New Genomic Marker for Tuberculosis May Help Identify Patients Who Will Develop the Disease

http://www.sciencedaily.com/releases/2010/08/100830073801.htm?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed%3A+sciencedaily+%28ScienceDaily%3A+Latest+Science+News%29

Matthew P. R. Berry, Christine M. Graham, Finlay W. McNab, Zhaohui Xu, Susannah A. A. Bloch, Tolu Oni, Katalin A. Wilkinson, Romain Banchereau, Jason Skinner, Robert J. Wilkinson, Charles Quinn, Derek Blankenship, Ranju Dhawan, John J. Cush, Asuncion Mejias, Octavio Ramilo, Onn M. Kon, Virginia Pascual, Jacques Banchereau, Damien Chaussabel, Anne O’Garra. An interferon-inducible neutrophil-driven blood transcriptional signature in human tuberculosis. Nature, 2010; 466 (7309): 973 DOI: 10.1038/nature09247

Videos in Gene Screen BC 2010

http://www.scivee.tv/node/16377/video

Genome-wide association study of migraine implicates a common susceptibility variant on 8q22.1. Nature Genetics, 2010; DOI: 10.1038/ng.652

Verneri Anttila, Hreinn Stefansson, Mikko Kallela, Unda Todt, Gisela M Terwindt, M Stella Calafato, Dale R Nyholt, Antigone S Dimas, Tobias Freilinger, Bertram Müller-Myhsok, Ville Artto, Michael Inouye, Kirsi Alakurtti, Mari A Kaunisto, Eija Hämäläinen, Boukje de Vries, Anine H Stam, Claudia M Weller, Axel Heinze, Katja Heinze-Kuhn, Ingrid Goebel, Guntram Borck, Hartmut Göbel, Stacy Steinberg, Christiane Wolf, Asgeir Björnsson, Gretar Gudmundsson, Malene Kirchmann, Anne Hauge, Thomas Werge, Jean Schoenen, Johan G Eriksson, Knut Hagen, Lars Stovner, H-Erich Wichmann, Thomas Meitinger, Michael Alexander, Susanne Moebus, Stefan Schreiber, Yurii S Aulchenko, Monique M B Breteler, Andre G Uitterlinden, Albert Hofman, Cornelia M van Duijn, Päivi Tikka-Kleemola, Salli Vepsäläinen, Susanne Lucae, Federica Tozzi, Pierandrea Muglia, Jeffrey Barrett, Jaakko Kaprio, Markus Färkkilä, Leena Peltonen, Kari Stefansson, John-Anker Zwart, Michel D Ferrari, Jes Olesen, Mark Daly, Maija Wessman, Arn M J M van den Maagdenberg, Martin Dichgans, Christian Kubisch, Emmanouil T Dermitzakis, Rune R Frants, Aarno Palotie. Genome-wide association study of migraine implicates a common susceptibility variant on 8q22.1. Nature Genetics, 2010; DOI: 10.1038/ng.652

Ten Simple Rules for a Good Poster Presentation

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1876493/

Ten Simple Rules for a Good Poster Presentation
Thomas C Erren* and Philip E Bourne

Coherent pipeline for biomarker discovery using mass spectrometry and bioinformatics
Al-Shahib A, Misra R, Ahmod N, Fang M, Shah H, Gharbia S
BMC Bioinformatics 2010, 11:437 (26 August 2010)
http://www.biomedcentral.com/1471-2105/11/437/abstract

A towards-multidimensional screening approach to predict candidate genes of rheumatoid arthritis based on SNP, structural and functional annotations
Zhang L, Li W, Song L, Chen L
BMC Medical Genomics 2010, 3:38 (20 August 2010)
http://www.biomedcentral.com/1755-8794/3/38/abstract

Curating the innate immunity interactome
Lynn DJ, Chan C, Naseer M, Yau M, Lo R, Sribnaia A, Ring G, Que J, Wee K, Winsor GL, Laird MR, Breuer K, Foroushani AK, Brinkman FSL, Hancock REW
BMC Systems Biology 2010, 4:117 (20 August 2010)
http://www.biomedcentral.com/1752-0509/4/117/abstract

