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Network-based analysis of transcriptional profiles from chemical perturbations experiments

Overview of attention for article published in BMC Bioinformatics, March 2017
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Title
Network-based analysis of transcriptional profiles from chemical perturbations experiments
Published in
BMC Bioinformatics, March 2017
DOI 10.1186/s12859-017-1536-9
Pubmed ID
Authors

Francesca Mulas, Amy Li, David H. Sherr, Stefano Monti

Abstract

Methods for inference and comparison of biological networks are emerging as powerful tools for the identification of groups of tightly connected genes whose activity may be altered during disease progression or due to chemical perturbations. Connectivity-based comparisons help identify aggregate changes that would be difficult to detect with differential analysis methods comparing individual genes. In this study, we describe a pipeline for network comparison and its application to the analysis of gene expression datasets from chemical perturbation experiments, with the goal of elucidating the modes of actions of the profiled perturbations. We apply our pipeline to the analysis of the DrugMatrix and the TG-GATEs, two of the largest toxicogenomics resources available, containing gene expression measurements for model organisms exposed to hundreds of chemical compounds with varying carcinogenicity and genotoxicity. Starting from chemical-specific transcriptional networks inferred from these data, we show that the proposed comparative analysis of their associated networks identifies groups of chemicals with similar functions and similar carcinogenicity/genotoxicity profiles. We also show that the in-silico annotation by pathway enrichment analysis of the gene modules with a significant gain or loss of connectivity for specific groups of compounds can reveal molecular pathways significantly associated with the chemical perturbations and their likely modes of action. The proposed pipeline for transcriptional network inference and comparison is highly reproducible and allows grouping chemicals with similar functions and carcinogenicity/genotoxicity profiles. In the context of drug discovery or drug repositioning, the methods presented here could help assign new functions to novel or existing drugs, based on the similarity of their associated network with those built for other known compounds. Additionally, the method has broad applicability beyond the uses here described and could be used as an alternative or as a complement to standard approaches of differential gene expression analysis.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 38 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 38 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 16%
Researcher 6 16%
Student > Bachelor 4 11%
Professor > Associate Professor 4 11%
Student > Master 3 8%
Other 4 11%
Unknown 11 29%
Readers by discipline Count As %
Agricultural and Biological Sciences 6 16%
Biochemistry, Genetics and Molecular Biology 5 13%
Computer Science 4 11%
Medicine and Dentistry 3 8%
Engineering 3 8%
Other 2 5%
Unknown 15 39%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 08 September 2017.
All research outputs
#14,909,862
of 24,143,470 outputs
Outputs from BMC Bioinformatics
#4,613
of 7,506 outputs
Outputs of similar age
#171,949
of 312,729 outputs
Outputs of similar age from BMC Bioinformatics
#64
of 122 outputs
Altmetric has tracked 24,143,470 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,506 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one is in the 35th percentile – i.e., 35% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 312,729 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 43rd percentile – i.e., 43% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 122 others from the same source and published within six weeks on either side of this one. This one is in the 45th percentile – i.e., 45% of its contemporaries scored the same or lower than it.