Searching the Resource Information Network

Our searching services are busy right now. Please try again later

  • Register
X
Forgot Password

If you have forgotten your password you can enter your email here and get a temporary password sent to your email.

X

Leaving Community

Are you sure you want to leave this community? Leaving the community will revoke any permissions you have been granted in this community.

No
Yes

Analysis of Genetically Diverse Macrophages Reveals Local and Domain-wide Mechanisms that Control Transcription Factor Binding and Function.

Verena M Link | Sascha H Duttke | Hyun B Chun | Inge R Holtman | Emma Westin | Marten A Hoeksema | Yohei Abe | Dylan Skola | Casey E Romanoski | Jenhan Tao | Gregory J Fonseca | Ty D Troutman | Nathanael J Spann | Tobias Strid | Mashito Sakai | Miao Yu | Rong Hu | Rongxin Fang | Dirk Metzler | Bing Ren | Christopher K Glass
Cell | 2018

Non-coding genetic variation is a major driver of phenotypic diversity and allows the investigation of mechanisms that control gene expression. Here, we systematically investigated the effects of >50 million variations from five strains of mice on mRNA, nascent transcription, transcription start sites, and transcription factor binding in resting and activated macrophages. We observed substantial differences associated with distinct molecular pathways. Evaluating genetic variation provided evidence for roles of ∼100 TFs in shaping lineage-determining factor binding. Unexpectedly, a substantial fraction of strain-specific factor binding could not be explained by local mutations. Integration of genomic features with chromatin interaction data provided evidence for hundreds of connected cis-regulatory domains associated with differences in transcription factor binding and gene expression. This system and the >250 datasets establish a substantial new resource for investigation of how genetic variation affects cellular phenotypes.

Pubmed ID: 29779944

Research resources used in this publication

None found

Antibodies used in this publication

None found

Associated grants

  • Agency: NIEHS NIH HHS, United States
    Id: P30 ES006694
  • Agency: NIDDK NIH HHS, United States
    Id: R01 DK091183
  • Agency: NIDDK NIH HHS, United States
    Id: P30 DK063491
  • Agency: NIDDK NIH HHS, United States
    Id: T32 DK007541
  • Agency: NHLBI NIH HHS, United States
    Id: R00 HL123485
  • Agency: NIGMS NIH HHS, United States
    Id: R01 GM065490
  • Agency: NIGMS NIH HHS, United States
    Id: P50 GM085764
  • Agency: NCI NIH HHS, United States
    Id: T32 CA009523
  • Agency: NIDDK NIH HHS, United States
    Id: P01 DK074868
  • Agency: NIDDK NIH HHS, United States
    Id: T32 DK007044
  • Agency: NCI NIH HHS, United States
    Id: R01 CA173903

Publication data is provided by the National Library of Medicine ® and PubMed ®. Data is retrieved from PubMed ® on a weekly schedule. For terms and conditions see the National Library of Medicine Terms and Conditions.

This is a list of tools and resources that we have found mentioned in this publication.


Cytoscape (tool)

RRID:SCR_003032

Software platform for complex network analysis and visualization. Used for visualization of molecular interaction networks and biological pathways and integrating these networks with annotations, gene expression profiles and other state data.

View all literature mentions

BWA (tool)

RRID:SCR_010910

Software for aligning sequencing reads against large reference genome. Consists of three algorithms: BWA-backtrack, BWA-SW and BWA-MEM. First for sequence reads up to 100bp, and other two for longer sequences ranged from 70bp to 1Mbp.

View all literature mentions

Bowtie 2 (tool)

RRID:SCR_016368

Ultrafast and memory efficient tool for aligning sequencing reads to long reference sequences. Supports gapped, local, and paired end alignment modes. More suited to finding longer, gapped alignments in comparison with original Bowtie method.

View all literature mentions

R package: lme4 (tool)

RRID:SCR_015654

Fit linear and generalized linear mixed-effects models. The models and their components are represented using S4 classes and methods. The core computational algorithms are implemented using the 'Eigen' C++ library for numerical linear algebra and 'RcppEigen' "glue."

View all literature mentions

DESeq2 (tool)

RRID:SCR_015687

Software package for differential gene expression analysis based on the negative binomial distribution. Used for analyzing RNA-seq data for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates.

View all literature mentions

STAR (tool)

RRID:SCR_015899

Software performing alignment of high-throughput RNA-seq data. Aligns RNA-seq reads to reference genome using uncompressed suffix arrays.

View all literature mentions

Bowtie (tool)

RRID:SCR_005476

Software ultrafast memory efficient tool for aligning sequencing reads. Bowtie is short read aligner.

View all literature mentions

STAR (tool)

RRID:SCR_004463

Software performing alignment of high-throughput RNA-seq data. Aligns RNA-seq reads to reference genome using uncompressed suffix arrays.

View all literature mentions