Posts Tagged ‘epigenetics’

Same but Different – The New Yorker

Saturday, August 6th, 2016

Annals of Science MAY 2, 2016 ISSUE

Same but Different

How epigenetics can blur the line between nature and nurture.


Same but Different by @DrSidMukherjee Nice #epigenetics overview, from Waddington to histone marks & ant castes

Some overlap w/ the book

C. H. Waddington – Wikipedia, the free encyclopedia

Sunday, July 24th, 2016


Epigenetic landscape

Waddington’s epigenetic landscape is a metaphor for how gene regulation modulates development.[10] Among other metaphors, Waddington asks us to imagine a number of marbles rolling down a hill.[11] The marbles will compete for the grooves on the slope, and come to rest at the lowest points. These points represent the eventual cell fates, that is, tissue types. Waddington coined the term chreode to represent this cellular developmental process. This idea was actually based on experiment: Waddington found that one effect of mutation (which could modulate the epigenetic landscape) was to affect how cells differentiated. He also showed how mutation could affect the landscape and used this metaphor in his discussions on evolution—he was the first person to emphasise that evolution mainly occurred through mutations that affected developmental anatomy.


Growing Pains for Field of Epigenetics as Some Call for Overhaul –

Monday, July 4th, 2016

Growing Pains for Field of #Epigenetics by
@CarlZimmer Reflects well on overhype & correlation vs causation issues

A Twist of Fate | The Scientist Magazine(R)

Sunday, May 18th, 2014

A Twist of Fate: Nice overview of switching between cell types, cellular reprogramming & #IPS cells #epigenetics

Epigenetic priors for identifying active tran… Bioinformatics. 2012 – PubMed – NCBI

Sunday, December 16th, 2012

Bioinformatics. 2012 Jan 1;28(1):56-62. doi:
10.1093/bioinformatics/btr614. Epub 2011 Nov 8.
Epigenetic priors for identifying active transcription factor binding sites. Cuellar-Partida G, Buske FA, McLeay RC, Whitington T, Noble WS, Bailey TL.

Score (posterior, at position i in the genome) = PWM for TF t at position i + priors at i (H3K4me + Dnase), gets 60% sens. at FPR of 1% averaged over all i & t, using essentially equal weighting on each functional genomics track. One issue here is because of the huge size of the genome the 1% FPR actually turns into a very low PPV, giving 5 FPs for each TP in practice.