Computational identification of <it>Drosophila </it>microRNA genes

gb-2003-4-7-r42 GBJ Research Computational identification of <it>Drosophila </it>microRNA genes Lai C Eric lai@fruitfly.org Tomancak Pavel Williams W Robert Rubin M Gerald

Howard Hughes Medical Institute, Department of Molecular and Cell Biology, University of California at Berkeley, 539 Life Sciences Addition, Berkeley, CA 94720, USA

Genome Biology 1465-6906 2003 4 7 R42 http://genomebiology.com/2003/4/7/R42 10.1186/gb-2003-4-7-r42 12844358 8 4 2003 16 5 2003 30 5 2003 30 6 2003 2003 Lai et al.; licensee BioMed Central Ltd. This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose, provided this notice is preserved along with the article's original URL.

Computational identification of Drosophila microRNA genes

An informatic procedure has been used to analyze the euchromatic sequences of Drosophila melanogaster and D. pseudoobscura for conserved sequences that adopt an extended stem-loop structure and display other characteristics of known miRNAs.

Abstract

Background

MicroRNAs (miRNAs) are a large family of 21-22 nucleotide non-coding RNAs with presumed post-transcriptional regulatory activity. Most miRNAs were identified by direct cloning of small RNAs, an approach that favors detection of abundant miRNAs. Three observations suggested that miRNA genes might be identified using a computational approach. First, miRNAs generally derive from precursor transcripts of 70-100 nucleotides with extended stem-loop structure. Second, miRNAs are usually highly conserved between the genomes of related species. Third, miRNAs display a characteristic pattern of evolutionary divergence.

Results

We developed an informatic procedure called 'miRseeker', which analyzed the completed euchromatic sequences of Drosophila melanogaster and D. pseudoobscura for conserved sequences that adopt an extended stem-loop structure and display a pattern of nucleotide divergence characteristic of known miRNAs. The sensitivity of this computational procedure was demonstrated by the presence of 75% (18/24) of previously identified Drosophila miRNAs within the top 124 candidates. In total, we identified 48 novel miRNA candidates that were strongly conserved in more distant insect, nematode, or vertebrate genomes. We verified expression for a total of 24 novel miRNA genes, including 20 of 27 candidates conserved in a third species and 4 of 11 high-scoring, Drosophila-specific candidates. Our analyses lead us to estimate that drosophilid genomes contain around 110 miRNA genes.

Conclusions

Our computational strategy succeeded in identifying bona fide miRNA genes and suggests that miRNAs constitute nearly 1% of predicted protein-coding genes in Drosophila, a percentage similar to the percentage of miRNAs recently attributed to other metazoan genomes.

Genome studies Bioinformatics Model organisms Molecular biology Methods

Background

Although the analysis of sequenced genomes to date has focused most heavily on the protein-coding set of genes, all genomes also contain a constellation of non-coding RNA genes. With the exception of certain classes of RNAs with strongly conserved sequences and/or structures, such as ribosomal and transfer RNAs, identification of most non-coding RNAs has historically been a relatively serendipitous affair. Only very recently have there been concerted efforts to identify such genes systematically, using both experimental and computational approaches 1.

Our collective ignorance of the totality of non-coding RNA genes was laid bare by recent work on microRNAs (miRNAs), an abundant family of 21-22 nucleotide non-coding RNAs 23. The founding members of this family, lin-4 and let-7, were identified through forward analysis of extant Caenorhabditis elegans mutants 45. Both of these RNAs are post-transcriptional regulators of developmental timing that function by binding to the 3' untranslated regions (3' UTRs) of target genes 5678. Although they were long regarded as genetic curiosities possibly specific to nematodes, let-7 was subsequently found to be broadly conserved across bilaterian evolution 9 and miRNA genes are now recognized as a pervasive and widespread feature of animal and plant genomes 10111213141516.

