Open Access

Comparative in silico analysis of SSRs in coding regions of high confidence predicted genes in Norway spruce (Picea abies) and Loblolly pine (Pinus taeda)

  • Sonali Sachin Ranade1,
  • Yao-Cheng Lin2,
  • Yves Van de Peer2, 3, 4 and
  • María Rosario García-Gil1Email author
BMC Genetics201516:149

https://doi.org/10.1186/s12863-015-0304-y

Received: 20 August 2015

Accepted: 10 December 2015

Published: 26 December 2015

Abstract

Background

Microsatellites or simple sequence repeats (SSRs) are DNA sequences consisting of 1–6 bp tandem repeat motifs present in the genome. SSRs are considered to be one of the most powerful tools in genetic studies. We carried out a comparative study of perfect SSR loci belonging to class I (≥20) and class II (≥12 and <20 bp) types located in coding regions of high confidence genes in Picea abies and Pinus taeda. SSRLocator was used to retrieve SSRs from the full length CDS of predicted genes in both species.

Results

Trimers were the most abundant motifs in class I followed by hexamers in Picea abies, while trimers and hexamers were equally abundant in Pinus taeda class I SSRs. Hexamers were most frequent within class II SSRs followed by trimers, in both species. Although the frequency of genes containing SSRs was slightly higher in Pinus taeda, SSR counts per Mbp for class I was similar in both species (P-value = 0.22); while for class II SSRs, it was significantly higher in Picea abies (P-value = 0.00009). AT-rich motifs were higher in abundance than the GC-rich motifs, within class II SSRs in both the species (P-values = 10−9 and 0). With reference to class I SSRs, AT-rich and GC-rich motifs were detected with equal frequency in Pinus taeda (P-value = 0.24); while in Picea abies, GC-rich motifs were detected with higher frequency than the AT-rich motifs (P-value = 0.0005).

Conclusions

Our study gives a comparative overview of the genome SSRs composition based on high confidence genes in the two recently sequenced and economically important conifers and, also provides information on functional molecular markers that can be applied in genetic studies in Pinus and Picea species.

Keywords

Norway spruce Picea abies Loblolly pine Pinus taeda Simple sequence repeats (SSR) Microsatellites High confidence genes

Background

Microsatellites or simple sequence repeats (SSRs) are DNA sequences consisting of 1–6 bp tandem repeat motifs widely distributed in the coding and non-coding parts of the genome [1], resulting from DNA-polymerase slippage during replication and unequal recombination [2]. Microsatellites are co-dominant, multi-allelic and reproducible besides having high mutation rates [3]. Microsatellite analysis is fast and cost effective with the present technology [46]. Due to these properties, they are considered to be one of the most powerful tools for analysis of genetic biodiversity [7], and are also widely used as molecular markers in marker-assisted selection [8], mapping and phylogeny [9].

SSRs are classified according to their length into class I composed of those with ≥20 bp repeats and class II containing repeats from 12 to 20 bp. Class I motifs are of prime importance from the point of view of applicability of the SSRs as markers due to their higher polymorphic nature compared to class II SSRs [10]. SSRs are also grouped into three types based on their complexity - perfect, imperfect and compound SSRs. Perfect SSRs are continuous repetitions of motifs without any interruption by any base (e.g. (AT)20), while in an imperfect SSR the repeated sequence is interrupted by different nucleotides that are not repeated (e.g., (AT)12GC(AT)8). Compound SSRs contain two adjacent distinct SSRs (e.g. (AT)7(GC)6).

Norway spruce (Picea abies) and Loblolly pine (Pinus taeda) are two important conifer species from an economical and ecological point of view. With the availability of the Picea abies [11] and Loblolly pine [12] genome assemblies, comparison between their genomes on various aspects is feasible and they have become the conifer model species to conduct further comparative research in gymnosperms [13, 14]. The distribution of long terminal repeat-retrotransposons (LTR-RTs: Ty1/Copia and Ty3/Gypsy) was similar in Picea (Picea abies) and Pinus (Pinus sylvestris) [11]. In this context the current analysis updates on the comparative distribution of the SSR loci within the two species.

