Large-scale analysis reveals that the genome features of simple sequence repeats are generally conserved at the family level in insects

Abstract

BackgroundSimple sequence repeats (SSR), also called microsatellites, have been widely used as genetic markers, and have been extensively studied in some model insects. At present, the genomes of more than 100 insect species are available. However, the features of SSRs in most insect genomes remain largely unknown.ResultsWe identified 15.01 million SSRs across 136 insect genomes. The number of identified SSRs was positively associated with genome size in insects, but the frequency and density per megabase of genomes were not. Most insect SSRs (56.2−93.1%) were perfect (no mismatch). Imperfect (at least one mismatch) SSRs (average length 22−73 bp) were longer than perfect SSRs (16−30 bp). The most abundant insect SSRs were the di- and trinucleotide types, which accounted for 27.2% and 22.0% of all SSRs, respectively. On average, 59.1%, 36.8%, and 3.7% of insect SSRs were located in intergenic, intronic, and exonic regions, respectively. The percentages of various types of SSRs were similar among insects from the same family. However, they were dissimilar among insects from different families within orders. We carried out a phylogenetic analysis using the SSR frequencies. Species from the same family were generally clustered together in the evolutionary tree. However, insects from the same order but not in the same family did not cluster together. These results indicated that although SSRs undergo rapid expansions and contractions in different populations of the same species, the general genomic features of insect SSRs remain conserved at the family level.ConclusionMillions of insect SSRs were identified and their genome features were analyzed. Most insect SSRs were perfect and were located in intergenic regions. We presented evidence that the variance of insect SSRs accumulated after the differentiation of insect families.Electronic supplementary materialThe online version of this article (10.1186/s12864-017-4234-0) contains supplementary material, which is available to authorized users.

Genome-enabled insights into the biology of thrips as crop pests

“…The assembly consists of 6263 scaffolds (N50 = 948 kb). One striking feature of the genome is the GC content of~50%, extraordinarily larger than other insects to date [32]. Updated assemblies with reduced proportions of gaps yielded total assembly sizes of 275-278 Mbp (see "Methods"); however, already accumulated manual annotations could not be comprehensively mapped to these new assemblies so the community reverted to using the original assembly.…”

Patterns of microsatellite distribution across eukaryotic genomes

“…A few studies [1, 3] have chosen a small subset of representative species across evolution to analyze their SSR content but the results may reflect trends that are specific to the chosen species rather than the group they represent, particularly if the sequence quality of the available genomes is variable. Other studies [17–20] have limited their analysis to a single taxonomic group, making their observations difficult to understand in terms of the broader evolutionary landscape. These issues can be overcome using an unbiased examination of the genome-wide distribution patterns of microsatellites in related clades, with large-scale comparisons revealing potentially relevant trends.…”

Patterns of microsatellite distribution reflect the evolution of biological complexity

“…15.5% of its genome is covered by SSRs ( Figure 2B, arrow). A recent analysis also made a similar observation, though from a comparison of only insect genomes (Ding, et al 2017). At 21292 bp/Mb (SSR coverage 2.13%), humans have almost an order of magnitude lower SSR density than their parasitic louse.…”

“…The caveat in such studies is that the results may reflect trends that are specific to the chosen species rather than the group they represent, particularly if the sequence quality of the available genomes is variable. Other studies (Hutter, et al 1998;Morgante, et al 2002;Ding, et al 2017;Liu, et al 2017) have limited their analysis to a single taxonomic group, making their observations difficult to understand in terms of the broader evolutionary landscape. Lastly, in silico SSR studies are limited by the efficiency, exhaustiveness and sensitivity of the various SSR identification programs they utilize and can be compromised by the quality of the SSR datasets generated (Lim, et al 2013;.…”