In general, it is thought that miRNA biogenesis proceeds via intermediate precursor transcripts of more than 70 nucleotides that have the capacity to form an extended stem-loop structure (pre-miRNA), although at least some pre-miRNAs are further derived from even longer transcripts (primary miRNA transcripts, or pri-miRNAs). These can exist as long individual pre-miRNA precursor transcripts, as operon-like multiple pre-miRNA precursors, or even as part of primary mRNA transcripts. Processing of pri-miRNA into the pre-miRNA stem-loop occurs in the nucleus, while subsequent processing of pre-miRNA into 21-22 mers is a cytoplasmic event mediated by the RNAse III enzyme Dicer 17181920; Dicer is also responsible for cleavage of long perfectly double-stranded RNA into 21-22 nucleotide fragments during RNA interference (RNAi) 221. These latter molecules, known as silencing RNA (siRNA), bind to and trigger the degradation of perfectly homologous mRNA molecules via RISC, a double-strand RNA-induced silencing complex containing nuclease activity 2223.

Although the in vivo function of only a few miRNAs is known so far, it is believed that the vast majority are likely to participate in post-transcriptional gene regulation of complementary mRNA targets. Interestingly, perfect or near-perfect target complementarity is associated with mRNA degradation 242526, similar to the effects of siRNA, whereas imperfect base-pairing is associated with regulation by translational inhibition 627. Recently, siRNAs with imperfect match to target mRNA were observed to function as translational inhibitors 28, suggesting that the type of 21-22 nucleotide RNA-mediated regulation may be largely determined by the quality of target complementarity.

The vast majority of the approximately 300 miRNAs currently known were identified through direct cloning of short RNA molecules. Although this method has been quite successful thus far, its practicality is limited by the necessity for a considerable amount of RNA as raw material for cloning, and cloned products are often dominated by a few highly expressed miRNAs. For example, 41% of miRNAs cloned from HeLa cells are variants of let-7, 28% of human brain miRNAs are variants of miR-124, and 45% of miRNAs cloned from human heart and 32% of those cloned from early Drosophila embryos are miR-1 1029. In fact, it has been opined that few additional mammalian miRNAs will be easily identified by the direct cloning method 30.

As a complementary approach to miRNA identification, we developed an informatic strategy ('miRseeker') and applied it to the completed genomes of Drosophila melanogaster and D. pseudoobscura, which are some 30 million years diverged. miRseeker subjects conserved intronic and intergenic sequences to an RNA folding and evaluation procedure to identify evolutionarily constrained hairpin structures with features characteristic of known miRNAs. The specificity of this computational procedure was shown by the presence of 18 out of 24 reference miRNAs within the top 124 candidates. We identified a total of 48 novel miRNA candidates whose existence was strongly supported by conservation in other insect, nematode or vertebrate genomes. Expression of 24 novel miRNA genes was verified by northern analysis (including 20 out of 27 candidates that were supported by third-species conservation and 4 out of 11 high-scoring predictions specific to Drosophila), demonstrating that the bioinformatic screen was successful. As might be expected, the newly verified miRNA genes vary tremendously with respect to abundance and developmental expression profile, suggesting diverse roles for these genes. Inference of our false-positive prediction and false-negative verification rates (based on our ability to identify known miRNAs and detect the expression of highly conserved, and thus presumed genuine, novel miRNAs) leads us to estimate that drosophilid genomes contain around 110 miRNA genes, or nearly 1% of the number of predicted protein-coding genes. In combination with other concurrent genomic analyses 31323334, it is likely that most miRNAs in completed animal genomes have now been identified. Collectively, this sets the stage for both genome-wide and targeted studies of this functionally elusive family of regulators.

Results

Evolutionarily conserved characteristics of miRNA genes

The starting point for our studies was a reference set of 24 Drosophila pre-miRNA sequences (let-7, the 21 originally identified by Lagos-Quintana and colleagues, mir-125, and a previously undescribed paralog of mir-2 that we named mir-2c 91029). We analyzed this set to derive rules and determine parameters that specifically describe known miRNA genes within anonymous genomic sequence.

Examination of the genomic sequence of D. melanogaster and D. pseudoobscura showed that all 24 members of the reference set are highly conserved along the entirety of the predicted precursor transcripts, which typically range between 70-100 nucleotides. When viewed in VISTA plot alignments 35, miRNA genes reside in short regions of exceptional conservation, easily seen as local 'peaks' (Figure 1). As is the case for many other non-coding RNA genes, their degree of conservation usually exceeds that of coding regions, due to their lack of third-position wobble. This suggested that miRNA genes might be found by folding fixed lengths of conserved sequence to identify ones that display the high degree of relatively continuous helicity characteristic of known pre-miRNAs. However, pilot studies identified a very large number of conserved stem-loops in genomic sequence, suggesting that additional criteria were necessary to make effective miRNA gene predictions.

