ArticleBMC bioinformatics2007
Comparative analysis of long DNA sequences by per element information content using different contexts.
Article in BMC bioinformatics, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
9 citing papers in PubMed, 34 citations in OpenAlex.
- On the Complexity Analysis and Visualization of Musical Information.Entropy (Basel, Switzerland) · 2019Article
- Complexity Analysis of Escher's Art.Entropy (Basel, Switzerland) · 2019Article
- An alignment-free method to find and visualise rearrangements between pairs of DNA sequences.Scientific reports · 2015Article
- Information theory applications for biological sequence analysis.Briefings in bioinformatics · 2014Review
- DNA sequences at a glance.PloS one · 2013Article
- On the representability of complete genomes by multiple competing finite-context (Markov) models.PloS one · 2011Article
- A genome alignment algorithm based on compression.BMC bioinformatics · 2010Article
- Entropic Profiler - detection of conservation in genomes using information theory.BMC research notes · 2009Article
- Defining diversity, specialization, and gene specificity in transcriptomes through information theory.Proceedings of the National Academy of Sciences of the United States of America · 2008Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundFeatures of a DNA sequence can be found by compressing the sequence under a suitable model; good compression implies low information content. Good DNA compression models consider repetition, differences between repeats, and base distributions. From a linear DNA sequence, a compression model can produce a linear information sequence. Linear space complexity is important when exploring long DNA sequences of the order of millions of bases. Compressing a sequence in isolation will include information on self-repetition. Whereas compressing a sequence Y in the context of another X can find what new information X gives about Y. This paper presents a methodology for performing comparative analysis to find features exposed by such models.
resultsWe apply such a model to find features across chromosomes of Cyanidioschyzon merolae. We present a tool that provides useful linear transformations to investigate and save new sequences. Various examples illustrate the methodology, finding features for sequences alone and in different contexts. We also show how to highlight all sets of self-repetition features, in this case within Plasmodium falciparum chromosome 2.
conclusionThe methodology finds features that are significant and that biologists confirm. The exploration of long information sequences in linear time and space is fast and the saved results are self documenting.
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Registered trials
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