Archive for the 'SciLit' Category

systematic comparison of dnase-seq & atac-seq

April 21, 2018

Reproducible inference of TF footprints in #ATACseq & DNase-seq…via protocol-specific bias modeling
https://www.BiorXiv.org/content/early/2018/03/19/284364 Systematic comparison of the 2 assay HT @gamzeandgursoy

Mapping the mouse Allelome reveals tissue-specific regulation of allelic expression | eLife

April 21, 2018

Mapping the mouse Allelome reveals tissue-specific regulation of allelic expression https://eLifeSciences.org/articles/25125 More noncoding than coding #allelic activity. Also, finding windows of allelic activity for chromatin

Daniel Andergassen,
Christoph P Dotter,
Daniel Wenzel,
Verena Sigl,
Philipp C Bammer,
Markus Muckenhuber,
Daniela Mayer,
Tomasz M Kulinski,
Hans-Christian Theussl,
Josef M Penninger,
Christoph Bock,
Denise P Barlow ,
Florian M Pauler ,
Quanah J Hudson

Research Center for Molecular Medicine of the Austrian Academy of Sciences, Austria;
Institute of Molecular Biotechnology of the Austrian Academy of Sciences, Austria;
Institute of Molecular Pathology, Austria

Fast, scalable prediction of deleterious noncoding variants from functional and population genomic data | Nature Genetics

March 18, 2018

Fast, scalable prediction of deleterious #noncoding variants from functional & population genomic data
https://www.Nature.com/articles/ng.3810 LINSIGHT, by @ASiepel et al., combines DNAse & conservation information

Yi-Fei Huang, Brad Gulko & Adam Siepel
Nature Genetics 49, 618–624 (2017)
doi:10.1038/ng.3810
Published online:
13 March 2017

New algorithm can create movies from just a few snippets of text | Science | AAAS

March 18, 2018

Interesting paper by alumnus Renqiang Min on “Video Generation from Text,” using a generative #MachineLearning model.
http://www.AAAI.org/GuideBook2018/16152-72279-GB.pdf (Press report by @SilverJacket: New algorithm can create movies from just a few snippets of text
http://www.ScienceMag.org/news/2018/02/new-algorithm-can-create-movies-just-few-snippets-text )

Video Generation from Text Yitong Li†∗, Martin Renqiang Min‡ , Dinghan Shen† , David Carlson† , Lawrence Carin† †

Nature 03142018 Single cell rnaseq of development of human brain Pfc

March 18, 2018

https://www.nature.com/articles/nature25980

A single-cell RNA-seq survey of the developmental landscape of the human prefrontal cortex

Suijuan Zhong
, Shu Zhang
[…]
Xiaoqun Wang
Nature
doi:10.1038/nature25980

ncdriver and ENCODE

March 17, 2018

Received: 13 November 2017 Revised: 22 November 2017 Accepted: 29 November 2017 https://www.nature.com/articles/s41525-017-0040-5.pdf

Fast, scalable prediction of deleterious noncoding variants from functional and population genomic data | Nature Genetics

March 14, 2018

Fast, scalable prediction of deleterious #noncoding variants from functional & population genomic data https://www.Nature.com/articles/ng.3810 LINSIGHT, by @ASiepel et al., combines DNAse & conservation information

Yi-Fei Huang, Brad Gulko & Adam Siepel

Nature Genetics 49, 618–624 (2017)
doi:10.1038/ng.3810
Published online:

13 March 2017

Postmortem examination of patient H.M.’s brain based on histological sectioning and digital 3D reconstruc tion | Nature Communications

March 11, 2018

https://www.nature.com/articles/ncomms4122

New GWAS SCZ loci (nature genetics 2018)

March 5, 2018

Common #schizophrenia alleles are enriched in mutation-intolerant genes & in regions under strong background selection
https://www.nature.com/articles/s41588-018-0059-2 50 novel SCZ loci & 145 loci in total, from #GWAS – associated w/ 33 candidate causal genes

QT:{{”
We report a new genome-wide association study of schizophrenia (11,260 cases and 24,542 controls), and through meta-analysis with existing data we identify 50 novel associated loci and 145 loci in total. Through integrating genomic fine-mapping with brain expression and chromosome conformation data, we identify candidate causal genes within 33 loci.
“}}

Common schizophrenia alleles are enriched in mutation-intolerant genes and in regions under strong background selection
Nature Genetics (2018)
doi:10.1038/s41588-018-0059-2

dynamic LDA

March 5, 2018

Dynamic Topic Models
https://mimno.infosci.cornell.edu/info6150/readings/dynamic_topic_models.pdf Classic work by @Blei_lab & J Lafferty adapts the #LDA formalism describing documents in terms of latent topics – to allow these to evolve over time