file:////home/bob/123.jpg
That's four / after :
Wednesday, August 10, 2011
Google Chart API / Infographics
http://code.google.com/apis/chart/infographics/docs/overview.html
QR code for "Hello world"
https://chart.googleapis.com/chart?chs=150x150&cht=qr&chl=Hello%20world
https://chart.googleapis.com/chart?chst=d_bubble_icon_text_small&chld=ski|bb|Wheeee!|FFFFFF|000000
Google Image Chart
https://chart.googleapis.com/chart?chs=250x100&chd=t:60,40&cht=p3&chl=Hello|World
Cytoscape custom graphic pass-through mapper
http://proteomics-ms.blogspot.com/2011/07/custom-graphics-for-node-in-cytoscape.html
QR code for "Hello world"
https://chart.googleapis.com/chart?chs=150x150&cht=qr&chl=Hello%20world
https://chart.googleapis.com/chart?chst=d_bubble_icon_text_small&chld=ski|bb|Wheeee!|FFFFFF|000000
Google Image Chart
https://chart.googleapis.com/chart?chs=250x100&chd=t:60,40&cht=p3&chl=Hello|World
Cytoscape custom graphic pass-through mapper
http://proteomics-ms.blogspot.com/2011/07/custom-graphics-for-node-in-cytoscape.html
Online course on Protein-Protein Interactions
-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
BI211 Protein-Protein Interactions
September 19-23, 2011
Online at Bioinformatics.Org
http://www.bioinformatics.org/wiki/BI211_Protein-Protein_Interactions
-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
OBJECTIVES:
This course will dig into some of the fundamental issues concerning protein-protein interactions (PPIs), including their need and use in research. It will introduce various tools and provide examples for finding true, positive interactors from Web searches and interfaces. The exercises and take-home messages will encourage participants to give the topics some thought and then hopefully build some excitement about the top-down approach of systems biology involving protein-protein interactions.
INSTRUCTORS:
Prashanth Suravajhala is on the Board of Directors of Bioinformatics.Org. His home page is at http://www.bioinformatics.org/wiki/Prash.
Gary D. Bader works on biological network analysis and pathway information resources as an Assistant Professor at The Donnelly Centre at the University of Toronto. His home page is at http://baderlab.org/.
TOPICS:
* Assays, tools and techniques in PPIs: Pros and Cons
* Types of interactions and networks
* Data validation and integration
* Capabilities of networks
* A look at sample PPI data
* Future challenges
* Introduction to Cytoscape, Osprey and Ingenuity
* Networkology: Data representations
* Exercises using Osprey: A short project
* Exercises with iHOP, String, Bind, PreBind, Genecards, MINT, HPRD
REGISTRATION:
http://www.bioinformatics.org/edu/ACAA
FOR MORE INFORMATION:
Please write to edu@bioinformatics.org.
BI211 Protein-Protein Interactions
September 19-23, 2011
Online at Bioinformatics.Org
http://www.bioinformatics.org/wiki/BI211_Protein-Protein_Interactions
-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
OBJECTIVES:
This course will dig into some of the fundamental issues concerning protein-protein interactions (PPIs), including their need and use in research. It will introduce various tools and provide examples for finding true, positive interactors from Web searches and interfaces. The exercises and take-home messages will encourage participants to give the topics some thought and then hopefully build some excitement about the top-down approach of systems biology involving protein-protein interactions.
INSTRUCTORS:
Prashanth Suravajhala is on the Board of Directors of Bioinformatics.Org. His home page is at http://www.bioinformatics.org/wiki/Prash.
Gary D. Bader works on biological network analysis and pathway information resources as an Assistant Professor at The Donnelly Centre at the University of Toronto. His home page is at http://baderlab.org/.
TOPICS:
* Assays, tools and techniques in PPIs: Pros and Cons
* Types of interactions and networks
* Data validation and integration
* Capabilities of networks
* A look at sample PPI data
* Future challenges
* Introduction to Cytoscape, Osprey and Ingenuity
* Networkology: Data representations
* Exercises using Osprey: A short project
* Exercises with iHOP, String, Bind, PreBind, Genecards, MINT, HPRD
REGISTRATION:
http://www.bioinformatics.org/edu/ACAA
FOR MORE INFORMATION:
Please write to edu@bioinformatics.org.