There are few investigations, which have reported the analysis of EST-SSRs (Expressed sequence tags) in Picea spp. [15, 16] and Pinus taeda [15, 1719]. Dimers were detected as the most abundant repeat motifs followed by trimers and hexamers in a majority of these analyses, similar to our earlier comparative study among gymnosperm tree species, which was somewhat limited by the data availability and the study was conducted only at the genus level [14]. Fluch et al. [16] is the only EST-SSR study so far conducted on Picea abies and this investigation reports the presence of trimers > pentamers > hexamers in the order of frequency of occurrence. In the current work, we carried out a comparative study of perfect SSRs belonging to the class I and class II types in Picea abies and Pinus taeda based on coding regions of genes predicted with high confidence CDS) [20]. As compared to previous studies in Picea and Pinus, our approach allows counting the precise numbers of all repeats motifs across the coding part of the genome, and it is expected that some degree of inconsistency would exist on the estimation of the number of class I SSRs with reference to those reported in previous studies on the basis of the data source and the methodology. We have considered only the high confidence full length genes (CDS) for detection of the repeat motifs and thus the detected loci could serve as robust molecular markers. Genic SSRs have advantages over the genomic SSRs as the putative function of the particular gene is known and they are highly transferrable across species [21]. The aims of this study are: (i) to analyse SSR motifs to identify the species-specific characteristics to gain insights into Pinaceae genome composition and (ii) to deliver a list of primers for the development of SSR molecular markers located in expressed genes, which can be applied to species of both genera, Pinus and Picea, for a range of different genetic studies such as population genetic studies, paternity analysis, genotyping, genetic mapping, molecular evolution and hybrid selection [22].

Methods

Genomic resources and procedure

Full length CDS of genes predicted with high confidence from Picea abies (26,437 genes) [11], (http://congenie.org/) and Pinus taeda (34,059 genes) [20] were included for the detection of SSRs in this work. SSRLocator [23] was used to retrieve the perfect SSR markers belonging to class I (≥20 bp) and class II (≥12 and <20 bp) in both species. SSRLocator was used with the following settings for class I SSRs, SSR repeat motifs and number of repeats as the calculated parameters, monomer-20, dimer-10, trimer-7, tetramer-5, pentamer-4, hexamer-4, heptamer-3, octamer-3, nonamer-3 and decamer-2 [10]. Likewise, following settings were used to detect class II SSRs - monomer-12, dimer-6, trimer-4, tetramer-3, pentamer-3, hexamer-2, heptamer-2, octamer-2 and nonamer-2. Since the class II search also retrieved the class I SSRs, the data was filtered for the redundant results with help of SQL queries. While recording the count of a particular repeat motif, circular permutations and/or reverse complements of each other were clustered together (e.g. AC = GT = CA = TG, ACG = CGA = GCA = TGC = GCT = CGT = AGC = TCG = CAG = GTC = CTG = GAC and AAC = ACA = CAA = TTG = TGT = GTT) [15]. Along with the in silico detection of the SSRs, SSRLocator provides list of putative primer pairs which are represented in the Additional file 1. Mononucleotides were included only for the calculation of counts per Mbp (Table 1) but were excluded from rest of the analysis to facilitate the comparison of the results with most other studies which did not consider the analysis of mononucleotides [14, 15, 19, 24, 25], as mononucleotide repeats can be difficult to accurately assay [26]. Moreover mononucleotides were excluded from this study also because of the possibility of sequencing or assembly errors [27, 28]. Blast2GO analysis [29] was performed for class I (≥20 bp) described as more efficient molecular markers [10].
Table 1

Counts per Mbp for class I and class II SSRs in Picea abies and Pinus taeda

 

Picea abies

 

Pinus taeda

 

No. of genes considered for the analysis

26,437

34,059

 

Class I SSRs

Class II SSRs

Class I SSRs

Class II SSRs

No. genes with SSRs

240

11380

337

14967

Motif lengtha (bp)

23.7 (4.6)

12.7 (1.6)

22.7 (4.1)

12.7 (1.6)

SSR counts per Mbp

54.7

1,768.2

42.7

1,541.9

No. genes with class I and class II SSRs

149

203

aStandard deviation for SSR length is shown in between parenthesis

Statistical analysis

We carried out a contingence χ2 test for heterogeneity of microsatellite counts (motif counts/total EST-fraction in Mbp) among different counts per Mbp within and between species. A t-test was applied to compare means among two groups of data. Statistical analyses were all carried out using the R software package [30].

Results

Number of genes containing SSRs and motif size

The percentage of genes containing class I and class II SSR loci in Picea abies was found to be 0.9 and 43 %, respectively; while in Pinus taeda it was 1 and 44 %, respectively. The percentage of genes containing both class I and class II loci was found to be 0.6 % in both species. Although the frequency of genes containing SSRs was similar in both species, counts per Mbp for class II SSRs was higher in Picea abies (chi-square = 15.4, P-value = 0.00009), while for class I the difference was not significant (chi-square = 1.5, P-value = 0.22) (Table 1). Motif lengths were significantly larger in Picea abies for class I motifs (P-value = 0.006), while lengths were identical in both species for class II SSRs.

