International Society for Complexity, Information, and Design (ISCID) Encyclopedia

http://www.iscid.org/encyclopedia/FRET

The International Society for Complexity, Information, and Design (ISCID) is a 501(c)(3) non-profit organization which provides a forum for free and uncensored inquiry into complex systems.

12 Useful FireFox Plugins For Safe Browsing

http://www.smashingdownloads.com/2009/07/13/12-useful-firefox-plugins-for-safe-browsing/

https://addons.mozilla.org/en-US/firefox/addon/9727/
https://addons.mozilla.org/en-US/firefox/addon/4429
https://addons.mozilla.org/en-US/firefox/addon/6623
https://addons.mozilla.org/en-US/firefox/addon/6718
https://addons.mozilla.org/en-US/firefox/addon/722/
http://noscript.net/

UBC Research Facilities

http://www.cellphys.ubc.ca/resfacilities.htm

HeLa cell - the "immortal cell"

A HeLa cell (also Hela or hela cell) is a cell type in an immortal cell line used in scientific research. It is one of the oldest and most commonly used human cell lines.[1] The line was derived from cervical cancer cells taken from Henrietta Lacks, a patient who eventually died of her cancer on October 4, 1951. The cell line was found to be remarkably durable and prolific as illustrated by its contamination of many other cell lines used in research.[2][3]

http://en.wikipedia.org/wiki/HeLa

Transgenic / Knockout Mice

http://www.cellmigration.org/resource/komouse/komouse_approaches.shtml

Cre - conditional gene modifications in mice

n extensive collection of mice have been generated, each line expressing Cre from a promoter that is either tissue specific, cell specific, developmentally specific or responsive to an exogenous agent like tetracycline.

proteomic fingerprints

Disease states might be associated with distinctive configurations of circulating proteins: so-called ‘proteomic fingerprints’.

Proteomic fingerprinting for the diagnosis of human African trypanosomiasis
Dan Agranoffa, August Stichb, Paulo Abelc and Sanjeev Krishnaa
Trends in Parasitology
Volume 21, Issue 4, April 2005, Pages 154-157

Christmas 2011 - Overdrive

http://www.watchanimeon.com/overdrive-episode-11/

Blood, cells, muscles and everything that has been sleeping within me until now.

Give me ... Give me ... strength.

Deeds, not words.

Prosperity makes friends, adversity tries them.

Joy and sorrow are next door neighbours.

You never know what you can do till you try.

Save You - Simple Plan

http://www.youtube.com/watch?v=eXT7iY2DgVo&feature=artistob&playnext=1&list=TL-N8vir6oee8

Overdrive

http://www.watchanimeon.com/overdrive-episode-1/

Biking , Bike

The Palme d'Or (English: Golden Palm)

The Palme d'Or (English: Golden Palm) is the highest prize awarded to competing films at the Cannes Film Festival.

http://en.wikipedia.org/wiki/Palme_d%27Or

Dropbox - file sharing

https://www.dropbox.com/referrals/NTExMTYyOTY5OQ?src=referrals_fb_post

Firefox flv downloader - Karbon

https://addons.mozilla.org/en-US/firefox/addon/54453/

noSQL

Casandra
Mongo
Dynamo
Amazon Simple DB
Google BigTable
javaCC - Query graph model (QGM)

http://en.wikipedia.org/wiki/NoSQL

BioPartnering North America

http://www.techvision.com/bpn/participants/index.php?src=lsbc&pageid=261#present

AAIPharma Services
ACT Biotech
Agere Pharmaceuticals, Inc.
AliX
Alligator Bioscience AB
Allon Therapeutics, Inc.
Allostera Pharma Inc.
Allphase Clinical Research Inc.
Alphora Research Inc.
Alsace BioValley
AltheaDx
Ambit Biosciences Corporation
AngioChem Inc
Aquinox Pharmaceuticals Inc.
Araclon Biotech
Augurex Life Sciences Corp.
AVOGADRO
Bridge Consulting
Calistoga Pharmaceuticals, Inc.
Cellectis SA
Cellvax
CIRION BioPharma Research Inc.
Conformetrix
Congenix, LLP
Crown Bioscience Inc.
CYTOO cell architects
Daiichi Sankyo Co., Ltd.
Debiopharm S.A.
DSM
Elan Corporation
Emerald Biostructures
Emmaus Medical, Inc.
Eurand International S.p.A.
Eurofins-ADME BIOANALYSES
Forma Therapeutics, Inc.
French Consulate
GlobalTox
Globe Laboratories Inc
Groupe FORENAP
i3 CanReg, Inc.
i3 Global
ImmunoVaccine Inc.
INC Research
InCode BioPharmaceutics, Inc.
Inimex Pharmaceuticals, Inc.
Inovio Biomedical Corp.
Invest in France Agency
iPierian, Inc.
Italian Trade Commission
KAI Pharmaceuticals Inc.
LEO Pharma A/S
London Genetics Ltd
Metabolys
NDA Regulatory Service AB
NeuMedics Inc.
NFX - Nord France eXperts
NicOx S.A.
Novozymes A/S
Oncodesign
Oncolytics Biotech Inc.
ORA, Inc.
OxyPharma AB
PATH
Pearl Therapeutics, Inc.
Pharmatek Laboratories, Inc.
PhysioStim
Piramal Healthcare
Plant Advanced Technologies
PlantForm
PolyMedix Inc.
Porsolt & Partners Pharmacology
Prestwick Chemical, Inc.
PROOF Centre of Excellence
ProPharma Partners International Inc.
Protaffin Biotechnologie AG
PX'Therapeutics
Rose Pharma A/S
Sandoz International GmbH
Sernova Corp
Shanghai Medicilon Inc.
Silence Therapeutics AG
Sirona Biochem
Skunkwerks Software Ltd
SuperGen Inc.
Synergy Pharmaceuticals, Inc.
Teva Pharmaceutical Industries Ltd
TFChem
Topica Pharmaceuticals Inc.
UBIFrance
University of Manitoba
Valocor Therapeutics, Inc.
Vifor Pharma Aspreva
Vifor Pharma Aspreva
VIVALIS
XenTech
YM BioSciences Inc.
Zoomedia Inc.
Zymeworks Inc. Contract Organization



