Posts Tagged ‘encode’

The Genomics Landscape: A monthly update from the NHGRI Director – July 2017

July 9, 2017

.@Genome_Gov Extramural Grant Portfolio
https://www.Genome.Gov/27569006/july-6-2017-the-nhgri-extramural-grant-portfolio-using-different-approaches-to-fund-genomics-research Nice grid divides programs into PI-initiated/consortia & RFA-solicited v not

promoter/enhancer categorization and Encyclopedia

July 1, 2017

Genome-wide characterization of..promoters w…enhancer functions http://www.Nature.com/ng/journal/v49/n7/full/ng.3884.html Blurs distinction betw these, suggests flexibility

Genome-wide characterization of mammalian promoters with distal enhancer functions

Lan T M Dao,
Ariel O Galindo-Albarrán,
Jaime A Castro-Mondragon,
Charlotte Andrieu-Soler,
Alejandra Medina-Rivera,
Charbel Souaid,
Guillaume Charbonnier,
Aurélien Griffon,
Laurent Vanhille,
Tharshana Stephen,
Jaafar Alomairi,
David Martin,
Magali Torres,
Nicolas Fernandez,
Eric Soler,
Jacques van Helden,
Denis Puthier
& Salvatore Spicuglia

Promoting transcription over long distances

Rui R Catarino,
Christoph Neumayr
& Alexander Stark

Nature Genetics 49, 972–973 (2017) doi:10.1038/ng.3904
28 June 2017

http://www.nature.com/ng/journal/v49/n7/full/ng.3884.html

http://www.nature.com/ng/journal/v49/n7/full/ng.3904.html

QT:{{”
“Should we be surprised that promoters can function as enhancers—or better—that enhancers and promoter regions can overlap? Probably not: the habit of annotating different genomic regions with distinct labels ignores the fact that DNA sequences typically encode different genetic functions in a rather flexible manner. Enhancers and promoters are determined by the presence of short degenerate motifs, and even protein-coding regions display flexibility due to the degeneracy of the genetic code. Therefore, a single DNA sequence can encode different types of functions, including enhancer function of protein-coding regions or—as shown now—enhancer function of
promoters.”
“}}

Journal Club Paper

June 18, 2017

Zhou, J. and Troyanskaya, O.G. (2015). Predicting effects of noncoding variants with deep learning–based sequence model. Nature Methods, 12, 931–934.

Predicting (& prioritizing) effects of noncoding variants w. [DeepSEA] #DeepLearning…model
https://www.Nature.com/nmeth/journal/v12/n10/full/nmeth.3547.html Trained w #ENCODE data

DNA’s secret weapon against knots and tangles

May 7, 2017

DNA’s secret weapon against knots & tangles http://www.Nature.com/news/dna-s-secret-weapon-against-knots-and-tangles-1.21838 Quick overview of recent models of loop extrusion w/ cohesin & CTCF

Shedding light on the dark proteome

April 24, 2017

QT:{{”
“The dark proteome could be an evolutionary playground for trying out new folds

Ultimately one would expect particularly useful variations to get fixed at the genetic level. But it needn’t be where that variation begins. What’s more, organisms needn’t be quite so dependent for their molecular repertoire on their evolutionary heritage. O’Donoghue thinks that all organisms probably have a significant fraction of proteins unique just to them.

‘The fact that the dark matter of the proteome has less evolutionary constraint than the other bits of proteome may suggest that it’s under less selection,’ says Gerstein. ‘This is perhaps because it’s more flexible structurally, but also in a sense more flexible in terms of accommodating various amino-acid changes compared to the structurally inflexible and fixed parts of the crystallised proteome.’ This adds momentum to the picture of genomics as a rather more fluid affair than is suggested by the old picture of identical proteins being
mass-produced from a fixed genetic template.

Gerstein feels that studying the dark proteome opens up a host of interesting questions. For example, although known bacteria have a smaller dark proteome than eukaryotes, there’s a huge ‘dark
microbiome’ of unculturable bacteria. Might that be more full of dark proteins – perhaps useful ones?