Tuesday, August 9, 2011
Chimpanzees Not as Selfish as We Thought
New research debunks a previous theory about how and why chimps share.
http://news.discovery.com/animals/chimps-share-110808.html
For each experiment, the tokens came in two different colors. Choosing tokens of a certain color would result in a "selfish outcome," which was a food reward for just the participant. Choosing tokens of the other color resulted in food rewards for both the token selector and another nearby chimp in an adjacent compartment.
The food rewards consisted of banana slices wrapped in butcher paper that made a loud sound when unwrapped.
The chimp participants nearly always chose the tokens that would yield food rewards for both the selector and the nearby observing chimp.
http://news.discovery.com/animals/chimps-share-110808.html
For each experiment, the tokens came in two different colors. Choosing tokens of a certain color would result in a "selfish outcome," which was a food reward for just the participant. Choosing tokens of the other color resulted in food rewards for both the token selector and another nearby chimp in an adjacent compartment.
The food rewards consisted of banana slices wrapped in butcher paper that made a loud sound when unwrapped.
The chimp participants nearly always chose the tokens that would yield food rewards for both the selector and the nearby observing chimp.
compendium
com·pen·di·um (km-pnd-m)
n. pl. com·pen·di·ums or com·pen·di·a (-d-)
1. A short, complete summary; an abstract.
2. A list or collection of various items.
n. pl. com·pen·di·ums or com·pen·di·a (-d-)
1. A short, complete summary; an abstract.
2. A list or collection of various items.
GHMM Library
http://ghmm.org/
The General Hidden Markov Model library (GHMM) is a freely available C library implementing efficient data structures and algorithms for basic and extended HMMs with discrete and continous emissions. It comes with Python wrappers which provide a much nicer interface and added functionality. The GHMM is licensed under the LGPL.
The General Hidden Markov Model library (GHMM) is a freely available C library implementing efficient data structures and algorithms for basic and extended HMMs with discrete and continous emissions. It comes with Python wrappers which provide a much nicer interface and added functionality. The GHMM is licensed under the LGPL.
Olga Troyanskaya
http://imperio.princeton.edu/cm/node/14
http://www.molbio.princeton.edu/index.php?option=content&task=view&id=243
Olga Troyanskaya
Troyanskaya Lab Webpage
ogt@princeton.eduThis e-mail address is being protected from spam bots, you need JavaScript enabled to view it Faculty Assistant:
Computer Science Bldg-204
Phone: 609-258-1749 Phone: 609-258-7014
Bioinformatics and genomics
The new era of high-throughput experimental methods in molecular biology has created exciting challenges for computer science to develop novel algorithms for complex, accurate, and consistent interpretation of diverse biological information. In the next decades, large-scale explorations of complex molecular, cellular, and organismic systems at complementary levels of resolution will allow us to integrate our understanding of macroscopic physiology and microscopic biology. To realize the full potential of these developments, we need to develop sophisticated bioinformatics frameworks to integrate and synthesize diverse biological data produced by these methods.
The goal of the research in my laboratory is to bring the capabilities of computer science and statistics to the study of gene function and regulation in the biological networks through integrated analysis of biological data from diverse data sources--both existing and yet to come (e.g. from diverse gene expression data sets and proteomic studies). We are designing systematic and accurate computational and statistical algorithms for biological signal detection in high-throughput data sets. More specifically, our lab is interested in developing methods for better gene expression data processing and algorithms for integrated analysis of biological data from multiple genomic data sets and different types of data sources (e.g. genomic sequences, gene expression, and proteomics data).
My laboratory combines computational methods with an experimental component in a unified effort to develop comprehensive descriptions of genetic systems of cellular controls, including those whose malfunctioning becomes the basis of genetic disorders, such as cancer, and others whose failure might produce developmental defects in model systems. The experimental component the lab focuses on is S. cerevisiae (baker's yeast).
http://www.molbio.princeton.edu/index.php?option=content&task=view&id=243
Olga Troyanskaya
Troyanskaya Lab Webpage
ogt@princeton.eduThis e-mail address is being protected from spam bots, you need JavaScript enabled to view it Faculty Assistant:
Computer Science Bldg-204
Phone: 609-258-1749 Phone: 609-258-7014
Bioinformatics and genomics
The new era of high-throughput experimental methods in molecular biology has created exciting challenges for computer science to develop novel algorithms for complex, accurate, and consistent interpretation of diverse biological information. In the next decades, large-scale explorations of complex molecular, cellular, and organismic systems at complementary levels of resolution will allow us to integrate our understanding of macroscopic physiology and microscopic biology. To realize the full potential of these developments, we need to develop sophisticated bioinformatics frameworks to integrate and synthesize diverse biological data produced by these methods.