3M Drug Delivery Systems
3M Drug Delivery Systems
3P Biopharmaceuticals, S.L.
4SC AG
Abbott Laboratories
Abeome Corporation
Abmedix Biomedical
Actelion Pharmaceuticals Ltd.
Affymax, Inc.
Ahngook Pharamceutical Co Ltd
Alder BioPharmaceuticals
Allergan, Inc.
Alphalyse, Inc.
Alvogen
Amicus Therapeutics
Aminex Therapeutics, Inc.
Anacor Pharmaceuticals, Inc.
AnaptysBio, Inc.
Antisoma Research Ltd
Apex Healthcare
Ark Therapeutics
ASEBIO, Spanish Bioindustry Association
Aska Research
Astellas
Astellas
Astellas
aTyr Pharma Inc.
Australian Research Network
Axon Clinical Research
BASF
Bayer AG
BC Advantage Funds (VCC) Ltd.
BC Cancer Research Centre (BCCRC)
BC Genome Sciences Centre, UBC
BCN Peptides
BELTAS Ltd
BioAlberta
biOasis Technologies Inc.
BIOCODEX
BioEnsemble
Biogen Idec, Inc.
BioMS Medical Corporation
Bioniche Life Sciences, Inc.
BIOPOLIS
Biopta Ltd
Bio-Sino Biotechnology&Science Inc.
BioTechLogic, Inc.
BioUETIKON Ltd
Biovail Corporation (Biovail Contract Research)
BioXcel Corporation
BITA
BC Ministry of Small Bus., Tech. and Econ. Dev.
BC Ministry of Small Bus., Tech. and Econ. Dev.
BC Ministry of Small Bus., Tech. and Econ. Dev.
Broadreach BioSystems Inc.
Business in Vancouver
Campbell Alliance
Canada's Research-Based Pharma Companies
Canadian Consulate General
Canadian Consulate General
Canadian Consulate General
Canadian Embassy
Canadian Embassy
Canadian Embassy
Canadian German Chamber of Ind. and Com. Inc
Canadian High Commission
Canadian HIV Trials Network
Can-Am Pharmaceutical Services, LLC
Cardiome Pharma Corp.
Catalent Pharma Solutions, Inc.
CCL Pharmaceuticals
Ceapro Inc.
Celator Pharmaceuticals, Inc.
Cell Therapy News
CellSeed Inc.
Cephalon, Inc.
CFC Underwriting Limited
Chelsea Therapeutics Inc.
ChemoCentryx Inc.
Christie Consulting Services
Chuncheon Bioindustry Foundation
Cipher Pharmaceuticals Limited
Circassia Ltd
Clinical Network Services (CNS) PTY Ltd
Clinimetrics, Inc.
CNETE
CombinatoRx, Incorporated
Compugen
Confederation Of Indian Industry
Consulate General of the Republic of Korea
Cook Pharmica LLC
Copenhagen Capacity
CPQ Ingenieros SL
Cqua Research International Inc
Creechurch Int'l Underwriters
Critical Outcome Technologies Inc.
Critical Pharmaceuticals Ltd
CrystalGenomics, Inc. / CG Pharmaceuticals
CrystalGenomics, Inc. / CG Pharmaceuticals
CSL Limited (CSL Biotherapies)
CTI Life Sciences Fund
Cytochroma Inc.
Dabur India Limited (Dabur Research Foundation)
Dalhousie University
Dalton Pharma Services, Inc.
DCI
deCODE genetics
DepoMed, Inc.
Diteba Research Laboratories Inc.
Donald MacPherson Inc.
Dong Wha Pharm. Co., Ltd.
Dong-A Pharmaceutical Co., Ltd.
DURECT Corporation
Dyax Corp.
E3 Resources
Economic and Commercial Office of Spain
Ecron AcuNova (Manipal Acunova Ltd)
Elevation Pharmaceuticals
EOStrat, Strategies & Management
ERA Consulting Group
Essa Pharma
Evotec AG
Exelixis, Inc.
Exploit Technologies Pte. Ltd
Fasken Martineau DuMoulin LLP
Fate Therapeutics Inc.
Fenwick & West LLP
Forest Laboratories, Inc.
Freeman
Galderma Laboratories, L.P.
GE Healthcare
Genhelix
Genoma Espana
GenomeDx Biosciences Inc.
Genzyme Corporation
GES Exposition
GlaxoSmithKline plc
GMD PharmaSolutions
Government of British Columbia
Government of Canada
Government of India
GP Pharm
Greenstone Venture Partners
Griffith University
GyeongGi Bio-center
Halozyme Therapeutics, Inc.
Histocell
HumanAutoCell GmbH
iCo Therapeutics Inc.
Ie Sung International
Illetrop Ltd
ImmuPharma PLC
Imperial Life Sciences USA, Inc,
IMS Health Inc.
Industry Canada / Industrie Canada
INIS Biotech
Innocoll Inc.
Innovaderm Research
Innovation PEI
Innovotech Inc.
Inogent Laboratories
Insymbiosis
International Science and Tech. Partnerships
International Trade Canada
Intrexon Corporation
IO Informatics
iProgen
Irwin Maclaren Enterprises
Ischemix
Japan Tobacco, Inc.
Jeollanamdo Biopharmaceutical Research Center
Jeonbuk Institute for Bioindustry
Kaken Pharmaceutical Company Limited
KaloBios, Inc.
Kareus Therapeutics, LLC
Korea Biotechnology Industry Organization
Korea Trade Centre, Vancouver (KOTRA)
KPMG LLP
Kyorin Pharmaceutical Co. Ltd
Kyukyu Pharmaceutical Co., Ltd
La Jolla Institute for Allergy & Immunology
LAB Research Inc
Laboratorios Farmaceuticos Rovi, S.A.
Life Science Analytics, Inc.
Ligand Pharmaceuticals, Inc.
Lupin Limited
Lupin Limited
Malachite Management Inc.
Marsh & McLennan Companies
Master of Biomedical Technology
Mayo Clinic
McGill University
MDMI Technologies Inc
MedGenesis Therapeutix Inc.
Medical College of Georgia
Medicon Valley Alliance
Menarini Group
Merck Serono International S.A
MergerMarket (PharmaWire)
Metaara Medical Technologies Inc.
Michael Smith Foundation for Health Research
Microbion BioSciences Corporation
Micromet AG
Micromyx, LLC
MIGENIX Inc.
MP Healthcare Venture Management, Inc.
MPI Research
MSI Methylation Sciences Inc.
multimmune
Myocept Inc.
Myriad Pharmaceuticals, Inc.
Nano Pacific Holdings,Inc.
Nanogen, Inc.
National Research Council Canada (IRAP)
National University of Singapore
NeoPharm Co., Ltd.
NewLink Genetics Corporation
Nexigen Therapeutics
NexMed, Inc.
NextPharma Technologies
Nexus Oncology Ltd
Nomura International plc
North Carolina Department of Commerce
Norwich Pharmaceuticals
Novartis International AG
Novartis International AG
Novo Nordisk A/S
Novo Nordisk A/S
Noxxon Pharma AG
NS Pharma, Inc.
Nucro-Technics Incorporated
Nuon Therapeutics Inc.
Nycomed Holding A/S
Nycomed Holding A/S
OCRI Life Sciences
OncoGenex Pharmaceuticals Inc.
Ono Pharmaceutical Co. Ltd.
OptiNose
Orcrist Bio Inc.
Osaka University, Inst. of Scientific & Ind. Res.
Otsuka Pharmaceutical Co. Ltd.
Pacific Pharmaceutical Co., Ltd. (PacificPharma)
PacificGMP
PAREXEL International Corp.
Penwest Pharmaceuticals Co.
PEPSCAN Systems B.V.
Pfenex Inc
Pharma 2.0 Inc.
PharmaGap
PharmaLegacy Laboratories
Physiosonics, Inc
Polypeptide Laboratories A/S (Group)
PREMAS Biotech Pvt. Ltd.
Priaxon AG
ProBioGen AG
ProteoGen Bio
ProtoKinetix Inc
Protox Therapeutics
PSI CRO AG
Purdue Pharma
QLT Inc.
Radient Technologies Inc.
ReachBio LLC
Regional Centre for Biotechnology
Regis Technologies, Inc.
ReSolution Latin America
Reuters Limited (Thomson Reuters)
Richcore Lifesciences Private Limited
Royal Thai Consulate General
Russian Federation
SageKey Software
Salus Technology Inc.
Sanofi-Aventis (Sanofi Pasteur)
Sanofi-Aventis (Sanofi Pasteur)
SBW Ltd. (Systems Biology Worldwide)
Scale Venture Partners
Scimega Research Inc.
Scottish Devel. International
SemBioSys Genetics Inc.
Siegfried AG
SierraSil Health Inc
Simon Fraser University
Sirius Genomics Inc.
Snowdon & Associates
Somnus Therapeutics Inc.
StemCell Technologies
Stiris Research, Inc.
SunConsult ApS
Superna Life Sciences
SurModics Pharmaceuticals, Inc
Synageva BioPharma
SynCo Bio Partners B.V.
Taiga Biotechnologies, Inc.
Takeda Pharmaceutical Company Limited
Takeda Research Investment, Inc.
Tectra
TetraQ
Teva Innovative Ventures
Tgen Drug Devel. Services (TD2)
TGen Translation Genomics Research Institute
The Hospital for Sick Children Research Institute
The Jackson Laboratory
The James Hogg iCAPTURE Centre
The Pfizer Incubator
The Prostate Centre at VGH
The Stiller Centre
The Student Biotechnology Network
Theracarb Inc.
Therapure Biopharma Inc.
Third Security, LLC
TianChen Scientific Inc.
Times of India
Tissue Solutions Ltd
Transdel Pharmaceuticals, Inc.
Trifermed Group
UCB Group
United Paragon Associates Inc.
Univalor
University Health Network, UHN
University of Alberta
University of British Columbia
University of Calgary
University of Ottawa
University of Prince Edward Island
University of Toronto
University of Victoria Innovation and Devel. Corp.
University Technologies International
UVic Genome BC Proteomics Centre
Vakzine Projekt Management GmbH (VPM)
Variation Biotechnologies
Vectura Limited
Verona Pharma plc
Versartis, Inc.
VIB, the Flanders Institute for Biotechnology
Victory Pharma
ViroMed Co., Ltd.
Washington Research Foundation
Wax-it Histology Services Inc.
Wellgenex
WEX Pharmaceuticals Inc.
Wingwalker Holding Inc.,
WITS Interactive Pvt. Ltd
World Courier
Xcovery, Inc.
Xenon Pharmaceuticals Inc.
XOMA Ltd. Drug Delivery

Seattle Proteome Centre - http://www.proteomecenter.org/software.php

Seattle Proteome Centre - http://www.proteomecenter.org/software.php

http://www.systemsbiology.org/

NIPS - Neural Information Processing Systems

http://nips.cc/Conferences/2010/Program/schedule.php

http://nips.cc/Conferences/2010/Program/event.php?ID=2291

University of Edinburgh; ; WTCHG Oxford; University of Edinburgh

Poster: Sparse Instrumental Variables (SPIV) for Genome-Wide Studies

Felix Agakov, Paul McKeigue, Jon Krohn, Amos Storkey

M74

This paper describes a probabilistic framework for studying associations between multiple genotypes, biomarkers, and phenotypic traits in the presence of noise and unobserved confounders for large genetic studies. The framework builds on sparse linear methods developed for regression and modified here for inferring causal structures of richer networks with latent variables. The method is motivated by the use of genotypes as ``instruments'' to infer causal associations between phenotypic biomarkers and outcomes, without making the common restrictive assumptions of instrumental variable methods. The method may be used for an effective screening of potentially interesting genotype phenotype and biomarker-phenotype associations in genome-wide studies, which may have important implications for validating biomarkers as possible proxy endpoints for early stage clinical trials. Where the biomarkers are gene transcripts, the method can be used for fine mapping of quantitative trait loci (QTLs) detected in genetic linkage studies. The method is applied for examining effects of gene transcript levels in the liver on plasma HDL cholesterol levels for a sample of sequenced mice from a heterogeneous stock, with $\sim 10^5$ genetic instruments and $\sim 47 \times 10^3$ gene transcripts.