And what about us? ‘How does the human dark proteome compare to that of eukaryotes as a whole?’ Gerstein wonders. How well, really, do we know ourselves?”
“}}

Shedding light on the dark proteome
BY PHILIP BALL13 FEBRUARY 2017
https://www.chemistryworld.com/feature/shedding-light-on-the-dark-proteome/2500392.article

The International Human Epigenome Consortium: A Blueprint for Scientific Collaboration and Discovery. – PubMed – NCBI

April 16, 2017

#IHEC: A Blueprint for…Collab. & Discovery
http://www.Cell.com/cell/abstract/S0092-8674(16)31528-8?_returnURL=http%3A%2F%2Flinkinghub.elsevier.com%2Fretrieve%2Fpii%2FS0092867416315288%3Fshowall%3Dtrue Summary bullets on heterogeneity, disease, rel. to SNPs, comp. tools

whole genome assembly from Hi-C data

April 2, 2017

De novo assembly of the A aegypti genome using #HiC, by @erezaterez et al http://science.ScienceMag.org/content/early/2017/03/22/science.aal3327.full Works on human too, w. promise for #SVs

De novo assembly of the Aedes aegypti genome using Hi-C yields chromosome-length scaffolds

Olga Dudchenko1,2,3,4,
Sanjit S. Batra1,2,3,*,
Arina D. Omer1,2,3,*,
Sarah K. Nyquist1,3,
Marie Hoeger1,3,
Neva C. Durand1,2,3,
Muhammad S. Shamim1,2,3,
Ido Machol1,2,3,
Eric S. Lander5,6,7,
Aviva Presser Aiden1,2,8,9,
Erez Lieberman Aiden1,2,3,4,5,†

Science 23 Mar 2017:
eaal3327
DOI: 10.1126/science.aal3327

on whole genome assembly from Hi-C reads. There is also some info on chromosomal rearrangement from Hi-C.

Inferring chromatin-bound protein complexes from genome-wide binding assays – Genome Research

February 26, 2017

Inferring [w. NMF] chromatin-bound protein complexes [of TFs] from [ENCODE ChIP-seq] binding assays, by @ElementoLab
http://genome.cshlp.org/content/23/8/1295.full

Giannopoulou E, Elemento O. 2013. Inferring chromatin-bound
protein complexes from genome-wide binding assays. Genome Research, Published in Advance April 3, 2013, doi: 10.1101/gr.149419.112.

This study uses nonnegative matrix factorization (NMF) of ENCODE CHIP-seq data (transcription
factors and histone modifications) to predict complexes of
transcription factors that bind DNA
together; it then assesses how these predicted complexes regulate gene expression. It goes beyond
previous studies in that it attempts to treat the TFs as complexes rather than individuals. A handful of
the predicted complexes correspond to known regulatory complexes, e.g. PRC2, and overall, the
complexes were enriched for known protein-protein interactions. Linear regression and random forest
models were then used to predict the effects of the complexes on the expression of adjacent genes. In
both models, the complexes performed better than those predicted from a scrambled TF read count
matrix. Overall, this study provides a large set of hypotheses for combinations of TFs that may
function together, as well as potential new components of known complexes.

The dark side of the human genome : Nature : Nature Research

November 27, 2016

Dark side of the..genome
http://www.Nature.com/nature/journal/v538/n7624/full/538275a.html QT: NextGen..has been..the tech engine of #ENCODE..but..hi-res livecell imaging [is coming]

Has figure from Khurana et al. Nat. Rev. Genet. (’16)

QT:{{”
“Next-generation sequencing has been — and still is — the
technological engine of ENCODE. But looking ahead, researchers might be able to roll out high-resolution live-cell imaging on a large scale to watch the state of the genome change in real time using specific markers. This technology could be disruptive. “If we had a better microscope, we wouldn’t be sequencing anymore,” says
Stamatoyannopoulos”
“}}

Species-Specific | The Scientist Magazine(R)

September 6, 2015

Scientists uncover striking differences between mouse and human gene expression across a variety of tissues.
By Jyoti Madhusoodanan | November 17, 2014

http://www.the-scientist.com/?articles.view/articleNo/41453/title/Species-Specific/

QT:{{”
The results “go a little against the grain,” said bioinformatician Mark Gerstein of Yale University who was not involved in the study. “We might think that humans and mice are very similar [genetically], but when we compare their transcriptomes, they’re more different than we thought.”
“}}