De novo assembly of short sequence reads
Konrad Paszkiewicz and David J. Studholme
Brief Bioinform published 19 August 2010, 10.1093/bib/bbq020
http://bib.oxfordjournals.org/cgi/content/abstract/bbq020v1

Perspectives on presentation and pedagogy in aid of bioinformatics education
Pier Luigi Buttigieg
Brief Bioinform published 19 August 2010, 10.1093/bib/bbq062
http://bib.oxfordjournals.org/cgi/content/abstract/bbq062v1

*omeSOM: a software for clustering and visualization of transcriptional and metabolite data mined from interspecific crosses of crop plants.
Milone DH, Stegmayer GS, Kamenetzky L, Lopez M, Lee J, Giovannoni JJ, Carrari F
BMC Bioinformatics 2010, 11:438 (26 August 2010)
http://www.biomedcentral.com/1471-2105/11/438/abstract

Predictive, preventive, personalized and participatory medicine: back to the future
Auffray C, Charron D, Hood L
Genome Medicine 2010, 2:57 (26 August 2010)
http://genomemedicine.com/content/2/8/57/abstract

Engineering of N. benthamiana L. plants for production of N-acetylgalactosamine-glycosylated proteins - towards development of a plant-based platform for production of protein therapeutics with mucin type O-glycosylation
Daskalova SM, Radder JE, Cichacz ZA, Olsen SH, Tsaprailis G, Mason H, Lopez LC
BMC Biotechnology 2010, 10:62 (24 August 2010)
http://www.biomedcentral.com/1472-6750/10/62/abstract

"Predicting protein complexes by data integration of different types of interactions", Powell Patrick Cheng Tan, Daryanaz Dargahi, Frederic Pio
DOI: 10.1504/IJCBDD.2010.034464
http://www.inderscience.com/search/index.php?action=record&rec_id=34464&prevQuery=&ps=10&m=or

Ensembles, CodonTest, Volumetric Analysis

http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1000911
Evidence of Functional Protein Dynamics from X-Ray Crystallographic Ensembles

There is a well-recognized gap between the dynamical motions of proteins required to execute function and the experimental techniques capable of capturing that motion at the atomic level. We show that much experimental detail of dynamical motion is already present in X-ray crystallographic data, which arises from being solved by different research groups using different methodologies under different crystallization conditions, which then capture an ensemble of structures whose variations can be quantified on a residue-by-residue level using local density correlations. We contrast the amino acid displacements below and above the protein dynamical transition temperature, TD∼215K, of hen egg white lysozyme by comparing the X-ray ensemble to MD ensembles as a function of temperature. We show that measuring structural variations across an ensemble of X-ray derived models captures the activation of conformational states that are of functional importance just above TD and they remain virtually identical to structural motions measured at 300K. It provides a novel analysis of large X-ray ensemble data that is useful for the broader structural biology community.

Jonathan E. Kohn1, Pavel V. Afonine2, Jory Z. Ruscio1, Paul D. Adams1,2, Teresa Head-Gordon1,2*

1 Department of Bioengineering, University of California, Berkeley, Berkeley, California, United States of America, 2 Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America


http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1000885
Wayne Delport1, Konrad Scheffler2, Gordon Botha2, Mike B. Gravenor3, Spencer V. Muse4, Sergei L. Kosakovsky Pond5*

1 Department of Pathology, University of California, San Diego, La Jolla, California, United States of America, 2 Computer Science Division, Department of Mathematical Sciences, Stellenbosch University, Stellenbosch, South Africa, 3 School of Medicine, University of Swansea, Swansea, United Kingdom, 4 Department of Statistics, North Carolina State University, Raleigh, North Carolina, United States of America, 5 Department of Medicine, University of California, San Diego, La Jolla, California, United States of America


VASP: A Volumetric Analysis of Surface Properties Yields Insights into Protein-Ligand Binding Specificity
Brian Y. Chen1,2, Barry Honig1,2*