Rails

SELECT
Customer.find(:all, :conditions=>["name LIKE ?", @message.to])

INSERT
@customer = Customer.new(:name => @message.to)
@customer.save

Autocomplete
1. app/views/layouts/messages.html.erb (javascript in header)
<%= javascript_include_tag :defaults %>
2. app/views/messages/new.html.erb (html form)
Replace
<%= f.text_field :to %>
with
<%= text_field_with_auto_complete 'message', 'to',{}, :skip_style => false  %>
3.app/controllers/messages_controller.rb (SQL select)
  def auto_complete_for_message_to() 
    user_name = '%' + params[:message][:to] + '%' 
    @customers = Customer.find(:all , :conditions=> ["name like ?", user_name.downcase]) 
    render :partial => 'username' 
  end
4. app/views/messages/_username.html.erb (partial output that is generated)

<% for customer in @customers do %>
• <%=h customer.name %>
 <% end %>

Scale-free network

A scale-free network is a network whose degree distribution follows a power law, at least asymptotically. That is, the fraction P(k) of nodes in the network having k connections to other nodes goes for large values of k as

P(k) \ \sim \ ck^\boldsymbol{-\gamma}

where c is a constant and γ is a constant whose value is typically in the range 2 < γ < 3, although occasionally it may lie outside these bounds. Scale-free networks are noteworthy because many empirically observed networks appear to be scale-free, including the world wide web, the Internet, citation networks, and some social networks. http://en.wikipedia.org/wiki/Scale-free_network

Random vs Scale-free networks
Random network (a) and scale-free network (b). In the scale-free network, the larger hubs are highlighted.

Firebug - The most popular and powerful web development tool

http://getfirebug.com/

JQuery java scripting widgets

http://www.salzburg.com/nwas/scripts/jquery-ui/

http://jqueryui.com/home

What is jQuery UI?

jQuery UI is a widget and interaction library built on top of the jQuery JavaScript Library, that you can use to build highly interactive web applications. This guide is designed to get you up to speed on how jQuery UI works. Follow along below to get started.

http://jqueryui.com/demos/accordion/


Stylesheets
http://ajax.googleapis.com/ajax/libs/jqueryui/1.8.7/themes/base/jquery-ui.css
http://static.jquery.com/ui/css/demo-docs-theme/ui.theme.css

Scripts
http://ajax.googleapis.com/ajax/libs/jquery/1.4.4/jquery.min.js
http://ajax.googleapis.com/ajax/libs/jqueryui/1.8.7/jquery-ui.min.js
http://jquery-ui.googlecode.com/svn/tags/latest/external/jquery.bgiframe-2.1.2.js
http://ajax.googleapis.com/ajax/libs/jqueryui/1.8.7/i18n/jquery-ui-i18n.min.js

Rails Autocomplete

http://railsforum.com/viewtopic.php?id=23188

http://share-facts.blogspot.com/2009/02/autocompleter-example-in-ruby-on-rails.html

http://railscasts.com/episodes/102

Git - http://whygitisbetterthanx.com

http://whygitisbetterthanx.com

3rd generation transcriptomics - CAGE

Monday Noon Seminar: Dr. Piero Carninci, RIKEN Yokohama Institute
12:00 PM-1:00 PM (Lecture Theatre)

3rd generation transcriptomics - CAGE

Breakthroughs in science depend on the development of novel technologies to solve outstanding biological problems. We have previously developed key technologies including the cap-trapper to comprehensively clone full-length cDNAs and the cap-analysis gene expression (CAGE). The mission of the Functional Genomics Technology Team is to develop novel original approaches to comprehensively study genes, their products and their interactions. The approaches should address biological questions that cannot be addressed with technologies that are the state of the art today. In contrast with classic approaches considering one gene at the time, our mission is to develop approaches that aim at targeting biological problem as systems. This requires to comprehensively analyzing various biological aspects.
The members of the laboratory are engaged in the development of original approaches and to design, propose and realize original genomics technologies and apply these to challenging biological problems. These include the discovery, validation and analysis of novel classes of non-coding RNAs; establishing single-cell CAGE methods and its application to reduced biological samples; and data mining, with particular interest at identifying biological patterns and rules, including genome-wise analysis of novel classes of RNAs and their network. To address these genomics approaches for samples derived from living cells in their biological context, particular emphasis consists in the miniaturization of these technologies, to analyze systematically the biology of few or single isolated cells, such as the characterization of the molecular networks of individual neurons, included in a larger cellular networks. Going beyond simple expression profiling, the ultimate mission is to understand the complex relationships between gene expression regulation, the non-coding RNA world, the epigenome and the biological output.


http://genome.gsc.riken.jp/osc/english/members/Piero_Carninci.html

http://fantom.gsc.riken.jp

The FANTOM consortium is an international collaborative research project initiated and organized by the RIKEN Omics Science Center. In earlier FANTOM efforts we cloned and annotated 103,000 full-length cDNAs from mouse and distributed them to researchers throughout the world. FANTOM1-3 focused on identifying the transcribed components of mammalian cells. This work improved estimates of the total number of genes and their alternative transcript isoforms in both human and mouse, expanded gene families, and revealed that a large fraction of the transcriptome is non-coding. In addition, with the development of Cap Analysis of Gene Expression (CAGE) FANTOM3 could map a large fraction of transcription start sites and revise our models of promoter structure. This updated web resource provides the previous FANTOM results mapped to current genome builds and presents the results of FANTOM4.

Rumbling Hearts - Kimi Ga Nozomu Eien

Rumbling Hearts

Genres: Drama, Psychological, Romance,

http://www.watchanimeon.com/anime/rumbling-hearts/

iText - Java PDF writer

http://itextpdf.com/

http://today.java.net/article/2007/06/21/generating-pdfs-fun-and-profit-flying-saucer-and-itext

iText is a library that allows you to create and manipulate PDF documents. It enables developers looking to enhance web- and other applications with dynamic PDF document generation and/or manipulation.

Developers can use iText to:

• Serve PDF to a browser
• Generate dynamic documents from XML files or databases
• Use PDF's many interactive features
• Add bookmarks, page numbers, watermarks, etc.
• Split, concatenate, and manipulate PDF pages
• Automate filling out of PDF forms
• Add digital signatures to a PDF file

Open Source PDF Libraries
http://java-source.net/open-source/pdf-libraries

Origami

http://www.chilloutpoint.com/featured/the-incredible-art-of-origami.html

Courses workshops

http://www.grantfacilitation.ubc.ca/continuing-education/courses-workshops/

ESI vs MALDI

http://www.biocompare.com/Articles/FeaturedArticle/921/ESI-Versus-MALDI-For-Protein-Characterization-By-Mass-Spec.html

Molecular biology glossary

http://tainano.com/chin/Molecular%20Biology%20Glossary.htm
http://www.sci.sdsu.edu/~smaloy/Glossary/H.html

Proteomics tools

http://www.psidev.info/index.php?q=node/95

http://www.geneinfinity.org/sp/sp_proteinptmodifs.html

http://www.thegpm.org/

http://www.brc.ubc.ca/drupal5/node/37

Ronald Beavis
Canada Research Chair in Bioinformatics
Professor, Medical Genetics
rbeavis@brc.ubc.ca
604-822-7805

http://www.ncbi.nlm.nih.gov/pubmed?term=%22Beavis%20RC%22[Author]

MGI - Mouse Genome Informatics

http://www.informatics.jax.org/

Mouse Genome Informatics

Mass spectrometry

http://www.21stcenturybio.com/mass_spec_analytical/ms_cid.shtml

One of the most powerful tools available to the mass spectrometrist is MS/MS. Whereas fragmentation is virtually absent in electrospray mass spectrometry, it can be deliberately induced by MS/MS techniques. Tandem MS employs collision-induced dissociation (CID) to fragment a precursor ion.

Similarly, various amino acid modifications can be determined due to the gentle nature of ESI (versus MALDI, which tends to remove some modifications, such as phosphates). These include phosphorylation, glycosylation, acylation, nitronation, and many others.

Ionization source Advantages Disadvantages
Matrix associated desorption Ionization
(MALDI)
Simple sample application and acquisition,
high-throughput capable, low down-time
Variable sample preparation for
different samples

Electrospray Ionization (ESI) Consistent sample ionization efficiency,
capable of ionizing complex mixtures
Rigid sample preparation
requirements, can have high
downtime

• Amino acid
sequence can be
determined by
examining the mass
of the nested
fragments.
• Powerful
technique, does not
require protein
information.
• Not suitable for
complex protein
mixtures.

