↓ Skip to main content

MissMax: alignment-free sequence comparison with mismatches through filtering and heuristics

Overview of attention for article published in Algorithms for Molecular Biology, April 2016
Altmetric Badge

Mentioned by

twitter
1 X user

Citations

dimensions_citation
26 Dimensions

Readers on

mendeley
13 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
MissMax: alignment-free sequence comparison with mismatches through filtering and heuristics
Published in
Algorithms for Molecular Biology, April 2016
DOI 10.1186/s13015-016-0072-x
Pubmed ID
Authors

Cinzia Pizzi

Abstract

Measuring sequence similarity is central for many problems in bioinformatics. In several contexts alignment-free techniques based on exact occurrences of substrings are faster, but also less accurate, than alignment-based approaches. Recently, several studies attempted to bridge the accuracy gap with the introduction of approximate matches in the definition of composition-based similarity measures. In this work we present MissMax, an exact algorithm for the computation of the longest common substring with mismatches between each suffix of a sequence x and a sequence y. This collection of statistics is useful for the computation of two similarity measures: the longest and the average common substring with k mismatches. As a further contribution we provide a "relaxed" version of MissMax that does not guarantee the exact solution, but it is faster in practice and still very precise.

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user 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 13 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 13 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 5 38%
Student > Ph. D. Student 2 15%
Professor > Associate Professor 2 15%
Researcher 1 8%
Professor 1 8%
Other 0 0%
Unknown 2 15%
Readers by discipline Count As %
Computer Science 3 23%
Agricultural and Biological Sciences 3 23%
Biochemistry, Genetics and Molecular Biology 2 15%
Physics and Astronomy 1 8%
Unknown 4 31%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 23 April 2016.
All research outputs
#22,758,309
of 25,373,627 outputs
Outputs from Algorithms for Molecular Biology
#235
of 265 outputs
Outputs of similar age
#270,634
of 313,604 outputs
Outputs of similar age from Algorithms for Molecular Biology
#12
of 14 outputs
Altmetric has tracked 25,373,627 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 265 research outputs from this source. They receive a mean Attention Score of 3.2. This one is in the 1st percentile – i.e., 1% 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 313,604 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 14 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.