysis. It can be used directly or through mVISTA, Genome VISTA, or VISTA Browser.

Submit Instructions About Cite
GenomeVISTA
compare your sequences with several whole genome assemblies. It will automatically find the ortholog, obtain the alignment and VISTA plot..

Submit About Cite
wgVISTA
Align sequences up to 10Mb long (finished or draft) including microbial whole-genome assemblies.

Submit Instructions About Cite
Phylo-Vista
Analyze multiple DNA sequence alignments of sequences from different species while considering their phylogenic relationships.

Submit

comparative genomics tools - whole genome alignments

Here you could find whole genome alignments for different genome pairs, these alignments are usually crucial for a comparative genomic study.

http://pipeline.lbl.gov/downloads.shtml

Alignments

Human - Rhesus - Horse - Dog - Mouse - Rat - Chicken	14-May-2008
Human - Orangutan - Rhesus - Marmoset	14-Dec-2007
Human - Chimp	09-Apr-2007
Human - Dog	02-Apr-2007
Human - Mouse	27-Mar-2007
Human - Dog - Mouse - Rat - Chicken	3-Dec-2006
Human (Jul. 2003) - Mouse - Rat	14-Mar-2004
Human (Apr. 2003) - Mouse - Rat	22-Aug-2003

A. thaliana Jun. 2009 (TAIR9)

Arabidopsis - Maize B73 v.2	16-Jun-2010
Arabidopsis - M. esculenta v.4	13-Jul-2010
Arabidopsis - M. guttatus v.1.0	27-Dec-2009
Arabidopsis - M. truncatula Mt3.0	25-Dec-2009
Arabidopsis - Papaya	25-Dec-2009
Arabidopsis - Poplar v.2.0	23-Dec-2009
Arabidopsis - P. patens v.1.1	07-Feb-2010
Arabidopsis - Rice v.6.0	25-Dec-2009
Arabidopsis - S. moellendorffii v.1.0	08-Feb-2010
Arabidopsis - Soybean	24-Dec-2009
Arabidopsis - Wine grape	17-Jul-2010
Arabidopsis - A. lyrata - T. halophila - C. papaya - C. clementina	11-Mar-2011

Poplar v.2.0

Poplar - Rice v.6.0	23-Dec-2009
Poplar - Papaya	26-Dec-2009

Rice v.6.0

Rice - Papaya	26-Dec-2009

Rice - Papaya

26-Dec-2009

A. thaliana Apr. 2008 (TAIR8)

Arabidopsis - A. lyrata	30-Jul-2008
Arabidopsis - Soybean	03-Dec-2008
Arabidopsis - Poplar v.1.1	30-Sep-2008
Arabidopsis - Medicago	18-Dec-2008
Arabidopsis - Papaya	17-Dec-2008
Arabidopsis - Wine grape	15-Aug-2008

A. thaliana Mar. 2004

Arabidopsis - Rice v.1.0	28-Jan-2005
Arabidopsis - Sorghum	04-Jan-2008
Arabidopsis - Poplar v.1.1	11-Aug-2005

Poplar v.1.1

Poplar - Arabidopsis	11-Aug-2005
Poplar - Rice v.3.0	11-Aug-2005
Poplar - Poplar	11-Aug-2005

Sorghum v.1.0

Sorghum - Arabidopsis - Rice v.5.0	22-Apr-2008
Sorghum - Sorghum	23-May-2007
Sorghum - Rice v.5.0	10-Apr-2007
Sorghum - Rice v.6.0	28-Dec-2009
Sorghum - Maize	06-Jun-2007

A. niger v.1.0

A. niger - A. fumigatus	23-Mar-2006
A. niger - A. nidulans	16-Mar-2006
A. niger - A. oryzae	23-Mar-2006

D. melanogaster Oct. 2006

D. melanogaster - D. pseudoobscura - D. yakuba - D. ananassae - D. erecta - D. simulans	13-Mar-2008

D. melanogaster - D. pseudoobscura - D. yakuba - D. ananassae - D. erecta - D. simulans

13-Mar-2008

D. melanogaster Apr. 2004

D. melanogaster - D. simulans	15-July-2005
D. melanogaster - D. pseudoobscura	29-Mar-2005
D. melanogaster - D. ananassae	29-Mar-2005
D. melanogaster - D. mojavensis	29-Mar-2005
D. melanogaster - D. yakuba	29-Mar-2005
D. melanogaster - D. virilis	29-Mar-2005
D. melanogaster - D. erecta	29-Mar-2005

