Skip to main content
  • Research article
  • Open access
  • Published:

Genome-wide annotation and analysis of zebra finch microRNA repertoire reveal sex-biased expression



MicroRNAs (miRNAs) are small noncoding RNAs that regulate gene expression post-transcriptionally in a wide range of biological processes. The zebra finch (Taeniopygia guttata), an oscine songbird with characteristic learned vocal behavior, provides biologists a unique model system for studying vocal behavior, sexually dimorphic brain development and functions, and comparative genomics.


We deep sequenced small RNA libraries made from the brain, heart, liver, and muscle tissues of adult male and female zebra finches. By mapping the sequence reads to the zebra finch genome and to known miRNAs in miRBase, we annotated a total of 193 miRNAs. Among them, 29 (15%) are avian specific, including three novel zebra finch specific miRNAs. Many of the miRNAs exhibit sequence heterogeneity including length variations, untemplated terminal nucleotide additions, and internal substitution events occurring at the uridine nucleotide within a GGU motif. We also identified seven Z chromosome-encoded miRNAs. Among them, miR-2954, an avian specific miRNA, is expressed at significantly higher levels in males than in females in all tissues examined. Target prediction analysis reveals that miR-2954, but not other Z-linked miRNAs, preferentially targets Z chromosome-encoded genes, including several genes known to be expressed in a sexually dimorphic manner in the zebra finch brain.


Our genome-wide systematic analysis of mature sequences, genomic locations, evolutionary sequence conservation, and tissue expression profiles of the zebra finch miRNA repertoire provides a valuable resource to the research community. Our analysis also reveals a miRNA-mediated mechanism that potentially regulates sex-biased gene expression in avian species.


MicroRNAs (miRNAs) are short non-coding RNA molecules that regulate gene expression post-transcriptionally[1]. miRNAs are transcribed by RNA polymerase II. Primary transcripts of miRNAs are cleaved in the nucleus by the nuclease Drosha to generate precursor miRNAs (pre-miRNAs) with a characteristic hairpin-like secondary structure[24]. The pre-miRNAs are then exported to the cytoplasm and further cleaved by the RNase III enzyme Dicer to release a 21–23 nt small RNA duplex from the stem region of the hairpins[2, 57]. In most cases, only one strand of the small RNA duplex is retained as mature miRNA, and the other strand, termed miRNA star (miRNA*), is degraded[8]. In some cases, however, both strands from the same hairpin are retained as functional miRNAs, which are annotated as miRNA-5p and miRNA-3p, respectively, depending on which arm of the hairpin they are derived[9]. Sequence variations, such as nucleotide deletion, insertion, untemplated terminal extension, and editing, have been observed for some miRNAs[1013]. The majority of animal miRNAs function by binding to the 3-untranslated regions (UTRs) of target mRNAs via sequence complementarity to induce mRNA degradation or repress protein translation[1416]. miRNAs have been found in most eukaryotes and many of them are conserved through evolution. The recent application of deep sequencing technology has also revealed an increasing number of species-specific miRNAs[10, 1721]. It is estimated that over 60% of human protein-coding genes might be regulated by miRNAs[22]. By fine-tuning gene expression, miRNAs regulate many biological processes ranging from cell proliferation, cell fate specification, cell differentiation, apoptosis, animal development, metabolism, to various disease conditions[1, 16, 23].

The zebra finch (Taeniopygia guttata), an oscine songbird, provides a unique animal model for neurobiological research with its characteristic learned vocal behavior[24]. Zebra finches use a discrete set of interconnected brain nuclei and pathways, commonly referred to as the song system, to control song behavior[25]. Song behavior in zebra finches is sexually dimorphic: only male birds sing, and the song system is highly developed in males[26, 27]. For decades, zebra finches have been widely used to study vocal learning, neuronal replacement, and sexually dimorphic development of male and female brains[24, 28]. In recent years, large efforts have been devoted to developing genomic resources and extending research in zebra finches to molecular and genomics levels[2935]. As part of this effort, we have systematically characterized miRNA expression in various tissues of adult female and male zebra finches using the Illumina high throughput sequencing platform. Taking advantage of the large sequence dataset now available, we analyzed features of these miRNAs, including miRNA sequence conservation through evolution, miRNA clusters, tissue-enriched expression, and sequence variations. Previously, using the 454 sequencing platform, we detected a Z chromosome encoded miRNA, miR-2954, in the male zebra finch brain[32]. This miRNA has recently been shown to be expressed in the auditory forebrain of zebra finches, and its expression is regulated by song exposure[35]. We have further explored the expression of miR-2954 and other Z chromosome encoded miRNAs in male and female tissues and have found that miR-2954 is predominantly expressed in all male tissues examined. Our target prediction analysis revealed that miR-2954, but not other Z chromosome encoded miRNAs, preferentially targets Z chromosome encoded genes.


