Open Access

Annotation of primate miRNAs by high throughput sequencing of small RNA libraries

Contributed equally
BMC Genomics201213:116

DOI: 10.1186/1471-2164-13-116

Received: 9 November 2011

Accepted: 27 March 2012

Published: 27 March 2012

Abstract

Background

In addition to genome sequencing, accurate functional annotation of genomes is required in order to carry out comparative and evolutionary analyses between species. Among primates, the human genome is the most extensively annotated. Human miRNA gene annotation is based on multiple lines of evidence including evidence for expression as well as prediction of the characteristic hairpin structure. In contrast, most miRNA genes in non-human primates are annotated based on homology without any expression evidence. We have sequenced small-RNA libraries from chimpanzee, gorilla, orangutan and rhesus macaque from multiple individuals and tissues. Using patterns of miRNA expression in conjunction with a model of miRNA biogenesis we used these high-throughput sequencing data to identify novel miRNAs in non-human primates.

Results

We predicted 47 new miRNAs in chimpanzee, 240 in gorilla, 55 in orangutan and 47 in rhesus macaque. The algorithm we used was able to predict 64% of the previously known miRNAs in chimpanzee, 94% in gorilla, 61% in orangutan and 71% in rhesus macaque. We therefore added evidence for expression in between one and five tissues to miRNAs that were previously annotated based only on homology to human miRNAs. We increased from 60 to 175 the number miRNAs that are located in orthologous regions in humans and the four non-human primate species studied here.

Conclusions

In this study we provide expression evidence for homology-based annotated miRNAs and predict de novo miRNAs in four non-human primate species. We increased the number of annotated miRNA genes and provided evidence for their expression in four non-human primates. Similar approaches using different individuals and tissues would improve annotation in non-human primates and allow for further comparative studies in the future.

Background

From a comparative genomics standpoint the great apes are among the most studied groups of organisms [1]. Since the completion of human genome sequencing in 2001 [2, 3] the genomes of all species belonging to this family have been or are being sequenced [4, 5]. Although only the human reference genome is considered of finished quality [2, 3], it is possible to compare and also use these genomes sequences as references for the alignment of reads generated in sequencing and gene expression studies. In addition to determine the DNA sequence of a genome, it is of particular importance to attach biological information to it e.g. determine the location and structure of protein-coding genes. Gene annotation is carried out both computationally and experimentally by sequencing cDNA e.g. traditionally using expressed sequence tags (ESTs) [6, 7] and more recently RNA-seq [8]. Human EST resources are also more abundant than their non-human counterparts and therefore human gene annotation is also the most accurate among great apes [9]. While the majority of efforts have focused on the annotation of protein-coding genes, the discovery of large-scale transcription outside of protein-coding genes [10, 11] has led to the identification of a great diversity of non-protein-coding RNA genes [12]. Among these are the microRNAs (miRNAs) which are short (~22 bp) RNA molecules [13] that post-transcriptionally down-regulate protein-coding gene expression [14, 15]. The official repository of miRNAs miRBase (v.17) [16, 17] contains 1,424 human miRNA, whereas fewer miRNAs are annotated in other primate genomes (chimpanzee: 600; bonobo: 88; gorilla: 85; orangutan: 581; rhesus macaque: 479), a fact that is explained by the larger number of human studies.

MiRNAs have been annotated in humans using a mixture of bioinformatics prediction and cDNA sequencing [18]. The identification of miRNAs in non-human primates has made use of a number of comparative methodologies such as sequence homology between closely related organisms [1922], the genomic search for RNA secondary structure patterns characteristic of miRNAs [23] and by direct sequencing of small RNA libraries [24, 25]. However, direct characterization of small RNA libraries by high throughput sequencing has been performed for a limited number of tissues in only chimpanzees and rhesus macaques[24, 25]. As a result the majority of non-human primate miRNAs in miRBase have no evidence for their expression and their existence is only supported by computational prediction. In the present study we sequenced small RNA libraries from multiple chimpanzee, gorilla, orangutan and rhesus macaque individuals and tissues using the Illumina high throughput sequencing platform. We applied an algorithm (miRDeep) that uses sequencing reads in conjunction with a model of miRNA biogenesis to predict miRNAs with high accuracy[26, 27].

Results

MiRNA prediction

We used the program miRDeep2 [27] to predict miRNAs from sequenced small RNAs. miRDeep2 takes as input the position and frequency of reads aligned to the genome ("signature") with respect to a putative RNA hairpin and scores the miRNA candidate employing a probabilistic model based on miRNA biogenesis [26]. The score produced by miRDeep takes into account the energetic stability of the putative hairpin and the compatibility of the observed read distribution with miRNA cleavage [26]. The more positive the score the more reliable the prediction. Additionally, miRDeep2 calculates false-positive rates by running the algorithm on a set of "signatures" and secondary structures that are paired by random permutation. Using predictions with a positive score and a significant folding p-value we identified from our sequences 47 (22 with expression evidence for star sequence) new miRNAs in chimpanzee, 240 (166 with expression evidence for star sequence) in gorilla, 55 (13 with expression evidence for star sequence) in orangutan and 47 (24 with expression evidence for star sequence) in rhesus macaque. miRDeep2 was able to predict 338 (64% of all annotated) known miRNAs (312 with a positive score) in chimpanzee, 75 (94% of all annotated, 73 with a positive score) in gorilla, 364 (61% of all annotated, 325 with a positive score) in orangutan and 348 (71% of all annotated, 312 with a positive score) in rhesus macaque (Figure 1). miRDeep2 performance statistics were similar to the ones reported in other species [27] (Figure 1).
https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-13-116/MediaObjects/12864_2011_Article_3956_Fig1_HTML.jpg
Figure 1

Expression of annotated and novel miRNAs for the four primate species. Column 1 illustrates the number of annotated miRBase (version 17) miRNAs. Columns 2-6 contain the number of expressed annotated (black) and novel (red) miRNAs for each separate tissue and column 7 for the union of all tissues. Columns 8-11 show miRDeep2 statistics and column 12 the number of miRNAs miRDeep2 defined as expressed and calculated its summary statistics on.

MiRNAs show high expression conservation between species, and tissue-specific expression patterns [28, 29]. In testis we found a lower fraction of the total reads align to miRNAs (Table 1) as a result of the expression of an additional class of small-RNAs in this tissue - piRNAs [29]. We were able to identify 11 tissue-specific miRNAs in chimpanzee (7 in brain, 1 in heart, 2 in kidney, 1 in testis), 110 in gorilla (100 in brain, 10 in liver), 28 in orangutan (25 in brain, 3 in liver) and 21 in rhesus macaque (11 in brain, 10 in testis).
Table 1

Samples' read alignment information.