Figure 1

miRNA genes are isolated, evolutionarily conserved genomic sequences that have the capacity to form extended stem-loop structures as RNA

miRNA genes are isolated, evolutionarily conserved genomic sequences that have the capacity to form extended stem-loop structures as RNA. Shown are VISTA plots of globally aligned sequence from D. melanogaster and D. pseudoobscura, in which the degree of conservation is represented by the height of the peak. This particular region contains a conserved sequence identified in this study that adopts a stem-loop structure characteristic of known miRNAs. Expression of this sequence was confirmed by northern analysis (Table 2), and it was subsequently determined to be the fly ortholog of mammalian mir-184. Most conserved sequences do not have the ability to form extended stem-loops, as evidenced by the fold adopted by the sequence in the neighboring peak.

We next aligned the 24 pairs of orthologous Drosophila pre-miRNA sequences and assessed their pattern of nucleotide divergence. There are only three pairs that have been completely conserved (Figure 2a, class 1), indicating that most pre-miRNAs have diverged to some extent within Drosophila. Unexpectedly, we detected mild selective pressure on the precise sequence of the non-miRNA-encoding arm. This attribute is not self-evident. It might have been the case that point mutations along an arm would be neutral as long as the status of base-pairing was maintained; this is possible due to the acceptability of G-U base-pairing in RNA. Instead, we observed preferential divergence within the loop sequence. Ten members of the reference set have diverged exclusively within their loop sequence (Figure 2a, class 2), whereas there are no members that have diverged exclusively along an arm (Figure 2a, class 5). This is well illustrated by the Drosophila let-7 orthologs, which have accumulated four mismatches and gaps in the loop sequence but maintain perfectly conserved arms (Figure 2b). Thus, the terminal loop is the most evolutionary labile portion of pre-miRNA.

Figure 2

Classification of conserved stem-loop sequences

Classification of conserved stem-loop sequences. (a) Patterns of Drosophila pre-miRNA nucleotide divergence patterns imply a canonical progression in miRNA evolution. The Drosophila orthologs of 23/24 previously described miRNAs are either completely conserved (class 1), contain one or more mismatches or gaps located exclusively in the loop (class 2) or contain an equal or greater number of mutations within the loop compared to the non-miRNA-encoding arm (class 3). We consider these to represent successive steps in the normal evolution of miRNAs and therefore connect them with arrows. Members of classes 1-3 are considered as equally good candidates while members of classes 4-6 are poor candidates. As we expect class 3 candidates to eventually evolve into class 6 candidates (broken arrow), these evolutionary considerations are most relevant to species separated by an evolutionary distance comparable to D. melanogaster and D. pseudoobscura. (b) Preferential divergence of miRNAs within their loop sequences is illustrated by let-7. The Drosophila orthologs of let-7 contain three mismatches and one gap within the loop, whereas both arms have been completely conserved.

Mutations do eventually accumulate on the non-miRNA-encoding arm, and orthologous pre-miRNAs from more diverged species will often preserve only the 21-24 nucleotide mature miRNA itself. However, because of preferential divergence within the loop, orthologous miRNAs from species of an appropriate evolutionary distance (such as D. melanogaster and D. pseudoobscura) show an equal or greater amount of change within the loop than on the non-miRNA-encoding arm (Figure 2a, class 3). This is the case despite the fact that the loop is typically only a third to a quarter the length of each arm. Of the eleven members of the reference set that show divergence on both an arm and the loop, seven show more changes in the loop than on the non-miRNA-encoding arm and three have an equal number of changes on the loop and non-miRNA-encoding arm (Figure 2a, class 3); only one member of the reference set (miR-2b-1) shows a greater number of changes on the non-miRNA arm than the loop (Figure 2a, class 6). Finally, there are no cases where both arms have diverged (irrespective of loop status), a situation that would imply that the miRNA sequence itself had diverged (Figure 2a, class 4).