General features of zebra finch miRNAs

We prepared eight small RNA libraries from four tissues – brain, heart, liver, and muscle – of adult female and male zebra finches. These libraries were sequenced using the Illumina Genome Analyzer II high throughput sequencing platform. We obtained a total of 23,366,676 raw sequence reads from all libraries combined. After adaptor trimming and removal of low quality reads and orphan sequences (single reads), 19,424,182 high quality sequence reads were retained for subsequent analysis (Additional file1). Of these reads, 60% mapped perfectly to the zebra finch genome assembly (release 1.0). We extracted flanking sequences around mapped reads and used mFold[36] to search for hairpin-like secondary structures. A total of 169 mature miRNAs were identified, which exhibited good hairpin-like precursor structures and matched known miRNAs recorded in miRBase (version 17.0) with high sequence homology (identical or with one mismatch). Considering that the current zebra finch genome assembly is relatively new and may contain gaps[32], we compared the unmapped sequence reads to known miRNA sequences in miRBase, and identified 21 additional miRNAs with high sequence homology (identical or with one mismatch) to known miRNAs in other species. Following the general criteria for miRNA annotation[37], we identified three novel zebra finch specific miRNA candidates, which are supported by good hairpin-like precursor structures, presence of corresponding star sequences, and relatively high expression (Figure1A). Taken together, we identified 193 distinct zebra finch miRNAs. The sequences, genomic locations, and relative expression levels of these miRNAs are summarized in Additional file2.

Figure 1
figure 1

miRNA annotation and expression analysis in zebra finch. (A) Mature sequences, expression counts, and precursor sequences with predicted hairpin-like secondary structures of three novel miRNAs identified in the zebra finch. Nucleotides labeled in red and blue in the precursor sequences represent mature miRNAs and their star sequences, respectively. The read numbers are combined from four tissues and two sexes. (B) Relative percentages of small RNA populations among the total sequence reads. Calculations were based on the total reads of the eight libraries combined. (C) The 20 most abundantly expressed miRNAs.

miRNAs and their variants accounted for ~64% of all high quality reads. The remaining sequence reads were classified as rRNA/tRNA fragments, repeat-associated small RNAs, degradation products of mRNAs, and undefined small RNAs (Figure1B). The expression levels of individual miRNAs spanned a wide range, from a few copies to thousands of copies (Additional file2). For example, tgu-miR-1, which was highly expressed in both heart and muscle, was represented by 2,541,528 reads, accounting for 25% of the total miRNA reads (Figure1C), whereas the three novel zebra finch miRNAs were expressed at relatively low levels, represented by 304, 66, and 73 reads respectively (Figure1A). (Note, these numbers represent combined totals from four tissues and two sexes. For read counts in individual tissues and sexes, see Additional file2). All of the top 20 most abundantly expressed miRNAs were conserved in vertebrates, and they comprised 76% of the total miRNA sequence reads (Figure1C).

Potential avian specific miRNAs

By comparing to mature miRNA sequences in miRBase, 33 of the 193 zebra finch miRNAs did not have homologs (identical or with one mismatch) outside of avian species. To investigate whether these 33 miRNAs had unidentified homologs in other genomes, we searched for homologous sequences in the genomes of 9 animal species including C. elegans, drosophila (D. melanogaster), zebrafish (Danio rerio), X. tropicalis (Xenopus tropicalis), lizard (Anolis carolinensis), platypus (Ornithorhynchus anatinus), chicken (Gallus gallus), mouse (Mus musculus), and human (Homo sapiens). Sequence homologs of 4 miRNAs (miR-2978, miR-2983, miR-2984, and miR-2987) were found in at least one non-avian vertebrate, and these were subsequently excluded from the list of avian-specific miRNAs. Thus, 29 miRNAs were classified as avian-specific miRNAs, of which 19 were zebra finch specific (Figure2). By extending this analysis to all the 193 zebra finch miRNAs, we found that 37 miRNAs were conserved from C. elegans through humans, 103 were conserved in vertebrates, and 24 were only conserved between avian and mammals (Additional file3).