Stars, ranking

aaaaaaddddd

adjust 30px to 50px to get 5 stars

aaaaaaddddd

Foreign Key Constraints

http://dev.mysql.com/doc/refman/5.0/en/innodb-foreign-key-constraints.html

mysql> SET foreign_key_checks = 0;
mysql> SOURCE dump_file_name;
mysql> SET foreign_key_checks = 1;

Pragmatic Bookshelf

Screencasts on coding, rails, xcode, git, etc.

http://www.pragprog.com/screencasts

xvidcap - screen capture, screen record, screencast

http://xvidcap.sourceforge.net/instruct-video1.mpeg

Rails: Couldn't find Product without an ID

Couldn't find Product without an ID

Couldn't find tablename without an ID

A possible reason is that the tablename, Product doesn't have a primary_key. So fix this by editing the model and add:

class Product < ActiveRecord::Base has_many :items, :as => :item_id

set_primary_key :item_id

end

Rename database column in Rails

llhttp://stackoverflow.com/questions/1992019/how-to-rename-a-database-column-in-rails-using-migration

$ script/generate migration FixColumnName

  def self.up
    rename_column :table_name, :old_column, :new_column
  end

$ rake db:migrate

Life Built With Toxic Chemical: First Known Microbe on Earth Able to Thrive and Reproduce Using Arsenic

http://www.sciencedaily.com/releases/2010/12/101202140622.htm

Too bad they haven't named it yet, but apparently, this organism uses arsenic (chemically similar to phosphorus) instead of phosphorous in it's DNA/RNA, etc.  It was discovered in Mono Lake in California.

CGH, Chromosomal Microarray Analysis (CMA), Copy number variation (CNV)

Comparative genomic hybridization (CGH) or Chromosomal Microarray Analysis (CMA) is a molecular-cytogenetic method for the analysis of copy number changes (gains/losses) in the DNA content of a given subject's DNA and often in tumor cells.

CGH will detect only unbalanced chromosomal changes. Structural chromosome aberrations such as balanced reciprocal translocations or inversions cannot be detected, as they do not change the copy number.

http://en.wikipedia.org/wiki/Comparative_genomic_hybridization

ncRNA.org - database of non-coding RNA tools and databases

http://www.ncrna.org/

Add comments to PDF files

http://www.afritz.org/freetools/adding_PDF_comments.pdf

Protocols JOVE - Large Insert Environmental Genomic Library Production

http://www.jove.com/index/Details.stp?ID=1387

Large Insert Environmental Genomic Library Production

Papers, 5 minute

RactIP: fast and accurate prediction of RNA-RNA interaction using integer programming
http://bioinformatics.oxfordjournals.org/content/26/18/i460.full
- it's fast, uses threshold-cut to limit search space
CopyMap: localization and calling of copy number variation by joint analysis of hybridization data from multiple individuals.
http://bioinformatics.oxfordjournals.org/content/26/21/2776.long
- compare many things at the same time, so better accuracy?
Using Sequence-Specific Chemical and Structural Properties of DNA to Predict Transcription Factor Binding Sites
http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1001007
- uses machine learning, lower false positive rate, compared with other methods, Match, MATRIX SEARCH, QPMEME, method of Berg and von Hippel, data from ChIP-chip assays to measure transcription binding on DNA sequences of Fis (TF), binding sites obtained from RegulonDB
-  A long-standing problem has been how to identify new TF binding sites given known binding sites.

SmashCommunity: a metagenomic annotation and analysis tool
http://bioinformatics.oxfordjournals.org/content/26/23/2977.full
- SmashCommunity (Simple Metagenomics Analysis SHell for microbial communities) to annotate shotgun metagenomes with inbuilt tools for quantitative and comparative analyses.
- Each task in metagenomic analysis, such as sequence assembly or gene prediction, is implemented as a module that is a wrapper around a software program that implements this task.

BigWig and BigBed: enabling browsing of large distributed datasets
http://bioinformatics.oxfordjournals.org/content/26/17/2204.full
- BigWig and BigBed files are compressed binary indexed files containing data at several resolutions that allow the high-performance display of next-generation sequencing experiment results in the UCSC Genome Browser
- BigBed and BigWig files are similar in many ways to BAM files (Li et al., 2009), which are commonly used to store mappings of short reads to the genome.
- http://samtools.sourceforge.net/ SAM (Sequence Alignment/Map) format is a generic format for storing large nucleotide sequence alignments.
MulteeSum: A Tool for Comparative Spatial and Temporal Gene Expression Data
http://gvi.seas.harvard.edu/paper/multeesum-tool-comparative-spatial-and-temporal-gene-expression-data
- Comparision of different Drosophila embryos (for morphological studies) from different Drosophila species, eg. Dmel, Dpse (Drosophila pseudoobscura, second species to be sequenced)
- Developed using the Processing language (Processing.org), based on PointCloudXplor
- http://multeesum.org/

Networks of gene sharing among 329 proteobacterial genomes reveal differences in lateral gene transfer frequency at different phylogenetic depths.
http://mbe.oxfordjournals.org/cgi/pmidlookup?view=long&pmid=21059789
- Using a minimal lateral network approach, we compared LGT rates at different phylogenetic depths.
- Hence our results indicate that the rate of gene acquisition per protein family are similar at the level of species (by recombination) and at the level of classes (by LGT).
is-rSNP: a novel technique for in silico regulatory SNP detection
http://bioinformatics.oxfordjournals.org/content/26/18/i524.short
- Determining the functional impact of non-coding disease-associated single nucleotide polymorphisms (SNPs) identified by genome-wide association studies (GWAS) is challenging. Many of these SNPs are likely to be regulatory SNPs (rSNPs): variations which affect the ability of a transcription factor (TF) to bind to DNA.

Modeling associations between genetic markers using Bayesian networks
http://bioinformatics.oxfordjournals.org/content/26/18/i632.short
- Many coefficients were proposed for measuring the degree of LD, but they provide only a static view of the current LD structure. Generative models (GMs) were proposed to go beyond these measures, giving not only a description of the actual LD structure but also a tool to help understanding the process that generated such structure.
- method is based on learning optimal BN structures (weighted) from haplotype data, extracting equivalence structure classes (PDAGs) and using them to model LD.
- HapMap database
- These plots provide a nice visualization of how single nucleotide polymorphisms (SNPs) travel together in human subpopulations and samples due to linkage disequilibrium (LD).
- http://www.goldenhelix.com/SNP_Variation/Manual/svs7/using_ld_plots.html
- http://bioinformatics.oxfordjournals.org/content/23/6/774/F1.medium.gif
- snp.plotter: an R-based SNP/haplotype association and linkage disequilibrium plotting package

Peripheral blood - circulating blood,

Peripheral blood is the flowing, circulating blood of the body. It is composed of erythrocytes, leukocytes and thrombocytes. These blood cells are suspended in blood plasma, through which the blood cells are circulated through the body. Peripheral blood is different from the blood whose circulation is enclosed within the liver, spleen, bone marrow and the lymphatic system. These areas contain their own specialized blood.

Read more: What is Peripheral Blood? | eHow.com http://www.ehow.com/about_4672930_what-peripheral-blood.html#ixzz16sx3Wl5v

http://www.ehow.com/about_4672930_what-peripheral-blood.html

----------

http://en.wikipedia.org/wiki/Lymph_node

Lymph nodes also have clinical significance. They become inflamed or enlarged in various conditions, which may range from trivial, such as a throat infection, to life-threatening such as cancers. In the latter, the condition of lymph nodes is so significant that it is used for cancer staging, which decides the treatment to be employed, and for determining the prognosis.

Lymphoma is a cancer in the lymphatic of the immune system and presents as a solid tumor of lymphoid cells. It is treatable with chemotherapy, and in some cases radiotherapy and/or bone marrow transplantation, and can be curable depending on the histology, type, and stage of the disease.[1]

Thomas Hodgkin published the first description of lymphoma in 1832, specifically of the form named after him, Hodgkin's lymphoma.[2]

http://en.wikipedia.org/wiki/Lymphoma

Non-hodgkin Lymphoma (Follicular lymphoma)
http://lymphoma.about.com/od/nonhodgkinlymphoma/p/follicularnhl.htm
Slow-growing cancer

Zombie Ants, Cat loving mice

Zombie ants
http://news.nationalgeographic.com/news/2009/05/photogalleries/zombie-ants/

Cat-loving mice
http://en.wikipedia.org/wiki/Toxoplasma_gondii
T. gondii infections have the ability to change the behavior of rats and mice, making them drawn to, rather than fearful of, the scent of cats. This effect is advantageous to the parasite, which will be able to sexually reproduce if its host is eaten by a cat.[11] The infection is highly precise, as it does not affect a rat's other fears such as the fear of open spaces or of unfamiliar smelling food.

Studies have also shown behavioral changes in humans, including slower reaction times and a sixfold increased risk of traffic accidents among infected males[12], as well as links to schizophrenia including hallucinations and reckless behavior[13]. A study of 191 young women in 1999 reported higher intelligence and lower guilt proneness in Toxoplasma-positive subjects[14].

from Robert Holt
http://www.bccrc.ca/dept/cmsgsc/faculty/rholt

Rene Warren
SSAKE - short read sequence aligner
http://www.renewarren.ca/main/Rene.html

Processing: A programming language for visual designers and artists

http://www.processing.org/
http://processing.org/learning/gettingstarted/

Processing is an open source programming language and environment for people who want to create images, animations, and interactions. Initially developed to serve as a software sketchbook and to teach fundamentals of computer programming within a visual context, Processing also has evolved into a tool for generating finished professional work. Today, there are tens of thousands of students, artists, designers, researchers, and hobbyists who use Processing for learning, prototyping, and production.