SSR frequency

Trimers were the most abundant motifs in class I SSRs in Picea abies (chi-square = 12.9, P-value = 0.0003), while trimers and hexamers were equally abundant in Pinus taeda (chi-square = 0.04, P-value = 0.95). Hexamers were significantly more abundant SSR motifs in class II SSR in both species (Picea abies, chi-square = 308, P-value = 0; Pinus taeda, chi-square = 446, P-value = 0) (Table 2). In Picea abies, the order of abundance in class I SSRs was trimers > hexamers > decamers, while in class II it was hexamers > trimers > heptamers. Likewise, in Pinus taeda the order was trimers = hexamers > dimers/decamers in class I SSRs, while it was hexamers > trimers > heptamers in the class II SSR motifs.
Table 2

Counts per Mbp of different SSR motifs for class I and class II SSRs in Picea abies and Pinus taeda

 

Picea abies

Pinus taeda

Motif

Counts per Mbp for class I SSRs

Counts per Mbp for class II SSRs

Counts per Mbp for class I SSRs

Counts per Mbp for class II SSRs

Monomer

0.0

8.6

6.2

39.7

Dimer

0.0

11.9

7.9

29

Trimer

36.4

409.6

11.4

231.3

Tetramer

0.0

43.5

0.9

70

Pentamer

0.7

6.4

2.1

13.6

Hexamer

11.5

1,088.0

11.1

959.8

Heptamer

0.5

120.0

0.8

143.9

Octamer

0.0

39.5

0.4

48.2

Nonamer

1.1

49.2

0.4

46.1

Decamer

4.5

0.0

7.7

0

With reference to class I trimers, AGG/CCT and ACG/CGT were both equally abundant and together were the most abundant motifs in Picea abies (chi-square = 4, P-value = 0.05). Likewise, in Pinus taeda, AAT/ATT, AAG/CTT, AGG/CCT and ACG/CGT motifs were equally abundant and together were the most abundant class I trimer motifs (chi-square = 6.3, P-value = 0.01) (Table 3). Similarly, regarding class II motifs, AAG/CTT, AGG/CCT and ACG/CGT motifs were significantly the most frequent in Picea abies (chi-square = 64.5, P-value = 0), and AAG/CTT, AGG/CCT, ACG/CGT and ACT/AGT motifs were the most abundant in Pinus taeda (chi-square = 54, P-value = 0) (Table 3). While comparing both species, the ranking of the most abundant motifs is not the same for the class I motifs, but is very similar for class II motifs. The total count per Mbp was significantly higher in Picea abies in both classes (class I, chi-square = 13.1, P-value = 0.0003; class II, chi-square = 49.7, P-value = 0).
Table 3

Counts per Mbp of trimer motifs for class I and class II SSRs in Picea abies and Pinus taeda

 

Picea abies

Pinus taeda

Motif

Counts per Mbp for class I SSRs

Counts per Mbp for class II SSRs

Counts per Mbp for class I SSRs

Counts per Mbp for class II SSRs

ACG/CGT

9.1

87.5

2

46.3

ACT/AGT

1.6

57.7

0.2

35

AAC/GTT

0.3

17.8

0

20

AAG/CTT

5.4

110.9

2.4

50

AAT/ATT

0.3

12.1

2.9

15.9

ACC/GGT

2.1

21.9

1

16.9

AGG/CCT

14.9

87.7

2.3

40.3

CCG/CCG

2.7

14.1

0.6

6.9

In both species the hexamer abundance in class I SSR was similar (Table 4). However, the most abundant motif type differenced among both species, in Picea abies, AAACCG was the most abundant, while AACGGT was the most frequent in Pinus taeda. With reference to class II hexamers, AACGGT was the most abundant motif type in both species followed by AACCGT in Picea abies, which was fourth in Pinus taeda; likewise, the fourth most abundant motif in Picea abies (AAACGT) was the second most frequent motif in Pinus taeda (Table 4). Furthermore, within the class I and II hexamers, total counts per Mbp were higher in Picea abies, although the differences were not statistically significant between the two species.
Table 4

Counts per Mbp of first two abundant hexamers motifs for class I and class II SSRs in Picea abies and Pinus taeda

Picea abies

Pinus taeda

Motif

Counts per Mbp for class I SSRs

Motif

Counts per Mbp for class II SSRs

Motif

Counts per Mbp for class I SSRs

Motif

Counts per Mbp for class II SSRs

AAACCG

1

AACGGT

88.4

AACGGG

1.1

AACGGT

69.9

AACCCG

0.9

AACCGT

68.8

AAGGGT

1

AAACGT

59.5

AACCGG

0.9

AAAGGT

64.8

  