We propose then that classes 1-3 represent the normal pattern of evolutionary divergence of miRNAs, and consider Drosophila candidates that fall into these classes to represent 'good' miRNA candidates. Conversely, we consider classes 4-6 to have a low probability of reflecting a genuine miRNA in Drosophila, regardless of how 'impressive' the helical nature of the conserved stem-loop is. Indeed, we tested one class 4 and seven class 5 candidates by northern blot and failed to observe expression for any of them, despite extensive, conserved stem-loop structure (data not shown). We emphasize that these evolutionary considerations are most relevant to relatively closely related species: since the loop is much shorter than the arms, we expect class 3 candidates to eventually evolve into class 6 candidates in species separated by greater evolutionary distance than the two Drosophila species analyzed in the present study (Figure 2a).

Computational prediction of Drosophila miRNA genes using miRseeker

Overall, the collected observations indicated that miRNAs are phylogenetically conserved, extended stem-loop structures that display a characteristic pattern of nucleotide divergence. We proceeded to identify such sequences in the completed drosophilid genomes using the following three-part computational pipeline that we call miRseeker (Figure 3).

Figure 3

Overview of miRseeker, a computational strategy for identifying Drosophila miRNAs

Overview of miRseeker, a computational strategy for identifying Drosophila miRNAs. See text for details.

Extraction of candidate, conserved, 'nongenic' Drosophila sequences

We first subdivided Release 3 of the D. melanogaster genome 36 into 1,287 contigs of 100,500 nucleotides each, with 500 nucleotides of overlap at either end. These were matched to the approximately 18,000 contigs in the first assembly of the D. pseudoobscura genome produced by the Human Genome Sequencing Center at the Baylor College of Medicine 37, using a dataset provided by the Berkeley Genome Pipeline 38. We then aligned the repeat-masked D. melanogaster sequence to corresponding D. pseudoobscura sequence using the global alignment tool AVID 3539. We subsequently eliminated from the alignment all sequences associated with the following Release 3.1 annotations 40: exons, transposable elements, snRNA, snoRNA, tRNA and rRNA genes. In total, we were able to align 51.3 out of 90.2 megabases of intronic and intergenic D. melanogaster sequence by this procedure.

Using the reference set, we empirically determined parameters for extracting conserved sequences that could contain miRNA genes (designated as 'regions'). We found that a 100-unit segment of the alignment (where a unit is either a single paired or gapped nucleotide) containing no more than 13% gaps or 15% mismatches, was sufficient to identify all known miRNA genes within their respective genomic neighborhoods, with a minimum of additionally selected sequences.

Conserved intergenic or intronic sequences are frequently longer than 100 nucleotides. We chose to evaluate these as a series of 'regions' that overlap each other by around 10 nucleotides, and grouped these regions into a single 'super-region'. The rationale for defining 'regions' and 'super-regions' came from our observation that RNA folding algorithms would not necessarily identify characteristic pre-miRNA structures if they were folded within the context of longer RNAs, owing to base-pairing with non-miRNA sequence. Thus, region-folding should offer optimal structures, while tracking of super-regions would ensure that multiple overlapping regions were evaluated as a single candidate gene. We took care to verify that our windowing parameters segregated individual miRNAs within known miRNA clusters (the mir-2, mir-13 and mir-3 → mir-6 clusters 10) into distinct super-regions. This analysis identified 436,000 conserved regions that originate from 118,000 super-regions.

Identification of conserved stem-loops and evaluation of their quality

We analyzed conserved regions with mfold 3.1, an RNA-folding algorithm 41. As miRNA genes can be located on either strand, and the quality of predicted hairpin structures can vary significantly between the respective strands (depending on the amount of G-U base-pairing), we folded each region as both the forward and the reverse complement of the extracted sequence (436,000 regions × 2 = 872,000 mfolds). Evaluation of candidate structures took into account the length of the longest helical arm (with a minimum cutoff of 23 base-pairs) and the free energy of this isolated arm (with a minimum cutoff of ΔG ≤-23.0 kcal/mol). The physical resemblance to the canonical stem-loop precursor was also assessed with metrics that reward continuous helical pairing and progressively penalize internal loops of increasing size. Since unpaired nucleotides in known miRNAs have a tendency to be found in symmetric loops, the presence of asymmetric loops and bulged nucleotides was further penalized. The size of the hairpin loop was not specifically evaluated as it appears to be variable in known pre-miRNAs; however it was indirectly assessed, as maximization of stem length concomitantly minimizes terminal loop size.