2010 Genome Sciences Centre Forum

miRNA / mRNA regulation

Talk by Phil Sharp at the 2010 Genome Sciences Centre Forum

- seed region (2-7nt) near the 5' end of the miRNA
- mIR-290-295, mIR-21, let-7
- fibroblast (extra-cellular matrices, ECM, connective tissue) converted to iPS (Induced pluripotent stem cell) via Oct4, Sox2, Nanog, Tck3 (http://en.wikipedia.org/wiki/Induced_pluripotent_stem_cell)
- Hanahan and Weinberg 2000, The hallmarks of cancer.
- loss in miRNA leads to increase in tumor formation
- there's a threshold when miRNA stops working ...

--------------
Talk by Angie-Brooks Wilson (G3, Genetics, Genomics, Gerentology)
- GWAS
- super seniors, healthy >85 year-olds
- ~20% genetics
- APOE4 - Alzheimer, heart disease (rs429358 SNP)
- BECN1 - lifespan in C. elegans (rs10512488 SNP)
- increase in cytokines -> increase in inflammation, tendency to age?

anisotropic - not the same direction

thus the origin of the word: "an" for not, "iso" for same, and "tropic" from tropism, relating to direction; anisotropic filtering does not filter the same in every direction

http://en.wikipedia.org/wiki/Anisotropic_filtering

LaTeX Subfigures to insert multiple figures

http://en.wikibooks.org/wiki/LaTeX/Floats,_Figures_and_Captions

RNAi off-target effects

However, ‘off-target effects’ compromise the specificity of RNAi if sequence identity between siRNA and random mRNA transcripts causes RNAi to knockdown expression of non-targeted genes. The complete off-target effects must be investigated systematically on each gene in a genome by adjusting a group of parameters, which is too expensive to conduct experimentally and motivates a study in silico.

http://nar.oxfordjournals.org/content/33/6/1834

nepotism

Nepotism is favoritism granted to relatives or friends regardless of merit.

w3m - a text based Web browser and pager

w3m - a text based Web browser and pager

$ w3m http://localhost:8080


Press 'Insert' to see the menu, Enter on a hyperlink

Linear Algebra - Eignen vector, Eigen value

These vectors are the eigenvectors of the matrix. A matrix acts on an eigenvector by multiplying its magnitude by a factor, which is positive if its direction is unchanged and negative if its direction is reversed. This factor is the eigenvalue associated with that eigenvector.

http://en.wikipedia.org/wiki/Eigenvalue,_eigenvector_and_eigenspace

http://www.mathworks.com/products/statistics/demos.html?file=/products/demos/shipping/stats/cmdscaledemo.html

R's or Matlab's cmdscale(D)

binding surface, hiv-1, h1n1 influenza, text-mining

Identification of protein binding surfaces using surface triplet propensities.

http://www.ncbi.nlm.nih.gov/pubmed/20819959


Computational Models of HIV-1 Resistance to Gene Therapy Elucidate Therapy Design Principles
http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1000883



Low-dimensional clustering detects incipient dominant influenza strain clusters
http://peds.oxfordjournals.org/content/23/12/935.full

EnvMine: A text-mining system for the automatic extraction of contextual information
http://www.biomedcentral.com/1471-2105/11/294

SVM, bagging, boosting, normalization

Sequential minimum optimization (SMO), a fast algorithm for
training SVM [26,27], was used to build MC-SVM kernel
function models, as implemented in WEKA.

Bagging vs. Boosting (Freund and Schapire 1996). Bagging (resampling) vs Boosting (iterative reweighting). -- these are used to eliminate bias in your samples

The development of PIPA: an integrated and automated pipeline for genome-wide protein function annotation

-- And with microarrays, it seems that the results are largely dependent on the data itself, not so much on the algorithms / classifiers used (so pick and choose which ones, and you might squeeze in a little performance above state of the art).

Nat Genet. 2002 Dec;32 Suppl:496-501.
Microarray data normalization and transformation.
Quackenbush J.

http://www.nature.com/ng/journal/v32/n4s/full/ng1032.html

The goal of most microarray experiments is to survey patterns of gene expression by assaying the expression levels of thousands to tens of thousands of genes in a single assay.

The hypothesis underlying microarray analysis is that the measured intensities for each arrayed gene represent its relative expression level. Biologically relevant patterns of expression are typically identified by comparing measured expression levels between different states on a gene-by-gene basis. But before the levels can be compared appropriately, a number of transformations must be carried out on the data to eliminate questionable or low-quality measurements, to adjust the measured intensities to facilitate comparisons, and to select genes that are significantly differentially expressed between classes of samples.

Using this approach, a normalization factor is calculated by summing the measured intensities in both channels
Locally weighted linear regression (lowess)⁶ analysis has been proposed^{4, 5} as a normalization method that can remove such intensity-dependent effects in the log₂(ratio) values.

Proteomics

Human Proteome Project (HPP)
Human Proteome Organisation (HUPO)

http://www.hupo.org/research/default.asp
http://en.wikipedia.org/wiki/Proteomics

Investigating the correspondence between transcriptomic and proteomic expression profiles using coupled cluster models
http://bioinformatics.oxfordjournals.org/content/24/24/2894

Chris Overall

http://www.clip.ubc.ca/personnel/alumni.html

Leonard Foster
http://www.chibi.ubc.ca/faculty/foster

Tools
PeptideProphet http://peptideprophet.sourceforge.net/
ProteinProphet
Sequence Logo iceLogo
Mascot (Matrix Science)
X! Tandem http://www.thegpm.org/tandem/
MSQuant is a tool for quantitative proteomics/mass spectrometry and processes spectra and LC runs to find quantitative information about proteins and peptides.
MSQuant http://msquant.sourceforge.net/ 

Research quote

In research you really have to love and be committed to your work because things have more of a chance of going wrong than right.  But when things go right, there's is nothing more exciting.

- Dr. Michael Smith

Google Docs has Drawing and Forms

docs.google.com

Google Refine

http://code.google.com/p/google-refine/

Google Refine is a power tool for working with messy data, cleaning it up, transforming it from one format into another, extending it with web services, and linking it to databases like Freebase.

Grad school

Advice for Undergraduates Considering Graduate School Phil Agre

 http://polaris.gseis.ucla.edu/pagre/grad-school.html

Graduate school is training in research. It is for people who love research, scholarship, and teaching for their own sake and for the difference they can sometimes make in the world. It is not for people who simply want more undergraduate courses. It is not for people who are in a hurry to get a real job. The eventual goal of many doctoral students is to get a job as a college professor, or perhaps in industrial or government research. Some in technical subjects go on to start companies. But many just do it because they like it.

The best part of graduate school, the part that makes it worthwhile, comes toward the end, when you begin to present your research in public. Suddenly you will begin to join the community of scholars who work in your chosen area; they will take you seriously and you will begin to make numerous professional acquaintances, some of whom you will probably keep for the rest of your life. (I've written another article, similar to this one, about this process of professional networking. It's online at http://dlis.gseis.ucla.edu/pagre/network.html .)

In graduate school, though, your personal identity will almost certainly undergo great change. In particular, you will acquire a particular sort of professional identity: you will become known as the person who wrote such-and-such a paper, who did such-and-such research, who refuted such-and-such theory, or who initiated such-and-such line of inquiry. This process can be tremendously satisfying. But it's not for everyone.

"Hello. I'd like to ask your advice. I am thinking I might want to go to graduate school, but I'm still uncertain about where I would go or what exactly I would study. I do know that I'm pretty interested in such-and-such. How would I find out about graduate schools in that area?" Some common responses to this are as follows:
(1) "I don't actually know much about that area, but you should talk to so-and-so who is really the expert on that." Go talk to so-and-so.
(2) "I think you're going to have to define your interests a little better before I can help you." Ask for help in defining your interests better.
(3) The response you're looking for, namely a list of all the good graduate programs in that area, with as much detailed description of them as you can possibly digest.
What next? Well, let's back up and talk about research.

Getting good grades in your undergraduate classes is important, but it's not really the main thing. The main thing is this: if you want to go to graduate school, you should start getting involved in research as an undergraduate.

Writing a grant proposal may be the single most valuable experience of your project.