AAAGGT

56.7

ACCCCG

0.9

AAACGT

62.5

  

AACCGT

53.3

AT-rich and GC-rich motifs

The differential counts of nucleotides per Mbp for class I and class II SSRs revealed that AT-rich motifs were more abundant within the class II SSRs in both species (Picea abies, chi-square = 28.6, P-value = 10−9; Pinus taeda, chi-square = 173, P-value = 0) (Table 5). Moreover, AT- and GC-rich motifs were equally abundant in class I SSRs in Pinus taeda (chi-square = 1.4, P-value = 0.24), while GC rich motifs showed higher frequency per Mbp in the class I SSRs in Picea abies (chi-square = 12.2, P-value = 0.0005). Differential G + C nucleotide count per Mbp was higher than that of A + T in the class I SSRs in Picea abies (chi-square = 4.3, P-value = 0.04), but the difference between both categories was not significant in Pinus taeda (chi-square = 3.3, P-value = 0.07). The differential A + T count per Mbp was higher in class II SSRs in both species (Picea abies, chi-square = 56.5, P-value = 0; Pinus taeda, chi-square = 239, P-value = 0).
Table 5

Differential counts per Mbp of nucleotides in repeat motifs for class I and class II SSRs in Picea abies and Pinus taeda

 

Picea abies

Pinus taeda

Nucleotides

Counts per Mbp for class I SSRs

Counts per Mbp for class II SSRs

Counts per Mbp for class I SSRs

Counts per Mbp for class II SSRs

AT-rich

12.8

791.2

20.8

818.6

GC-rich

37.5

542.6

14.3

366.0

A

75.3

3002.1

72.2

2717

T

28.7

2158.9

51.9

2354.1

G

79.2

2581.9

52.4

2134

C

57

1842.6

44.6

1493.8

Gene ontology and amino acid distribution

The GO distribution of functional annotations in both species shows that the highest number of genes containing class I SSRs represent metabolic process, cell and binding for three main GO categories respectively (Fig. 1). Glutamic acid (Glu) is the most frequently occurring amino acid among the class I SSR loci in both species. With reference to class II SSRs, Serine (Ser) is the most commonly occurring amino in Picea abies, while Leucine (Leu) was most common in Pinus taeda (Fig. 2).
Fig. 1

GO distribution by Level 2: Distribution of functional annotations among SSR containing genes in Picea abies and Pinus taeda. Results are summarized for three main GO categories: a) biological process, b) cellular component and c) molecular function. a Picea abies. b Pinus taeda

Fig. 2

Amino acid occurrences in SSR loci in Picea abies and Pinus taeda: a) Class I SSRs b) Class II SSRs. a Picea abies. b Pinus taeda

Discussion

We have considered the high confidence full length coding regions of genes for the SSR analysis for the first time in gymnosperm species, while all the earlier studies involving gymnosperms have been carried out on ESTs. In addition, previously applied methodology also differs from ours (reviewed by [14]), e.g. some studies have considered 5′ UTR, ORF and 3′ UTR separately [14], while some have considered only 5′ESTs and 3′ESTs [15]. In the current study we have also analysed the class I and class II separately.

Overall abundance of SSRs in Picea abies

Counts per Mbp SSR motifs were higher in Picea abies (Table 1), which is in partial agreement with earlier investigations [14, 15, 19] considering that in the current study the difference in counts per Mbp SSR motifs between the two species was significant only for class II SSRs. The motif length detected in the current study (class I SSRs) was lower as compared to the earlier studies in both genera [14, 18], but it is noteworthy that the standard error reported in the current study is also very low. In Picea abies, the overall abundance of SSR loci in class I is primarily the result of a higher frequency of trimers, which is three times higher compared to Pinus taeda (count per Mbp of hexamers in both species is similar – Table 2), whereas the higher frequency of SSRs in class II in Picea abies is largely as a result of additive effect of trimers and hexamers. This is again not in favour of an earlier study where the count per Mbp of trimers in both species was similar whereas the count per Mbp of hexamers was higher in Pinus taeda [19].