1 Department of Biochemistry and Molecular Biophysics, Center for Computational Biology and Bioinformatics, Columbia University, New York, New York, United States of America, 2 Howard Hughes Medical Institute, Columbia University, New York, New York, United States of America
Many algorithms that compare protein structures can reveal similarities that suggest related biological functions, even at great evolutionary distances. Proteins with related function often exhibit differences in binding specificity, but few algorithms identify structural variations that effect specificity. To address this problem, we describe the Volumetric Analysis of Surface Properties (VASP), a novel volumetric analysis tool for the comparison of binding sites in aligned protein structures. VASP uses solid volumes to represent protein shape and the shape of surface cavities, clefts and tunnels that are defined with other methods. Our approach, inspired by techniques from constructive solid geometry, enables the isolation of volumetrically conserved and variable regions within three dimensionally superposed volumes. We applied VASP to compute a comparative volumetric analysis of the ligand binding sites formed by members of the steroidogenic acute regulatory protein (StAR)-related lipid transfer (START) domains and the serine proteases. Within both families, VASP isolated individual amino acids that create structural differences between ligand binding cavities that are known to influence differences in binding specificity. Also, VASP isolated cavity subregions that differ between ligand binding cavities which are essential for differences in binding specificity. As such, VASP should prove a valuable tool in the study of protein-ligand binding specificity.

Bioinformatics Standards for Flow Cytometry

http://flowcyt.sourceforge.net/

New Twist on Drug Screening to Treat Common Childhood Cancer

http://www.sciencedaily.com/releases/2010/08/100818112709.htm?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed%3A+sciencedaily+(ScienceDaily%3A+Latest+Science+News)

Neuroblastoma, a solid tumour found outside the brain in the nervous system, is the most frequent cause of disease-related death in children.

"We conducted our drug discovery by targeting the cells that we think are responsible for the cancer coming back," says Kaplan, Senior Scientist at SickKids and Professor in the Department of Molecular Genetics at the University of Toronto. "This is a new way of developing drugs for kids, as we are taking the patients' own cancer stem cells and testing them in the lab."

If the clinical trial shows positive results, this could be the beginning of a personalized medicine approach, Kaplan says. "Our dream is that children will come to SickKids, we'll isolate their cancer stem cells, screen them with libraries of drugs and find out whether Patient A will respond to Therapy B.

Kristen M. Smith, Alessandro Datti, Mayumi Fujitani, Natalie Grinshtein, Libo Zhang, Olena Morozova, Kim M. Blakely, Susan A. Rotenberg, Loen M. Hansford, Freda D. Miller, Herman Yeger, Meredith S. Irwin, Jason Moffat, Marco A. Marra, Sylvain Baruchel, Jeffrey L. Wrana, David R. Kaplan. Selective targeting of neuroblastoma tumour-initiating cells by compounds identified in stem cell-based small molecule screens. EMBO Molecular Medicine, 2010; DOI: 10.1002/emmm.201000093

Wireless client bridge

http://linksys.custhelp.com/cgi-bin/linksys.cfg/php/enduser/std_adp.php?p_faqid=3733&p_created=1152002311&p_sid=pvAKyxlj&p_accessibility=0&p_redirect=&p_lva=4579&p_sp=cF9zcmNoPTEmcF9zb3J0X2J5PSZwX2dyaWRzb3J0PSZwX3Jvd19jbnQ9MjcsMjcmcF9wcm9kcz0wJnBfY2F0cz0wJnBfcHY9JnBfY3Y9JnBfc2NmXzM9MSZwX3BhZ2U9MSZwX3NlYXJjaF90ZXh0PWNhc2NhZGluZw**&p_li=&p_topview=1

Wireless client bridge

LAN-LAN

1. TEW-432BRP as the main router, 192.168.1.1, DHCP Server enabled
2. Linksys WRT160N as the client router, 192.168.1.2, DHCP Server disabled

Hook up the cable as ethernet port to ethernet port

To get WRT160N to work, I had to enter the correct MAC address from my old router ...