Given a 100-nucleotide input sequence, mfold 3.1 typically returns one to six alternate structures, each containing one to four helical arms; thus, the structure containing the highest-scoring helical arm had to be determined for each folded sequence. The highest-scoring region in each super-region (which could be located on either strand) was then saved as its representative, and these were ordered. For the top 25,000 D. melanogaster super-regions, we repeated this analysis on all regions in the corresponding D. pseudoobscura super-regions. We averaged the scores obtained for each aligned pair of Dm/Dp regions (termed a region-pair) and selected the highest-scoring region-pair within each super-region as its most probable miRNA candidate sequence. We then searched for homologs of these selected regions in Anopheles gambiae using WU-BLAST of the Dm sequence 42, extending the returned sequences as necessary on their 5' and 3' ends to make 100-nucleotide windows equivalent to the queried sequence. The top three mosquito hits were then folded and scored as before. However, as a large fraction of known fly miRNAs lack mosquito counterparts (Table 1), we decided to rank the candidates solely on their average Dm/Dp score.

Table 1

List of Drosophila miRNAs and additional unverified candidates supported by third-species conservation

Reference set miRNAs

Rank

Score

miR name

miR position

Cytological position

Sequence

Ano

Apis

Other

Nearest gene

Comment

26.15

miR-2a-2 (1)

2L:19547562

37E

UAUCACAGCCAAGCUUUGAUGAGC

In the intron of spi (sense)

10; 3 miR cluster

26.00

miR-2a-1 (2)

2L:19547974

37E

UAUCACAGCCAGCUUUGAUGAGC

Worm

In the intron of spi (sense)

10; 3 miR cluster

24.16

miR-2b-2 (3)

2L:19548259

37E

UAUCACAGCCAGCUUUGAGGAGC

In the intron of spi (sense)

10; 3 miR cluster

(8)

24.01*

miR-2b-1

2L:8250840

28B

UAUCACAGCCAGCUUUGAGGAGC

895 upstream of Btk29A

10; failed conservation filter

23.52

miR-13b-2 (4)

X:8830202

UAUCACAGCCAUUUUGACGAGU

In the intron of CG7033 (sense)

23.45

miR6-3 (5)

2R:14724424

56E

UAUCACAGUGGCUGUUCUUUUU

1732 upstream of CG11018

10; 7 miR cluster

22.89

miR-12 (6)

X:15240478

13D

UGAGUAUUACAUCAGGUACUGGU

1986 upstream of Ac13E

10; 2 miR cluster

22.84

miR-7 (7)

2R:15669777

57A

UGGAAGACUAGUGAUUUUGUUGU

Vertebrate

816 upstream of CG30147

22.45

miR-14 (8)

2R:4614375

45E

UCAGUCUUUUUCUCUCUCCUA

5855 upstream of Or45b

20.94

miR-9 (9)

3L:19515075

76C

UCUUUGGUUAUCUAGCUGUAUGA

6462 upstream of Shal

20.77

miR6-2 (10)

2R:14724582

56E

UAUCACAGUGGCUGUUCUUUUU

1574 upstream of CG11018

10; 7 miR cluster

20.65

miR6-1 (11)

2R:14724711

56E

UAUCACAGUGGCUGUUCUUUUU

1445 upstream of CG11018

10; 7 miR cluster

20.38

miR-13a (12)

3R:11243269

88F

UAUCACAGCCAUUUUGAUGAGU

4626 upstream of CG6118

10; 3 miR cluster

20.08

miR-5 (13)

2R:14724858

56E

AAAGGAACGAUCGUUGUGAUAUG

1298 upstream of CG11018

10; 7 miR cluster

18.86

let-7 (14)

2L:18450101

36E

UGAGGUAGUAGGUUGUAUAGU

Vertebrate/ worm

932 upstream of CG10283

9; 3 miR cluster

18.45*

miR-10

3R:2635277

84B

ACCCUGUAGAUCCGAAUUUGU

Vertebrate

13566 upstream of Scr

10; (not on aligned contig)

18.42

miR-1 (15)

2L:20457182

38D

UGGAAUGUAAAGAAGUAUGGAG

Vertebrate/ worm

14444 upstream of CG15476

17.79

miR-3 (16)

2R:14725313

56E

UCACUGGGCAAAGUGUGUCUCA

843 upstream of CG11018

10; 7 miR cluster

114

17.49

miR-11 (17)