Your statement should demonstrate that you know what research is, that you have had at least one idea in your life, and that you have an interesting and tractable idea about your research for the future. The problem, of course, is that you probably have only the sketchiest idea of what your research in graduate school will be about. That doesn't matter. You are not promising to do the research you describe in your statement (although I am told that this is changing in some areas of the hard sciences); you are only spelling out a single plausible scenario, one that fairly reflects your interests. Try to be concrete, but also include a few hedges such as "perhaps" and "these possibilities include". Good writing counts. Project sobriety and maturity. Avoid frivolity, boasting, and self-deprecation. Show that you've read the research literature, but go easy on academic jargon. Minimize adverbs. Eschew the words "interesting" and "important", which say little. Many people start their statements with a paragraph or two of commonplaces; cut this material until you reach a statement that says something non-obvious about the world and your research involvements. Don't talk about your family, your feelings, or your non-professional interests. Don't say anything bad about anyone, including yourself. And make sure that you are not simply describing the year's most fashionable cliche of a research project -- ask for advice about this issue specifically. Put yourself in the shoes of the graduate admissions committee: they're looking at hundreds of applications and they're only going to take a second look at the ones that stand out. If you follow the above advice then your application will make the first cut and receive the serious consideration it deserves.


Meanwhile, apply for fellowships, that is, grants from foundations and other sources that pay your tuition and a small salary so that you can commit yourself full-time to studying. Don't wait until you're accepted somewhere to apply for outside funding! Deadlines typically fall between November and January in the United States and a few months later in many other countries. Ask someone in your department which are the major fellowships in your area and apply for them all. Also, at each university it is usually somebody's job to keep a list, maybe on the Web, of obscure graduate fellowships. It might be called the office of research development. You might also look in the acknowledgements sections of papers written by younger researchers in your field. Find such lists and write away for applications forms for all of the fellowships that seem relevant. Get advice about which ones are worth applying for. When in doubt, apply. Fellowships are good because they give you much more freedom to choose your own research topics. Without a fellowship, you will have to work for someone else as a teaching assistant or research assistant. Assistantships are often perfectly fine, but a fellowship is always better.

One issue that you should definitely be aware of is that people are going to really want to see you have a definite course of research in your statement of purpose. Unless you know what you want to do, pick two or three different topics that you're interested in and write up something short about each of them. Then let them sit for a day or two and see which one you feel best about. Definitely ask a professor to read over them for you if you have someone who would be willing to do so. If you don't feel comfortable asking a professor, ask other people to read them for you. Graduate students you know are a good choice; all of them have been through this process, and they remember how difficult it was.

It almost never hurts to have extra letters, and don't feel bad about asking people for letters; it's part of their job.

http://www.cs.ubc.ca/~rap/crossroads.html

1. how they like the department
2. can they live on their stipend
3. what is the worst thing about the department
4. how are the resources (building, computers, etc)
5. if there is a specific professor who you'd like to work with, find some of her students and ask them how they like working with the faculty member, how many students the professor has, how much interaction they have with her, etc.
6. how many people who enter the program finish with a Ph.D.
7. why did the people who don't finish leave
8. what happens if you decide to leave the program (some places are considering making you pay back all of the tuition if you leave)
9. are they happy there
10. how many hours a week they spend at work
11. what the classes are like
12. how many classes they have to take, and can you place out of them
13. if there are no classes, what do you have to do instead
14. what hurdles (like preliminary exams) do you have to take, and what type are they (oral, written, etc)
15. anything else that's important to you; for example, if you are female ask the female students how they are treated as females. This is important; don't feel silly for asking. 

1. After a certain point, you cannot make a wrong decision. Chances are good that there is no one perfect place for you to go to, and any where that you go will be fine. You're just trying to optimize. This may not make you feel a whole lot better, but keep it in mind; it really is true.

2. No decision that you make will make everyone happy. Someone will think that you've made the wrong decision no matter where you decide to go. Accept that and when the first person expresses that you've made the wrong choice, try not to let it bother you. 

protein-dna binding

Discovering protein–DNA binding sequence patterns using association rule mining
http://nar.oxfordjournals.org/content/early/2010/06/06/nar.gkq500.full.pdf+html

GOing Bayesian: model-based gene set analysis of genome-scale data
http://nar.oxfordjournals.org/content/38/11/3523.long

Mapping the Druggable Allosteric Space of G-Protein Coupled Receptors: a Fragment-Based Molecular Dynamics Approach
http://onlinelibrary.wiley.com/doi/10.1111/j.1747-0285.2010.01012.x/full

Identification and Optimization of Classifier Genes from Multi-Class Earthworm Microarray Dataset
http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0013715

GWAS and health care

http://www.bioworld.com/servlet/com.accumedia.web.Dispatcher?next=bioWorldHeadlines_article&forceid=53907

Labs

http://compbio.bccrc.ca/?page_id=39
http://molonc.bccrc.ca/?page_id=217
http://www.chibi.ubc.ca/
http://www.chibi.ubc.ca/training

Omics

http://omics.org/index.php/Degradomics

The major role of matrix metalloproteinases (MMPs) is for homeostatic regulation of the extracellular environment, not simply to degrade matrix as their name suggests.

http://www.nature.com/nrm/journal/v3/n7/full/nrm858.html

Degradomics — the application of genomic and proteomic approaches to identify the protease and protease-substrate repertoires, or 'degradomes', on an organism-wide scale — promises to uncover new roles for proteases in vivo. This knowledge will facilitate the identification of new pharmaceutical targets to treat disease. Here, we review emerging degradomic techniques and concepts.

GWAS

Genome-wide association studies for complex traits: consensus, uncertainty and challenges
http://dx.doi.org/10.1038/nrg2344

Finding genes underlying human disease
http://www.ncbi.nlm.nih.gov/pubmed/18783406

Genomewide Association Studies and Assessment of the Risk of Disease
http://www.ncbi.nlm.nih.gov/pubmed/20647212

http://bioinformatics.oxfordjournals.org/content/26/21/2664.full?sid=1f67c073-33fd-40cc-8196-a8ea07ce3e9c

These results show an increasing proportion of newly determined sequences falling within existing islands, which may indicate an approach to the representative map of the protein universe. If this trend continues, by approximately 2017 at least 80% of new sequences will fall within an existing island (Fig. 4) that is, have a sequence identity >50% with sequences already present in the database.

Git vs SVN (subversion) version control system (VCS)

Git
- Distriubted, users have their own copy, fast - no network latency (except for push and fetch/pull) for branch switch, diff, status, commit, merge
- Better branch handling, every working directory is a branch
- Easily switch branches without creating a separate checkout
- Takes up less space, only one copy is kept
- SHA1 to identify a commit, use a tag instead

Svn
- more mature user interface eg. Tortoise, RapidSVN
- single repository, know where files are stored
- access control
- revision numbers, easy to track



https://git.wiki.kernel.org/index.php/GitSvnComparison

git clone https://github.com/proj/proj my_proj
cd my_proj
git pull
git add new_file
git commit -m 'Adding new file'
git pull
git push
git checkout revert_file

GWAS

http://www.genomesunzipped.org/2010/07/how-to-read-a-genome-wide-association-study.php

The basic GWAS approach is to look at approximately a million positions in the human genome (called ‘SNPs’) where different people carry different versions of the genetic code (so at some particular position I might have an ‘A’ and you might have a ‘C’). I’m going to focus here on the most common GWAS design, called case-control, where the goal is to compare the frequencies of these different versions between a group of healthy individuals (controls) and another group of people with a specific disease (cases). The places where the frequencies between cases and controls are significantly different are therefore associated with risk of developing the disease.

Intergenic regions

An Intergenic region (IGR) is a stretch of DNA sequences located between clusters of genes that contain few or no genes. Occasionally some intergenic DNA acts to control genes close by, but most of it has no currently known function. It is one of the DNA sequences collectively referred to as junk DNA, though it is only one phenomenon labeled such and in scientific studies today, the term is less used. In humans, intergenic regions comprise a large percentage of the genome.
This could also be where noncoding RNAs are located. Though little is known about them, they are thought to have regulatory functions.
Intergenic regions are different from intragenic regions (or introns), which are short, non-coding regions that are found within genes, especially within the genes of eukaryotic organisms.
Scientists have now artificially synthesized proteins from intergenic regions. <sup>[1]

http://en.wikipedia.org/wiki/Intergenic_region</sup>

1000Genomes

http://www.1000genomes.org/page.php?page=about

The goal of the 1000 Genomes Project is to find most genetic variants that have frequencies of at least 1% in the populations studied. This goal can be attained by sequencing many individuals lightly. To sequence a person's genome, many copies of the DNA are broken into short pieces and each piece is sequenced. The many copies of DNA mean that the DNA pieces are more-or-less randomly distributed across the genome. The pieces are then aligned to the reference sequence and joined together. To find the complete genomic sequence of one person with current sequencing platforms requires sequencing that person's DNA the equivalent of about 28 times (called 28X). If the amount of sequence done is only an average of once across the genome (1X), then much of the sequence will be missed, because some genomic locations will be covered by several pieces while others will have none. The deeper the sequencing coverage, the more of the genome will be covered at least once. Also, people are diploid; the deeper the sequencing coverage, the more likely that both chromosomes at a location will be included. In addition, deeper coverage is particularly useful for detecting structural variants, and allows sequencing errors to be corrected.