D. melanogaster Jan. 2003

D. melanogaster - D. pseudoobscura (LAGAN)	20-Sep-2004
D. melanogaster - D. pseudoobscura (AVID)	13-Aug-2004
D. melanogaster - D. ananassae	20-Sep-2004
D. melanogaster - D. mojavensis	20-Sep-2004
D. melanogaster - D. virilis	21-Sep-2004
D. melanogaster - D. yakuba	21-Aug-2004

Ciona intestinalis v.1.0 - Ciona savignyi	13-Aug-2004
Ciona intestinalis v.2.0 - Ciona savignyi	29-Nov-2005

Human (NCBI build 30) - Mouse (MGSCv3) (all alignments)	04-Nov-2002
Human (NCBI build 30) - Mouse (MGSCv3) (filtered alignments)	4-Nov-2002
Notes for this run

Escherichia coli O6 CFT073	25-May-2005 SLAGAN

2011年10月19日星期三

the Systematic Investor Toolbox - useful tools

1. the Systematic Investor Toolbox is here:
https://github.com/systematicinvestor/SIT

2. it has been introduced here:
http://systematicinvestor.wordpress.com/

3. plot.table function

plot.table function in the Systematic Investor Toolbox is a flexible table drawing routine. plot.table has a simple interface and takes following parameters:

plot.matrix – matrix with data you want to plot
smain – text to draw in (top, left) cell; default value is blank string
highlight – Either TRUE/FALSE to indicate if you want to color each cell based on its numeric value Or a matrix with colors for each cell
colorbar – TRUE/FALSE flag to indicate if you want to draw colorbar

4.
PloTA ( plot + ta ) library in the Systematic Investor Toolbox is a simple plot interface to charting Time Series and Technical Analysis plots. I created it as an alternative to charting functionality in quantmod package. It is designed to mimic default plot interface and works with xts objects. PloTA implements following methods:

plota – main plot method
plota2Y – add second Y axis to existing plot

plota.lines – plot lines
plota.candle – plot Candle
plota.ohlc – plot Open/High/Low/Close
plota.hl – plot High/Low
plota.volume – plot Volume
plota.scale.volume – scale Volume

plota.grid – add grid
plota.legend – plot legend
plota.layout – specify plot layout

plota.theme.blue.red – color theme
plota.theme.green.orange – color theme
plota.theme.gray.orange – color theme

10 rules for genomics

Here from Ewan's blog, 10 rules for genomics

http://genomeinformatician.blogspot.com/2011/07/10-rules-of-thumb-in-genomics.html

2011年10月18日星期二

GeCo++

GeCo++: a C++ library for genomic features computation and annotation in the presence of variants

http://bioinformatics.oxfordjournals.org/content/27/9/1313.full

http://bioinformatics.emedea.it/geco/

http://bioinformatics.emedea.it/geco/doc/geco_tutorial.html, this tutorial give a comparison between GeCo and R on NGS analysis. So you could learn much about R/bioconductor.

two variables correlation can be presented as smoothed color density plot

1. Here is an example:
(http://sas-and-r.blogspot.com/2011/07/example-92-transparency-and-bivariate.html?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed%3A+SASandR+%28SAS+and+R%29)
(please go to scatterSmooth function)

2. another example

http://princeofslides.blogspot.com/2011/02/fixing-up-smoothscatter-heat-maps.html

blogs on SAS

If you are using SAS, please come here:

1.
http://blogs.sas.com/content/iml/

2.
http://sas-and-r.blogspot.com/

2011年10月17日星期一

Linear mixed modelers in R

1. Here Luis, gave a very nice introduction on mixed modeling with R
http://www.quantumforest.com/2011/10/linear-mixed-models-in-r/#idc-container

2. please go the the comments of this blog post, there are many informative comments, especially Lars pointed out his hglm package. It is very attractive.