Frequency of dimer motifs

Dimers were not detected in the class I SSR type in Picea abies and although were detected in the class II SSRs, they were not the most abundant types as found previously [14, 15, 18]. In a broader view, dimers are more frequent in lower plant species (algae and mosses), while trimer motifs are more frequent for the majority of higher plant groups (flowering plants) [18]. With reference to Picea abies, higher abundance of dimers was detected in EST-SSRs, but the majority of the studies were conducted on Picea spp. [15, 19, 24]. The only study conducted on Picea abies detected trimers (trimers > pentamers > hexmers) as the most abundant repeat [16]. Therefore, either the trimer frequency is species specific or the analysis is dependent on the data source involved and the parameters used for the detection of SSR repeats. In Pinus taeda on the other hand, trimers were most frequently detected in Pinus spp. [25], while the majority of the studies involving Pinus taeda [15, 18, 19], except one [17], showed dimers as the most abundant repeats. In our study, dimers represented the most abundant motifs after hexamers and trimers in class I SSRs, while it was the least detected category of SSR repeats in class II (Table 2). Overall, trimers were the most abundant motifs together with dimers in most of the studies in both species [15, 17, 19, 24]. Previously, it was reported that although a higher abundance of dimers was detected in EST-SSRs, the proportion of dimers to trimers decreased significantly in the ORF fraction in the majority of the genera including both angiosperm and gymnosperm species [14]. The sequence data is being updated continuously with recent advancements and as explained earlier, the use of a different sequence dataset for the SSR analysis is the most likely reason for not finding dimers as the most abundant motifs in both species.

Trimers and hexamers are the most abundant motif types

Genome wide studies conducted to estimate the SSR distribution in eukaryotes reveal abundance of trimers and hexamers in the coding regions in lower single cellular organisms e.g. yeast [31] as well as higher organisms e.g. model plant systems like Arabidopsis [32, 33] and also in more complex organisms like human beings [34]. Trimers and hexamers are predominant as they are favoured by the selective pressures compared to the other repeats (e.g. dimers, tetramers and pentamers) considering that they do not alter the coding frame due to frameshift unless the length of the indel is divisible by three, e.g. in case of dimers an addition of three repeat motifs (e.g. ATATAT) will not modify the reading frame [35].

Although trimers were the most frequent motifs detected in the class I category, hexamers ranked as the next most abundant motifs in this class in Picea abies, while in Pinus taeda trimers and hexamers were equally abundant (Table 2). It is noteworthy that in Picea abies the proportion of trimers to hexamers in the same class is 3.1. The higher and lower proportion of trimers to hexamers in Picea and Pinus taeda, respectively, is similar to what has been reported by Berube et al. [15], but contrasts with the recent comparative study where the proportion of trimers to hexamers was lower in Picea spp. (1.5) and slightly higher in Pinus (1.3) [14]. Hexamers were the most abundant among the class II SSR types in both species and their count per Mbp was very high as compared to the other motif types. Predominance of trimers in Picea abies [16] and Pinus taeda [17] was reported earlier only in two studies, likewise Yan et al. [25] demonstrated higher frequency of trimers it in Pinus spp. Abundance of hexamers in gymnosperms is in accordance with earlier results in Picea [15, 16], Pinus [15], and Cryptomeria [36], as well as in comparative studies, which report hexamers to be more common among EST-SSRs in gymnosperms than angiosperms [14, 18]. The estimation of hexamer repeats was however under-estimated in earlier studies [14, 15], as a consequence of analysing only class I SSRs, whereas the current analysis reveals that there is very high abundance of hexamer repeats if class II SSRs are also taken into consideration (1100 and 971 per Mbp in spruce and pine, respectively).

Similar to previous investigations, AAT/ATT was one among the most frequent class I trimers in Pinus taeda [19] (Table 3). AAG/CTT was also one among the most abundant trimers, which was reported as the most frequent trimer in other studies in Pinus [17, 25] closely followed by ACG/CGT and AGG/CCT [17]. AGG/CCT and ACG/CGT were the most frequent trimer motifs within the class I category in Picea abies, which is similar to our previous results in the ORF fractions of Picea [14]. ACG/CGT was also the most abundant trimer detected by Berube et al. [15] in Picea and Pinus taeda. AAG/CTT motif was among the most abundant trimer repeats in class II SSRs of both species and class I SSRs of Pinus taeda, which was reported to be the second most frequent in Pinus and third most frequent in Picea within the class I trimers [14]. It is noteworthy that AGG/CCT and ACG/CGT are the trimer repeats detected in class I and class II as the most and equally abundant motifs among the others in both species.

Frequency of AT-rich and GC-rich motifs

Abundance of AT-rich motifs was detected in class II SSRs in both species, which is in agreement with earlier studies in conifers [14, 15] (Table 5). Equal frequency of AT-rich and GC-rich motifs were found in class I SSRs of Pinus taeda while class I SSRs in Picea abies showed higher abundance of GC-rich motifs in contrast to earlier reports [14, 15]. This could be attributed to the difference in the data source considered, as the method used for detection of SSRs was similar as our previous study [14]. AT-rich segments in the coding region regulate DNA replication [37], while GC-rich elements in the coding region play important role in gene regulation [38].