3R:17439181

93E

CAUCACAGUCUGAGUUCUUGC

In the intron of E2f (sense)

124

17.36

miR-4 (18)

2R:14724998

56E

AUAAAGCUAGACAACCAUUGA

1158 upstream of CG11018

10; 7 miR cluster

172

16.54

miR-13b-1 (19)

3R:11243135

88F

UAUCACAGCCAUUUUGACGAGU

4760 upstream of CG6118

10; 3 miR cluster

192

16.27

miR-8 (20)

2R:11895154

53D

UAAUACUGUCAGGUAAAGAUGUC

3783 downstream of CG6301

14.20

miR-125

2L:18450405

36E

UCCCUGAGACCCUAACUUGUGA

Vertebrate/ worm

628 upstream of CG10283

29: low score; 3 miR cluster

miR-2c

3R:11243493

88F

UAUCACAGCCAGCUUUGAUGGGC

4402 upstream of CG6118

Hom; score n/a; 3 miR cluster

Newly verified miRNAs

Rank

Score

miR name

miR position

Cytological position

Sequence

Anos

Apis

Other

Nearest gene

Comment

24.67

miR-184

2R:8394117

50A

UGGACGGAGAACUGAUAAGGG

Vertebrate

24406 upstream of CG17048

Expression verified

24.15

miR-274

3L:11614451

68C

UUUUGUGACCGACACUAACGGGUAAU

In the intron of CG32085 (antisense)

Expression verified

23.10

miR-275

2L:7418027

27F

cAGUCAGGUACCUGAAGUAGCGCGCG

1070 upstream of CG5261

2miR cluster (+Ano and Apis); expression verified

22.57

miR-92a

3R:21461594

96E

CAUUGCACUUGUCCCGGCCUG

Vertebrate

6578 upstream of BcDNA:LD22548

2miR cluster

21.72

miR-219

3L:17263886

74A

UGAUUGUCCAAACGCAAUUCUUG

Vertebrate

4955 upstream of CG6485

Expression not seen

21.12

miR-276a

3L:10322758

67E

CAGCGAGGUAUAGAGUUCCUACG

47587 upstream of CG12362

Duplicated, 45 kb apart; expression verified; 1copy in Ano and Apis

20.88

miR-277

3R:5925763

85F

UGUAAAUGCACUAUCUGGUACGACAU

1391 upstream of Fmr1

2 miR cluster; expression verified

20.73

miR-278

2R:10720792

52B

ggUGGGACUUUCGUCCGUUUGUAA

386 upstream of fus

Expression verified

20.27

miR-133

2L:20586360

38D

UUGGUCCCCUUCAACCAGCUGU

Vertebrate

1059 downstream of CG15475

3 miR cluster; expression verified; 45

20.03

miR-279

3R:25030674

99A

UGUGACUAGAUCCACACUCAU

1328 upstream of CG31044

Related to miR-286; expression verified

19.90

miR-33

3L:19716503

76C

AGGUGCAUUGUAGUCGCAUUG

Vertebrate

In the intron of HLH106 (sense)