Individual

Tissue

Genome

miRBase miRNAs

Predictions

Unknown

Total reads

Chimp 1

Brain

42.3

78.8

2.9

18.3

12211879

Chimp 2

Brain

54.3

90

2.2

7.7

11658357

Chimp 3

Brain

54.8

73.5

2.4

24.2

8627942

Chimp 4

Brain

52.5

88.1

2.3

9.6

10381037

Chimp 5

Brain

18.7

79.4

2.2

18.4

13977547

Chimp 1

Liver

57.4

92.3

1.1

6.6

8262666

Chimp 2

Liver

63.9

89.3

0.9

9.8

8088806

Chimp 3

Liver

51.7

88.1

0.9

11

11017642

Chimp 4

Liver

52.3

93.8

1.1

5.1

10449677

Chimp 5

Liver

29.9

57.5

0.5

41.9

16283995

Chimp 2

Testis

49.1

4.2

1.6

94.2

11361816

Chimp 3

Testis

63

5.8

2.1

92

8899032

Chimp 4

Testis

40.7

8.6

3.4

88

11965804

Chimp 5

Testis

43.2

5.8

2.1

92.1

11875495

Chimp 6

Testis

51.3

6.8

4

89.2

11166737

Chimp 1

Kidney

60.3

91

2.8

6.2

9702033

Chimp 2

Kidney

44.5

83.3

2.8

13.9

7774225

Chimp 3

Kidney

61.6

86.4

3.4

10.2

10250184

Chimp 5

Kidney

57.9

83.4

3.6

12.9

10264521

Chimp 1

Heart

63.2

94.7

2.2

3.1

7818504

Chimp 2

Heart

63.6

96.4

1.5

2

8644295

Chimp 3

Heart

65.4

95.3

1.1

3.6

9426585

Chimp 4

Heart

61.3

88

1.6

10.5

9449302

Chimp 5

Heart

60.8

88

1.3

10.7

9124991

Rhesus 1

Brain

36.1

72.3

4.8

23

12946219

Rhesus 2

Brain

38

81.8

4.5

13.7

12258382

Rhesus 3

Brain

47.5

82.3

5.6

12.1

11623674

Rhesus 4

Brain

44.9

90.6

3.6

5.8

11490940

Rhesus 5

Brain

48.9

88.4

3.8

7.8

10898842

Rhesus 1

Liver

51.4

93.4

1.3

5.4

8615049

Rhesus 2

Liver

58.2

95.1

1.1

3.8

8617533

Rhesus 3

Liver

54.7

95

2

3

9668109

Rhesus 4

Liver

45.6

94.6

1.8

3.7

10620490

Rhesus 5

Liver

34.5

90.6

1.9

7.5

10750399

Rhesus 1

Testis

44.4

36.3

1.1

62.5

12068068

Rhesus 2

Testis

25.7

40.2

2.7

57

14533174

Rhesus 3

Testis

47

29.9

1.1

69.1

11467601

Rhesus 4

Testis

50.5

15.2

0.4

84.3

10760301

Rhesus 1

Kidney

39.5

59.4

1.3

39.2

10730625

Rhesus 2

Kidney

52.4

87.9

2.4

9.6

12158274

Rhesus 3

Kidney

58.5

86.5

2.8

10.7

10683932

Rhesus 4

Kidney

55.8

81.5

2.3

16.2

10704780

Rhesus 6

Kidney

57.8

86.3

2.4

11.3

10530708

Rhesus 1

Heart

57.8

92.9

1.1

5.9

9116454

Rhesus 2

Heart

24.8

52.9

0.6

46.5

19394080

Rhesus 3

Heart

61.9

96.7

0.8

2.5

9093491

Rhesus 4

Heart

57.8

90.8

1.2

8

9824696

Rhesus 5

Heart

66.8

95

1.1

3.9

9018713

Orang 1

Brain

42.5

78.6

0.6

20.8

11307562

Orang 2

Brain

40.7

64.7

0.2

35

11449064

Orang 3

Liver

53.4

91.7

0.2

8.1

7111233

Orang 4

Liver

38.8

91.3

0.1

8.5

10302589

Gorilla 1

Brain

41.5

6.7

56.4

37

11931502

Gorilla 2

Brain

37.6

3.2

32.6

64.3

9534826

Gorilla 3

Liver

35

1.8

72.8

25.4

12400172

Gorilla 4

Liver

38.8

2.4

61.2

36.4

12018826

Column 1: individual information; column 2: tissue; column 3: fraction of reads that could be mapped perfectly to species corresponding genome; columns 4-6 are based on the reads that could be mapped to the corresponding species genome and contain how many of these reads could be aligned to known miRNAs (column 4), newly predicted miRNAs (column 5) and to neither of these 2 categories (column 6); column 7: total number of sequenced reads.

To identify miRNAs which are shared between all the primates studied here we examined miRNAs that are encoded in orthologous locations in all four primate species and in human. For the miRNAs present in miRBase (v.17) we found 60 miRNAs that are located in orthologous regions in human and the four non-human primate species. When we included the set of miRNAs predicted in this study we increased this number to 175 miRNAs. This set of miRNAs can be considered prediction of high confidence since they were known in human and either known or predicted by us in all other four primate species.

Sequence identity

All 60 of the known miRNAs present in all four species and human showed a high sequence identity i.e. the sequence is completely identical between the mature sequences for all of them. Using the set of 175 miRNAs we were able to reconstruct the expected phylogenetic relationships between the species studied for both the hairpin and the mature sequence. A principle component analysis on the sequence identity between hairpin sequences (Figure 2) shows a close relationship between chimpanzee and gorilla while both species are distant from orangutan and even more afar to rhesus macaque.
https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-13-116/MediaObjects/12864_2011_Article_3956_Fig2_HTML.jpg
Figure 2

Principle Component Analysis (PCA) using sequence similarity between mature (above) and hairpin (below) sequences. The plots show the first two components of the corresponding PCAs and the amount of variance explained by each component.

Secondary structure

For some stages during their biogenesis miRNAs form a secondary structure that resembles a hairpin [30]. Since the endonuclease that processes miRNAs recognizes them based on their three-dimensional structure [30], the stability of the secondary structure can be considered a proxy for miRNA functionality and therefore for the reliability of miRNAs predictions. We used the minimum free energy (MFE) as a measure of structure stability. We found that the hairpins of predicted miRNAs are as stable as hairpins from known miRNAs, which is not unexpected given that the score calculated by miRDeep2 takes into account the stability of the miRNA hairpin secondary structure.

Discussion

Although the genomes of multiple non-human primates have been sequenced, the functional annotation of the human genome remains the most complete among primates. This is the case for miRNAs annotated in miRBase, where the number of human miRNAs is double than miRNAs annotated in chimpanzee (the second-best annotated genome) [16, 17]. In the present study we sequenced small RNA libraries from multiple individuals and tissues in four non-human primates in order to identify from expression data new miRNA genes. We identified these new miRNAs using miRDeep2 [27], which uses a model for miRNA precursor processing by Dicer to score miRNA predictions. Using this approach we predicted 47 new miRNAs in chimpanzee, 240 in gorilla, 55 in orangutan and 47 in rhesus macaque (Figure 1). We found that the secondary structures from our new miRNAs were as stable as miRNAs previously described in miRBase.

A similar number of new miRNAs were identified in chimpanzee, orangutan and rhesus macaque, whereas the number of new miRNA predictions in gorilla was much higher. While the genomes of the chimpanzee, orangutan and rhesus have been available for some time, and a number of miRNA studies in these species published, the gorilla genome has not yet been published and fully annotated [4, 5, 31], and no published description of miRNAs in gorilla - a requirement for inclusion of new miRNAs in miRBase - exists The majority of annotated miRNAs in the non-human primates are based on homology with human miRNAs [2022]. However, the presence of a given locus in a genome is not a guarantee of its expression. We have, in this study, provided evidence of expression for 51% of the homology-based annotated miRNAs in gorilla, 49% in chimpanzee and 60% in rhesus macaque. We increased from 60 to 175 the number of miRNAs, which are located in orthologous regions in the four non-human primate genomes studied here and in human. This is a set of high confidence miRNAs based on homology, expression and miRNA biogenesis signatures.

In addition to the analysis of expression and folding, miRDeep incorporates a model of miRNA biogenesis, which makes its predictions more accurate than other software [27]. While the sequencing of small RNA libraries is now technically feasible, the accurate identification of novel miRNAs remains challenging. A pioneer study in primates sequenced small RNAs libraries from human and chimpanzee brains [24]. They predicted a large number (268 in human and 257 in chimpanzee) of new miRNAs in both species based on small RNA sequencing. Only few of these miRNAs have been included in miRBase, the public, curated repository for miRNAs (49 in human and 19 in chimpanzee). It is important to identify novel miRNAs accurately, and therefore particularly important to take into account the effect of genome quality and completeness on the ability to determine whether particular miRNAs are species-specific In primate comparisons the higher quality and completeness of the human genome means that miRNAs are frequently described as human-specific when in fact they are simply missed in related primate genomes due to sequence quality issues.