GO annotation

Among genes containing class I SSRs in both species, GO distributions show that the highest numbers of genes belong to the metabolic process, cell and binding, respectively for three main GO categories (Fig. 1). Similar results were reported in Physcomitrella patens and Arabidopsis thaliana [18]. However, the GO term with the highest number of genes containing SSR loci in Cryptomeria [36] was cellular process instead of metabolic process as is the case in Pinus taeda and Picea abies. Therefore, we suggest that the GO distribution may be species specific rather than generalised for gymnosperms as such.

Among class I SSR loci, glutamine (Glu) is the most represented amino acid in both conifer species studied (Fig. 2). In contrast, serine (Ser) was found to be the most frequent in Gnetum while arginine (Arg) was the most frequent in Pinus taeda [18]. In class II, Ser is the most frequent amino acid followed by Arg and leucine (Leu) in Picea abies, while Leu ranks first, followed by Ser and Arg in Pinus taeda. It is worth noticing that tyrosine (Tyr) ranks last in all cases. In this context, Glu and Ser repeats are amongst the few single amino acid repeats which are incorporated into many proteins to a considerable extent [39] and polyserine repeats are the most abundant in Arabidopsis [40].

Conclusions

While several previous studies were based on EST datasets, for the first time in conifers, we report SSR loci in high confidence coding regions, which provides information on functional molecular markers that can be applied to genetic studies in Pinus and Picea species having prime economical and ecological importance. This analysis reveals an overall higher frequency of microsatellite repeats per Mbp in Picea abies as compared to Pinus taeda. It also supports abundance of hexamers in conifers. Although AT-rich and GC-rich repeats were equally abundant in Pinus taeda, GC-rich were found to be common in Picea abies in the class I SSR category.

Availability of supporting data

All the supporting data are included as additional files.

Abbreviations

SSR: 

Simple sequence repeats

CDS: 

Coding sequence

GO: 

Gene ontology

EST: 

Expressed sequence tags

UTR: 

Untranslated region

ORF: 

Open reading frame

Declarations

Acknowledgements

SSR was supported with a stipend from Kempe foundation. Travel cost for SSR was covered by the travel grant from Foundation Fund for Forestry Science Research, Faculty of Forest Sciences, SLU, Umeå. We acknowledge the support from Berzelii Centre of excellence at Umeå Plant Science Centre, Umeå, Sweden. We also acknowledge the Swedish research Council (VR) and the Swedish Governmental Agency for Innovation Systems (VINNOVA) for supporting the infrastructure to maintain P. abies genome assembly as publically available at Umeå Plant Science Centre (UPSC), Umeå, Sweden. Authors also acknowledge the support of computational resources from Picea abies genome consortium (http://congenie.org/) and Dendrome project.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Department of Forest Genetics and Plant Physiology, Umeå Plant Science Centre, Swedish University of Agricultural Sciences
(2)
Department of Plant Systems Biology (VIB) and Department of Plant Biotechnology and Bioinformatics, Ghent University
(3)
Genomics Research Institute, University of Pretoria
(4)
Bioinformatics Institute Ghent, Ghent University