The goal of the research in my laboratory is to bring the capabilities of computer science and statistics to the study of gene function and regulation in the biological networks through integrated analysis of biological data from diverse data sources--both existing and yet to come (e.g. from diverse gene expression data sets and proteomic studies). We are designing systematic and accurate computational and statistical algorithms for biological signal detection in high-throughput data sets. More specifically, our lab is interested in developing methods for better gene expression data processing and algorithms for integrated analysis of biological data from multiple genomic data sets and different types of data sources (e.g. genomic sequences, gene expression, and proteomics data).
My laboratory combines computational methods with an experimental component in a unified effort to develop comprehensive descriptions of genetic systems of cellular controls, including those whose malfunctioning becomes the basis of genetic disorders, such as cancer, and others whose failure might produce developmental defects in model systems. The experimental component the lab focuses on is S. cerevisiae (baker's yeast).
Global Prediction of Tissue-Specific Gene Expression and Context-Dependent Gene Networks in Caenorhabditis elegans
http://www.ploscompbiol.org/article/info:doi%2F10.1371%2Fjournal.pcbi.1000417
Abstract
Tissue-specific gene expression plays a fundamental role in metazoan biology and is an important aspect of many complex diseases. Nevertheless, an organism-wide map of tissue-specific expression remains elusive due to difficulty in obtaining these data experimentally. Here, we leveraged existing whole-animal Caenorhabditis elegans microarray data representing diverse conditions and developmental stages to generate accurate predictions of tissue-specific gene expression and experimentally validated these predictions. These patterns of tissue-specific expression are more accurate than existing high-throughput experimental studies for nearly all tissues; they also complement existing experiments by addressing tissue-specific expression present at particular developmental stages and in small tissues. We used these predictions to address several experimentally challenging questions, including the identification of tissue-specific transcriptional motifs and the discovery of potential miRNA regulation specific to particular tissues. We also investigate the role of tissue context in gene function through tissue-specific functional interaction networks. To our knowledge, this is the first study producing high-accuracy predictions of tissue-specific expression and interactions for a metazoan organism based on whole-animal data.
Abstract
Tissue-specific gene expression plays a fundamental role in metazoan biology and is an important aspect of many complex diseases. Nevertheless, an organism-wide map of tissue-specific expression remains elusive due to difficulty in obtaining these data experimentally. Here, we leveraged existing whole-animal Caenorhabditis elegans microarray data representing diverse conditions and developmental stages to generate accurate predictions of tissue-specific gene expression and experimentally validated these predictions. These patterns of tissue-specific expression are more accurate than existing high-throughput experimental studies for nearly all tissues; they also complement existing experiments by addressing tissue-specific expression present at particular developmental stages and in small tissues. We used these predictions to address several experimentally challenging questions, including the identification of tissue-specific transcriptional motifs and the discovery of potential miRNA regulation specific to particular tissues. We also investigate the role of tissue context in gene function through tissue-specific functional interaction networks. To our knowledge, this is the first study producing high-accuracy predictions of tissue-specific expression and interactions for a metazoan organism based on whole-animal data.
CPAWS | Canadian Parks and Wilderness Society
www.cpaws.org
Founded in 1963, the Canadian Parks and Wilderness Society (CPAWS) has helped protect over 300000 square kilometers of Canada's threatened wild areas.
Founded in 1963, the Canadian Parks and Wilderness Society (CPAWS) has helped protect over 300000 square kilometers of Canada's threatened wild areas.
Monday, August 8, 2011
Sample Wiggle UCSC custom track
track type=wiggle_0 name=fooWig descript="bla"
variableStep chrom=chr4
265 0.202
270 0.202
275 0.202
280 0.202
285 0.202
290 0.202
http://genome.ucsc.edu/goldenPath/help/wiggle.html
variableStep chrom=chr4
265 0.202
270 0.202
275 0.202
280 0.202
285 0.202
290 0.202
http://genome.ucsc.edu/goldenPath/help/wiggle.html
wig tools
http://hgdownload.cse.ucsc.edu/admin/exe/linux.x86_64/
bedGraphToBigWig v 4 -
Convert a bedGraph program to bigWig.
bedItemOverlapCount
bedToBigBed
http://genome.ucsc.edu/goldenPath/help/bigWig.html
bedGraphToBigWig v 4 -
Convert a bedGraph program to bigWig.
bedItemOverlapCount
bedToBigBed
http://genome.ucsc.edu/goldenPath/help/bigWig.html
Subscribe to:
Posts (Atom)