We sought to identify miRNAs that are expressed in tissue-specific manner. For species where we had samples from five tissues (chimpanzee and rhesus) we could say with more confidence that a given miRNA is tissue-specific than for the species where we had only two tissues (orangutan and gorilla). Brain was the tissue with both more miRNAs in total, and more tissue-specific miRNAs both in chimpanzee and marginally in rhesus. In orangutan and gorilla we could only identify miRNAs that are expressed mutually exclusively in either liver or brain. We found more miRNAs expressed exclusively in brain than in liver. This is in agreement with the fact that the miRNA repertoire in humans, chimpanzees and rhesus macaques is more diverse in brain compared to other tissues [29].

Conclusion

We have sequenced small RNA libraries from multiple individuals and tissues from chimpanzee, gorilla, orangutan and rhesus macaque. We identified known miRNAs and used miRDeep2 to predict de novo microRNAs in these four primate species. Our new expression-based predictions increased the number of known miRNAs in all four species. In addition, we showed the first expression evidence for miRNAs that were previously only annotated by sequence homology with humans. Accurate annotation of miRNAs in multiple primate species provides a fundamental to carry out evolutionary, comparative and functional studies of miRNAs.

Methods

MiRNA samples

We sequenced 56 small RNA libraries (24 from chimpanzees, 24 from rhesus macaques, four from orangutan and four from gorilla). The chimpanzee and rhesus macaque samples have been published [29]. We added to this set eight samples from orangutan and gorilla (four liver and four brain samples from each species). All the individuals used in this study were adults and suffered sudden death that did not involve the tissues sampled. A description of the samples is available in Table 1.

Library preparation and sequencing

We used the individuals presented in [29] including 24 chimpanzee and rhesus macaque samples. Additionally, we sequenced four gorilla and four orangutan samples from brain and liver (two from each species and tissue). Total RNA was prepared as described in the Illumina Inc. manual "Small RNA Sample Preparation Guide" (Part # 1004239 Rev. A Illumina Inc. San Diego). Illumina Genome Analyzer I and II sequencing runs were analyzed starting from raw intensities. A detailed summary about the platform each sample was sequenced on, how many cycles and which chemistry was used can be found in Table 2. Base calling and quality score calculation was performed for all runs using the IBIS base caller [32].
Table 2

Sequencing information.

Individual

Tissue

Sex

Platform

Chemistry

Cycles

Orang 1

Brain

Male

GA 1

V2

26

Orang 2

Brain

Female

GA 1

V2

36

Orang 3

Liver

Male

GA 2

V1

26

Orang 4

Liver

Male

GA 1

V1

36

Gorilla 1

Brain

Female

GA 1

V2

26

Gorilla 2

Brain

Female

GA 1

V2

36

Gorilla 3

Liver

Female

GA 2

V1

26

Gorilla 4

Liver

Female

GA 1

V1

36

Sample composition and read annotation

Read alignments were performed using PatMaN [33] allowing no mismatches. We mapped reads against miRBase [16, 17] version 17 and the corresponding species genomes - chimpanzee (panTro3), rhesus macaque (rheMac2), orangutan (ponAbe2) and the draft genome of gorilla (gorGor3).

Sequence data

MiRNA data was uploaded to the European Nucleotide Archive hosted by the European Bioinformatics Institute with the study accession number ERP000973 and ArrayExpress with accession number E-MTAB-828.

MiRNAs prediction

We used miRDeep2 prediction algorithm [27]. All reads from each species were used for the corresponding predictions. We excluded redundant predictions for the same genomic location and only kept the prediction with the highest score. We used the mapper module (mapper.pl) provided by miRDeep2 with the following parameters: -n -d -c -i -j -l 18 -m -k TCGTATGCCGTCTTCTGCTTG. We ran miRDeep2 with default parameters. Newly predicted miRNAs that were found in orthologous genomic regions in all four species were submitted to miRBase. Names were assigned by miRBase and are available in Table 3.
Table 3