And if you get a blue screen of death in windows while using TEW-424UB, SiS163U WLAN,
802.11 USB Wireless LAN Adapterthe USB key, saying something about sis163u.sys, move this file from c:\windows\system32 over to a c:\temp folder and install

http://www.sis.com/download/agreement.php?url=/download/

Python coding tips

http://allendowney.com/sd/notes/notes11.txt

Generalized Linear Model

http://www.stat.ubc.ca/~gustaf/stat538.html

Description: Generalized Linear Models (GLMs) extend much of the `niceness' of linear models to situations where the response variable is not continuous. Consequently these models are popular for analysis in the common scenarios of response variables which are binary, categorical, counts, proportions, or directions. GLMs have become a big part of the `statistical toolbox' in most applicaton areas. This course will be a core introduction to GLMs, including a quick review of linear models, the fundamental formulation of GLMs, discussion of link functions, iterative least-squares algorithms, deviance and asymptotic theory, residuals, quasi-likelihood, and quadratic variance functions. A wide range of GLM applications will be discussed.

Weaver [Bui, Yu, Thain] a Python workflow app

Weaver [Bui, Yu, Thain] a Python workflow app
* http://bitbucket.org/pbui/weaver/src
* http://cse.nd.edu/~ccl/research/pubs/weaver-clade2010.pdf
* Peter Bui, Li Yu and Douglas Thain. "Weaver: Integrating Distributed Computing Abstractions into Scientific Workflows using Python", CLADE, Chicago Illinois, June 2010.

OpenSource project hosting

http://bitbucket.org/ - uses mercurial
http://github.com/ - uses git
http://code.google.com/ - uses svn
http://sourceforge.net/

Sequence Manipulation Suite

http://www.bioinformatics.org/sms2/

Fejes Bioinformatics

http://blogs.nature.com/fejes/

The Northern Lights Light Up Vancouver

http://www.associatedcontent.com/article/5656058/the_northern_lights_light_up_vancouver.html?cat=8

A solar storm on Aug. 3 and 4 allowed people to see a spectacle that is usually just reserved for those who live in the northernmost regions — the northern lights. Technically referred to as aurora borealis, the
northern lights are a natural phenomenon resulting from the emission of photons and chemicals from the sun's surface that are usually only seen in areas around the north poles, such as in northern Alaska.

FoldIt

http://arstechnica.com/science/news/2010/08/gamers-beat-algorithms-for-finding-protein-structures.ars

Gamers beat algorithms at finding protein structures

Foldit takes a hybrid approach. The Rosetta algorithm is used to create some potential starting structures, but users are then given a set of controls that let them poke and prod the protein's structure in three dimensions; displays provide live feedback on the energy of a configuration.

ROC Curve

Receiver operating characteristic (ROC) curve

Exposing the Coadaptive Potential of Protein-protein Interfaces through Computational Sequence Design
Menachem Fromer 1 and Michal Linial 2,*
1School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel
2Department of Biological Chemistry, Institute of Life Sciences, Sudarsky Center for Computational Biology, The Hebrew University of Jerusalem, Jerusalem, Israel
*To whom correspondence should be addressed. Michal Linial, E-mail: michall@cc.huji.ac.il
http://bioinformatics.oxfordjournals.org/cgi/content/abstract/btq412v1?etoc

Sample data
http://mark.goadrich.com/programs/AUC/

Example List file

0.9 1
0.8 1
0.7 0
0.6 1
0.55 1
0.54 1
0.53 0
0.52 0
0.51 1
0.505 0

Example ROC file for same dataset as above, using 6 pos and 4 neg examples

0       0
0.0     0.16666666666666666
0.0     0.3333333333333333
0.25    0.3333333333333333
0.25    0.5
0.25    0.6666666666666666
0.25    0.8333333333333334
0.5     0.8333333333333334
0.75    0.8333333333333334
0.75    1.0
1.0     1.0

AUC 

0.75

> AUC
function(ranks, n) {
 #ranks : ranks = c( 1,2,3);
#n = 5 ; total number
Npos =length(ranks)
Nneg = n - Npos
AUC = 1 - ( sum(ranks) - Npos*(Npos+1)/2 ) / (Npos * Nneg)
return(AUC)
}


> ROC
function(ranks , n) {
#doesn't work for ties !
TF = rep(0,n)
TF[ranks] = 1;
fp= cumsum(!TF/sum(!TF))
tp=cumsum(TF/sum(TF))
return( list( fp = fp / max(fp), tp = tp))
}

