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The Rate and Tract Length of Gene Conversion between Duplicated Genes.

Mansai SP, Kado T, Innan H - Genes (Basel) (2011)

Bottom Line: To understand the rate and tract length of gene conversion, there are two major approaches.One is based on mutation-accumulation experiments, and the other uses natural DNA sequence variation.In this review, we overview the two major approaches and discuss their advantages and disadvantages.

View Article: PubMed Central - PubMed

Affiliation: Graduate University for Advanced Studies, Hayama, Kanagawa 240-0193, Japan. sayaka@soken.ac.jp.

ABSTRACT
Interlocus gene conversion occurs such that a certain length of DNA fragment is non-reciprocally transferred (copied and pasted) between paralogous regions. To understand the rate and tract length of gene conversion, there are two major approaches. One is based on mutation-accumulation experiments, and the other uses natural DNA sequence variation. In this review, we overview the two major approaches and discuss their advantages and disadvantages. In addition, to demonstrate the importance of statistical analysis of empirical and evolutionary data for estimating tract length, we apply a maximum likelihood method to several data sets.

No MeSH data available.


Illustration of the effect of multiple gene conversions on the performance of GENECONV. See text for details.
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f4-genes-02-00313: Illustration of the effect of multiple gene conversions on the performance of GENECONV. See text for details.

Mentions: As was demonstrated by our recent simulation work [75], it is not appropriate to use GENECONV to infer the actual tract length. There is no doubt that the regions identified by GENECONV are strong candidate regions that have undergone recent gene conversion. However, the identified region is not necessarily to correspond to the region that was really transferred by a single gene conversion event. Accordingly, the result of GENECONV is sometimes misinterpreted as if the output (a list of candidate converted tracts) reflects the distribution of the tract length of gene conversion (i.e., Ref. [76]). This effect is easily demonstrated by simple illustrations in Figure 4. In the left panel, two conversions in the opposite directions share a part of the tract. If GENECONV is applied to the sequence data in the box, it will likely identify two regions (with red lines in Figure 4) with lengths much shorter than the real converted tract lengths. In contrast, the two overlapping conversion events in the same direction result in a large region identified by GENECONV. The real situation should be much more complicated with a number of overlapping recurrent gene conversion events, indicating that the empirical approach would be the only reliable means to investigate the tract length of gene conversion. It should be noted that there are several algorithms for detecting gene conversion tracts [77–81], and we here treated GENECONV as a representative of them because they share the basic idea. Thus, using natural variation in DNA sequences is not very suitable to investigate the tract length of gene conversion.


The Rate and Tract Length of Gene Conversion between Duplicated Genes.

Mansai SP, Kado T, Innan H - Genes (Basel) (2011)

Illustration of the effect of multiple gene conversions on the performance of GENECONV. See text for details.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3924818&req=5

f4-genes-02-00313: Illustration of the effect of multiple gene conversions on the performance of GENECONV. See text for details.
Mentions: As was demonstrated by our recent simulation work [75], it is not appropriate to use GENECONV to infer the actual tract length. There is no doubt that the regions identified by GENECONV are strong candidate regions that have undergone recent gene conversion. However, the identified region is not necessarily to correspond to the region that was really transferred by a single gene conversion event. Accordingly, the result of GENECONV is sometimes misinterpreted as if the output (a list of candidate converted tracts) reflects the distribution of the tract length of gene conversion (i.e., Ref. [76]). This effect is easily demonstrated by simple illustrations in Figure 4. In the left panel, two conversions in the opposite directions share a part of the tract. If GENECONV is applied to the sequence data in the box, it will likely identify two regions (with red lines in Figure 4) with lengths much shorter than the real converted tract lengths. In contrast, the two overlapping conversion events in the same direction result in a large region identified by GENECONV. The real situation should be much more complicated with a number of overlapping recurrent gene conversion events, indicating that the empirical approach would be the only reliable means to investigate the tract length of gene conversion. It should be noted that there are several algorithms for detecting gene conversion tracts [77–81], and we here treated GENECONV as a representative of them because they share the basic idea. Thus, using natural variation in DNA sequences is not very suitable to investigate the tract length of gene conversion.

Bottom Line: To understand the rate and tract length of gene conversion, there are two major approaches.One is based on mutation-accumulation experiments, and the other uses natural DNA sequence variation.In this review, we overview the two major approaches and discuss their advantages and disadvantages.

View Article: PubMed Central - PubMed

Affiliation: Graduate University for Advanced Studies, Hayama, Kanagawa 240-0193, Japan. sayaka@soken.ac.jp.

ABSTRACT
Interlocus gene conversion occurs such that a certain length of DNA fragment is non-reciprocally transferred (copied and pasted) between paralogous regions. To understand the rate and tract length of gene conversion, there are two major approaches. One is based on mutation-accumulation experiments, and the other uses natural DNA sequence variation. In this review, we overview the two major approaches and discuss their advantages and disadvantages. In addition, to demonstrate the importance of statistical analysis of empirical and evolutionary data for estimating tract length, we apply a maximum likelihood method to several data sets.

No MeSH data available.