Large-scale culture of radiation hybrid clones is a time-consuming process and moreover causes the loss of donor fragments in the hybrids. To avoid the necessity of cultivating the radiation hybrid clones at a large scale, we tested three approaches.
The advantage of the Fluidigm approaches is low cost combined with simple and rapid high-scale genotyping. Duck radiation hybrids were obtained by fusing female duck embryonic fibroblasts irradiated at a dose of 6, rads, with HPRT-deficient hamster cells from the Wg3hCl 2 cell line. Five fusion experiments were carried out to produce duck radiation hybrids, suggesting that one hybrid clone was recovered per , duck fibroblasts, corresponding to an average fusion efficiency of 3.
Retention frequencies in the hybrids were estimated by using a set of 31 microsatellite markers distributed along the duck genome, whose positions were estimated on the basis of a low resolution genetic map Marie-Etancelin et al. Genotyping was performed by conventional PCR followed by agarose gel electrophoresis. As microchromosomes and the regions close to centromeres were reported to be better retained in chicken radiation hybrids [ 36 - 38 ], we decided to focus more on macrochromosomes and use a higher proportion of markers from macrochromosomes.
Altogether, 20 markers from macrochromosomes 1 to 7 and chromosome Z were selected and the rest 11 markers were from identified microchromosomes. By using the genetic maps and comparative mapping with chicken, we avoided the clustering of markers.
As a result from genotyping, we estimated the average retention frequency of duck genome fragments in the hybrids to be Finally, the 90 hybrids selected for the definitive panel were chosen with the highest possible marker retentions for macrochromosomes while maintaining good values for microchromosomes.
Final retention frequency values are Several thousand markers are needed to build genome-wide maps, requiring large amounts of DNA, usually prepared by large-scale culture of the radiation hybrid clones. However, this is a time-consuming task and moreover, donor chromosome fragments are lost during the culture process. To avoid this, we tested three alternative methods allowing minute amounts of DNA from the hybrids to be used.
Whole genome amplifications with MDA were performed for all the 90 selected radiation hybrids. Each sample was amplified in three replicates which were pooled together to avoid representation bias in the final working panel. As the smallest microchromosomes have always proved difficult to sequence and to clone in chicken, we supposed a bias could also exist for WGA.
These 4 markers were added to our first set of 31 microsatellite markers we used primarily for selecting the 90 clones for the panel. In contrast, a slight retention loss was found for S, the other scaffold marker from APL Thus amplifying the panel by WGA and genotyping by the conventional PCR and agarose gel electrophoresis approach WGA-PCR appears to be a good option for mapping without having to perform large-scale culture of the hybrids, at least for macrochromosomes and medium-sized microchromosomes.
Estimations of duck genome retention in the RH clones. A: retention frequencies of thirty-one microsatellite markers and four scaffold markers before white and after grey whole genome amplification. The test was done on the 90 selected hybrids by conventional Agarose genotyping. B : Retention frequencies of thirty-nine scaffolds markers obtained using three different genotyping strategies.
The markers are distributed along the X axis from the lowest to the highest retention frequencies obtained by the first method the amplified panel with conventional agarose genotyping WGA-PCR in blue. However, genotyping several thousand markers by individual PCR and gel electrophoresis would require a lot of time and effort and a higher throughput method would be more appropriate, if feasible.
Diagonal in bold : mean number of positive hybrids in the panel 90 hybrids; 39 markers tested. Above the diagonal: mean number of positive hybrids in common between two conditions. Below the diagonal: P-values adjusted by Bonferroni correction for the differences in marker retention between two techniques.
Right: melting curve of the final product. Green: positive control duck DNA. Red: a hybrid which was positive containing duck DNA corresponding to the marker tested. Blue: a negative hybrid. Yellow: negative control hamster DNA. The same markers and controls are used as in A. The sensitivity is higher in B , with a lower number of cycles necessary for detection of duck DNA. The negative control and the hybrid not containing duck DNA amplify at a much higher number of cycles and the non-specific products amplified can easily be distinguished by their different melting temperature values right.
In both experiments, no amplification was obtained from water data not shown. To build a RH map of microchromosome APL22, two methods were used: one using the Carthagene software with the usual method [ 41 ] and a second using a comparative approach based on the chicken genome, and the construction of a robust map see Methods.