> ROC(x[x$V2==1,'rank'],nrow(x))
$fp
 [1] 0.25 0.25 0.50 0.75 0.75 0.75 0.75 1.00 1.00 1.00

$tp
 [1] 0.0000000 0.1666667 0.1666667 0.1666667 0.3333333 0.5000000 0.6666667
 [8] 0.6666667 0.8333333 1.0000000

> x$rank <- rank(-1*x$V1)
> ROC(x[x$V2==1,'rank'],nrow(x))
$fp
 [1] 0.00 0.00 0.25 0.25 0.25 0.25 0.50 0.75 0.75 1.00

$tp
 [1] 0.1666667 0.3333333 0.3333333 0.5000000 0.6666667 0.8333333 0.8333333
 [8] 0.8333333 1.0000000 1.0000000

> AUC(x[x$V2==1,'rank'],nrow(x))
[1] 0.75


Comparing experimental and computational alanine scanning techniques for probing a prototypical protein–protein interaction
Richard T. Bradshaw, Bhavesh H. Patel, Edward W. Tate, Robin J. Leatherbarrow and Ian R. Gould1
Department of Chemistry and Chemical Biology Centre, Imperial College London, South Kensington Campus, London SW7 2AZ, UK
1 To whom correspondence should be addressed. E-mail: i.gould@imperial.ac.uk
http://peds.oxfordjournals.org/cgi/content/abstract/gzq047v1?etoc

Comparison study of microarray meta-analysis methods
Anna Campain email and Yee Hwa Yang email
BMC Bioinformatics 2010, 11:408doi:10.1186/1471-2105-11-408
Published: 3 August 2010
http://peds.oxfordjournals.org/cgi/content/abstract/gzq047v1?etoc

Background

Meta-analysis methods exist for combining multiple microarray datasets. However, there are a wide range of issues associated with microarray meta-analysis and a limited ability to compare the performance of different meta-analysis methods.
Results

We compare eight meta-analysis methods, five existing methods, two naive methods and a novel approach (mDEDS). Comparisons are performed using simulated data and two biological case studies with varying degrees of meta-analysis complexity. The performance of meta-analysis methods is assessed via ROC curves and prediction accuracy where applicable.
Conclusions

Existing meta-analysis methods vary in their ability to perform successful meta-analysis. This success is very dependent on the complexity of the data and type of analysis. Our proposed method, mDEDS, performs competitively as a meta-analysis tool even as complexity increases. Because of the varying abilities of compared meta-analysis methods, care should be taken when considering the meta-analysis method used for particular research.


The diagnostic performance of a test, or the accuray of a test to discriminate diseased cases from normal cases is evaluated using Receiver Operating Characteristic (ROC) curve analysis (Metz, 1978; Zweig & Campbell, 1993). ROC curves can also be used to compare the diagnostic performance of two or more laboratory or diagnostic tests (Griner et al., 1981).
http://www.medcalc.be/manual/roc.php

'Designer Protein' Opens New Door in Cancer Research

http://www.sciencedaily.com/releases/2010/08/100803112813.htm?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed%3A+sciencedaily+%28ScienceDaily%3A+Latest+Science+News%29


Recently, the researchers were able to do just that, creating a designer protein that not only targets a specific cell type, but then invades that cell and is drawn directly to a chosen compartment.

New Drug Target for Immune Diseases Discovered

http://www.sciencedaily.com/releases/2010/08/100803132738.htm?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed%3A+sciencedaily+%28ScienceDaily%3A+Latest+Science+News%29

Led by Dr. Andrea Cerutti, MD, Professor of Medicine at Mount Sinai School of Medicine, researchers studied human tissue and immune cells from people with mutations of TACI and MyD88, two proteins required to activate the immune system. MyD88 is a signaling protein that alerts the so-called innate immune system -- the immune system encoded at birth that remains unchanged -- to the presence of pathogens. TACI is a receptor protein used to activate immune cells in the so-called adaptive immune system, a more sophisticated immune system than the innate, which is dynamic and combats pathogens.