References

  1. Tautz D, Renz M. Simple sequences are ubiquitous repetitive components of eukaryotic genomes. Nucleic Acids Res. 1984;12(10):4127–38.PubMedPubMed CentralView ArticleGoogle Scholar
  2. Schlotterer C, Tautz D. Slippage synthesis of simple sequence DNA. Nucleic Acids Res. 1992;20(2):211–5.PubMedPubMed CentralView ArticleGoogle Scholar
  3. Powell W, Machray GC, Provan J. Polymorphism revealed by simple sequence repeats. Trends Plant Sci. 1996;1(7):215–22.View ArticleGoogle Scholar
  4. Nguyen TTM, Lakhan SE, Finette BA. Development of a cost-effective high-throughput process of microsatellite analysis involving miniaturized multiplexed PCR amplification and automated allele identification. Hum Genomics. 2013;7:6.PubMedPubMed CentralView ArticleGoogle Scholar
  5. Yu JN, Won C, Jun J, Lim Y, Kwak M. Fast and cost-effective mining of microsatellite markers using NGS technology: an example of a Korean water deer Hydropotes inermis argyropus. Plos One. 2011;6(11):e26933.PubMedPubMed CentralView ArticleGoogle Scholar
  6. Zhang S, Tang CJ, Zhao Q, Li J, Yang LF, Qie LF, et al. Development of highly polymorphic simple sequence repeat markers using genome-wide microsatellite variant analysis in Foxtail millet [Setaria italica (L.) P. Beauv.]. Bmc Genomics. 2014;15:78.PubMedPubMed CentralView ArticleGoogle Scholar
  7. Muzzalupo I, Vendramin GG, Chiappetta A. Genetic biodiversity of Italian olives (Olea europaea) germplasm analyzed by SSR markers. The Sci World J. 2014;2014(2014):12. Article ID 296590. http://dx.doi.org/10.1155/2014/296590.
  8. Ashkani S, Rafii MY, Rusli I, Sariah M, Abdullah SNA, Rahim HA, et al. SSRs for marker-assisted selection for blast resistance in Rice (Oryza sativa L.). Plant Mol Biol Rep. 2012;30(1):79–86.View ArticleGoogle Scholar
  9. Stagel A, Portis E, Toppino L, Rotino GL, Lanteri S. Gene-based microsatellite development for mapping and phylogeny studies in eggplant. Bmc Genomics 2008;9:357. doi: https://doi.org/10.1186/1471-2164-9-357.
  10. Temnykh S, DeClerck G, Lukashova A, Lipovich L, Cartinhour S, McCouch S. Computational and experimental analysis of microsatellites in rice (Oryza sativa L.): frequency, length variation, transposon associations, and genetic marker potential. Genome Res. 2001;11(8):1441–52.PubMedPubMed CentralView ArticleGoogle Scholar
  11. Nystedt B, Street NR, Wetterbom A, Zuccolo A, Lin YC, Scofield DG, et al. The Norway spruce genome sequence and conifer genome evolution. Nature. 2013;497(7451):579–84.PubMedView ArticleGoogle Scholar
  12. Zimin A, Stevens KA, Crepeau M, Holtz-Morris A, Koriabine M, Marcais G, et al. Sequencing and assembly of the 22-Gb Loblolly Pine genome. Genetics. 2014;196(3):875–90.PubMedPubMed CentralView ArticleGoogle Scholar
  13. Buschiazzo E, Ritland C, Bohlmann J, Ritland K. Slow but not low: genomic comparisons reveal slower evolutionary rate and higher dN/dS in conifers compared to angiosperms. Bmc Evol Biol. 2012;12:8.PubMedPubMed CentralView ArticleGoogle Scholar
  14. Ranade SS, Lin YC, Zuccolo A, Van de Peer Y, Garcia-Gil MR. Comparative in silico analysis of EST-SSRs in angiosperm and gymnosperm tree genera. Bmc Plant Biol. 2014;14:220. doi: https://doi.org/10.1186/s12870-014-0220-8.
  15. Berube Y, Zhuang J, Rungis D, Ralph S, Bohlmann J, Ritland K. Characterization of EST SSRs in loblolly pine and spruce. Tree Genet Genomes. 2007;3(3):251–9.View ArticleGoogle Scholar
  16. Fluch S, Burg A, Kopecky D, Homolka A, Spiess N, Vendramin GG. Characterization of variable EST SSR markers for Norway spruce (Picea abies L.). BMC Res Notes. 2011;4:401.PubMedPubMed CentralView ArticleGoogle Scholar
  17. Chagne D, Chaumeil P, Ramboer A, Collada C, Guevara A, Cervera MT, et al. Cross-species transferability and mapping of genomic and cDNA SSRs in pines. Theor Appl Genet. 2004;109(6):1204–14.PubMedView ArticleGoogle Scholar
  18. Victoria FC, da Maia LC, de Oliveira AC. In silico comparative analysis of SSR markers in plants. Bmc Plant Biol. 2011;11:15.PubMedPubMed CentralView ArticleGoogle Scholar
  19. von Stackelberg M, Rensing SA, Reski R. Identification of genic moss SSR markers and a comparative analysis of twenty-four algal and plant gene indices reveal species-specific rather than group-specific characteristics of microsatellites. Bmc Plant Biol. 2006;6:9.View ArticleGoogle Scholar