Online LaTeX editor

http://www.codecogs.com/latex/eqneditor.php

score = \sum\limits_{i=0}^{o,s,it,d}w_i \cdot \sum\limits_{j=0}^{n_i}(\frac{v_j}{w_j})

Linkage disequilibrium (LD), linkage study

When the transmission of genotype at locus A is DEPENDENT on the genotype at another locus B.

bio.classes.ucsc.edu/bio107/Class%20pdfs/W05_lecture15.pdf

Random genetic drift is a stochastic process (by definition). One aspect of genetic drift is the random nature of transmitting alleles from one generation to the next given that only a fraction of all possible zygotes become mature adults. The easiest case to visualize is the one which involves binomial sampling error. If a pair of diploid sexually reproducing parents (such as humans) have only a small number of offspring then not all of the parent's alleles will be passed on to their progeny due to chance assortment of chromosomes at meiosis.


http://www.talkorigins.org/faqs/genetic-drift.html

Agreement in the types of data that occur in natural pairs. For example, in a trait like schizophrenia, a pair of identical twins is concordant if both are affected or both are unaffected; it is discordant if one of them only is affected. Likewise, the pairs might be non-identical twins, or sibs, or husband and wife, etc.


http://www.mondofacto.com/facts/dictionary?concordance


www-gene.cimr.cam.ac.uk/clayton/courses/florence05/lectures/linkage-lecture.pdf
“Genetic linkage analysis is a statistical method that is used to associate functionality of genes to their location on chromosomes.“ 
http://bioinfo.cs.technion.ac.il/superlink/

Neighboring genes on the chromosome have a tendency to stick together when passed on to offsprings.
Therefore, if some disease is often passed to offsprings along with specific marker-genes , then it can be concluded that the gene(s) which are responsible for the disease are located close on the chromosome to these markers.

Feyerabend

Feyerabend’s “there is no idea that is not capable of improving our knowledge.”

‘remote applicability’, “it is easy to massage a problem in biology, say, until it succumbs to the tricks of our trade -- and is of no use to biologists”. Database metatheory: asking the big queries, Christos Papadimitriou, PODS 95..

It is darkest before the dawn
http://www.weekdaywisdom.com/mm030705.htm

"Those whose acquaintance with scientific research is derived chiefly from its practical results easily develop a completely false notion of the mentality of the men who, surrounded by a sceptical world, have shown the way to those like-minded with themselves, scattered through the earth and the centuries. " Religion and Science, The following excerpt was published in The World as I See It (1999). by Albert Einstein

RIDICULED DISCOVERERS, VINDICATED MAVERICKS, revolutionary science

http://amasci.com/weird/vindac.html

Robert L. Folk (existence and importance of nanobacteria)

Discovered bacteria with diameters far below 200nM widely present in mineral samples, able to both metabolize metals and to create calcium encrustations. Proposed their large role in creation of "metamorphic" rock and everyday metal corrosion. These ideas were rejected with hostility because the bacterial diameter is too small to include enough genetic material or ribosomes, and they seem immune to common sterilization techniques.

Galileo (supported the Copernican viewpoint)

It was not the church authorities who refused to look through his telescope. It was his fellow scientists! They thought that using a telescope was a waste of time, since even if they did see evidence for Galileo's claims, it could only be because Galileo had bewitched them.

LaTeX Beamer

http://www.math-linux.com/spip.php?article77

Basic presentation with Beamer

Expand Your Professional-Skills Training

http://sciencecareers.sciencemag.org/career_magazine/previous_issues/articles/2010_10_01/caredit.a1000096

On Negative Results

"Negativity is to a large extent in the eye of the beholder" - Database metatheory: asking the big queries, Christos Papadimitriou, PODS 95

Delivering effective presentations

http://www.methink.com/3-techniques-for-delivering-engaging-presentations/

1. 10 slides for 20 minutes with 30 size font
2. Picture is worth a thousand words - slides are only aids
3. Tell a story - put the problem in the context of a story that your audience can relate to

X-Ray for Your Genes: Researcher Takes the Next Step in Personalized Medicine

http://www.sciencedaily.com/releases/2010/10/101007111459.htm

ggplot2

http://learnr.wordpress.com/2009/08/20/ggplot2-version-of-figures-in-lattice-multivariate-data-visualization-with-r-part-13-2/

> library("flowViz")
> data(GvHD, package = "flowCore")

> pl <- densityplot(Visit ~ `FSC-H` | Patient, data = GvHD)
> print(pl) 

# Pie charts words grouped by journals
fig3a <- ggplot(melted, aes(x = factor(1), fill=variable)) +  # x and y
            geom_bar(width=1) +                              # layers
            coord_polar(theta = "y") +                       # pie chart
            facet_wrap(~journal, nrow = 1) +                # group
            scale_fill_manual(values = myColors) +           # some matching colors
            opts(axis.text.x = theme_blank(),               # remove x-label
                axis.title.x=theme_blank(),
                title = 'Journal word frequencies') +    
            opts(legend.position="right")                   # legend to the bottom

grid.arrange(fig3a, fig3b, nrow = 2, ncol = 1)

Highlight syntax color code

http://www.andre-simon.de/doku/highlight/en/highlight.html

EST

to map ESTs and variable reads (multiple fasta-format files) to an already known related prokaryotic genome

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2789075/

http://en.wikipedia.org/wiki/Expressed_sequence_tag

The current commercially available high-throughput methodologies rely on primers or probes designed to detect each of the current reference miRNA sequences residing in miRBase, which acts as the central repository for known miRNAs (Griffiths-Jones 2006).

However, probe-based methodologies are generally restricted to the detection and profiling of only the known miRNA sequences previously identified by sequencing or homology searches.

Sequencing-based applications for identifying and profiling miRNAs have been hindered by laborious cloning techniques and the expense of capillary DNA sequencing (Pfeffer et al. 2005; Cummins et al. 2006).

In contrast with capillary sequencing, recently available “next-generation” sequencing technologies offer inexpensive increases in throughput, thereby providing a more complete view of the miRNA transcriptome.

Pluripotent human embryonic stem cells (hESCs) can be cultured under nonadherent conditions that induce them to differentiate into cells belonging to all three germ layers and form cell aggregates termed embryoid bodies (EBs) (Itskovitz-Eldor et al. 2000; Bhattacharya et al. 2004).

Samples of undifferentiated hESCs and differentiated cells from EBs were chosen for miRNA profiling, first because the pluripotency of ESCs is known to require the presence of miRNAs (Bernstein et al. 2003; Song and Tuan 2006; Wang et al. 2007) and second because specific changes in miRNA expression are thought to accompany differentiation (Chen et al. 2007).

These reads were mapped to the genome by forcing perfect alignments beginning at the first nucleotide and retaining the longest region of each read that could be aligned to the reference genome, along with all alignment positions. After mapping, a total of 766,199 (hESC) and 724,091 (EB) unique error-free trimmed small RNA sequences were represented by 4,351,479 and 3,886,865 reads.

Sequences deriving from 334 distinct miRNA genes were identified. The miRNAs were the most abundant class of small RNAs on average, but spanned the entire range of expression, with sequence counts up to ~120,000 (Fig. 1A).

Virtually no reads aligned to the genome after position 28, so we trimmed all reads at 30 nt to reduce the number of unique sequences.

For every read, the longest alignment was determined, and this subsequence, as well as the positions for every alignment of this length, was stored in a database (to a maximum of 100 alignments). 

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2279248/?tool=pubmed

Nanopore sequencing

http://www.youtube.com/watch?v=HbjAMJehSlg&feature=related

Perl database interface DBI

http://www.perl.com/pub/1999/10/DBI.html
# db settings
my $db = "mydbname";
my $host = "127.0.0.1";
my $port = 3306;

# connect
my $dsn = "DBI:mysql:database=$db;host=$host;port=$port";
my $fosdb = DBI->connect( $dsn, $user, $pass) or die ( "Couldn't connect to database: " . DBI->errstr . "\n";

# Read the matching records and print them out          
          while (@data = $sth->fetchrow_array()) {
            my $firstname = $data[1];
            my $id = $data[2];
            print "\t$id: $firstname $lastname\n";
          }

my $sth = $dbh->prepare('SELECT age FROM people WHERE id = ?')
            or die "Couldn't prepare statement: " . $dbh->errstr;

$sth->execute($id) 
            or die "Couldn't execute statement: " . $sth->errstr;

$dbh->disconnect;

perlconsole - An interactive Perl console like Python's

Installing the package libterm-readline-gnu-perl should get you readline support. 

$ apt-cache show perlconsole
...
Description: small program that lets you evaluate Perl code interactiv
+ely
 Perl Console is a light program that lets you evaluate Perl code
 interactively. It uses Readline for grabing input and provides comple
+tion
 with all the namespaces loaded during your session.
 .
 This is pretty useful for Perl developers that write modules.
 You can load a module in your session and test a function exported by
+ the
 module.

http://www.perlmonks.org/?node_id=816352

MiPred

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1933124/

In this article, in order to achieve higher performance of distinguishing the real pre-miRNAs from the pseudo ones, a hybrid feature by incorporating the local contiguous structure-sequence composition, the minimum of free energy (MFE) of the secondary structure and the P-value of randomization test was used.

