Evidence map›Paper›PMID 20122211›Full record

ArticleBMC bioinformatics2010

Comparative classification of species and the study of pathway evolution based on the alignment of metabolic pathways.

Adi Mano, Tamir Tuller, Oded Béjà, Ron Y Pinter

Abstract readComparative Study
In one paragraph

Article in BMC bioinformatics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Adi ManoDept, of Computer Science, Technion - Israel Institute of Technology, Haifa 32000, Israel. amano@cs.technion.ac.il
Tamir Tuller
Oded Béjà
Ron Y Pinter

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPathways provide topical descriptions of cellular circuitry. Comparing analogous pathways reveals intricate insights into individual functional differences among species. While previous works in the field performed genomic comparisons and evolutionary studies that were based on specific genes or proteins, whole genomic sequence, or even single pathways, none of them described a genomic system level comparative analysis of metabolic pathways. In order to properly implement such an analysis one should overcome two specific challenges: how to combine the effect of many pathways under a unified framework and how to appropriately analyze co-evolution of pathways. Here we present a computational approach for solving these two challenges. First, we describe a comprehensive, scalable, information theory based computational pipeline that calculates pathway alignment information and then compiles it in a novel manner that allows further analysis. This approach can be used for building phylogenies and for pointing out specific differences that can then be analyzed in depth. Second, we describe a new approach for comparing the evolution of metabolic pathways. This approach can be used for detecting co-evolutionary relationships between metabolic pathways.

resultsWe demonstrate the advantages of our approach by applying our pipeline to data from the MetaCyc repository (which includes a total of 205 organisms and 660 metabolic pathways). Our analysis revealed several surprising biological observations. For example, we show that the different habitats in which Archaea organisms reside are reflected by a pathway based phylogeny. In addition, we discover two striking clusters of metabolic pathways, each cluster includes pathways that have very similar evolution.

conclusionWe demonstrate that distance measures that are based on the topology and the content of metabolic networks are useful for studying evolution and co-evolution.

Indexed as

Evolution, MolecularArchaeaGenomeMetabolic Networks and PathwaysPhylogenyProteinsSpecies SpecificityProteins

Identifiers

PMID20122211
PMCPMC3009510

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Registered trials

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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.