Evidence map›Paper›PMID 12695327›Full record

ArticleGenome research2003

LineUp: statistical detection of chromosomal homology with application to plant comparative genomics.

Steve Hampson, Aoife McLysaght, Brandon Gaut, Pierre Baldi

Abstract readComparative Study
In one paragraph

Article in Genome research, 2003. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing 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

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3 · Its place in the literature

Who cites it

19 citing papers in PubMed.

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  11. CHSMiner: a GUI tool to identify chromosomal homologous segments.Algorithms for molecular biology : AMB · 2009
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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.

Steve HampsonInstitute for Genomics and Bioinformatics, Department of Information and Computer Science and Department of Ecology and Evolutionary Biology, and Department of Biological Chemistry, University of California at Irvine, Irvine, California 92697, USA.
Aoife McLysaght
Brandon Gaut
Pierre Baldi

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The identification of homologous regions between chromosomes forms the basis for studies of genome organization, comparative genomics, and evolutionary genomics. Identification of these regions can be based on either synteny or colinearity, but there are few methods to test statistically for significant evidence of homology. In the present study, we improve a preexisting method that used colinearity as the basis for statistical tests. Improvements include computational efficiency and a relaxation of the colinearity assumption. Two algorithms perform the method: FullPermutation, which searches exhaustively for runs of markers, and FastRuns, which trades faster run times for exhaustive searches. The algorithms described here are available in the LineUp package (http://www.igb.uci.edu/ approximately baldig/lineup). We explore the performance of both algorithms on simulated data and also on genetic map data from maize (Zea mays ssp. mays). The method has reasonable power to detect a homologous region; for example, in >90% of simulations, both algorithms detect a homologous region of 10 markers buried in a random background, even when the homologous regions have diverged by numerous inversion events. The methods were applied to four maize molecular maps. All maps indicate that the maize genome contains extensive regions of genomic duplication and multiplication. Nonetheless, maps differ substantially in the location of homologous regions, probably reflecting the incomplete nature of genetic map data. The variation among maps has important implications for evolutionary inference from genetic map data.

Indexed as

Models, StatisticalSequence Homology, Nucleic AcidChromosome MappingChromosomes, PlantComputational BiologyDNA, PlantGenomicsZea maysDNA, Plant

Identifiers

PMID12695327
PMCPMC430881

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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.