By the classical Carthagene approach, 24 markers were included in a single linkage group with a LOD score threshold of 11, and a framework map containing 12 markers and spanning cR was obtained. The average retention frequency for the markers is A maximum marker retention around marker scaB, suggests the centromere could be in that region data not shown , which would be compatible with an acrocentric microchromosome.
Comparative mapping with chicken chromosome GGA21 suggests several intrachromosomal rearrangements within this microchromosome. Developing markers using comparative mapping data. Right: GGA21, with gene names. Left: white cylinders represent duck scaffolds or portions of duck scaffolds aligned to the chicken genome.
Grey and green arrows represent portions of conserved synteny between the chicken chromosome and the duck scaffolds and their orientation. Left: names of the markers developed for RH mapping.
For large scaffolds, such as sca, one marker every kb was developed to ensure RH linkage by optimizing inter-marker distances. Red: scaffold and green: scaffold These two scaffolds each seem to be split in chicken into three and two different regions respectively. At least one marker per region was developed, so as to check duck scaffold integrity. Comparative mapping between chicken chromosome 21 GGA21 sequence map and duck chromosome 22 APL22 radiation hybrid maps.
Left and right: position of duck scaffold markers on the chicken genome. Middle left: RH map built with the Carthagene software.
Middle right: RH map built with the comparative approach, followed by statistical confidence measures for genome maps. Genotyping data for ten APL12 microsatellites and thirty-one markers designed from 18 scaffolds aligned to GGA11 were successfully obtained using the Pre-ampFLDMqPCR method and used to generate a RH map by the classical approach with the Carthagene software [ 41 ] and by the comparative mapping approach.
After two-point analysis at a LOD threshold of 6, three linkage groups were defined among which the largest one contained 38 markers. The order of the 38 markers from the largest linkage group was determined by multipoint analysis with Carthagene and a framework map of APL12 bearing eighteen markers was obtained.
The map obtained by the comparative mapping approach is cR long. As a result, the whole chromosome has a relatively high retention rate and no position for the centromeric region can be suggested from the RH map. Comparative mapping between chicken chromosome 11 GGA11 sequence map and duck chromosome 12 APL12 radiation hybrid maps. Framework markers for the CarthageneRH map and robust markers for the ComparativeRH map are in red or in green inversion. One additional minor inversion is observed only when comparing GGA11 with the map of APL12 built with the classical Carthagene approach.
Chromosomes are stained by DAPI. Centromere positions cen are indicated by arrows. Left: BAC clone WAG19G7, corresponding to duck scaffold sca is located in the centromeric region of GGA11 top , whereas it is clearly located in the middle of the q arm of APL12 bottom , suggesting the occurrence of an intrachromosomal rearrangement. This suggests the occurrence of an inversion between the two species.
Next, we wanted to test the three genotyping methods for mapping the smallest microchromosomes, orthologous to those absent from the current chicken sequence assembly and maps.
We previously reported a strategy for mapping genes on the smallest microchromosomes absent from the chicken genome assembly [ 42 , 43 ]. Most of these markers, which we named the no hit markers see materials and methods , were found to cluster in specific regions of the human genome, likely corresponding to conserved syntenies missing in chicken and corresponding to the missing microchromosomes [ 42 ].
To increase chances of our markers showing linkage in duck, we focused marker development on duck EST contig sequence having sequence similarity to HSA19, in a region that was already shown to have synteny conservation with some of the smallest chicken chromosomes and being absent in the chicken genome assembly [ 43 ].
These were genotyped by all three techniques. The latter method seems thus the only one suited for mapping the smallest microchromosomes. Markers were developed from chicken EST contigs absent from the chicken assembly no hit markers , presenting sequence similarity to HSA Markers in black got subsequently included in the linkage groups.
For each marker, the name of the gene is added. The duck EST markers are shown on both sides of the map to allow visualization of all possible pair wise map comparisons. Markers were developed from duck EST contigs, presenting sequence similarity to HSA19 and for which no sequence similarity could be found on the chicken genome.
Genotyping 8 no hit markers using three different genotyping strategies. Pos: number of hybrids positive for the assay out of 90 hybrids tested ; 4 Nb.