http://users.du.se/~lrn/DUweb/
http://www.quantumforest.com/2011/10/linear-mixed-models-in-r/#idc-container

detecting orthologs among species

1. Markov Cluster Algorithm
http://micans.org/mcl/

2. OrthoMCL
http://orthomcl.org/cgi-bin/OrthoMclWeb.cgi?rm=indx

OrthoMCL-DB Version 5 is released. We have included 150 genomes in this release

2011年10月16日星期日

plots side by side - R

http://www.quantumforest.com/2011/10/setting-plots-side-by-side/

library(ggplot2)
 
# Create first plot and assign it to variable
p1 = qplot(wt, mpg, data = mtcars,
           xlab = 'Car weight', ylab = 'Mileage')
 
# Create second plot and assign it to variable
p2 = qplot(wt, mpg, color = factor(cyl), data = mtcars,
           geom = c('point', 'smooth'),
           xlab = 'Car weight', ylab = 'Mileage')
 
# Define grid layout to locate plots and print each graph
pushViewport(viewport(layout = grid.layout(1, 2)))
print(p1, vp = viewport(layout.pos.row = 1, layout.pos.col = 1))
print(p2, vp = viewport(layout.pos.row = 1, layout.pos.col = 2))

"not in" - R command line

http://www.quantumforest.com/2011/10/not-in-in-r/

When processing data it is common to test if an observation belongs to a set. Let’s suppose that we want to see if the sample code belongs to a set that includes A, B, C and D. In R it is easy to write something like:

inside.set = subset(my.data, code %in% c('A', 'B', 'C', 'D'))

Now, what happens if what we want are the observations that are not in that set? Simple, we use the negation operator (!) as in:

outside.set = subset(my.data, !(code %in% c('A', 'B', 'C', 'D')))

In summary, surround the condition by !().

2011年10月13日星期四

some sentences on correlation

Correlation measures are among the most basic tools in statistical data analysis and machine learning.
They are applied to pairs of observations to measure to which extent the two observations
comply with a certain model. The most prominent representative is surely Pearson’s product
moment coefficient [1, 13], often nonchalantly called correlation coefficient for short. Pearson’s
product moment coefficient can be applied to numerical data and assumes a linear relationship as
the underlying model; therefore, it can be used to detect linear relationships, but no non-linear
ones.

Rank correlation measures [7, 10, 12] are intended to measure to which extent a monotonic
function is able to model the inherent relationship between the two observables. They neither
assume a specific parametric model nor specific distributions of the observables. They can be
applied to ordinal data and, if some ordering relation is given, to numerical data too. Therefore,
rank correlation measures are ideally suited for detecting monotonic relationships, in particular, if
more specific information about the data is not available. The two most common approaches are
Spearman’s rank correlation coefficient (short Spearman’s rho) [14, 15] and Kendall’s tau (rank
correlation coefficient) [2, 9, 10]. Another simple rank correlation measure is the gamma rank
correlation measure according to Goodman and Kruskal [7].

The rank correlation measures cited above are designed for ordinal data. However, as argued in
[5], they are not ideally suited for measuring rank correlation for numerical data that are perturbed
by noise. Consequently, [5] introduces a family of robust rank correlation measures. The idea
is to replace the classical ordering of real numbers used in Goodman’s and Kruskal’s gamma [7]
by some fuzzy ordering [8, 3, 4] with smooth transitions — thereby ensuring that the correlation
measure is continuous with respect to the data.

cited from "RoCoCo-An R Package Implementing a Robust Rank Correlation Coefficient and a Corresponding Test".

Screening synteny blocks in pairwise genome comparisons

(1) the tools
http://www.biomedcentral.com/1471-2105/12/102

(2) the web-interfaced
http://genomevolution.org/CoGe/

2011年10月11日星期二

Inferring the Rate of Occurrence and Fitness Effects of Advantageous Mutations

A Method for Inferring the Rate of Occurrence and Fitness Effects of Advantageous Mutations

http://www.genetics.org/content/early/2011/09/26/genetics.111.131730.abstract

http://homepages.ed.ac.uk/eang33/

Simulating tools for landscape genetics

Simulating natural selection in landscape genetics

http://onlinelibrary.wiley.com/doi/10.1111/j.1755-0998.2011.03075.x/full

2011年10月10日星期一

annotate your gene with gene superfamily and GO

Three tools you need to take their advantages:
1. BioMart, this is just a interface for many databases
http://www.biomart.org/index.html

2. InterPro, is an integrated database of predictive protein "signatures" used for the classification and automatic annotation of proteins and genomes. InterPro classifies sequences at superfamily, family and subfamily levels, predicting the occurrence of functional domains, repeats and important sites. InterPro adds in-depth annotation, including GO terms, to the protein signatures.

http://www.ebi.ac.uk/interpro/

the publication for InterPro
http://database.oxfordjournals.org/content/2011/bar033.full

all the gene superfamily list
http://www.ebi.ac.uk/interpro/ISearch?mode=db&query=Y

3.