Novel miRNAs

species

miRBase id

mature sequence

chromosome

miRDeep2 score

chimpanzee

ptr-mir-4423

AUAGGCACCAAAAAGCAACAA

1

24.7

chimpanzee

ptr-mir-3121

UAAAUAGAGUAGGCAAAGGACA

1

25919

chimpanzee

ptr-mir-3117

AUAGGACUCAUAUAGUGCCAGG

1

4.2

chimpanzee

ptr-mir-4742

UCAGGCAAAGGGAUAUUUACAGA

1

4.7

chimpanzee

ptr-mir-4428

CAAGGAGACGGGAACAUGGAGCC

1

5.2

chimpanzee

ptr-mir-4654

UGUGGGAUCUGGAGGCAUCUGGG

1

5.7

chimpanzee

ptr-mir-92b

UAUUGCACUCGUCCCGGCCUCC

1

9795.4

chimpanzee

ptr-mir-3127

AUCAGGGCUUGUGGAAUGGGAAG

2A

103.7

chimpanzee

ptr-mir-3132

UGGGUAGAGAAGGAGCUCAGA

2B

5.5

chimpanzee

ptr-mir-3129

GCAGUAGUGUAGAGAUUGGU

2B

92.4

chimpanzee

ptr-mir-378b

ACUGGACUUGGAGGCAGAAA

3

5.2

chimpanzee

ptr-mir-4446

CAGGGCUGGCAGUGAGAUGGG

3

5.3

chimpanzee

ptr-mir-3136

CUGACUGAAUAGGUAGGGUCA

3

5.5

chimpanzee

ptr-mir-3138

ACAGUGAGGUAGAGGGAGUG

4

148.4

chimpanzee

ptr-mir-3660

ACUGACAGGAGAGCGUUUUGA

5

120.4

chimpanzee

ptr-mir-378e

ACUGGACUUGGAGUCAGG

5

5

chimpanzee

ptr-mir-449c

AGGCAGUGUAUUGCUAGCGGCUGU

5

5.4

chimpanzee

ptr-mir-3943

UAGCCCCCAGGCUUCACUUGGCG

7

47.7

chimpanzee

ptr-mir-4660

UGCAGCUCUGGUGGAAAAUGGA

8

45124

chimpanzee

ptr-mir-3151

GGUGGGGCAAUGGGAUCAGGUG

8

500.7

chimpanzee

ptr-mir-3149

UUUGUAUGGAUAUGUGUGUGUA

8

5.3

chimpanzee

ptr-mir-4667

ACUGGGGAGCAGAAGGAGAACC

9

5.5

chimpanzee

ptr-mir-548e

AAAAACUGCGACUACUUUUG

10

5.4

chimpanzee

ptr-mir-3664

UCAGGAGUAAAGACAGAGU

11

5.6

chimpanzee

ptr-mir-1260b

AUCCCACCACUGCCACCAU

11

5.8

chimpanzee

ptr-mir-3165

AGGUGGAUGCAAUGUGACCUCA

11

5.9

chimpanzee

ptr-mir-1252

AGAAGGAAGUUGAAUUCAUU

12

4.6

chimpanzee

ptr-mir-200c

UAAUACUGCCGGGUAAUGAUGGA

12

5.8

chimpanzee

ptr-mir-655

AUAAUACAUGGUUAACCUCUU

14

246.1

chimpanzee

ptr-mir-3173

AAAGGAGGAAAUAGGCAGGCCA

14

344.5

chimpanzee

ptr-mir-2392

UAGGAUGGGGGUGAGAGGUG

14

5

chimpanzee

ptr-mir-4504

UGUGACAAUAGAGAUGAACAUGG

14

5.8

chimpanzee

ptr-mir-4510

UGAGGGAGUAGGAUGUAUGGU

15

4.2

chimpanzee

ptr-mir-4524a

UGAGACAGGCUUAUGCUGCUA

17

195.8

chimpanzee

ptr-mir-4743

UGGCCGGAUGGGACAGGAGGCA

18

5.4

chimpanzee

ptr-mir-320e

AAAAGCUGGGUUGAGAAGGUGA

19

4.5

chimpanzee

ptr-mir-548o

AAAAGUAAUUGCGGUUUUUGCC

20

105.8

chimpanzee

ptr-mir-3193

CUCCUGCGUAGGAUCUGAGGAG

20

4.7

chimpanzee

ptr-mir-3192

UCUGGGAGGUUGUAGCAGUGGA

20

5

chimpanzee

ptr-mir-3200

CACCUUGCGCUACUCAGGUCUG

22

270.9

chimpanzee

ptr-mir-23c

AUCACAUUGCCAGUGAUUACCC

X

4.4

chimpanzee

ptr-mir-2114

CGAGCCUCAAGCAAGGGACUUCA

X

50.6

chimpanzee

ptr-mir-767

UGCACCAUGGUUGUCUGAGCA

X

5.3

chimpanzee

ptr-mir-4536

UGUGGUAGAUAUAUGCACGA

X

5.3

chimpanzee

ptr-mir-222

AGCUACAUCUGGCUACUGGGUC

X

5.6

chimpanzee

ptr-mir-3937

ACAGGCGGCUGUAGCAAUGGGGGG

X

6.1

chimpanzee

ptr-mir-676

CUGUCCUAAGGUUGUUGAGU

X

79.5

gorilla

ggo-mir-135b

UAUGGCUUUUCAUUCCUAUGUGA

1

10.3

gorilla

ggo-mir-3605

GAUGAGGAUGGAUAGCAAGGAAG

1

1.1

gorilla

ggo-mir-29c

UAGCACCAUUUGAAAUCGGUUA

1

11813.8

gorilla

ggo-mir-197

UUCACCACCUUCUCCACCCAGC

1

119.9

gorilla

ggo-mir-92b

UAUUACACUCGUCCCGGCCUCC

1

1589.6

gorilla

ggo-mir-30e

UGUAAACAUCCUUGACUGGAAGC

1

3114.3

gorilla

ggo-mir-556

AUAUUACCAUUAGCUCAUCU

1

36.8

gorilla

ggo-mir-488

CCCAGAUAAUGGCACUCUCAA

1

4.7

gorilla

ggo-mir-320b

AGAAGCUGGGUUGAGAGGGCAA

1

5

gorilla

ggo-mir-190b

UGAUAUGUUUGAUAUUGGGUUG

1

5.1

gorilla

ggo-mir-429

UAAUACUGUCUGGUAAAACCG

1

5.3

gorilla

ggo-mir-760

CGGCUCUGGGUCUGUGGGGAG

1

5.4

gorilla

ggo-mir-1278

UAGUACUGUGCAUAUCAUCUA

1

5.6

gorilla

ggo-mir-551a

GCGACCCACUCUUGGUUUCCA

1

83

gorilla

ggo-mir-200b

UAAUACUGCCUGGUAAUGAUGAC

1

86.9

gorilla

ggo-mir-200a

UAACACUGUCUGGUAACGAUGU

1

99.7

gorilla

ggo-mir-4429

AAAAGCUGGGCUGAGAGGCGA

2A

1

gorilla

ggo-mir-3126

UGAGGGACAGAUGCCAGAAGCA

2A

5.3

gorilla

ggo-mir-1301

UUGCAGCUGCCUGGGAGUGACU

2A

5.5

gorilla

ggo-mir-3127

AUCAGGGCUUGUGGAAUGGGA

2A

5.6

gorilla

ggo-mir-26b

UUCAAGUAAUUCAGGAUAGGU

2B

15749.2

gorilla

ggo-mir-375

UUUGUUCGUUCGGCUCGCGUGA

2B

1.7

gorilla

ggo-mir-128

UCACAGUGAACCGGUCUCUU

2B

22571.1

gorilla

ggo-mir-149

UCUGGCUCCGUGUCUUCACUCCC

2B

357.8

gorilla

ggo-mir-3129

GCAGUAGUGUAGAGAUUGGU

2B

4

gorilla

ggo-mir-191

CAACGGAAUCCCAAAAGCAGC

3

13047.6

gorilla

ggo-let-7g

UGAGGUAGUAGUUUGUACAGU

3

134084.7

gorilla

ggo-mir-3923

AACUAGUAAUGUUGGAUUAGGGC

3

1.5

gorilla

ggo-mir-28

CACUAGAUUGUGAGCUCCUGGA

3

-4.8

gorilla

ggo-mir-4446

CAGGGCUGGCAGUGAGAUGGG

3

5.2

gorilla

ggo-mir-378b

ACUGGACUUGGAGGCAGAAAG

3

5.2

gorilla

ggo-mir-885

AGGCAGCGGGGUGUAGUGGA

3

5.7

gorilla

ggo-mir-551b

GCGACCCAUACUUGGUUUCAG

3

74.8

gorilla

ggo-mir-1255a

AGGAUGAGCAAAGAAAGUAGAU

4

122.2

gorilla

ggo-mir-548d

CAAAAACUGCAGUUACUUUUG

4

17.8

gorilla

ggo-mir-577

AUAGAUAAAAUAUUGGUACCUG

4

1.8

gorilla

ggo-mir-3138

ACAGUGAGGUAGAGGGAGUG

4

2.3

gorilla

ggo-mir-574

CACGCUCAUGCACACACCCACA

4

510.5

gorilla

ggo-mir-378e

ACUGGACUUGGAGUCAGGAC

5

0.5

gorilla

ggo-mir-3615