Genotype calling

www.cbs.dtu.dk/chipcourse/Lectures/genotype_calling.pdf

SNP call rate?  Plot of SNPs along allele A (eg. A) and allele B (eg. C) 

You can either get AA (AA), AB (AC), or BB (CC).

R draw.key positioning

https://stat.ethz.ch/pipermail/r-help/2009-February/187229.html

The simplest way to change position is to supply a simple 'vp' argument.

xyplot(1~1,
       panel = function(...) {
           require(grid)
           panel.xyplot(...)
           draw.key(list(text=list(lab='catch'),
                         lines=list(lwd=c(2)),
                         text=list(lab='landings'),
                         rectangles=list(col=rgb(0.1, 0.1, 0, 0.1))),
                    draw = TRUE,
                    vp = viewport(x = unit(0.75, "npc"), y = unit(0.9, "npc")))
       })

MiR-107 and MiR-185 Can Induce Cell Cycle Arrest in Human Non Small Cell Lung Cancer Cell Lines

MiR-107 and MiR-185 Can Induce Cell Cycle Arrest in Human Non Small Cell Lung Cancer Cell Lines

http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0006677

Algorithm text books

• the book "Biological sequence analysis" by Durbin et al. (Cambridge University Press, ISBN-13: 978-0521629713) will serve as our main reference book (the BSA book)
• if you do not have a strong Biology background, I suggest "Molecular Biology of the Gene" by James Watson et al. (Benjamin Cummings, 6th edition (2007), ISBN-13 978-0805395921) and, to a lesser extent, "Molecular Biology of the Cell" by Bruce Alberts which is also a fine book (Garland, 4th edition (2002), ISBN-13: 978-0815332183) as your reference books. Make sure you are dealing with the latest editions of these books.

http://www.cs.ubc.ca/~irmtraud/cs_545/

Analytic Bridge

Data mining, statistics, quant, operations research, six sigma, econometrics, web analytics, text mining, business intelligence, SAS, biostatistics, machine learning, artificial intelligence, decision sciences, cloud computing, SaaS.

http://www.analyticbridge.com/

Deep Sequencing - sequence coverage

http://scienceblogs.com/mikethemadbiologist/2010/03/what_is_deep_sequencing.php

Git basics

Upload it again: 'git push'
So, to have a successful work session do the following:
   Sit down at computer
   type 'git pull'
   make changes
   test your changes
   type 'git commit'
   describe your changes
   type 'git pull'
   type 'git push'
   Get up and walk away from the computer

You can type 'git pull' every minute, if you like.
You can type 'git commit -a' just after you've
made a change and
have tested it a little (Any syntax errors?
Does it run at all?).
You can type 'git push' after each commit; it has
no effect until then.


~$ git config --global user.email my@email.com
~$ git config --global user.name myusrname

Alignment

*Free shift (or semi-global) alignments will ignore gaps at the beginning and end of the sequence, while Global alignments try to consider all positions.
Use Free shift alignments when some of the sequences are terminally truncated. Local alignments (such as BLAST) are useful for
finding short stretches of homology and are useful for finding sequence overlap or detecting a short internal sequence stretches that
are shared. 


http://www.uoguelph.ca/plant/depttools/dnaanalysis.htm
www.cs.ecu.edu/hochberg/spring2006/LocalAlign.pdf
birg.cs.wright.edu/text/Ch2.ppt 

Nominees for the 2010 M-net Asian Music Awards announced!

http://musicbang.myx.tv/nominees-for-the-2010-m-net-asian-music-awards-announced/

Rspec

$ jruby -S gem install rspec -v=1.1.12
$ spec -v
rspec 1.1.12
$ RAILS_ENV=test jruby -S rake db:test:prepare
$ RAILS_ENV=test jruby -S rake db:migrate


$ RAILS_ENV=development jruby -S rake db:migrate:redo VERSION=20101027194336


$ RAILS_ENV=development jruby -S rake db:rollback STEP=5


http://cheat.errtheblog.com/s/rspec/

http://guides.rubyonrails.org/migrations.html

Genomics: 1000 Genomes Project Gives New Map of Genetic Diversity

http://www.sciencemag.org/cgi/content/full/330/6004/574

GPL (viral) vs Artistic 2.0

http://stackoverflow.com/questions/1699619/artistic-license-v2-0-vs-gpl-v2-differences

ncRNA non-coding RNA review papers

1: Galasso M, Elena Sana M, Volinia S. Non-coding RNAs: a key to future
personalized molecular therapy? Genome Med. 2010 Feb 18;2(2):12. PubMed PMID: 20236487; PubMed Central PMCID: PMC2847703.
http://www.ncbi.nlm.nih.gov/pubmed/20236487

1: Harrison BR, Yazgan O, Krebs JE. Life without RNAi: noncoding RNAs and their functions in Saccharomyces cerevisiae. Biochem Cell Biol. 2009 Oct;87(5):767-79. Review. PubMed PMID: 19898526.
http://www.ncbi.nlm.nih.gov/pubmed/19898526

1: Fabbri M, Calin GA. Beyond genomics: interpreting the 93% of the human genome that does not encode proteins. Curr Opin Drug Discov Devel. 2010 May;13(3):350-8. Review. PubMed PMID: 20443168.
http://www.ncbi.nlm.nih.gov/pubmed/20443168

1: Majer A, Booth SA. Computational methodologies for studying non-coding RNAs relevant to central nervous system function and dysfunction. Brain Res. 2010 Jun 18;1338:131-45. Epub 2010 Apr 8. Review. PubMed PMID: 20381467.
http://www.ncbi.nlm.nih.gov/pubmed/20381467

1: Zheng L, Qu L. Computational RNomics: structure identification and functional
prediction of non-coding RNAs in silico. Sci China Life Sci. 2010
May;53(5):548-62. Epub 2010 May 23. PubMed PMID: 20596938.
http://www.ncbi.nlm.nih.gov/pubmed/20596938

ncRNA, probe, GWAS

A method for automatically extracting infectious disease-related primers and probes from the literature
http://www.biomedcentral.com/1471-2105/11/410

Classification of ncRNAs using position and size information in deep
sequencing data
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2935403/?tool=pubmed

Forward-time simulation of realistic samples for genome-wide association studies
http://www.biomedcentral.com/1471-2105/11/442
Genetic drift or allelic drift is the change in the frequency of a gene variant (allele) in a population due to random sampling.  The alleles in the offspring are a sample of those in the parents, and chance has a role in determining whether a given individual survives and reproduces. vs (natural selection)

In population genetics, linkage disequilibrium is the non-random association of alleles at two or more loci, not necessarily on the same chromosome. It is not the same as linkage, which describes the association of two or more loci on a chromosome with limited recombination between them. Linkage disequilibrium describes a situation in which some combinations of alleles or genetic markers occur more or less frequently in a population than would be expected from a random formation of haplotypes from alleles based on their frequencies. Non-random associations between polymorphisms at different loci are measured by the degree of linkage disequilibrium (LD). Numerically, it is the difference between observed and expected (assuming random distributions) allelic frequencies.

Detection and characterization of novel sequence insertions using paired-end next-generation sequencing.
http://bioinformatics.oxfordjournals.org/content/26/10/1277.full

imagemagick convert - split single multi-pdf to many pdfs

http://ardvaark.net/useful-pdf-imagemagick-recipes

Split single multi-pdf to many pdfs

$ convert -quality 100 -density 300x300 in.pdf multi%d.pdf

# combine, may increase in size by a lot
$ convert -density 150 pdf1.pdf pdf2.pdf out.pdf

or better
$ pdftk pdf1.pdf pdf2.pdf cat output temp.pdf

R aggregation

> x
  j word journ
1 1    p     b
2 2    g     b
3 3    p     d
4 4    p     b
5 5    p     d
> with(x, tapply(word, journ, length))
b d
3 2

Perl monks for your PERL programming needs

http://www.perlmonks.org

Critical thinking

http://en.wikipedia.org/wiki/Critical_thinking

Critical thinking clarifies goals, examines assumptions, discerns hidden values, evaluates evidence, accomplishes actions, and assesses conclusions.
"Critical" as used in the expression "critical thinking" connotes the importance or centrality of the thinking to an issue, question or problem of concern. "Critical" in this context does not mean "disapproval" or "negative." There are many positive and useful uses of critical thinking, for example formulating a workable solution to a complex personal problem, deliberating as a group about what course of action to take, or analyzing the assumptions and the quality of the methods used in scientifically arriving at a reasonable level of confidence about a given hypothesis. Using strong critical thinking we might evaluate an argument, for example, as worthy of acceptance because it is valid and based on true premises. Upon reflection, a speaker may be evaluated as a credible source of knowledge on a given topic.
Critical thinking can occur whenever one judges, decides, or solves a problem; in general, whenever one must figure out what to believe or what to do, and do so in a reasonable and reflective way. Reading, writing, speaking, and listening can all be done critically or uncritically. Critical thinking is crucial to becoming a close reader and a substantive writer. Expressed most generally, critical thinking is "a way of taking up the problems of life."^[2]