Thursday, June 30, 2011
Phylop vs Phastcons
PhastCons (region) (which has been used in previous Conservation tracks) is a hidden Markov model-based method that estimates the probability that each nucleotide belongs to a conserved element, based on the multiple alignment. It considers not just each individual alignment column, but also its flanking columns.
By contrast, phyloP (position) separately measures conservation at individual columns, ignoring the effects of their neighbors. As a consequence, the phyloP plots have a less smooth appearance than the phastCons plots, with more "texture" at individual sites. The two methods have different strengths and weaknesses. PhastCons is sensitive to "runs" of conserved sites, and is therefore effective for picking out conserved elements. PhyloP, on the other hand, is more appropriate for evaluating signatures of selection at particular nucleotides or classes of nucleotides (e.g., third codon positions, or first positions of miRNA target sites).
http://compgen.bscb.cornell.edu/phast/faq.php
http://genome.ucsc.edu/cgi-bin/hgTrackUi?hgsid=200695383&g=cons30way#a_cfg_phastcons
By contrast, phyloP (position) separately measures conservation at individual columns, ignoring the effects of their neighbors. As a consequence, the phyloP plots have a less smooth appearance than the phastCons plots, with more "texture" at individual sites. The two methods have different strengths and weaknesses. PhastCons is sensitive to "runs" of conserved sites, and is therefore effective for picking out conserved elements. PhyloP, on the other hand, is more appropriate for evaluating signatures of selection at particular nucleotides or classes of nucleotides (e.g., third codon positions, or first positions of miRNA target sites).
http://compgen.bscb.cornell.edu/phast/faq.php
http://genome.ucsc.edu/cgi-bin/hgTrackUi?hgsid=200695383&g=cons30way#a_cfg_phastcons
Publishing in the web for free
Google Sites
https://www.google.com/accounts
The key feature of Google Sites is its collaboration abilities
Wordpress
http://wordpress.com/
Powerful design
http://productivewise.com/free-website-building-platforms-compared-wordpress-google-sites/
https://www.google.com/accounts
The key feature of Google Sites is its collaboration abilities
Wordpress
http://wordpress.com/
Powerful design
http://productivewise.com/free-website-building-platforms-compared-wordpress-google-sites/
What Makes You Irresistible to Employers
In a tight market, every job seeker needs to find a way to stand out from the crowd. What separates the "great" from the "good" and makes a particular candidate just too irresistible to pass up?
Oftentimes, it is one of these three things:
- Ability to Prove Worth,
- More than a Simple Knowledge of the Company,
- and Enthusiasm.
http://www.careerbuilder.ca/Article/CB-774-Job-Search-What-makes-you-irresistible-to-employers/?sc_extcmp=cbca_9774&cblang=CAEnglish&SiteId=cbca_9774
Tips for landing higher pay in a new role
Here's how to excel in the art of negotiation:
- Know Your Benchmark
- Set a Minimum Threshold
- Don't Start Too Early
- Demonstrate Your Value
- Be Assertive
BGI to expand into clinical, drug-development services
Earlier this month, Adam Bonislawski at GenomeWeb’s Proteomonitor reported that China-based BGI intends to expand its proteomics offerings and buy 50 new mass spectrometers over the next two years. This diversification into clinical and drug-development services is a forward-thinking move for the genomics/bioinformatics giant and one that should be taken seriously by current proteomics service providers.
http://www.medcitynews.com/2011/06/bgi-to-expand-into-clinical-drug-development-services/
http://www.medcitynews.com/2011/06/bgi-to-expand-into-clinical-drug-development-services/
new generation of ExPASy
SIB Swiss Institute of Bioinformatics just launched the new generation of ExPASy, a web portal which offers a point of entry to more than 120 SIB bioinformatics resources
http://www.expasy.org/
http://www.expasy.org/
Crows learning
Not so bird-brained: Clever crows recognise faces
http://www.google.com/hostednews/afp/article/ALeqM5gDb05xN18drPIMfucOK6FaDQqcog?docId=CNG.b543d6b84be023e079ab0236d9bba908.491
http://www.google.com/hostednews/afp/article/ALeqM5gDb05xN18drPIMfucOK6FaDQqcog?docId=CNG.b543d6b84be023e079ab0236d9bba908.491
Wednesday, June 29, 2011
A New Bioinformatics System Improves Medical Diagnosis
http://www.sciencedaily.com/releases/2011/06/110628094833.htm
The algorithm, called CliDaPa (Clinical Data Partitioning), uses histological and clinical data and pharmacological treatments to partition patients by means of a tree representation for a particular disease (called clinical tree), used to cluster patients according to similar behaviour. It then uses data mining techniques to analyse each patient partition with the associated genetic information.
CliDaPa is a different method of DNA microarray analysis that aims to generate a model representing different patient behaviours (gathered from clinical data). These behaviours will then be examined separately and specifically by means of data mining. New learning methods were also proposed in the course of the research.
The algorithm, called CliDaPa (Clinical Data Partitioning), uses histological and clinical data and pharmacological treatments to partition patients by means of a tree representation for a particular disease (called clinical tree), used to cluster patients according to similar behaviour. It then uses data mining techniques to analyse each patient partition with the associated genetic information.
CliDaPa is a different method of DNA microarray analysis that aims to generate a model representing different patient behaviours (gathered from clinical data). These behaviours will then be examined separately and specifically by means of data mining. New learning methods were also proposed in the course of the research.
Literature mining, ontologies and information visualization for drug repurposing
http://bib.oxfordjournals.org/content/early/2011/06/28/bib.bbr005.abstract?papetoc
Abstract
The immense growth of MEDLINE coupled with the realization that a vast amount of biomedical knowledge is recorded in free-text format, has led to the appearance of a large number of literature mining techniques aiming to extract biomedical terms and their inter-relations from the scientific literature. Ontologies have been extensively utilized in the biomedical domain either as controlled vocabularies or to provide the framework for mapping relations between concepts in biology and medicine. Literature-based approaches and ontologies have been used in the past for the purpose of hypothesis generation in connection with drug discovery. Here, we review the application of literature mining and ontology modeling and traversal to the area of drug repurposing (DR). In recent years, DR has emerged as a noteworthy alternative to the traditional drug development process, in response to the decreased productivity of the biopharmaceutical industry. Thus, systematic approaches to DR have been developed, involving a variety of in silico, genomic and high-throughput screening technologies. Attempts to integrate literature mining with other types of data arising from the use of these technologies as well as visualization tools assisting in the discovery of novel associations between existing drugs and new indications will also be presented.
http://nar.oxfordjournals.org/content/39/suppl_2/W450.abstract?etoc
http://nar.oxfordjournals.org/content/39/suppl_2/W450.abstract?etoc
Prevent ssh connection from disconnecting
$ man ssh_config
$ cat /etc/ssh/ssh_config
...
# ProxyCommand ssh -q -W %h:%p gateway.example.com
SendEnv LANG LC_*
HashKnownHosts yes
# GSSAPIAuthentication yes
# GSSAPIDelegateCredentials no
ServerAliveInterval 30
ServerAliveCountMax 4
$ cat /etc/ssh/ssh_config
...
# ProxyCommand ssh -q -W %h:%p gateway.example.com
SendEnv LANG LC_*
HashKnownHosts yes
# GSSAPIAuthentication yes
# GSSAPIDelegateCredentials no
ServerAliveInterval 30
ServerAliveCountMax 4
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