UCUCUCCGCUCCUCGCGGCUCGC

5

11.9

gorilla

ggo-mir-423

UGAGGGGCAGAGAGCGAGACUU

5

12767.2

gorilla

ggo-mir-4524a

UGAGACAGGCUUAUGCUGCUA

5

150

gorilla

ggo-mir-338

UCCAGCAUCAGUGAUUUUGUUGA

5

1509.7

gorilla

ggo-mir-193a

AACUGGCCUACAAAGUCCCAG

5

1740.8

gorilla

ggo-mir-1180

UUUCCGGCUCGCGUGGGUGUG

5

1.9

gorilla

ggo-mir-144

GGAUAUCAUCAUAUACUGUAAG

5

245.3

gorilla

ggo-mir-454

UAGUGCAAUAUUGCUUAUAGGGUU

5

4.9

gorilla

ggo-mir-152

UCAGUGCAUGACAGAACUUGG

5

5070.4

gorilla

ggo-mir-146a

UGAGAACUGAAUUCCAUGGGU

5

5.2

gorilla

ggo-mir-874

CUGCCCUGGCCCGAGGGACCGA

5

526.7

gorilla

ggo-mir-142

CCCAUAAAGUAGAAAGCACUA

5

5.3

gorilla

ggo-mir-1250

ACGGUGCUGGAUGUGGCCUU

5

5.4

gorilla

ggo-mir-4738

UGAAACUGGAGCGCCUGGAG

5

5.5

gorilla

ggo-mir-584

UUAUGGUUUGCCUGGGACUGA

5

5.8

gorilla

ggo-mir-1271

CUUGGCACCUAGCAAGCACUCA

5

58.5

gorilla

ggo-mir-378

ACUGGACUUGGAGUCAGAAGGCC

5

7592.3

gorilla

ggo-mir-340

UUAUAAAGCAAUGAGACUGAU

5

8919.2

gorilla

ggo-mir-877

GUAGAGGAGAUGGCGCAGGGGACA

6

1.5

gorilla

ggo-mir-30c

UGUAAACAUCCUACACUCUCAGC

6

1740.7

gorilla

ggo-mir-548b

CAAAAACCUCAGUUGCUUUUG

6

17.9

gorilla

ggo-mir-548a

AAAAGUAAUUGUGGUUUUUGC

6

30.4

gorilla

ggo-mir-133b

UUUGGUCCCCUUCAACCAGC

6

4

gorilla

ggo-mir-206

UGGAAUGUAAGGAAGUGUGUGG

6

5.4

gorilla

ggo-mir-1273c

GGCGACAAAACGAGACCCUG

6

8.4

gorilla

ggo-mir-671

UCCGGUUCUCAGGGCUCCACC

7

24.5

gorilla

ggo-mir-3943

UAGCCCCCAGGCUUCACUUGGCG

7

34

gorilla

ggo-mir-148a

UCAGUGCACUACAGAACUUUG

7

3957.5

gorilla

ggo-mir-339

UGAGCGCCUCGACGACAGAGCCG

7

429.6

gorilla

ggo-mir-592

UUGUGUCAAUAUGCGAUGAUG

7

45.6

gorilla

ggo-mir-548f

CAAAAGUGAUCGUGGUUUUUG

7

4.6

gorilla

ggo-mir-589

UGAGAACCACGUCUGCUCUGA

7

5.3

gorilla

ggo-mir-182

UUUGGCAAUGGUAGAACUCACA

7

5.4

gorilla

ggo-mir-590

GAGCUUAUUCAUAAAAGUGCAG

7

57.4

gorilla

ggo-mir-490

CAACCUGGAGGACUCCAUGCUG

7

73.8

gorilla

ggo-mir-335

UCAAGAGCAAUAACGAAAAAUG

7

785.9

gorilla

ggo-mir-486

UCCUGUACUGAGCUGCCCCGAG

8

1100

gorilla

ggo-mir-383

AGAUCAGAAGGUGAUUGUGGC

8

1642.2

gorilla

ggo-mir-3151

GGUGGGGCAAUGGGAUCAGGUG

8

18.3

gorilla

ggo-mir-598

UACGUCAUCGUUGUCAUCGUCA

8

5151.1

gorilla

ggo-mir-4660

UGCAGCUCUGGUGGAAAAUGGA

8

5.2

gorilla

ggo-mir-320a

AAAAGCUGGGUUGAGAGGGCGA

8

5.5

gorilla

ggo-mir-151a

UCGAGGAGCUCACAGUCUAG

8

5.6

gorilla

ggo-mir-455

GCAGUCCAUGGGCAUAUACAC

9

1166.5

gorilla

ggo-let-7f

UGAGGUAGUAGAUUGUAUAGU

9

1167727.6

gorilla

ggo-mir-873

GCAGGAACUUGUGAGUCUCC

9

197.5

gorilla

ggo-mir-27b

UUCACAGUGGCUAAGUUCUGC

9

2594.1

gorilla

ggo-mir-23b

AUCACAUUGCCAGGGAUUACCA

9

5

gorilla

ggo-mir-3927

CAGGUAGAUAUUUGAUAGGCA

9

6

gorilla

ggo-mir-491

AGUGGGGAACCCUUCCAUGAGGA

9

92.5

gorilla

ggo-mir-1287

UGCUGGAUCAGUGGUUCGAG

10

0.8

gorilla

ggo-mir-146b

UGAGAACUGAAUUCCAUAGGCUGU

10

10004.3

gorilla

ggo-mir-2110

UUGGGGAAGCGGCCGCUGAGUGA

10

1.4

gorilla

ggo-mir-346

UGUCUGCCCGCAUGCCUGCCUC

10

1.8

gorilla

ggo-mir-4484

GAAAAAGGCGGGAGAAGCCCCA

10

-2.5

gorilla

ggo-mir-202

AAGAGGUAUAGGGCAUGGGAAA

10

4.3

gorilla

ggo-mir-609

AGGGUGUUUCUCUCAUCUCUGG

10

4.3

gorilla

ggo-mir-548e

AAAAACUGCGACUACUUUUG

10

5.4

gorilla

ggo-mir-1296

UUAGGGCCCUGGCUCCAUCUCC

10

5.6

gorilla

ggo-mir-548c

AAAAGUACUUGCGGAUUUUG

11

12.7

gorilla

ggo-mir-34c

AGGCAGUGUAGUUAGCUGAUUG

11

1287.5

gorilla

ggo-mir-483

AAGACGGGAGGAAAGAAGGGAG

11

1967.6

gorilla

ggo-mir-4488

UAGGGGGCGGGCUCCGGCG

11

2

gorilla

ggo-mir-192

CUGACCUAUGAAUUGACAGCC

11

243338.1

gorilla

ggo-mir-34b

AGGCAGUGUCAUUAGCUGAUUG

11

28.3

gorilla

ggo-mir-210

CUGUGCGUGUGACAGCGGCUGA

11

323

gorilla

ggo-mir-675b

UGGUGCGGAGAGGGCCCACAGUG

11

41.1

gorilla

ggo-mir-139

UCUACAGUGCACGUGUCUCCAG

11

4363.3

gorilla

ggo-mir-1260b

AUCCCACCACUGCCACCA

11

5.6

gorilla

ggo-mir-326

CCUCUGGGCCCUUCCUCCAG

11

5.7

gorilla

ggo-mir-129

AAGCCCUUACCCCAAAAAGCA

11

7084.6

gorilla

ggo-mir-331

GCCCCUGGGCCUAUCCUAGAAC

12

1050.8

gorilla

ggo-mir-3612

AGGAGGCAUCUUGAGAAAUGG

12

12.5

gorilla

ggo-mir-1252

AGAAGGAAGUUGAAUUCAUU

12

16

gorilla

ggo-mir-148b

UCAGUGCAUCACAGAACUUUG

12

2086.5

gorilla

ggo-let-7i

UGAGGUAGUAGUUUGUGCUGU

12

25708.1

gorilla

ggo-mir-1228

GUGGGCGGGGGCAGGUGUGUGG

12

30.4

gorilla

ggo-mir-1291

GUGGCCCUGACUGAAGACCAGCA

12

5.3

gorilla

ggo-mir-1197

UAGGACACAUGGUCUACUUC

14

-0.3

gorilla

ggo-mir-370

GCCUGCUGGGGUGGAACCUGGUC

14

0.6

gorilla

ggo-mir-431

UGCAGGUCGUCUUGCAGGGCU

14

1

gorilla

ggo-mir-380

UAUGUAAUAUGGUCCACAUC

14

106

gorilla

ggo-mir-3545

UUGAACUGUUAAGAACCACUGG

14

12.6

gorilla

ggo-mir-433

AUCAUGAUGGGCUCCUCGGUG

14

1331

gorilla

ggo-mir-376a

AUCAUAGAGGAAAAUCCACG

14

156.3

gorilla

ggo-mir-655

AUAAUACAUGGUUAACCUCUU

14

158.8

gorilla

ggo-mir-379

UGGUAGACUAUGGAACGUAGG

14

1946

gorilla

ggo-mir-624

UAGUACCAGUACCUUGUGUUCA

14

2

gorilla

ggo-mir-409

AGGUUACCCGAGCAACUUUGCA

14

233

gorilla

ggo-mir-487a

AAUCAUACAGGGACAUCCAGU

14

245.1

gorilla

ggo-mir-495

AAACAAACAUGGUGCACUUCU

14

