Convert SRA to FASTQ

http://www.ncbi.nlm.nih.gov/sites/books/NBK158900/

./sratoolkit.2.4.2-ubuntu64/bin/fastq-dump --split-3 -L info --gzip -v SRR1131659

Oracle Health Sciences Translational Research Center: A Translational Medicine Platform to Address the Big Data Challenge

http://www.oracle.com/us/industries/healthcare/translational-medicine-platform-wp-1840042.pdf

Oracle, on the Oracle Health Sciences Translational Research Center, a scalable informatics solution for translational research. Its back-end data components seamlessly integrate clinical and omics data from diverse clinical data sources as well as from vendor-specific and modality-specific omics data silos, providing standardized data readily available for the front-end application. 

Finding overlap between two lists in Excel

Function

=COUNTIF(C:C,K4)

Where C:C contains one list and K4 is the value. Simply drag and drop this formula for all other values in the "K" column!

Eclipse misbehaving

If eclipse is misbehaving for some reasons, context menu not appearing, right-click does nothing ...

backup and remove ~/workspace/.metadata/.plugins

Or

Switch to a new workspace

Compound heterozygote

http://www.medicinenet.com/script/main/art.asp?articlekey=33675

Compound heterozygote: The presence of two different mutant alleles at a particular gene locus, one on each chromosome of a pair.

The human genome contains two copies of each gene, a paternal and a maternal allele. A mutation affecting only one allele is called heterozygous. Ahomozygous mutation is the presence of the identical mutation on both alleles of a specific gene. However, when both alleles of a gene harbor mutations, but the mutations are different, these mutations are called compound heterozygous. Also called a genetic compound.

http://ghr.nlm.nih.gov/glossary=compoundheterozygote

An individual who has two different abnormal alleles at a particular locus, one on each chromosome of a pair; usually refers to individuals affected with an autosomal recessive disorder

or they can be mutations at different locus of the same gene.

Syndromic vs non-syndromic (not associated with anything else, specific)

http://ghr.nlm.nih.gov/condition/nonsyndromic-deafness

Nonsyndromic deafness is hearing loss that is not associated with other signs and symptoms. In contrast, syndromic deafness involves hearing loss that occurs with abnormalities in other parts of the body. Different types of nonsyndromic deafness are named according to their inheritance patterns.

Javascript Closures -- callbacks in a loop

http://geekabyte.blogspot.ca/2013/04/callback-functions-in-loops-in.html

Solution for callbacks in a loop:
1. callbacks
2. recursive loop

Continuous integration servers

Jenkins
http://jenkins-ci.org/

Bamboo
https://confluence.atlassian.com/display/BAMBOO/Bamboo+Documentation+Home

PLOS Computational Biology: Translational Bioinformatics

http://www.ploscollections.org/article/browseIssue.action?issue=info:doi/10.1371/issue.pcol.v03.i11

Let's Make Those Book Chapters Open Too!

Philip E. Bourne

PLOS Computational Biology: published 21 Feb 2013 | info:doi/10.1371/journal.pcbi.1002941

Education Articles

Introduction to Translational Bioinformatics Collection

Russ B. Altman

PLOS Computational Biology: published 27 Dec 2012 | info:doi/10.1371/journal.pcbi.1002796

Chapter 1: Biomedical Knowledge Integration

Philip R. O. Payne

PLOS Computational Biology: published 27 Dec 2012 | info:doi/10.1371/journal.pcbi.1002826

Chapter 2: Data-Driven View of Disease Biology

Casey S. Greene, Olga G. Troyanskaya

PLOS Computational Biology: published 27 Dec 2012 | info:doi/10.1371/journal.pcbi.1002816

Chapter 3: Small Molecules and Disease

David S. Wishart

PLOS Computational Biology: published 27 Dec 2012 | info:doi/10.1371/journal.pcbi.1002805

Chapter 4: Protein Interactions and Disease

Mileidy W. Gonzalez, Maricel G. Kann

PLOS Computational Biology: published 27 Dec 2012 | info:doi/10.1371/journal.pcbi.1002819

Chapter 5: Network Biology Approach to Complex Diseases

Dong-Yeon Cho, Yoo-Ah Kim, Teresa M. Przytycka

PLOS Computational Biology: published 27 Dec 2012 | info:doi/10.1371/journal.pcbi.1002820

Chapter 6: Structural Variation and Medical Genomics

Benjamin J. Raphael

PLOS Computational Biology: published 27 Dec 2012 | info:doi/10.1371/journal.pcbi.1002821

Chapter 7: Pharmacogenomics

Konrad J. Karczewski, Roxana Daneshjou, Russ B. Altman

PLOS Computational Biology: published 27 Dec 2012 | info:doi/10.1371/journal.pcbi.1002817