Controls: total number of controls which are positive over the number of controls tested; 5 Mean pos: mean number of positive hybrids observed over the whole panel; NA : not applicable. To test the quality of scaffold assembly, we selected the 13 largest duck scaffolds whose length ranged from 4. These 70 markers were genotyped by Pre-ampFLDMqPCR and the results allowed the detection of one possible misassembly in scaffold, for which a marker located at one end showed no linkage with the others.
Results for all the scaffolds are showed in Additional File 2 : Figure S1. As no inter-chromosomal rearrangements have been described to date between duck and chicken, we suspected assembly errors could have occurred and therefore tested 19 of the breakpoints by RH mapping with 45 markers.
Testing duck scaffolds aligning to two chicken chromosomes. Based on previous observations, duck scaffolds aligning to two chicken chromosomes were suspected to be misassembled and one example is shown here. B: Markers scaA green and scaB purple , very close to one another on sca, but spanning the putative breakpoint, were genotyped on the RH panel, but failed to show linkage, indicating that the scaffold is indeed misassembled.
Results for other scaffolds are shown in Additional File 3 : Figure S2. Overall, the pattern of retention for the broken duck chromosome fragments in the hamster cells obtained here is very similar to that observed for the chicken radiation hybrid panel, with higher retentions for microchromosomes than for macrochromosomes. Indeed, although the fusion efficiency for chicken-hamster hybrids was reported to be as high as 2—9 x 10 -6 by Kwok et al.
Here, the fusion efficiency is close to 3. For instance, the HPRT gene used as a selection marker for the clones is on the short arm of macrochromosome GGA4 in chicken [ 45 ] and thus very likely to be on microchromosome APL10 in duck. Microchromosomes being better retained than the macrochromosomes, having the selection gene on one of them could help recovering a higher number of clones in each fusion experiment. Similarly, Ekker et al. More generally, the difference in optimal temperatures for the growth of donor and recipient cells may be one of the possible causes for the lower retention frequencies usually observed for somatic and radiation hybrids in non mammalian species.
To obtain the DNA quantity required for building genome-wide maps, large-scale culture of the hybrid clones is necessary. However, in this process, donor DNA is lost. For instance, Karere et al. This problem, in addition to the fact that large-scale culture of a RH panel requires lots of labor, encouraged us to find an alternative, such as WGA or scaling down the reaction volumes.
Since the s three major whole genome amplification techniques including primer extension pre-amplification PEP [ 48 ], degenerate oligonucleotide primed DOP PCR [ 49 ] and multiple displacement amplification MDA have been developed to address the problem of limiting amounts of DNA samples.
The genome coverage is much improved, with an estimation of only 2. Karere et al. However, even if this is true for mammals, it might not be the case of microchromosomes in an avian genome. When comparing retention frequencies before and after WGA in the 90 hybrids, with the 35 markers used for clone selection, only slight variations of retention, either gains or losses, were usually observed. Therefore, we suggest that the genomic features in the smallest microchromosomes causing coverage problems in whole genome sequencing projects may also interfere with the efficiency of WGA.
As we have already shown, RH mapping can allow building maps for non-sequenced chromosomes [ 42 , 43 ], it is important that we produce genotyping results for them. High throughput gene expression analysis by real time PCR in a microfluidic dynamic array was first introduced by Spurgeon et al. In our case, by performing qPCR with the Fluidigm BioMark TM IFC Dynamic Array TM genotyping, the additional benefit is high throughput, as the identification of bands on gel electrophoresis is replaced by monitoring the PCR with Ct Cycle threshold and end point Tm melting temperature values, allowing the distinction between specific and non-specific amplification profiles.
The Tm value is mainly influenced by base composition of amplicons, making it a specifically interesting parameter to follow when using markers defined from coding regions, which are more prone to cross-amplifying the hamster DNA.
These high Ct values suggested the quantity of DNA template was too low [ 55 ]. Apart from improving the genome assembly by assigning and ordering scaffolds to chromosomes, the duck RH panel can be used to test the scaffold assemblies.
