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HiC-bench: comprehensive and reproducible Hi-C data analysis designed for parameter exploration and benchmarking

Overview of attention for article published in BMC Genomics, January 2017
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (72nd percentile)
  • Good Attention Score compared to outputs of the same age and source (73rd percentile)

Mentioned by

twitter
5 tweeters
facebook
1 Facebook page

Citations

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61 Dimensions

Readers on

mendeley
140 Mendeley
citeulike
3 CiteULike
Title
HiC-bench: comprehensive and reproducible Hi-C data analysis designed for parameter exploration and benchmarking
Published in
BMC Genomics, January 2017
DOI 10.1186/s12864-016-3387-6
Pubmed ID
Authors

Charalampos Lazaris, Stephen Kelly, Panagiotis Ntziachristos, Iannis Aifantis, Aristotelis Tsirigos

Abstract

Chromatin conformation capture techniques have evolved rapidly over the last few years and have provided new insights into genome organization at an unprecedented resolution. Analysis of Hi-C data is complex and computationally intensive involving multiple tasks and requiring robust quality assessment. This has led to the development of several tools and methods for processing Hi-C data. However, most of the existing tools do not cover all aspects of the analysis and only offer few quality assessment options. Additionally, availability of a multitude of tools makes scientists wonder how these tools and associated parameters can be optimally used, and how potential discrepancies can be interpreted and resolved. Most importantly, investigators need to be ensured that slight changes in parameters and/or methods do not affect the conclusions of their studies. To address these issues (compare, explore and reproduce), we introduce HiC-bench, a configurable computational platform for comprehensive and reproducible analysis of Hi-C sequencing data. HiC-bench performs all common Hi-C analysis tasks, such as alignment, filtering, contact matrix generation and normalization, identification of topological domains, scoring and annotation of specific interactions using both published tools and our own. We have also embedded various tasks that perform quality assessment and visualization. HiC-bench is implemented as a data flow platform with an emphasis on analysis reproducibility. Additionally, the user can readily perform parameter exploration and comparison of different tools in a combinatorial manner that takes into account all desired parameter settings in each pipeline task. This unique feature facilitates the design and execution of complex benchmark studies that may involve combinations of multiple tool/parameter choices in each step of the analysis. To demonstrate the usefulness of our platform, we performed a comprehensive benchmark of existing and new TAD callers exploring different matrix correction methods, parameter settings and sequencing depths. Users can extend our pipeline by adding more tools as they become available. HiC-bench consists an easy-to-use and extensible platform for comprehensive analysis of Hi-C datasets. We expect that it will facilitate current analyses and help scientists formulate and test new hypotheses in the field of three-dimensional genome organization.

Twitter Demographics

The data shown below were collected from the profiles of 5 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 140 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 1%
India 1 <1%
Lithuania 1 <1%
Mexico 1 <1%
United Kingdom 1 <1%
Unknown 134 96%

Demographic breakdown

Readers by professional status Count As %
Researcher 33 24%
Student > Ph. D. Student 31 22%
Student > Master 15 11%
Student > Bachelor 13 9%
Student > Doctoral Student 8 6%
Other 20 14%
Unknown 20 14%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 60 43%
Agricultural and Biological Sciences 32 23%
Computer Science 10 7%
Engineering 5 4%
Medicine and Dentistry 5 4%
Other 11 8%
Unknown 17 12%

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 07 February 2017.
All research outputs
#2,521,500
of 11,217,345 outputs
Outputs from BMC Genomics
#1,514
of 6,776 outputs
Outputs of similar age
#87,602
of 316,101 outputs
Outputs of similar age from BMC Genomics
#52
of 194 outputs
Altmetric has tracked 11,217,345 research outputs across all sources so far. Compared to these this one has done well and is in the 77th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 6,776 research outputs from this source. They receive a mean Attention Score of 4.2. This one has done well, scoring higher than 77% of its peers.
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 316,101 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 72% of its contemporaries.
We're also able to compare this research output to 194 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 73% of its contemporaries.