Friday, July 30, 2010
Thursday, July 29, 2010
Tuesday, July 27, 2010
Key Milestone Towards the Development of a New Clinically Useful Antibiotic
http://www.sciencedaily.com/releases/2010/07/100712154426.htm?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed%3A+sciencedaily+%28ScienceDaily%3A+Latest+Science+News%29
The producing bacterium, Microbispora corallina, is difficult to work with. It grows very slowly and no tools existed for its genetic manipulation. PhD student Lucy Foulston developed the tools herself. She then took advantage of new developments in genome sequencing to identify and then isolate the M. corallina gene cluster responsible for microbisporicin production.
The producing bacterium, Microbispora corallina, is difficult to work with. It grows very slowly and no tools existed for its genetic manipulation. PhD student Lucy Foulston developed the tools herself. She then took advantage of new developments in genome sequencing to identify and then isolate the M. corallina gene cluster responsible for microbisporicin production.
Tuesday, July 20, 2010
Choosing a Rotation Lab
http://artsci.wustl.edu/~sac/document/ChoosingaRotationLab.htm
http://www.med.upenn.edu/mstp/lab_rotation.shtml
http://thelindberglab.com/papers/rotations.pdf
http://www.med.upenn.edu/mstp/lab_rotation.shtml
http://thelindberglab.com/papers/rotations.pdf
Thursday, July 15, 2010
10 Places to Find Opportunities:
10 Places to Find Opportunities:
1. Job boards & job board aggregators
2. Conferences (Look at vendor lists)
3. Twitter feeds (often people will retweet jobs)
4. Company websites (some companies don’t post their jobs on boards)
5. Linkedin Groups
6. Facebook company pages
7. Business & Investment Blogs
8. Chamber of commerce
9. Local & National newspapers
10. Bumping into opportunity (running into people at coffee shops, sports teams etc)
http://blog.workopolis.com/en/2010/07/ten-places-to-find-career-opportunities.html
1. Job boards & job board aggregators
2. Conferences (Look at vendor lists)
3. Twitter feeds (often people will retweet jobs)
4. Company websites (some companies don’t post their jobs on boards)
5. Linkedin Groups
6. Facebook company pages
7. Business & Investment Blogs
8. Chamber of commerce
9. Local & National newspapers
10. Bumping into opportunity (running into people at coffee shops, sports teams etc)
http://blog.workopolis.com/en/2010/07/ten-places-to-find-career-opportunities.html
Thursday, July 8, 2010
Cholesterol's Other Way out
"In parallel, we believe that the liver also plays a gatekeeper role for nonbiliary fecal sterol loss by repackaging peripheral cholesterol into nascent plasma lipoproteins that are destined for subsequent intestinal delivery."
http://www.sciencedaily.com/releases/2010/07/100707102443.htm
http://www.sciencedaily.com/releases/2010/07/100707102443.htm
Tuesday, July 6, 2010
Avatar / The Last Air Bender
http://www.watchanimeon.com/avatar-the-last-airbender-book-1-chapter-1-2/
Wednesday, June 30, 2010
HTC Heror and VillainROM10
http://www.villainrom.co.uk/wiki/index.php?title=VillainROM_10_Series
http://www.villainrom.co.uk/releases/VillainROM10.3/download.php
http://forum.xda-developers.com/showthread.php?t=645253
http://www.youtube.com/watch?v=VxkWFsJC9lY
http://www.villainrom.co.uk/releases/VillainROM10.3/download.php
http://forum.xda-developers.com/showthread.php?t=645253
http://www.youtube.com/watch?v=VxkWFsJC9lY
CTI Life Sciences
http://www.ctisciences.com/english/
Based in Montreal, CTI Life Sciences Fund L.P. is a limited partnership formed in 2006. The Fund make venture capital investments in high quality emerging life sciences companies at the start-up and clinical development stage primarily in Canada. This fund is the first of its kind created in Quebec since 2002.
Based in Montreal, CTI Life Sciences Fund L.P. is a limited partnership formed in 2006. The Fund make venture capital investments in high quality emerging life sciences companies at the start-up and clinical development stage primarily in Canada. This fund is the first of its kind created in Quebec since 2002.
Prediction of protease substrates using sequence and structure features
http://bioinformatics.oxfordjournals.org/cgi/content/abstract/26/14/1714?etoc
Prediction of protease substrates using sequence and structure features
David T. Barkan 1,2,3,4,{dagger}, Daniel R. Hostetter 3,4,{dagger}, Sami Mahrus 3,4, Ursula Pieper 2,3,4, James A. Wells 3,4,5, Charles S. Craik 3,4,* and Andrej Sali 2,3,4,*
Abstract
Motivation:Granzyme B (GrB) and caspases cleave specific protein substrates to induce apoptosis in virally infected and neoplastic cells. While substrates for both types of proteases have been determined experimentally, there are many more yet to be discovered in humans and other metazoans. Here, we present a bioinformatics method based on support vector machine (SVM) learning that identifies sequence and structural features important for protease recognition of substrate peptides and then uses these features to predict novel substrates. Our approach can act as a convenient hypothesis generator, guiding future experiments by high-confidence identification of peptide-protein partners.