Chapter 8: Biological Knowledge Assembly and Interpretation

Ju Han Kim

PLOS Computational Biology: published 27 Dec 2012 | info:doi/10.1371/journal.pcbi.1002858

Chapter 9: Analyses Using Disease Ontologies

Nigam H. Shah, Tyler Cole, Mark A. Musen

PLOS Computational Biology: published 27 Dec 2012 | info:doi/10.1371/journal.pcbi.1002827

Chapter 10: Mining Genome-Wide Genetic Markers

Xiang Zhang, Shunping Huang, Zhaojun Zhang, Wei Wang

PLOS Computational Biology: published 27 Dec 2012 | info:doi/10.1371/journal.pcbi.1002828

Chapter 11: Genome-Wide Association Studies

William S. Bush, Jason H. Moore

PLOS Computational Biology: published 27 Dec 2012 | info:doi/10.1371/journal.pcbi.1002822

Chapter 12: Human Microbiome Analysis

Xochitl C. Morgan, Curtis Huttenhower

PLOS Computational Biology: published 27 Dec 2012 | info:doi/10.1371/journal.pcbi.1002808

Chapter 13: Mining Electronic Health Records in the Genomics Era

Joshua C. Denny

PLOS Computational Biology: published 27 Dec 2012 | info:doi/10.1371/journal.pcbi.1002823

Chapter 14: Cancer Genome Analysis

Miguel Vazquez, Victor de la Torre, Alfonso Valencia

PLOS Computational Biology: published 27 Dec 2012 | info:doi/10.1371/journal.pcbi.1002824

Chapter 15: Disease Gene Prioritization

Yana Bromberg

PLOS Computational Biology: published 25 Apr 2013 | info:doi/10.1371/journal.pcbi.1002902

Chapter 16: Text Mining for Translational Bioinformatics

K. Bretonnel Cohen, Lawrence E. Hunter

PLOS Computational Biology: published 25 Apr 2013 | info:doi/10.1371/journal.pcbi.1003044

Chapter 17: Bioimage Informatics for Systems Pharmacology

Fuhai Li, Zheng Yin, Guangxu Jin, Hong Zhao, Stephen T. C. Wong

PLOS Computational Biology: published 25 Apr 2013 | info:doi/10.1371/journal.pcbi.1003043

threejs - makes WebGL - 3D in the browser - very easy

http://threejs.org/docs/index.html#Manual/Introduction/Creating_a_scene

Three.js is a library that makes WebGL - 3D in the browser - very easy. While a simple cube in raw WebGL would turn out hundreds of lines of Javascript and shader code, a Three.js equivalent is only a fraction of that.

Google Genomics

https://cloud.google.com/genomics/v1beta2/visualization

Google Genomics provides an API to store, process, explore, and share DNA sequence reads, reference-based alignments, and variant calls, using Google's cloud infrastructure.

    Store alignments and variant calls for one genome or a million.
    Process genomic data in batch by running principal component analysis or Hardy-Weinberg equilibrium, in minutes or hours, by using parallel computing frameworks like MapReduce.
    Explore data by slicing alignments and variants by genomic range across one or multiple samples -- for your own algorithms or for visualization; or interactively process entire cohorts to find transition/transversion ratios, allelic frequency, genome-wide association and more using BigQuery.
    Share genomic data with your research group, collaborators, the broader community, or the public. You decide.

Google Genomics is implementing the API defined by the Global Alliance for Genomics and Health for visualization, analysis and more. Compliant software can access Google Genomics, local servers, or any other implementation.

Cluster computing
https://spark.apache.org/downloads.html

Google BigQuery
https://cloud.google.com/bigquery/what-is-bigquery

10 New Breakthrough Technologies 2014

http://www.technologyreview.com/lists/technologies/2014/


Agricultural Drones
Ultraprivate Smartphones
Brain Mapping
Neuromorphic Chips
Genome Editing
Microscale 3-D Printing
Mobile Collaboration
Oculus Rift
Agile Robots
Smart Wind and Solar Power

Alzheimer's drug sneaks through blood–brain barrier

Neurobiologist Ryan Watts and his colleagues at the biotechnology company Genentech in South San Francisco have sought to break through the barrier by exploiting transferrin, a protein that sits on the surface of blood vessels and carries iron into the brain. The team created an antibody with two ends. One end binds loosely to transferrin and uses the protein to transport itself into the brain. And once the antibody is inside, its other end targets an enzyme called β-secretase 1 (BACE1), which produces amyloid-β. Crucially, the antibody binds more tightly to BACE1 than to transferrin, and this pulls it off the blood vessel and into the brain. It locks BACE1 shut and prevents it from making amyloid-β.

http://www.nature.com/news/alzheimer-s-drug-sneaks-through-blood-brain-barrier-1.16291