We tested this by genotyping markers at Mb density on the 13 scaffolds larger than 4 Mb, spanning altogether 60 Mb and thus accounting for 5. A total of 70 markers were genotyped and only one marker scaF on the end of sca was not linked with other markers derived from the same scaffold Additional File 2 : Figure S1 , suggesting an overall good quality of the final genome.
The 41 scaffolds including sca we detected as potential chimeras by comparative mapping had poor pair-end sequence support BGI, personal communication , suggesting most of them could indeed be misassembled Additional file 4 : Table S2. We tested nineteen of them by genotyping markers flanking the potential breakpoints Additional File 3 : Figure S2.
As a result, all but one scaffold sca could be misassembled, and sca possibly suggesting the first detection of a small inter-chromosomal rearrangement between the duck and chicken genomes, or perhaps more likely a segmental duplication in duck or in the last common ancestor of the two species. It can be noted that the pair-end sequence support for this scaffold was high, showing an agreement between sequencing and RH mapping data.
When disagreements between assembly and our RH data are detected in large scaffolds, they tend to happen towards the end. To achieve better assembly accuracy, higher sequencing depth or more efforts on developing sequencing libraries with longer inserts are needed. Concerning the smallest duck microchromosomes, paralogous to those absent from the chicken assembly, we suspect similar problems will arise: lack of sequence information, difficulties in cloning, in genetic mapping, etc.
RH mapping has proved useful for getting a grip on these regions and one striking example is the case of some regions of HSA19, to which no corresponding chicken genome data could be assigned by sequence similarity and to which many chicken no hit EST showed significant sequence similarity.
By developing markers targeted to this region, a small linkage group composed of 4 no hit markers absent in the chicken genome assembly orthologous to HSA19 was obtained. Partitioning markers into 20 linkage groups took less time with concorde compared to having all the markers in one group because each individual problem was easier for concorde.
However, the same behavior is not seen with linkern because the parameters specified find a solution from 10 starting points called restarts for each linkage group for a total of restarts with 3 criteria and 20 groups compared to only a total of 30 restarts when all markers were in one group.
As before, linkern also found an optimal solution for the set with markers. However, linkern did not always find an optimal solution for the set with and markers, suggesting that the parameters for linkern are not conservative enough for large problem sizes.
Because of decades of research into better algorithms and provable upper bounds for the length of TSP tours, instances with thousands of cities can be solved to guaranteed optimality quickly using concorde , even though TSP is NP-complete Applegate et al. Another recent addition allows users to express preferences for which marker to use in a framework map when two markers are too close to each other and, at most, one of them can be selected.
This makes it possible to systematically increase the number of markers on the framework map about which one also has external evidence, improving the utility of the resulting framework maps for comparative genomics. The MLE or OCB score of the best placement is compared to the score of the second best placement, and only markers with a score difference above some user-specified threshold are retained on the map. To produce a globally integrated map combining framework markers and placed markers, we define and solve instances of a restricted TSP in which the framework markers are required to stay in the framework map order, placed markers are required to stay in their best intervals, but if there are multiple placed markers in the same framework interval, their order is chosen optimally.
Order for placed markers is tested using flips in a restricted mode that considers only those permutations that retain placed markers in the same interval. This global positioning of the placed markers avoids the incremental addition of markers used by other packages that causes the resulting maps to be highly sensitive to the order in which placed markers are added Hitte et al.
We include new code to systematically handle markers with identical RHvectors, so that although only one of them is used in any flips test, all identical markers can appear in the final map with identical position. We include new options to assign markers that cannot be placed with high enough score to bins that are wider than intervals bounded by adjacent framework markers.
This allows users to increase the number of markers shown with some positional information without corrupting the robustness of the total ordering of framework and placed markers. Some of these programs create UNIX shell scripts. Google Scholar. Google Preview. Oxford University Press is a department of the University of Oxford. It furthers the University's objective of excellence in research, scholarship, and education by publishing worldwide.
Sign In or Create an Account. Sign In. Advanced Search. Search Menu. Article Navigation. Close mobile search navigation Article Navigation. Volume Article Contents Abstract. Oxford Academic. Edward Stallknecht Rice. Create distance radiuses around specific data points to gain additional insights. You can analyze your data within a radius, export data points, and draw on census data within the radius.
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