Epistasis Blog

From the Computational Genetics Laboratory at Dartmouth Medical School (www.epistasis.org)

Sunday, December 30, 2012

My Year on Twitter 2012

This is a summary of my tweets from 2012 via Vizify

Follow me on Twitter here.


Saturday, December 29, 2012

Epistasis Blog Posts from 2012

January, 2012

Imaging genetics

Lower-order effects adjustment in quantitative traits model-based multifactor

February, 2012

Genetic epidemiology with a capital E

Six degrees of epistasis

March, 2012

Ten years of pathway analysis: Current approaches and outstanding challenges

Is life law-like?

April, 2012

The predictive capacity of personal genome sequencing and missing heritability

May, 2012

Gene-based multifactor dimensionality reduction (MDR)

June, 2012

The challenges of personalized medicine and genomics

Dynamic epistasis for different alleles of the same gene

From the reaktionsnorm to the adaptive norm: The norm of reaction, 1909–1960

July, 2012

Biological basis of epistasis

Machine learning that matters

Risk estimation and risk prediction using machine-learning methods

Two opposing views of academic life

Top 10 reasons to study bioinformatics

August, 2012

New NIH R01: Bioinformatics approaches to visual disease genetics

Modular biological complexity

Epistasis in yeast

Journal impact factor

September, 2012

Epistasis dominates the genetic architecture of Drosophila quantitative traits

New NIH R01: Bioinformatics strategies for brain imaging genetics

October, 2012

Big data in the biological and biomedical sciences: biodata mining challenges and opportunities

November, 2012

Genetic influences on disease remain hidden

December, 2012

Auctioning your papers to journals

Human diseases through the lens of network biology

Translational bioinformatics collection

Friday, December 28, 2012

Translational Bioinformatics Collection

We participated (Chapter 11) in this nice collection of translational bioinformatics papers published by PLoS Computational Biology.

Introduction to Translational Bioinformatics Collection
Russ B. Altman

Chapter 1: Biomedical Knowledge Integration
Philip R. O. Payne

Chapter 2: Data-Driven View of Disease Biology
Casey S. Greene, Olga G. Troyanskaya

Chapter 3: Small Molecules and Disease
David S. Wishart

Chapter 4: Protein Interactions and Disease
Mileidy W. Gonzalez, Maricel G. Kann

Chapter 5: Network Biology Approach to Complex Diseases
Dong-Yeon Cho, Yoo-Ah Kim, Teresa M. Przytycka

Chapter 6: Structural Variation and Medical Genomics
Benjamin J. Raphael

Chapter 7: Pharmacogenomics
Konrad J. Karczewski, Roxana Daneshjou, Russ B. Altman

Chapter 8: Biological Knowledge Assembly and Interpretation
Ju Han Kim

Chapter 9: Analyses Using Disease Ontologies
Nigam H. Shah, Tyler Cole, Mark A. Musen

Chapter 10: Mining Genome-Wide Genetic Markers
Xiang Zhang, Shunping Huang, Zhaojun Zhang, Wei Wang

Chapter 11: Genome-Wide Association Studies
William S. Bush, Jason H. Moore

Chapter 12: Human Microbiome Analysis
Xochitl C. Morgan, Curtis Huttenhower

Chapter 13: Mining Electronic Health Records in the Genomics Era
Joshua C. Denny

Chapter 14: Cancer Genome Analysis
Miguel Vazquez, Victor de la Torre, Alfonso Valencia

Monday, December 10, 2012

Human diseases through the lens of network biology

A nice new review on the role of network biology in human genetics.

Furlong, LI. Human diseases through the lens of network biology. Trends in Genetics, in press (2012)[Cell]

Abstract

One of the challenges raised by next generation sequencing (NGS) is the identification of clinically relevant mutations among all the genetic variation found in an individual. Network biology has emerged as an integrative and systems-level approach for the interpretation of genome data in the context of health and disease. Network biology can provide insightful models for genetic phenomena such as penetrance, epistasis, and modes of inheritance, all of which are integral aspects of Mendelian and complex diseases. Moreover, it can shed light on disease mechanisms via the identification of modules perturbed in those diseases. Current challenges include understanding disease as a result of the interplay between environmental and genetic perturbations and assessing the impact of personal sequence variations in the context of networks. Full realization of the potential of personal genomics will benefit from network biology approaches that aim to uncover the mechanisms underlying disease pathogenesis, identify new biomarkers, and guide personalized therapeutic interventions.

Tuesday, December 04, 2012

Auctioning Your Papers to Journals

I read an interesting blog post from Richard Smith today that brings up some ideas about how to avoid the rat race of publishing in top journals that have too much control over your work. Smith discusses the idea of auctioning your paper to different journals. He talks about writing a paper and then advertising its availability for publishing on twitter. He got four offers from journals to publish his paper and he picked the one he liked the best. You could imagine a scenario like this:

1) Write a paper
2) Post the paper to arXiv or similar
3) Revise the paper and address the comments and cristicisms raised on arXiv
4) Advertise the availability of the updated paper for publishing on twitter, your blog, or by emailing editors directly
5) Select from the interested journals

Once it catches on I think editors would start to search for worthy papers more actively.

As Editor-in-Chief of BioData Mining, I would be happy to hear from authors who have gone through this process.