19.77

miR-280

2R:3358854

44C

UGUAUUUACGUUGCAUAUGAAAUGAUA

21740 upstream of CG30358

Expression verified

19.73

miR-281a

2R:7235078

48E

ACUGUCGACGGACAGCUCUCUU

356 downstream of SmD3

Duplicated cluster; expression verified; 1 copy in Ano

19.64

miR-282

3L:3231652

63C

aaucUAGCCUCUACUAGGCUUUGUCUGU

7132 upstream of CG14959

Expression verified

19.55

miR-283

X:15238971

13D

AAAUAUCAGCUGGUAAUUCUGGG

3493 upstream of Ac13E

2 miR cluster; expression verified

19.52

miR-284

3R:8377257

87C

UGAAGUCAGCAACUUGAUUCCAGCAAUUG

1128 upstream of CG6989

Expression verified

19.47

miR-281b

2R:7234866

48E

ACUGUCGACGGAUAGCUCUCUU

144 downstream of SmD3

Duplicated cluster; expression verified

19.35

miR-34

3R:5926677

85F

UGGCAGUGUGGUUAGCUGGUUG

Vertebrate/ worm

477 upstream of Fmr1

2 miR cluster; expression verified; 45

19.27

miR-263a

2L:11942273

33B

aAUGGCACUGGAAGAAUUCACg

Vertebrate

4764 downstream of CG16964

Expression verified; 34

18.89

miR-124

2L:17544454

36D

AUAAGGCACGCGGUGAAUGCCA

Vertebrate/ worm

10606 downstream of CG7094

2 miR cluster; 45

18.58

miR-79

2L:16676639

36A

AUAAAGCUAGAUUACCAAAGC

Worm

822 upstream of CG31782

3 miR cluster; expression verified; 45

18.57

miR-276b

3L:10277315

67E

CAGCGAGGUAUAGAGUUCCUACG

Vertebrate

7073 downstream of CG6559

Duplicated, 45 kb apart; expression verified; 1copy in Ano and Apis

18.36

miR-210

X:17859179

16F

UUGUGCGUGUGACAGCGGCUA

Vertebrate

1193 downstream of CG32553

18.11

miR-285

3L:11903642

68E

UAGCACCAUUCGAAAUCAGUGCU

Vertebrate

1592 upstream of CG7252

Similar to miR-29

18.08*

miR-100

2L:18449518

36E

AACCCGUAAAUCCGAACUUGUG

Vertebrate

1515 upstream of CG10283

Failed conservation filter; 3 miR cluster; expression verified; 45

17.93

miR-92b

3R:21466486

96E

AAUUGCACUAGUCCCGGCCU

Vertebrate

1686 upstream of BcDNA:LD22548

Expression verified; 2 miR cluster; 45

145

17.12

miR-286

2R:14724858

56E

AGUGACUAGACCGAACACUCG

1013 upstream of CG11018

Expression verified; 7 miR cluster; related to miR-279

146

17.11

bantam

3L:622845

61C

AGUGAGAUCAUUUUGAAAGCUG

Worm

6301 upstream of CG12030

208

16.09

miR-289

3L:13578391

70C

UAAAUAUUUAAGUGGAGCCUGCGACU

In the intron of bru-3 (antisense)

Expression verified

13.73

miR-287

2L:17552694

36D

UGUGUUGAAAAUCGUUUGCAC

14896 upstream of Oli

Very low score;found by proximity to miR-124; expression verified

13.35

miR-87

2L:9942828

30D

UGAGCAAAAUUUCAGGUGUG

Worm

2009 upstream of CG13126

Hom: very low score

miR-263b

3L:15666960

72D

cuUGGCACUGGGAGAAUUCACa

Vertebrate

4243 upstream of comm

Hom; score n/a

miR-288

2L:20588106

38D

UUUCAUGUCGAUUUCAUUUCAUG

2805 downstream of CG15475

Score n/a; found by proximity to miR-133; expression verified

Unverified Ano-conserved candidates

Rank

Score

miR name

miR position

Cytological position

Sequence

Ano

Apis

Other

Nearest gene

Comment

26.76

2R:4681879

46A

CAUCACACCCAGGUUGAGUGAGU

In the intron of Mmp2 (antisense)

24.35

3R:121090

82A

AAAUUGACUCUAGUAGGGAGUCC

533 downstream of CG9780

22.63

X:1545630

UGCAGGUUUCGUCGACAACGA

732 upstream of CG32806

22.13

3L:21585985

79A

CGAUUUGUCUUUUUCCGCUUACUG

1727 downstream of CG7160

21.95

3L:18809845

75E

UUUUGAUUGUUGCUCAGAAAGCC

3283 upstream of CG6865

No expression seen either strand

21.38

3L:8530512

66D

GUGAGAUAUGUUUGAUAUUCUUGGUUGUU

2374 upstream of CG6638

19.75

X:12366993

11B

UAUCAUAAGACACACGCGGCUAU

in the intron of tomosyn (sense)

19.06

2R:11128979

52E

guUAUUGCUUGAGAAUACACGUAGUU

15915 upstream of Dg

No expression seen either strand

18.86

2L:859210

21D

AGUUUGUUCGUUUGGCUCGAGUUAU

2208 downstream of CG13949

104

17.64

2L:16676008

36A

UCUUUGGUAUUCUAGCUGUAGA

1453 upstream of CG31782

No expression seen; miR-79 cluster

117

17.44

3R:21403955

96E

UGAUAUUGUCCUGUCACAGCAGUA

3265 upstream of CG12250

No expression seen

123

17.36

2L:7418192

27F