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MRFalign: Protein Homology Detection through Alignment of Markov Random Fields

Overview of attention for article published in PLoS Computational Biology, March 2014
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (83rd percentile)
  • Above-average Attention Score compared to outputs of the same age and source (62nd percentile)

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11 X users
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1 patent

Citations

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86 Mendeley
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2 CiteULike
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Title
MRFalign: Protein Homology Detection through Alignment of Markov Random Fields
Published in
PLoS Computational Biology, March 2014
DOI 10.1371/journal.pcbi.1003500
Pubmed ID
Authors

Jianzhu Ma, Sheng Wang, Zhiyong Wang, Jinbo Xu

Abstract

Sequence-based protein homology detection has been extensively studied and so far the most sensitive method is based upon comparison of protein sequence profiles, which are derived from multiple sequence alignment (MSA) of sequence homologs in a protein family. A sequence profile is usually represented as a position-specific scoring matrix (PSSM) or an HMM (Hidden Markov Model) and accordingly PSSM-PSSM or HMM-HMM comparison is used for homolog detection. This paper presents a new homology detection method MRFalign, consisting of three key components: 1) a Markov Random Fields (MRF) representation of a protein family; 2) a scoring function measuring similarity of two MRFs; and 3) an efficient ADMM (Alternating Direction Method of Multipliers) algorithm aligning two MRFs. Compared to HMM that can only model very short-range residue correlation, MRFs can model long-range residue interaction pattern and thus, encode information for the global 3D structure of a protein family. Consequently, MRF-MRF comparison for remote homology detection shall be much more sensitive than HMM-HMM or PSSM-PSSM comparison. Experiments confirm that MRFalign outperforms several popular HMM or PSSM-based methods in terms of both alignment accuracy and remote homology detection and that MRFalign works particularly well for mainly beta proteins. For example, tested on the benchmark SCOP40 (8353 proteins) for homology detection, PSSM-PSSM and HMM-HMM succeed on 48% and 52% of proteins, respectively, at superfamily level, and on 15% and 27% of proteins, respectively, at fold level. In contrast, MRFalign succeeds on 57.3% and 42.5% of proteins at superfamily and fold level, respectively. This study implies that long-range residue interaction patterns are very helpful for sequence-based homology detection. The software is available for download at http://raptorx.uchicago.edu/download/. A summary of this paper appears in the proceedings of the RECOMB 2014 conference, April 2-5.

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 86 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 5 6%
United Kingdom 2 2%
France 2 2%
Netherlands 1 1%
Korea, Republic of 1 1%
Germany 1 1%
Switzerland 1 1%
Israel 1 1%
Ukraine 1 1%
Other 1 1%
Unknown 70 81%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 36 42%
Researcher 22 26%
Student > Master 7 8%
Other 3 3%
Professor > Associate Professor 3 3%
Other 7 8%
Unknown 8 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 35 41%
Biochemistry, Genetics and Molecular Biology 22 26%
Computer Science 11 13%
Neuroscience 2 2%
Immunology and Microbiology 2 2%
Other 4 5%
Unknown 10 12%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 9. 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 June 2017.
All research outputs
#4,332,572
of 25,838,141 outputs
Outputs from PLoS Computational Biology
#3,504
of 9,050 outputs
Outputs of similar age
#40,141
of 239,036 outputs
Outputs of similar age from PLoS Computational Biology
#54
of 146 outputs
Altmetric has tracked 25,838,141 research outputs across all sources so far. Compared to these this one has done well and is in the 83rd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 9,050 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one has gotten more attention than average, scoring higher than 61% 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 239,036 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 83% of its contemporaries.
We're also able to compare this research output to 146 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 62% of its contemporaries.