2528.9

gorilla

ggo-mir-543

AAACAUUCGCGGUGCACUUCU

14

260.4

gorilla

ggo-mir-432

UCUUGGAGUAGGUCAUUGGGUG

14

2631.8

gorilla

no id*1

AGGGGGAAAGUUCUAUAG

14

3.4

gorilla

ggo-mir-493

UUGUACAUGGUAGGCUUUCAU

14

38.4

gorilla

ggo-mir-889

UUAAUAUCGGACAACCAUUG

14

3.9

gorilla

ggo-mir-485

AGAGGCUGGCCGUGAUGAAU

14

3983.2

gorilla

ggo-mir-299

UGGUUUACCGUCCCACAUACA

14

446.3

gorilla

ggo-mir-494

UGAAACAUACACGGGAAACCUC

14

4.7

gorilla

ggo-mir-329b

AACACACCUGGUUAACCUCU

14

4.7

gorilla

ggo-mir-1185

AGAGGAUACCCUUUGUAUGU

14

5

gorilla

ggo-mir-496

UGAGUAUUACAUGGCCAAUC

14

5

gorilla

ggo-mir-487b

AAUCGUACAGGGUCAUCCACU

14

5.1

gorilla

ggo-mir-127

UCGGAUCCGUCUGAGCUUGGC

14

5.2

gorilla

ggo-mir-323b

CCCAAUACACGGUCGACCUC

14

5.3

gorilla

ggo-mir-337

GAACGGCUUCAUACAGGAG

14

5.3

gorilla

ggo-mir-668

AUGUCACUCGGCUCGGCCCAC

14

5.3

gorilla

ggo-mir-342

UCUCACACAGAAAUCGCACCCG

14

5.4

gorilla

ggo-mir-1193

GGGAUGGUAGACCGGUGACGUGC

14

5.4

gorilla

ggo-mir-376c

AACAUAGAGGAAAUUCCACG

14

558

gorilla

ggo-mir-3173

AAAGGAGGAAAUAGGCAGGCCAG

14

5.7

gorilla

ggo-mir-654

UGGUGGGCCGCAGAACAUGUGC

14

58.5

gorilla

ggo-mir-411

AUAGUAGACCGUAUAGCGUACG

14

587.6

gorilla

ggo-mir-656

AAUAUUAUACAGUCAACCUC

14

59.4

gorilla

ggo-mir-410

AAUAUAACACAGAUGGCCUG

14

644.2

gorilla

ggo-mir-376b

AUCAUAGAGGAAAAUCCAUG

14

71.1

gorilla

ggo-mir-377

AUCACACAAAGGCAACUUUUG

14

83.6

gorilla

ggo-mir-381

UAUACAAGGGCAAGCUCUCUG

14

86.1

gorilla

ggo-mir-345

GCUGACUCCUAGUCCAGGGCUCG

14

88.9

gorilla

ggo-mir-323a

CACAUUACACGGUCGACCUC

14

894

gorilla

ggo-mir-628

AUGCUGACAUAUUUACUAGAGG

15

141.7

gorilla

ggo-mir-1179

AAGCAUUCUUUCAUUGGUUGG

15

27.1

gorilla

ggo-mir-4510

UGAGGGAGUAGGAUGUAUGGU

15

4.7

gorilla

ggo-mir-1266

CCUCAGGGCUGUAGAACAGGGCUG

15

5.9

gorilla

ggo-mir-629

UGGGUUUAUGUUGGGAGAACU

15

78.2

gorilla

ggo-mir-1343

CUCCUGGGGCCCGCACUC

16

1

gorilla

ggo-mir-484

UCAGGCUCAGUCCCCUCCCGA

16

1.1

gorilla

ggo-mir-328

CUGGCCCUCUCUGCCCUUCCG

16

116.1

gorilla

ggo-mir-193b

CGGGGUUUUGAGGGCGAGAUGA

16

1197.1

gorilla

ggo-mir-940

AAGGCAGGGCCCCCGCUCCCC

16

1.9

gorilla

ggo-mir-138

AGCUGGUGUUGUGAAUCAGGCCG

16

3411

gorilla

ggo-mir-365a

UAAUGCCCCUAAAAAUCCUUA

16

698

gorilla

ggo-mir-140

ACCACAGGGUAGAACCACGGAC

16

97632.3

gorilla

ggo-mir-324

CGCAUCCCCUAGGGCAUUGGUG

17

550.3

gorilla

ggo-mir-497

CAGCAGCACACUGUGGUUUG

17

5.6

gorilla

ggo-mir-4520b

UUUGGACAGAAAACACGCAGG

17

5.6

gorilla

ggo-mir-887

GUGAACGGGCGCCAUCCCGAGGCU

17

81.3

gorilla

ggo-mir-22

AAGCUGCCAGUUGAAGAACUG

17

8262.6

gorilla

ggo-mir-582

UUACAGUUGUUCAACCAGUUAC

17

86.1

gorilla

ggo-mir-4529

UCAUUGGACUGCUGAUGGCCUG

18

0.8

gorilla

ggo-mir-122

UGGAGUGUGACAAUGGUGUUUG

18

2545110.2

gorilla

ggo-mir-4743

UGGCCGGAUGGGACAGGAGGCA

18

5.4

gorilla

ggo-mir-1

UGGAAUGUAAAGAAGUAUGUA

18

54001.2

gorilla

ggo-mir-517c

AUCGUGCAUCCCUUUAGAGUG

19

3

gorilla

ggo-mir-516b

AUCUGGAGGUAAGAAGCACUU

19

3.9

gorilla

ggo-mir-371b

ACUCAAAAGAUGGCGGCACUU

19

5.3

gorilla

ggo-mir-330

GCAAAGCACACGGCCUGCAGAGA

19

5.4

gorilla

ggo-mir-769

UGAGACCUCUGGGUUCUGAGC

19

545.2

gorilla

ggo-mir-125a

UCCCUGAGACCCUUUAACCUG

19

5.5

gorilla

ggo-mir-641

AAAGACAUAGGAUAGAGUCACC

19

6

gorilla

ggo-mir-181d

AACAUUCAUUGUUGUCGGUGGGU

19

6323.7

gorilla

ggo-mir-150

UCUCCCAACCCUUGUACCAGUG

19

64.7

gorilla

ggo-let-7e

UGAGGUAGGAGGUUGUAUAGU

19

86198.3

gorilla

ggo-mir-1289

UGGAAUCCAGGAAUCUGCAUUU

20

5.2

gorilla

ggo-mir-499a

UUAAGACUUGCAGUGAUGUU

20

5.5

gorilla

ggo-mir-296

AGGGUUGGGUGGAGGCUCUCC

20

6.2

gorilla

ggo-let-7c

UGAGGUAGUAGGUUGUAUGGU

21

270515.7

gorilla

ggo-mir-155

UUAAUGCUAAUCGUGAUAGGGG

21

5.3

gorilla

ggo-mir-1306

ACGUUGGCUCUGGUGGUGAUG

22

1.1

gorilla

ggo-mir-1286

UGCAGGACCAAGAUGAGCCCU

22

1.3

gorilla

ggo-let-7b

UGAGGUAGUAGGUUGUGUGGU

22

224101.1

gorilla

ggo-mir-1249

ACGCCCUUCCCCCCCUUCUUCA

22

29.3

gorilla

ggo-let-7a

UGAGGUAGUAGGUUGUAUAGU

22

523694.4

gorilla

ggo-mir-130b

CAGUGCAAUGAUGAAAGGGCA

22

548.3

gorilla

ggo-mir-185

UGGAGAGAAAGGCAGUUCCUGA

22

9137.4

gorilla

ggo-mir-18b

UAAGGUGCAUCUAGUGCAGU

X

-0.1

gorilla

ggo-mir-4536

UAUCGUGCAUAUAUCUACCACA

X

0.4

gorilla

ggo-mir-508

ACUGUAGCCUUUCUGAGUAGA

X

0.7

gorilla

ggo-mir-374b

AUAUAAUACAACCUGCUAAGUG

X

1006.8

gorilla

ggo-mir-532

CAUGCCUUGAGUGUAGGACCG

X

1105.2

gorilla

ggo-mir-542

UGUGACAGAUUGAUAACUGAAA

X

121

gorilla

ggo-mir-450b

UUUUGCAAUAUGUUCCUGAAUA

X

16

gorilla

ggo-mir-502a

AAUGCACCUGGGCAAGGAUUCA

X

164

gorilla

ggo-mir-503

UAGCAGCGGGAACAGUUCUGCAG

X

180.3

gorilla

ggo-mir-504

GACCCUGGUCUGCACUCUA

X

2

gorilla

ggo-mir-188

CAUCCCUUGCAUGGUGGAGGGUG

X

20.1

gorilla

ggo-mir-424

CAGCAGCAAUUCAUGUUUUGA

X

2017.9

gorilla

ggo-mir-509

UACUGCAGACGUGGCAAUCAUG

X

20.9

gorilla

ggo-mir-660

UACCCAUUGCAUAUCGGAGUUG

X

247.5

gorilla

ggo-mir-652

AAUGGCGCCACUAGGGUUGUG

X

291.5

gorilla

ggo-mir-363

AAUUGCACGGUAUCCAUCUGUAA

X

362.8

gorilla

