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

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/

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）。

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

Bayesian inference of ancient human demography from individual genome sequences

依基因组序列数据，推断 demography history
http://www.nature.com/ng/journal/vaop/ncurrent/full/ng.937.html

A competitive assessment of de novo short read assembly methods

http://genome.cshlp.org/content/early/2011/09/16/gr.126599.111.abstract

datatable - an R package

http://datatable.r-forge.r-project.org/

历史时期北半球积雪厚度的重建

http://onlinelibrary.wiley.com/doi/10.1111/j.2041-210X.2011.00144.x/abstract

Reconstructing historical snow depth surfaces to evaluate changes in critical demographic rates and habitat components of snow-dependent and snow-restricted species

区分进化历史中不同的变异类型

Distinguishing Driver and Passenger Mutations in an Evolutionary History Categorized by Interference

http://www.genetics.org/content/early/2011/09/05/genetics.111.133975.abstract

从数量性状推测选择迹象

A New Method to Uncover Signatures of Divergent and Stabilizing Selection in Quantitative Traits

http://www.genetics.org/content/early/2011/08/10/genetics.111.129387.abstract

MicroRNA 与种内功能分化

MicroRNA Sequence Variation Potentially Contributes to Within-species Functional Divergence in the Nematode Caenorhabditis briggsae

http://www.genetics.org/content/early/2011/08/30/genetics.111.132795.abstract

linux command - select a subset of lines in a file

What if the lines you want to extract are not in sequence, say lines 1 to 2, and line 4?

$ sed -n -e 1,2p -e 4p somefile.txt
Line 1
Line 2
Line 4

nice plot on - the correlation between geographic distance and genetic diversity

http://www.sciencedirect.com/science/article/pii/S0960982210006573

write list object to a text file

http://stackoverflow.com/questions/7351049/write-csv-a-list-of-unequally-sized-data-frames 

outputs <- list() 
outputs$fivenum <- fivenum(rnorm(100))
outputs$summary <- as.data.frame(as.vector(summary(rnorm(100))))

tmp <- lapply(outputs, as.data.frame)

# can not work
write.csv(tmp, file="Output.csv",append=T)

# workable

sapply(tmp, write.table, file="Output.csv", append=TRUE, sep=",")

heritability of height in pine

Patterns of genetic parameters for height in field genetic tests of Picea abies and Pinus sylvestris in Sweden 

http://www.springerlink.com/content/17272771167530j3/

Impact of selection on genes involved in regulatory network - a modelling study

http://onlinelibrary.wiley.com/doi/10.1111/j.1420-9101.2011.02335.x/abstract

Complex phenotypes are often controlled by many interacting genes. One question emerging from such organization is how selection, acting at the phenotypic level, shapes the evolution of genes involved in regulatory networks controlling the phenotypes. We studied this issue through a matrix model of such networks. In a population submitted to selection, we simulated the evolution of a quantitative trait controlled by a set of loci that regulate each other through positive or negative interactions. Investigating several levels of selection intensity on the trait, we studied the evolution of regulation intensity between the genes and the evolution of the genetic diversity of those genes as an indirect measure of the strength of selection acting on them. We show that an increasing intensity of selection on the phenotype leads to an increased level of regulation between the loci. Moreover, we found that the genes responding more strongly to selection within the network were those evolving towards stronger regulatory action on the other genes and/or those that are the less regulated by the other genes. This observation is strongest for an intermediate level of selection. This may explain why several experimental studies have shown evidence of selection on regulatory genes inside gene networks.

interpolation of XYZ data and projection onto a map - R

http://menugget.blogspot.com/2011/05/mapxyz-interpolation-of-xyz-data-and.html

Temporal variation in genetic diversity and effective population size of Mediterranean and subalpine Arabidopsis thaliana populations

http://onlinelibrary.wiley.com/doi/10.1111/j.1365-294X.2011.05193.x/full

recombination and selection

Comparison of recent patterns of recombination derived from linkage maps to historical patterns of recombination from linkage disequilibrium (LD) could help identify genomic regions affected by strong artificial selection, appearing as reduced recent recombination. Norwegian Red cattle (NRF) make an interesting case study for investigating these patterns as it is an admixed breed with an extensively recorded pedigree. NRF have been under strong artificial selection for traits such as milk and meat production, fertility and health.
While measures of LD is also crucial for determining the number of markers required for association mapping studies, estimates of recombination rate can be used to assess quality of genomic assemblies.

http://www.biomedcentral.com/1471-2164/12/33