Name
Abstract | ||
|---|---|---|
Radiation hybrid (RH) mapping is a somatic cell technique that is used for ordering markers along a chromosome and estimating physical distances between them. It nicely complements the genetic mapping technique, allowing for finer resolution. Like genetic mapping, RH mapping consists in finding a marker ordering that maximizes a given criteria. Several software packages have been recently proposed to solve RH mapping problems. Each package offers specific criteria and specific ordering techniques. The most general packages look for maximum likelihood maps and may cope with errors, unknowns and polyploid hybrids at the cost of limited computational efficiency. More efficient packages look for minimum breaks or two-points approximated maximum likelihood maps but ignore errors, unknowns and polyploid hybrids. In this paper, we present a simple improvement of the EM algorithm [5] that makes maximum likelihood estimation much more efficient (in practice and to some extent in theory too). The boosted EM algorithm can deal with unknowns in both error-free haploid data and error-free backcross data. Unknowns are usually quite limited in RH mapping but cannot be ignored when one deals with genetic data or multiple populations/panels consensus mapping (markers being not necessarily typed in all panels/populations). These improved EM algorithms have been implemented in the CARTHAGÈNE software. We conclude with a comparison with similar packages (RHMAP and MapMaker) using simulated data sets and present preliminary results on mixed simultaneous RH/genetic mapping on pig data. |
Year | Venue | Keywords |
|---|---|---|
2001 | WABI | boosting em,error-free backcross data,panels consensus mapping,error-free haploid data,genetic mapping technique,polyploid hybrid,radiation hybrid,rh mapping problem,maximum likelihood map,em algorithm,genetic mapping,rh mapping,genetics,somatic cells,maximum likelihood,maximum likelihood estimate |
Field | DocType | ISBN |
Data set,Expectation–maximization algorithm,Computer science,Gene mapping,Maximum likelihood,Software,Travelling salesman problem,Boosting (machine learning),Bioinformatics,Radiation hybrid mapping | Conference | 3-540-42516-0 |
Citations | PageRank | References |
1 | 0.72 | 2 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Thomas Schiex | 1 | 1509 | 123.12 |
| patrick chabrier | 2 | 20 | 3.64 |
| martin bouchez | 3 | 20 | 2.97 |
| david j milan | 4 | 1 | 0.72 |
| ai lab | 5 | 1 | 0.72 |