The only secret of style

Matthew Arnold (1822 -1888):

“Have something to say, and say it as clearly as you can. That is the only secret of style.”

E.g. Bill Clinton full DNC Speech 2012
http://www.youtube.com/watch?v=uzDhk3BHi6Q

2007 Microsoft Office Add-in: Microsoft Save as PDF or XPS

http://r.office.microsoft.com/r/rlidMSAddinPDFXPS

http://office.microsoft.com/en-us/help/print-share-and-protect-files-in-the-pdf-and-xps-file-formats-HA010167527.aspx?CTT=5&origin=HA010067494

Install this plugin so that you can export / save PowerPoint slides to PDF.

yED - Graph Editor

http://www.yworks.com/en/products_yed_about.html

Workflow visualization tool.

Coursera

https://www.coursera.org/ubc

Free online course

Useful Genetics from Rosy Redfield

Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources

http://www.nature.com/nprot/journal/v4/n1/full/nprot.2008.211.html

Da Wei Huang^1,2, Brad T Sherman^1,2 & Richard A Lempicki¹

Abstract

DAVID bioinformatics resources consists of an integrated biological knowledgebase and analytic tools aimed at systematically extracting biological meaning from large gene/protein lists. This protocol explains how to use DAVID, a high-throughput and integrated data-mining environment, to analyze gene lists derived from high-throughput genomic experiments. The procedure first requires uploading a gene list containing any number of common gene identifiers followed by analysis using one or more text and pathway-mining tools such as gene functional classification, functional annotation chart or clustering and functional annotation table. By following this protocol, investigators are able to gain an in-depth understanding of the biological themes in lists of genes that are enriched in genome-scale studies.

Science and the Semantic Web

http://www.sciencemag.org/content/299/5606/520.summary

James Hendler*[HN19]

As modern science grows in complexity and scope, the need for more collaboration between scientists at different institutions, in different areas, and across scientific disciplines becomes increasingly important. An emerging generation of World Wide Web technology, known as the Semantic Web, offers tremendous potential for collaborative and interdisciplinary science. However, to realize this potential, scientists and information technologists must forge new models of cooperation, and new thinking must go into the funding and dissemination of this next generation of scientific tools on the Web.      

Science 24 January 2003:
Vol. 299 no. 5606 pp. 520-521 DOI: 10.1126/science.1078874

Force fsck on boot

Tried to run fsck but your drive is already mounted? Force run it on the next boot by typing

$ sudo touch /forcefsck

Edit the drives that you want checked by putting a 1 on the last column

$ sudo gedit /etc/fstab

First day of Teaching

http://www2.honolulu.hawaii.edu/facdev/guidebk/teachtip/dayone.htm

Don't just present the slides, also read the expression of students and see if they follow you.

http://gradschool.about.com/od/collegeteaching/p/How-To-Lecture-Teaching-Tips-For-Graduate-Students.htm

Place yourself in their shoes

http://gradschool.about.com/od/becomingaprofessional/g/impostor.htm

Definition: The impostor syndrome is feeling that one hasn't earned his or her achievements - that the achievements are the result of luck. It is very common among high achieving persons, particularly women, who worry that some day they will be "found out" - that others will discover that they really do not know what they are doing. A professor who experiences the impostor syndrome may feel that he or she is faking it and worry that others know that they are really a phony and that their achievements are simply a matter of luck, not ability.

Remind yourself that you know your stuff. Visualize yourself succeeding and be your own cheerleader.  

