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Quantiprot - a Python package for quantitative analysis of protein sequences

Overview of attention for article published in BMC Bioinformatics, July 2017
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (52nd percentile)
  • Above-average Attention Score compared to outputs of the same age and source (61st percentile)

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6 X users
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1 Facebook page

Citations

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7 Dimensions

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36 Mendeley
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Title
Quantiprot - a Python package for quantitative analysis of protein sequences
Published in
BMC Bioinformatics, July 2017
DOI 10.1186/s12859-017-1751-4
Pubmed ID
Authors

Bogumił M. Konopka, Marta Marciniak, Witold Dyrka

Abstract

The field of protein sequence analysis is dominated by tools rooted in substitution matrices and alignments. A complementary approach is provided by methods of quantitative characterization. A major advantage of the approach is that quantitative properties defines a multidimensional solution space, where sequences can be related to each other and differences can be meaningfully interpreted. Quantiprot is a software package in Python, which provides a simple and consistent interface to multiple methods for quantitative characterization of protein sequences. The package can be used to calculate dozens of characteristics directly from sequences or using physico-chemical properties of amino acids. Besides basic measures, Quantiprot performs quantitative analysis of recurrence and determinism in the sequence, calculates distribution of n-grams and computes the Zipf's law coefficient. We propose three main fields of application of the Quantiprot package. First, quantitative characteristics can be used in alignment-free similarity searches, and in clustering of large and/or divergent sequence sets. Second, a feature space defined by quantitative properties can be used in comparative studies of protein families and organisms. Third, the feature space can be used for evaluating generative models, where large number of sequences generated by the model can be compared to actually observed sequences.

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X Demographics

The data shown below were collected from the profiles of 6 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 36 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 36 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 9 25%
Student > Ph. D. Student 8 22%
Student > Master 4 11%
Student > Bachelor 3 8%
Student > Doctoral Student 2 6%
Other 2 6%
Unknown 8 22%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 8 22%
Agricultural and Biological Sciences 7 19%
Computer Science 6 17%
Immunology and Microbiology 2 6%
Chemistry 2 6%
Other 3 8%
Unknown 8 22%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 28 July 2018.
All research outputs
#13,363,602
of 23,881,329 outputs
Outputs from BMC Bioinformatics
#3,690
of 7,454 outputs
Outputs of similar age
#133,553
of 285,024 outputs
Outputs of similar age from BMC Bioinformatics
#37
of 96 outputs
Altmetric has tracked 23,881,329 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,454 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 48th percentile – i.e., 48% 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 285,024 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 52% of its contemporaries.
We're also able to compare this research output to 96 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 61% of its contemporaries.