ggo-mir-676

CUGUCCUAAGGUUGUUGAGUUG

X

4

gorilla

ggo-mir-374a

CUUAUCAGAUUGUAUUGUAAU

X

414.8

gorilla

ggo-mir-105

CCACGGAUGUUUGAGCAUGUG

X

-4.4

gorilla

ggo-mir-23c

AUCACAUUGCCAGUGAUUACCC

X

4.4

gorilla

ggo-mir-421

AUCAACAGACAUUAAUUGGGCG

X

5

gorilla

ggo-mir-20b

CAAAGUGCUCAUAGUGCAGGUAG

X

5

gorilla

ggo-mir-651

UUUAGGAUAAGCUUGACUUUUG

X

5

gorilla

ggo-mir-452

AACUGUUUGCAGAGGAAACUGA

X

5.2

gorilla

ggo-mir-767

UGCACCAUGGUUGUCUGAGCA

X

5.3

gorilla

ggo-mir-502b

AUGCACCUGGGCAAGGAUUCUGA

X

5.3

gorilla

ggo-mir-505

GUCAACACUUGCUGGUUUCC

X

5.4

gorilla

ggo-mir-1298

UUCAUUCGGCUGUCCAGAUG

X

5.4

gorilla

ggo-mir-222

AGCUACAUCUGGCUACUGGGUC

X

5.6

gorilla

ggo-mir-361

UUAUCAGAAUCUCCAGGGGUAC

X

615.7

gorilla

ggo-mir-450a

UUUUGCGAUGUGUUCCUAAUA

X

69.1

gorilla

ggo-mir-448

UUGCAUAUGUAGGAUGUCCCA

X

70

gorilla

ggo-mir-362

AACACACCUAUUCAAGGAUUCA

X

70.8

gorilla

ggo-mir-766

ACUCCAGCCCCACAGCCUCAGC

X

72.8

gorilla

ggo-mir-1264

ACAAGUCUUAUUUGAGCACCUG

X

7.8

gorilla

ggo-mir-1277

UACGUAGAUAUAUAUGUAUUU

X

93.5

orangutan

ppy-mir-4427

UCUGAAUAGAGUCUGAAGAG

1

0.2

orangutan

ppy-mir-3121

UAAAUAGAGUAGGCAAAGGACA

1

1.2

orangutan

ppy-mir-1976

CUCCUGCCCUCCUUGCUGUAG

1

3.8

orangutan

ppy-mir-4774

UCUGGUAUGUAGUAGGUAAUAA

2B

2.1

orangutan

ppy-mir-4782

UUCUGGAUAUGAAGACAAUCA

2B

3.2

orangutan

ppy-mir-4791

UGGAUAUGAUGACUGAAA

3

0.8

orangutan

ppy-mir-4446

CAGGGCUGGCAGUGAGAUGGG

3

2829

orangutan

ppy-mir-4796

UAAAGUGGCAGAGUAUAGACACA

3

3.3

orangutan

ppy-mir-378b

ACUGGACUUGGAGGCAGAAAG

3

5.3

orangutan

ppy-mir-4788

ACGGACCAGCUAAGGGAGGCAU

3

5.9

orangutan

ppy-mir-3938

AAUUCCCUUGUAGAUAACCUGG

3

8.5

orangutan

ppy-mir-4798

UUCGGUAUACUUUGUGAAUUGG

4

11.1

orangutan

ppy-mir-4451

UGGUAGAGCUGAGGACAG

4

4.6

orangutan

ppy-mir-3661

UGACCUGGGACUCGGAUAGCUGC

5

1.5

orangutan

ppy-mir-548h

AAAAGUAAUUGCGGUUUUUG

5

23.7

orangutan

ppy-mir-4637

UACUAACUGCAGAUUCAAGUGA

5

3

orangutan

ppy-mir-378e

ACUGGACUUGGAGUCAGG

5

4.1

orangutan

ppy-mir-3912

UAACGCAUAAUAUGGACAUG

5

4.5

orangutan

ppy-mir-548f

CAAAAACUGUAAUUACUUUUG

5

5.1

orangutan

ppy-mir-3660

CACUGACAGGAGAGCAUUUUGA

5

5.3

orangutan

ppy-mir-548a

AAAAGUAAUUGUGGUUUUUG

6

4.9

orangutan

ppy-mir-1273e

GAGGCAGGAGAAUCGCUUG

6

5

orangutan

ppy-mir-3934

UCAGGUGUGGAAUCUGAGGCA

6

5.3

orangutan

ppy-mir-3145

AACUCCAAGCAUUCAAAACUCA

6

5.4

orangutan

ppy-mir-3943

UAGCCCCCAGGCUUCACUUGGCG

7

22.2

orangutan

ppy-mir-4667

UGACUGGGGAGCAGAAGGAGA

9

1.6

orangutan

ppy-mir-3154

CAGAAGGGGAGUUGGGAGCAG

9

1.9

orangutan

ppy-mir-4672

ACACAGCUGGACAGAGGGACGA

9

4.8

orangutan

ppy-mir-2861

GGCGGCGGGCGUCGGGCG

9

6

orangutan

ppy-mir-2278

GAGGGCAGUGUGUGUUGUGUGG

9

8.8

orangutan

ppy-mir-4484

AAAAAGGCGGGAGAAGCCCCG

10

3.9

orangutan

ppy-mir-548e

AAAACGGUGACUACUUUUGCA

10

4.8

orangutan

ppy-mir-202

UUCCUAUGCAUAUACUUCUU

10

49.7

orangutan

ppy-mir-3155a

CAGGCUCUGCAGUGGGAACGGA

10

6.1

orangutan

ppy-mir-548c

AAAAGUACUUGCGGAUUUUG

11

5

orangutan

ppy-mir-1260b

AUCCCACCACUGCCACCA

11

5.5

orangutan

ppy-mir-3170

CUGGGGUUCUGAGACAGACAG

13

2.4

orangutan

ppy-mir-151b

UCCAGGAGCUCACAGUCUAG

14

2.6

orangutan

ppy-mir-1193

GGGAUGGUAGACCGGUGACGUGC

14

5

orangutan

ppy-mir-3173

AAGGAGGAAAUAGGCAGGCCAG

14

5.8

orangutan

ppy-mir-3174

UAGUGAGUUAGAGAUGCAGAGC

15

1.7

orangutan

ppy-mir-4515

AGGACUGGACUCCCGGCGGC

15

2.9

orangutan

ppy-mir-10a

UACCCUGUAGAUCCGAAUUUG

17

4.3

orangutan

ppy-mir-454

UAGUGCAAUAUUGCUUAUAGGG

17

5

orangutan

ppy-mir-4520a

UGGACAGAAAACACGCAGGAAG

17

5.2

orangutan

ppy-mir-152

UCAGUGCAUGACAGAACUUGG

17

8232.8

orangutan

ppy-mir-4526

GCUGACAGCAGGGCCGGCCAC

18

2.8

orangutan

ppy-mir-4529

AUUGGACUGCUGAUGGCCUG

18

3.6

orangutan

ppy-mir-4743

UGGCCGGAUGGGACAGGAGGCA

18

5.4

orangutan

ppy-mir-3188

AGAGGCUUUGUGCGGACUCGG

19

1.1

orangutan

ppy-mir-3940

CAGCCCGGAUCCCAGCCCACUCA

19

1.5

orangutan

ppy-mir-320e

AAAAGCUGGGUUGAGAAGGUGA

19

4.6

orangutan

ppy-mir-3617

AAAGACAUAGUUGCAAGAUGGG

20

1.6

orangutan

ppy-mir-378d

ACUGGACUUGGAGUCAGA

X

4.3

orangutan

ppy-mir-676

CCGUCCUAAGGUUGUUGAGUUG

X

5.1

rhesus macaque

mml-mir-1255b

UACGGAUAAGCAAAGAAAGUGG

1

2.1

rhesus macaque

mml-mir-320b

AAAAGCUGGGUUGAGAGGGCAA

1

5.1

rhesus macaque

mml-mir-3122

GUUGGGACAAGAGAACGGUCU

1

5.5

rhesus macaque

mml-mir-1262

UGAUGGGUGAAUUUGUAGAAGG

1

647.1

rhesus macaque

mml-mir-4446

CAGGGCUGGCAGUGAGAUGGG

2

26007.7

rhesus macaque

mml-mir-1284

UCUGUACAGACCCUGGCUUU

2

4.5

rhesus macaque

mml-mir-4796

AAGUGGCAGAGUGUAGACACAA

2

5.9

rhesus macaque

mml-mir-3146

CAUGCUAGAACAGAAAGAAUGGG

3

5

rhesus macaque

mml-mir-4650

UGGAAGGUAGAAUGAGGCCUGAU

3

5.8

rhesus macaque

mml-mir-3145

UAUUUUGAGUGUUUGGAAUUGA

4

4.8

rhesus macaque

mml-mir-1243

AAACUGGAUCAAUUAUAGGAG

5

17.7

rhesus macaque

mml-mir-378d

ACUGGACUUGGAGUCAGAAGCA

5

4.8

rhesus macaque

mml-mir-3140

AAGAGCUUUUGGGAAUUCAGG

5

5.3

rhesus macaque

mml-mir-1255a