Miniature science: How microfluidics is powering biology

http://www.youtube.com/watch?v=66Oc8fLKXlE

... so basically, use laser tweezers to isolate an unculturable bacteria (eg. SFB segmented filamentous bacteria, found in the lining of intestines) so that you can amplify them and sequence
.... in a microfluidic environment, you can minimize the contamination that comes with amplification reagents due to smaller volume in chambers and it's a closed environment so contamination from air can't come in

Table of Contents
00:00 - Introduction
01:33 - Talk begins
03:26 - What is microfluidics?
09:24 - Microelectronics: a recent micro-tech revolution
13:12 - The complexity of biological systems
17:50 - How to unravel biological mysteries
21:24 - Is there a Moore's law for experimental biology?
23:55 - Methods and possibilities of modern microfluidics
30:23 - Studying single cells with microfluidics
34:33 - Moving cells with laser tweezers
35:18 - Preventing contamination using microfluidics
40:38 - Intestinal bacteria and their DNA
44:55 - A new revolution in life science is beginning...
46:30 - Acknowledgements
47:10 - Q&A

Miniaturized lab-on-a-chip methods are being deployed as labor-saving devices in biological research, through the advent of a suite of microfluidics technologies. Microfluidics enables large-scale studies that provide the means to better understand, prevent, and treat human disease. In his 2012 Midsummer Nights' Science lecture, Paul Blainey discusses the promise of using microfluidics to transform our industrial infrastructure to operate more efficiently, while protecting the natural environment.

Find out more about Paul Blainey's work at the Broad Institute and about our public lecture series and other educational opportunities:

-Blainey lab page (http://www.broadinstitute.org/node/4165/)
-Broad news story (http://www.broadinstitute.org/news/3068)
-Education (http://www.broadinstitute.org/node/635/)
-Broad Institute Midsummer Nights' Science series (http://www.broadinstitute.org/midsummer)

Category:

Science & Technology

License:

Standard YouTube License

Vancouver Prostate Centre Next-Gen Sequencing

slebihan@prostatecentre.com

Metagenomic research

Energy production, bioremediation

microbe, dentistry

marine biology

soil

for:
microbial and metabolic diversity
microbial abundance
gene content
metatranscriptomics
metabolic signature profiling
viral pathogen detection

16S/18S rRNA Amplicon Sequencing and Shotgun Sequencing

Free online brainstorming diagraming tool

https://bubbl.us/

brainstorming and workflow made simple

R Bootstrapping, stratification

http://www.statmethods.net/advstats/bootstrapping.html

Bootstrapping a Single Statistic (k=1)

The following example generates the bootstrapped 95% confidence interval for R-squared in the linear regression of miles per gallon (mpg) on car weight (wt) and displacement (disp). The data source is mtcars. The bootstrapped confidence interval is based on 1000 replications.
# Bootstrap 95% CI for R-Squared
library(boot)
# function to obtain R-Squared from the data
rsq <- function(formula, data, indices) {
  d <- data[indices,] # allows boot to select sample
  fit <- lm(formula, data=d)
  return(summary(fit)$r.square)
}
# bootstrapping with 1000 replications
results <- boot(data=mtcars, statistic=rsq,
   R=1000, formula=mpg~wt+disp)

# view results
results
plot(results)

# get 95% confidence interval
boot.ci(results, type="bca")




http://rss.acs.unt.edu/Rdoc/library/sampling/html/strata.html

library('sampling')



############
## Example 3
############
# Uses the 'swissmunicipalities' data for drawing a sample of units
data(swissmunicipalities)
# the variable 'REG' has 7 categories in the population; it is used as stratification variable
# Computes the population stratum sizes
table(swissmunicipalities$REG)
# do not run
#  1   2   3   4   5   6   7 
# 589 913 321 171 471 186 245 
# the sample stratum sizes are given by size=c(30,20,45,15,20,11,44)
# the method is simple random sampling without replacement (equal probability, without replacement)
st=strata(swissmunicipalities,stratanames=c("REG"),size=c(30,20,45,15,20,11,44), method="srswor")
# extracts the observed data
# the order of the columns is different from the order in the swsissmunicipalities database
table(getdata(swissmunicipalities, st)$REG)
# do not run
# 1  2  3  4  5  6  7 
# 20 15 45 30 20 11 44

z<-getdata(swissmunicipalities, st)
dim(z)
#[1] 185  25
dim(swissmunicipalities)
#[1] 2896   22




Differential gene expression

Differential Gene Expression

http://estbioinfo.stat.ub.es/docs/jlmosquera/teaching/UVic/tia/Lecture3-DifferentialGeneExpression.pdf