The program RECORD (REcombination Counting and ORDering) can be used for the ordering of loci on genetic linkage maps. The method minimises the total number of recombination events. The search algorithm is a heuristic procedure, combining elements of branch-and-bound with local reshuffling.
Since the criterion does not require intensive calculations, the algorithm rapidly produces an optimal ordering as well as a series of near-optimal ones. The latter provides insight into the local certainty of ordering along the map.
RECORD can deal with the following types of mapping populations: BC1, F2, F3, RILs (in fact any generation obtained by repeated selfing of a hybrid between homozygous parents). Mapping populations from non-inbreds should be split into BC1 or HAP data that represent the maternal and paternal gametes, according to the two-way pseudo-testcross method. Please provide the data in a .LOC file in JoinMap format.
In 2011 Wu et al published a comparisons of four approximation algorithms for large-scale linkage map construction in Theoretical and Applied Genetics 123(4):649-655. Wu et al conclude that RECORD consistently outperformed other mapping software in accuracy and computational speed. Wu et al describe RECORD as the IN and COUNT algorithm, where IN represents the one-by-one insertion of markers into the map and COUNT the optimisation criterion (or cost function) where the map with the smallest number of recombination events is regarded as the best map.
- Having established the superior performance of RECORD, we used a combination of RECORD and a purpose-built perl script to construct pilot maps of individual populations. Wenzl et al. (2006) A high-density consensus map of barley linking DArT markers to SSR, RFLP and STS loci and agricultural traits. BMC Genomics 7:206
- The number of crossovers forms the basis for RECORD since the algorithms minimise the total number of recombination events (Van Os et al. 2005). Total map length has been used to evaluate different marker orders, and generally the shorter the map (produced from the datasets under investigation) the more accurate the marker order.
- Comparison of SALOD with number of crossovers between true, MMQTX, and RECORD maps clearly indicated that the minimum number of crossovers is a far superior determinant of marker order accuracy compared with SALOD. Collard et al (2009) How accurate are marker orders in crop linkage maps. Crop & Pasture Science 60(4)362-372.
- The order of markers as given by RECORD is better than the order of markers as given by traditional linkage mapping software programs… Varshney et al (2007) A high density barley microsatellite consensus map with 775 SSR loci. Theor Appl Genet 114(6):1091-1103.
Concepts from RECORD and SMOOTH have inspired the development of MSTMAP. Tests show that MSTMAP consistently outperforms the best available methods in the literature. This is because of the integration of a mapping algorithm with detection and removal of noise from the dataset. However, when the error detection feature is turned off and the dataset is noisy, the authors conclude:
- ”CARTHAGENE appears to be better than RECORD when the data are clean (g =0). When the data are noisy, RECORD constructs more accurate maps than CARTHAGENE. MSTMAP and RECORD are both very efficient in terms of running time, and they are much faster than CARTHAGENE. Wu et al (2008) Efficient and Accurate Construction of Genetic Linkage Maps from the Minimum Spanning Tree of a Graph. PLoS Genet 4(10):e1000212.
- Cheema and Dicks (2009) Approaches and software tools for genetic linkage map estimation in plants. Briefings in Bioinformatics 2009 10(6):595-608.