Google Reader - Play

Oh wow what's this?

New to me!

http://www.google.com/reader/play/

Keybindings - iterm2 - How to set up your shell for accepting proper key bindings. - Development site for iTerm2 - Google Project Hosting

Gah!
when i solve one issue for tweaking my shell/terminal/ env another one pops up

I like the alt+<- for jumping words in Terminal but it's missting in iTerm2
iTerm2 will select the entire line when i double tap (e.g. folder
path) but it lacks the above ..

Readline also supports jumping between words, that is re-positioning
the cursor before or after the current/next word.

The default binding for this is ESC-f for forward jumping and ESC-b
for backward jumping.

Most users will find this inconvenient, as the escape key is far away
from the default finger position. While iTerm2 can be configured to
use the Option (ALT) key for entering escape sequences, it should be
better used as the Meta key or for entering "special" characters like
greek letters, symbols, umlauts etc.
http://code.google.com/p/iterm2/wiki/Keybindings

PLoS ONE: Cancer Gene Prioritization for Targeted Resequencing Using FitSNP Scores

http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0031333

PLoS One. 2012;7(3):e31333. Epub 2012 Mar 1.

Cancer Gene Prioritization for Targeted Resequencing Using FitSNP Scores.

Fieuw A, De Wilde B, Speleman F, Vandesompele J, De Preter K.

Source

Center for Medical Genetics, Ghent University Hospital, Ghent, Belgium.

Abstract

BACKGROUND:

Although the throughput of next generation sequencing is increasing and at the same time the cost is substantially reduced, for the majority of laboratories whole genome sequencing of large cohorts of cancer samples is still not feasible. In addition, the low number of genomes that are being sequenced is often problematic for the downstream interpretation of the significance of the variants. Targeted resequencing can partially circumvent this problem; by focusing on a limited number of candidate cancer genes to sequence, more samples can be included in the screening, hence resulting in substantial improvement of the statistical power. In this study, a successful strategy for prioritizing candidate genes for targeted resequencing of cancer genomes is presented.

RESULTS:

FOUR PRIORITIZATION STRATEGIES WERE EVALUATED ON SIX DIFFERENT CANCER TYPES: genes were ranked using these strategies, and the positive predictive value (PPV) or mutation rate within the top-ranked genes was compared to the baseline mutation rate in each tumor type. Successful strategies generate gene lists in which the top is enriched for known mutated genes, as evidenced by an increase in PPV. A clear example of such an improvement is seen in colon cancer, where the PPV is increased by 2.3 fold compared to the baseline level when 100 top fitSNP genes are sequenced.

CONCLUSIONS:

A gene prioritization strategy based on the fitSNP scores appears to be most successful in identifying mutated cancer genes across different tumor entities, with variance of gene expression levels as a good second best.

rehh : An R package to detect footprints of selection in genome-wide SNP data from haplotype structure.[Bioinformatics. 2012] - PubMed - NCBI

http://www.ncbi.nlm.nih.gov/pubmed/22402612

Bioinformatics. 2012 Mar 7. [Epub ahead of print]

rehh : An R package to detect footprints of selection in genome-wide SNP data from haplotype structure.

Gautier M, Vitalis R.

Source

INRA, INRA-CNRS, UMR CBGP (INRA - IRD - Cirad - Montpellier SupAgro), Campus International de Baillarguet, CS 30016, F-34988 Montferrier-sur-Lez Cedex, France.

Abstract

SUMMARY:

With the development of next-generation sequencing and genotyping approaches, large SNP haplotype data sets are becoming available in a growing number of both model and non-model species. Identifying genomic regions with unexpectedly high local haplotype homozygosity relatively to neutral expectation represents a powerful strategy to ascertain candidate genes responding to natural or artificial selection. To facilitate genome-wide scans of selection based on the analysis of long-range haplotypes, we developed the R package rehh , which provides a versatile tool to detect the footprints of recent or ongoing selection, thanks to several graphical functions that help visual interpretation of the results.

AVAILABILITY:

rehh is available for Windows, Mac OS X and Linux platforms from http://www1.montpellier.inra.fr/URLB/Rpackages/rehh_1.0_all.zip. Function documentation and example data files are provided within the package and a tutorial is available as supplementary information. rehh is distributed under the GNU General Public Licence (GPL ≥ 2).

A modified method for whole exome resequencing from... [PLoS One. 2012] - PubMed - NCBI

http://www.ncbi.nlm.nih.gov/pubmed/22403682

A modified method for whole exome resequencing from minimal amounts of starting DNA.

Kozarewa I, Rosa-Rosa JM, Wardell CP, Walker BA, Fenwick K, Assiotis I, Mitsopoulos C, Zvelebil M, Morgan GJ, Ashworth A, J C.

Source

The Breakthrough Breast Cancer Research Centre, The Institute of Cancer Research, London, United Kingdom.

Abstract

Next generation DNA sequencing (NGS) technologies have revolutionized the pace at which whole genome and exome sequences can be generated. However, despite these advances, many of the methods for targeted resequencing, such as the generation of high-depth exome sequences, are somewhat limited by the relatively large amounts of starting DNA that are normally required. In the case of tumour analysis this is particularly pertinent as many tumour biopsies often return submicrogram quantities of DNA, especially when tumours are microdissected prior to analysis. Here, we present a method for exome capture and resequencing using as little as 50 ng of starting DNA. The sequencing libraries generated by this minimal starting amount (MSA-Cap) method generate datasets that are comparable to standard amount (SA) whole exome libraries that use three micrograms of starting DNA. This method, which can be performed in most laboratories using commonly available reagents, has the potential to enhance large scale profiling efforts such as the resequencing of tumour exomes.

PMID:

 

22403682

 

[PubMed - in process]

Cookbook for R » Colors (ggplot2) for the color blind (Not a joke!)

Problem

You want to use colors in a graph with ggplot2.

Solution

The default colors in ggplot2 can be difficult to distinguish from one another because they have equal luminance. They are also not friendly for colorblind viewers.

A good general-purpose solution is to just use the colorblind-friendly palette below.
http://wiki.stdout.org/rcookbook/Graphs/Colors%20(ggplot2)/

Learning From Our GWAS Mistakes: From experimental design to scientific method | Our 2 SNPs…(R)

Learning From Our GWAS Mistakes
by Christophe Lambert, CEO and Chairman

This month Biostatistics published online an open access article I co-authored with Dr. Laura Black from Montana State University: "Learning From Our GWAS Mistakes: From Experimental Design To Scientific Method." The paper version is expected to come out in April, along with an editorial introduction. I'm hoping that you will take the time to read it. This post extends on some of the themes of the paper, and continues my ongoing effort to examine the systems and paradigms in which we work and how we can improve or even replace them. I'm going to try to be more provocative here than the paper itself. It may insult you, but I'll make sure to insult myself as well. Continue reading »