  20. Wegrzyn JL, Liechty JD, Stevens KA, Wu LS, Loopstra CA, Vasquez-Gross H, et al. Unique features of the Loblolly Pine (Pinus taeda L.) megagenome revealed through sequence annotation. Genetics. 2014;196(3):891.PubMedPubMed CentralView ArticleGoogle Scholar
  21. Kalia RK, Rai MK, Kalia S, Singh R, Dhawan AK. Microsatellite markers: an overview of the recent progress in plants. Euphytica. 2011;177(3):309–34.View ArticleGoogle Scholar
  22. Plomion C, Bousquet J, Kole C. Genetics, genomics and breeding of conifers. New York: Edenbridge Science Publishers and CRC Press; 2011.Google Scholar
  23. da Maia LC, Palmieri DA, de Souza VQ, Kopp MM, de Carvalho FI, Costa de Oliveira A. SSR Locator: Tool for simple sequence repeat discovery integrated with primer design and PCR simulation. Int J Plant Genomics. 2008;2008:412696.PubMedPubMed CentralGoogle Scholar
  24. Rungis D, Berube Y, Zhang J, Ralph S, Ritland CE, Ellis BE, et al. Robust simple sequence repeat markers for spruce (Picea spp.) from expressed sequence tags. Theor Appl Genet. 2004;109(6):1283–94.PubMedView ArticleGoogle Scholar
  25. Yan M, Dai X, Li S, Yin T. A meta-analysis of EST-SSR sequences in the genomes of Pine, Poplar and Eucalyptus. Tree Genetics and Molecular Breeding. 2012;2(1):1–7.Google Scholar
  26. Guichoux E, Lagache L, Wagner S, Chaumeil P, Leger P, Lepais O, et al. Current trends in microsatellite genotyping. Mol Ecol Resour. 2011;11(4):591–611.PubMedView ArticleGoogle Scholar
  27. Mun JH, Kim DJ, Choi HK, Gish J, Debelle F, Mudge J, et al. Distribution of microsatellites in the genome of Medicago truncatula: a resource of genetic markers that integrate genetic and physical maps. Genetics. 2006;172(4):2541–55.PubMedPubMed CentralView ArticleGoogle Scholar
  28. Vasquez A, Lopez C. In Silico Genome Comparison and Distribution Analysis of Simple Sequences Repeats in Cassava. Int J Genomics. 2014;2014(2014):9.Article ID 471461. http://dx.doi.org/10.1155/2014/471461.
  29. Conesa A, Gotees S, García-Gómez J, Terol J, Talon M, Robles M. Blast2GO: A universal annotation and visualization tool in functional genomics research. Application note Bioinformatics. 2005;21:3674–6.View ArticleGoogle Scholar
  30. R Development Core Team R. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria, ISB 2006.Google Scholar
  31. Richard GF, Kerrest A, Dujon B. Comparative genomics and molecular dynamics of DNA repeats in eukaryotes. Microbiol Mol Biol Rev. 2008;72(4):686–727.PubMedPubMed CentralView ArticleGoogle Scholar
  32. Wang Y, Yang C, Jin Q, Zhou D, Wang S, Yu Y, et al. Genome-wide distribution comparative and composition analysis of the SSRs in Poaceae. Bmc Genet. 2015;16:18.PubMedPubMed CentralView ArticleGoogle Scholar
  33. Lawson MJ, Zhang LQ. Distinct patterns of SSR distribution in the Arabidopsis thaliana and rice genomes. Genome Biol. 2006;7(2):R14.PubMedPubMed CentralView ArticleGoogle Scholar
  34. Subramanian S, Mishra RK, Singh L. Genome-wide analysis of microsatellite repeats in humans: their abundance and density in specific genomic regions. Genome Biol. 2003;4(2):R13.PubMedPubMed CentralView ArticleGoogle Scholar
  35. Metzgar D, Bytof J, Wills C. Selection against frameshift mutations limits microsatellite expansion in coding DNA. Genome Res. 2000;10(1):72–80.PubMedPubMed CentralGoogle Scholar
  36. Ueno S, Moriguchi Y, Uchiyama K, Ujino-Ihara T, Futamura N, Sakurai T, et al. A second generation framework for the analysis of microsatellites in expressed sequence tags and the development of EST-SSR markers for a conifer, Cryptomeria japonica. Bmc Genomics. 2012;13:136.PubMedPubMed CentralView ArticleGoogle Scholar
  37. Rajewska M, Wegrzyn K, Konieczny I. AT-rich region and repeated sequences - the essential elements of replication origins of bacterial replicons. FEMS Microbiol Rev. 2012;36(2):408–34.PubMedView ArticleGoogle Scholar
  38. Hohn T, Corsten S, Rieke S, Muller M, Rothnie H. Methylation of coding region alone inhibits gene expression in plant protoplasts. P Natl Acad Sci USA. 1996;93(16):8334–9.View ArticleGoogle Scholar
  39. Katti MV, Sami-Subbu R, Ranjekar PK, Gupta VS. Amino acid repeat patterns in protein sequences: their diversity and structural-functional implications. Protein Sci. 2000;9(6):1203–9.PubMedPubMed CentralView ArticleGoogle Scholar
  40. Zhang L, Yu S, Cao Y, Wang J, Zuo K, Qin J, et al. Distributional gradient of amino acid repeats in plant proteins. Genome. 2006;49(8):900–5.PubMedView ArticleGoogle Scholar

Copyright

© Ranade et al. 2015

Advertisement