ArticleBioinformatics (Oxford, England)2013
Multiple alignment-free sequence comparison.
Article in Bioinformatics (Oxford, England), 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- A survey and evaluations of histogram-based statistics in alignment-free sequence comparison.Briefings in bioinformatics · 2019Article
- Alignment-Free Sequence Analysis and Applications.Annual review of biomedical data science · 2018Article
- Alignment-free sequence comparison: benefits, applications, and tools.Genome biology · 2017Review
- Inference of Markovian properties of molecular sequences from NGS data and applications to comparative genomics.Bioinformatics (Oxford, England) · 2016Article
- On the comparison of regulatory sequences with multiple resolution Entropic Profiles.BMC bioinformatics · 2016Article
- Cnidaria: fast, reference-free clustering of raw and assembled genome and transcriptome NGS data.BMC bioinformatics · 2015Article
- Evidence for deep regulatory similarities in early developmental programs across highly diverged insects.Genome biology and evolution · 2014Article
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Authors and funding
5 authors.
Funding
Abstract
motivationRecently, a range of new statistics have become available for the alignment-free comparison of two sequences based on k-tuple word content. Here, we extend these statistics to the simultaneous comparison of more than two sequences. Our suite of statistics contains, first, C(*)1 and C(S)1, extensions of statistics for pairwise comparison of the joint k-tuple content of all the sequences, and second, C(*)2, C(S)2 and C(geo)2, averages of sums of pairwise comparison statistics. The two tasks we consider are, first, to identify sequences that are similar to a set of target sequences, and, second, to measure the similarity within a set of sequences.
resultsOur investigation uses both simulated data as well as cis-regulatory module data where the task is to identify cis-regulatory modules with similar transcription factor binding sites. We find that although for real data, all of our statistics show a similar performance, on simulated data the Shepp-type statistics are in some instances outperformed by star-type statistics. The multiple alignment-free statistics are more sensitive to contamination in the data than the pairwise average statistics. AVAILABILITY: Our implementation of the five statistics is available as R package named 'multiAlignFree' at be http://www-rcf.usc.edu/∼fsun/Programs/multiAlignFree/multiAlignFreemain.html. CONTACT: reinert@stats.ox.ac.uk. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.