Figure 2
figure 2

Conservation status of avian-specific miRNAs in nine animal species. The orange color indicates miRNA sequence homologs with predicted hairpin-like structures were found in a genome by our analysis. Black dots indicate homolog miRNAs are already recorded in miRBase as known miRNAs. Green color indicates mature miRNA sequences, but not precursor sequences, were found in a genome, and they were not counted as having homologs in other genomes. Blue is the background. Note, sequence homologs for miR-2978, miR-2983, miR-2984, miR-2987, and miR-2956 were found in the chicken genome, but they were not recorded in miRBase, so they didn′t get a black dot. We reported zebra finch specific miRNAs (miR-2955 through miR-2997) previously[32], and they are already in miRBase, so they were not considered to be novel miRNAs.

miRNA clusters

Using a distance of 10 kb between any two miRNA genes as a cutoff, 87 miRNA genes were grouped into 36 genomic clusters. We named these miRNA gene clusters according to the name of the first member of each cluster followed by the number of miRNA genes within the cluster in parenthesis, e.g., cluster tgu-mir-24(3) contains tgu-mir-24, tgu-mir-27b, and tgu-mir-23b (Additional file4). Among these clusters, 3 appeared to be formed by tandem duplications of a single miRNA gene, e.g., the tgu-mir-2989(2) cluster on chromosome 8 contains 2 copies of the tgu-mir-2989 gene (Additional file4). A majority of clusters were conserved in vertebrates (having more than 2 miRNA genes in the same cluster in other species); one cluster (tgu-let-7a-2(3)) was also conserved in D. melanogaster, but none was conserved in C. elegans. One cluster (tgu-mir-2989(2)) appeared to be zebra finch specific.


We found 150 miRNA* sequences for 131 mature miRNAs (Additional file5). For miRNAs encoded by multiple genomic loci, a single mature sequence can have more than one star sequence, presumably originating from different precursors. For example, three different star sequences were detected for tgu-miR-7, which has three genomic loci. These three tgu-miR-7* sequences had different read counts, indicating that the three tgu-miR-7 genes were transcribed with different promoter activities and/or different efficacies of precursor processing (Additional file6). Most of the star sequences were detected at considerably lower levels compared to their respective dominant strands. Yet, for 16 miRNAs, both strands were detected at comparable levels (< 10 fold differences, Additional file7), suggesting that both strands may function as mature miRNAs. The relative read counts for the two strands of these miRNAs were similar across the four tissues examined (Additional file5), indicating that the mechanisms retaining both strands were tissue independent. Interestingly, for several miRNAs (e.g., tgu-miR-142, tgu-miR-214, and tgu-miR-455), both strands have been detected at similar levels in mice and chickens as well[12, 38], suggesting a functional conservation of these star sequences in vertebrates.

miRNAs generated by atypical biogenesis pathways

Mirtrons are a special group of miRNAs that are derived from short intronic sequences by splicing machinery rather than by Drosha cleavage[3941]. Recently, Glazov and colleagues reported the identification of 12 mirtrons in chickens[38]. We did not, however, find sequence homologs of any of the chicken mirtrons in our miRNA set, nor did we find any miRNAs mapping to either boundaries of a short intron. This discrepancy might be partly explained by the fact that the chicken mirtrons were detected in embryos and exhibited low copy numbers[38], whereas our miRNAs were from adult tissues. Alternatively, it may simply be a consequence of the incomplete annotation of intronic regions in the zebra finch genome.

miR-451 is a vertebrate miRNA whose maturation depends on Ago2 cleavage rather than the common Dicer pathway[42, 43]. We found that tgu-miR-451 was located on chromosome 19, about 100 nt downstream from miR-144, and the mature and precursor sequences of tgu-miR-451 were highly conserved with those in chickens, mice, and humans. Similar to its mammalian counterparts, the 5′-end of tgu-miR-451 was well defined, whereas its 3′-end was highly variable, extending into the loop region of the hairpin structure, presumably reflecting imprecise cleavage by Ago2 (Additional file8).

miRNA expression patterns in tissues

We next examined the expression patterns of zebra finch miRNAs in brain, heart, liver, and muscle. Overall, the brain displayed the most diverse miRNA expression and had the largest number of tissue-enriched miRNAs (Figure3A). About 17% of all miRNAs (32 of 193) showed enriched expression in a single tissue, and two thirds of them (20 of 32) were highly enriched in brain based on combined read counts from males and females (Figure3A and3B). However, the two most abundantly expressed miRNAs (miR-1 expressed in heart and muscle and miR-499 in heart) showed little or no expression in brain. Since the sequences of many miRNAs are conserved through evolution, we asked whether the tissue-enriched miRNA expression patterns were also conserved. To this end, we analyzed previously published data from human, mouse, and sea slug (Aplysia)[10, 19]. Seven miRNAs (miR-9, miR-124, miR-137, mir-153, miR129, mir-218, and miR-138b) showed brain-enriched expression in both humans and zebra finches, and miR-124, miR-137, miR-153, and miR-34b have also been detected in the Aplysia nervous system (Table1). We validated the conservation of tissue expression patterns of five miRNAs in human, mouse, and zebra finch by Northern blot analysis (Figure3C).