AGGAUGAGCAAAGGAAGUAGU

5

5.7

rhesus macaque

mml-mir-4803

UAACAUAAUAGUGUGGACUGA

6

5.6

rhesus macaque

mml-mir-1271

CUUGGCACCUAGCAAGCACUCA

6

980.3

rhesus macaque

mml-mir-1179

AAGCAUUCUUUCAUUGGUUGG

7

16.9

rhesus macaque

mml-mir-1185

AGAGGAUACCCUUUGUAUGU

7

5.2

rhesus macaque

mml-mir-3173

GAAGGAGGAAACAGGCAGGCCAG

7

5.8

rhesus macaque

mml-mir-4716

AAGGGGGAAGGACACAUGGAGA

7

6.1

rhesus macaque

mml-mir-3151

ACGGGUGGCGCAAUGGGAUCAG

8

223.8

rhesus macaque

mml-mir-1296

UUAGGGCCCUGGCUCCAUCUCCU

9

5.5

rhesus macaque

mml-mir-1249

ACGCCCUUCCCCCCCUUCUUCA

10

118

rhesus macaque

mml-mir-3200

CACCUUGCGCUACUCAGGUCUG

10

202.6

rhesus macaque

mml-mir-1258

AGUUAGGAUUAGGUCGUGGAA

12

5.9

rhesus macaque

mml-mir-217b

UACUGCAUCAGGAACUGAUUGGA

13

4.3

rhesus macaque

mml-mir-1260b

AUCCCACCACUGCCACCA

14

5.6

rhesus macaque

mml-mir-1304

UUCGAGGCUACAAUGAGAUGUG

14

5.8

rhesus macaque

no id*2

CCAGGCUGGAGUGCAGUGG

15

4.1

rhesus macaque

mml-mir-873

GCAGGAACUUGUGAGUCUCC

15

4275.6

rhesus macaque

mml-mir-4667

ACUGGGGAGCAGAAGGAGAAC

15

5.5

rhesus macaque

mml-mir-3927

CAGGUAGAUAUUUGAUAGGCA

15

6.1

rhesus macaque

mml-mir-1250

ACGGUGCUGAAUGUGGCCUU

16

5.6

rhesus macaque

mml-mir-320c

AAAAGCUGGGUUGACAGGGUAA

18

3.8

rhesus macaque

mml-mir-4743

UGGCCGGAUGGGACAGGAGGCA

18

5.3

rhesus macaque

mml-mir-518d

CUCUAGAGGAAAGCGCUUACUG

19

103

rhesus macaque

mml-mir-517c

AUCGUGCAGCCUUUUAGAGUG

19

106.7

rhesus macaque

mml-mir-519e

UUCUCCAAUGGGAAGCACCUUC

19

132.7

rhesus macaque

mml-mir-1283

CUACAAAGGAAAGCACUUUC

19

4.9

rhesus macaque

mml-mir-1323

UCAAAACUGAGGGGCAUUUUC

19

6232.9

rhesus macaque

mml-mir-1298

UUCAUUCGGCUGUCCAGAUGUA

X

198.4

rhesus macaque

mml-mir-891b

UGCAACGAACUUGAGCCAUUGA

X

24.7

rhesus macaque

mml-mir-2114

CGAGCCUCAAGCAAGGGACUUC

X

25.3

rhesus macaque

mml-mir-4536

UGUGGUAGAUAUAUGCACGA

X

4.2

rhesus macaque

mml-mir-1277

UACGUAGAUAUAUAUGUAUUU

X

543.7

rhesus macaque

mml-mir-676

CCGUCCUAAGGUUGUUGAGU

X

766.4

rhesus macaque

mml-mir-514b

AUUGACACCUCUGUGAGUAGA

X

997.4

*1,2 miRBase did not provide names due to ambiguous N bases in the hairpin sequence or missing relationships to existing miRNAs in the database.

Orthology of miRNAs

We identified orthologous regions starting from human hg19-based miRBase (version 17) hairpin locations [16, 17]. The genome coordinates were transferred to hg18 coordinates using liftOver [34] with the 95% identity cutoff. Human mature sequences from miRBase were aligned to the human genome (hg18) and their corresponding hairpin sequences were assigned by overlapping genome coordinates using intersectBed from Bedtools [35]. All other primate miRNA mature sequences (known and predicted) were aligned against the corresponding genome and their genome locations were transferred to hg18 coordinates. The mature miRNA sequences found in the other primates that overlapped with human coordinates were defined as orthologous. The corresponding primate hairpin sequence was obtained by transferring the human genome hairpin coordinates to the corresponding primate genome. We excluded regions where liftOver was unable to identify an orthologous region.

Tissue specificity

MiRNAs were defined to be tissue specific when less than 5% of reads map to other tissues. This means that at least 80% of the perfectly aligned reads in chimpanzee and rhesus macaque (where we have reads from 4 tissues), and 95% of the perfectly aligned reads in gorilla and orangutan (where we have reads from 2 tissues) that were used for the prediction of the miRNA came from one tissue.

Sequence comparison

Sequence identity of miRNAs (mature/hairpin) in orthologous regions was computed using the multiple sequence alignment tool MUSCLE [36] and the identity function of the R package bio3d [37].

Secondary structure analysis

We calculated the minimum free energy (MFE) of known and predicted hairpin sequences by using RNAfold algorithm with default parameters [38]. The MFE for each group of annotated/predicted miRNAs was computed by averaging the MFEs.

Notes

Declarations

Acknowledgements

We would like to thank Thomas Giger for the dissection of the frozen tissues; Ines Drinnenberg, Matthias Meyer and the Sequencing Group of the MPI-EVA for coordinating sequencing runs; Martin Kircher for technical assistance with sequencing runs processing; Marike Schreiber for assistance with the figure preparation. The project was founded by a grant of the Max Planck Society.

Authors’ Affiliations

(1)
Department of Evolutionary Genetics, Max Planck Institute for Evolutionary Anthropology
(2)
Department of Genetic Causes of Disease, Center for Genomic Regulation
(3)
Department of Molecular Biology, Max Planck Institute for Developmental Biology

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© Dannemann et al; licensee BioMed Central Ltd. 2012

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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