Results:The method is benchmarked on the known substrates of both protease types, including our literature-curated GrB substrate set (GrBah). On these benchmark sets, the method outperforms a number of other methods that consider sequence only, predicting at a 0.87 true positive rate (TPR) and a 0.13 false positive rate (FPR) for caspase substrates, and a 0.79 TPR and a 0.21 FPR for GrB substrates. The method is then applied to ~25 000 proteins in the human proteome to generate a ranked list of predicted substrates of each protease type. Two of these predictions, AIF-1 and SMN1, were selected for further experimental analysis, and each was validated as a GrB substrate.
Availability: All predictions for both protease types are publically available at http://salilab.org/peptide. A web server is at the same site that allows a user to train new SVM models to make predictions for any protein that recognizes specific oligopeptide ligands.
Contact: craik@cgl.ucsf.edu; sali@salilab.org
Prediction of protease substrates using sequence and structure features
David T. Barkan 1,2,3,4,{dagger}, Daniel R. Hostetter 3,4,{dagger}, Sami Mahrus 3,4, Ursula Pieper 2,3,4, James A. Wells 3,4,5, Charles S. Craik 3,4,* and Andrej Sali 2,3,4,*
Abstract
Motivation:Granzyme B (GrB) and caspases cleave specific protein substrates to induce apoptosis in virally infected and neoplastic cells. While substrates for both types of proteases have been determined experimentally, there are many more yet to be discovered in humans and other metazoans. Here, we present a bioinformatics method based on support vector machine (SVM) learning that identifies sequence and structural features important for protease recognition of substrate peptides and then uses these features to predict novel substrates. Our approach can act as a convenient hypothesis generator, guiding future experiments by high-confidence identification of peptide-protein partners.
Results:The method is benchmarked on the known substrates of both protease types, including our literature-curated GrB substrate set (GrBah). On these benchmark sets, the method outperforms a number of other methods that consider sequence only, predicting at a 0.87 true positive rate (TPR) and a 0.13 false positive rate (FPR) for caspase substrates, and a 0.79 TPR and a 0.21 FPR for GrB substrates. The method is then applied to ~25 000 proteins in the human proteome to generate a ranked list of predicted substrates of each protease type. Two of these predictions, AIF-1 and SMN1, were selected for further experimental analysis, and each was validated as a GrB substrate.
Availability: All predictions for both protease types are publically available at http://salilab.org/peptide. A web server is at the same site that allows a user to train new SVM models to make predictions for any protein that recognizes specific oligopeptide ligands.
Contact: craik@cgl.ucsf.edu; sali@salilab.org
Fast integration of heterogeneous data sources for predicting gene function with limited annotation Sara Mostafavi 1,2,* and Quaid Morris 1,2,*
http://bioinformatics.oxfordjournals.org/cgi/content/abstract/26/14/1759?etoc
Motivation: Many algorithms that integrate multiple functional association networks for predicting gene function construct a composite network as a weighted sum of the individual networks and then use the composite network to predict gene function. The weight assigned to an individual network represents the usefulness of that network in predicting a given gene function. However, because many categories of gene function have a small number of annotations, the process of assigning these network weights is prone to overfitting.
Results: Here, we address this problem by proposing a novel approach to combining multiple functional association networks. In particular, we present a method where network weights are simultaneously optimized on sets of related function categories. The method is simpler and faster than existing approaches. Further, we show that it produces composite networks with improved function prediction accuracy using five example species (yeast, mouse, fly, Esherichia coli and human).
Availability: Networks and code are available from: http://morrislab.med.utoronto.ca/sara/SW
Contact: smostafavi@cs.toronto.edu; quaid.morris@utoronto.ca
Motivation: Many algorithms that integrate multiple functional association networks for predicting gene function construct a composite network as a weighted sum of the individual networks and then use the composite network to predict gene function. The weight assigned to an individual network represents the usefulness of that network in predicting a given gene function. However, because many categories of gene function have a small number of annotations, the process of assigning these network weights is prone to overfitting.
Results: Here, we address this problem by proposing a novel approach to combining multiple functional association networks. In particular, we present a method where network weights are simultaneously optimized on sets of related function categories. The method is simpler and faster than existing approaches. Further, we show that it produces composite networks with improved function prediction accuracy using five example species (yeast, mouse, fly, Esherichia coli and human).
Availability: Networks and code are available from: http://morrislab.med.utoronto.ca/sara/SW
Contact: smostafavi@cs.toronto.edu; quaid.morris@utoronto.ca
Tuesday, June 29, 2010
Relying on Origami Techniques, Researchers Show Programmable Matter Folding Into a Boat Or Plane-Shape
ScienceDaily (June 29, 2010) — "More than meets the eye" may soon become more than just a tagline for a line of popular robotic toys.
http://www.sciencedaily.com/releases/2010/06/100628152641.htm?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed%3A+sciencedaily+%28ScienceDaily%3A+Latest+Science+News%29
http://www.sciencedaily.com/releases/2010/06/100628152641.htm?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed%3A+sciencedaily+%28ScienceDaily%3A+Latest+Science+News%29
Monday, June 28, 2010
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