SUPERFAMILY is a database of structural and functional annotation for all proteins and genomes.

The SUPERFAMILY annotation is based on a collection of hidden Markov models, which represent structural protein domains at the SCOP superfamily level. A superfamily groups together domains which have an evolutionary relationship. The annotation is produced by scanning protein sequences from over 1,700 completely sequenced genomes against the hidden Markov models.

http://www.supfam.org/SUPERFAMILY/index.html

2011年10月7日星期五

libsequence - for population genomic studies 群体基因组学必备

libsequence，无疑是群体基因组学的必备利器。它可以帮你方便的计算多数群体遗传参数，尤其是对于大规模、高通量的数据，它的效用就更明显。

这是一些注释文件：

http://molpopgen.org/software/libsequence/doc/html/index.html
http://molpopgen.org/software/libsequence/doc/html/classSequence_1_1PolySNP.html

2011年10月2日星期日

Venn diagram tools

1. venn {gplots} works on up to 5 sets,
2. Plotting Venn diagrams in R
https://stat.ethz.ch/pipermail/r-help/2003-February/029393.html
3. Venn Diagrams with R?
http://stackoverflow.com/questions/1428946/venn-diagrams-with-r
4. Vennerable package not available now for R.
5. venneuler package, not accurate
6. VennDiagram package, not work on my data
7. http://biostar.stackexchange.com/questions/7736/tool-to-generate-proportional-venn-diagrams

GWAS 中LD和haplotype长度的影响

Mapping Rare and Common Causal Alleles for Complex Human Diseases

http://www.cell.com/abstract/S0092-8674%2811%2901069-5#MainText

Linkage Disequilibrium and Haplotype Lengths

2011年9月30日星期五

R package on GoogleMaps plotting

plotGoogleMaps: Plot HTML output with Google Maps API and your own data
R2GoogleMaps: Provides a mechanism to generate JavaScript code from R that displays data using Google Maps
RgoogleMaps: Overlays on Google map tiles in R

2011年9月27日星期二

two annotation tools

1. annotation of genomic variations for SNP/indel effects
http://snpeff.sourceforge.net/

2. annotation of gene_ID/transcript_ID by GO
http://david.abcc.ncifcrf.gov/

2011年9月25日星期日

Plant RNAs Found in Mammals

有文章证实：植物源RNA能在人体里积累，并且影响人的基因表达。请看评论：

http://the-scientist.com/2011/09/20/plant-rnas-found-in-mammals/

我在想，这似乎为中药和很多中国饮食学说找到了根据（必须澄清：我不太了解西方的药物和饮食理论）。

2011年9月22日星期四

batch - tools for submitting R code in parallel on cluster

batch: Batching Routines in Parallel and Passing Command-Line Arguments to R

Functions to allow you to easily pass command-line arguments into R, and functions to aid in submitting your R code in parallel on a cluster and joining the results afterward (e.g. multiple parameter values for simulations running in parallel, splitting up a permutation test in parallel, etc.). See ‘parseCommandArgs(...)’ for the main example of how to use this package.

Inference of human population history from individual whole-genome sequences

依据基因组序列数据，推断群体历史

http://www.nature.com/nature/journal/vaop/ncurrent/full/nature10231.html

a, Population sizes inferred from autosomes of six individuals. 5%, 10% and 29% of heterozygotes are assumed to be missing in CHN.A, KOR.A and EUR1.A, respectively. b, Population sizes inferred from male-combined X chromosomes and the simulated African–Asian combined sequences from the best-fit model

The PSMC software package is freely available at http://github.com/lh3/psmc.

Evaluation from Faculty 1000:
http://f1000.com/11961958?key=x2xx78wtvjh8qn9

install python modules without root

You need follow this guide:

http://docs.python.org/install/index.html#alternate-installation-the-home-scheme

# in the uncompressed folder of the module, run
python setup.py install --home=/your/home/path