Figure 3
figure 3

Tissue enriched expression of miRNAs. (A) Summary of percentages of miRNAs with enriched expression in each tissue. (B) miRNAs with the most tissue-enriched expression. Note, calculations in A and B were based on reads combined from both sexes. (C) Northern blot analysis showing the expression of miR-7, miR-9, miR-124, miR-143, and miR-122 in the brain and liver tissues of zebra finches, mice and humans.

Table 1 Conservation analysis of brain enriched miRNAs in three other species

miRNAs within a genomic cluster tend to exhibit parallel relative expression patterns across tissues, probably reflecting their shared promoter and tissue specific cistronic transcriptional control. However, the expression abundance of different members of a cluster in a given tissue can be drastically different. For example, miR-133a and miR-1 of the 133a(2) cluster were both expressed in heart and muscle, but the expression of miR-1 was about 10 times higher than that of miR-133a in each of these tissues. This difference in expression levels between members was characteristic of many genomic clusters (Additional file9), suggesting that a precursor specific event during the miRNA maturation process might regulate the expression levels of individual miRNAs in a genomic cluster.

Sequence variations

Taking advantage of the large sequence dataset, we analyzed miRNA sequence variations. We classified miRNA isoforms into three major groups: length variations, untemplated terminal nucleotide additions, and internal substitutions (Figure4A). The length variants accounted for 25% of the total miRNA reads, a majority of which (> 80%) were 3' variants (Figure4A). This is in good agreement with observations in other species, further supporting the notion that precision at cleavage events at the 5'-termini is necessary to protect the seed sequence at positions 2–8 of the mature miRNA[12, 44, 45]. Nonetheless, the read numbers of 5' offset isoforms of several miRNAs were relatively high. For example, miR-124, a brain enriched miRNA, had several 5' offset isoforms, with the combined reads accounting for 15% of all reads (Figure4B). Another prominent example was miR-133a, which had two main 5'-isoforms, miR-133a1 and miR-133a2, of which the 5' terminus of miR-133a2 was shifted 1 nucleotide in the 3' direction. These two isoforms were each represented by 77,642 and 98,840 reads, accounting for 32% and 41% of the total reads. Interestingly, similar patterns of 5' heterogeneity are also observed in mouse miR-124 and miR-133a[12, 46], indicating that the alternative processing mechanisms giving raise to these isoforms might be evolutionarily conserved.

Figure 4
figure 4

miRNA sequence variants. (A) Distribution of sequence variation types among all miRNA reads. (B) Sequence variants in miR-124. The nucleotides differing from the template genome are highlighted in red. Blue bars to the right indicate frequencies of these variants. (C) Motifs identified among internal substitution sites. The overall height of a stack indicates sequence conservation at that position: the higher the stack, the more the position is conserved. The heights of nucleotides within a stack indicate the relative frequency for each nucleotide at that position. We analyzed 6-nucleotide sequences containing an internal substitution site having a substitution rate > 5%, and found that a GGU motif was preferentially present among all sequences. The numbers (1–6) below the X axis indicate nucleotide positions in the motifs.

Untemplated nucleotide additions to miRNA 3' ends are observed for many miRNAs in worms, flies, and mammals[10, 12, 44, 45]. Similar to those observed in other species, nucleotides most frequently added to zebra finch miRNAs were U and A, with the U additions occurring more frequently than the A additions (66 miRNAs had U additions and 35 miRNAs had A additions, Additional file10). For some miRNAs, the extended isoforms outnumbered the canonical forms. For example, miR-456 had 7 fold more extended reads than non-extended reads, and miR-24 had 23,754 extended reads compared to 7,212 non-extended reads (Additional file10). We also observed that A addition had a greater tendency to occur on 5p arms, occurring 26 times on 5p arms compared to 9 on 3p arms, while U addition occurred slightly more often on 3p arms, occurring 36 times on 3p arms compared to 30 on 5p arms. Compared with previously published data, it appeared that patterns of untemplated extension of some miRNAs were conserved among multiple vertebrate species. For example, the A extensions of miR-99 and miR-101 and the U extensions of miR-15a, miR-24, miR-106, miR-124, and miR-425-5p were observed in zebra finch, human, and mouse[13, 46].

In mammals, miRNA editing events in which adenosine is converted to inosine by adenosine deaminases (ADARs) are observed for several miRNAs; the resultant inosine is detected in sequencing as an A-to-G conversion[10, 12, 4749]. Among our data, however, the A-to-G change was found in just ~0.3% of total miRNA reads in the brain library and in all 8 libraries combined. This frequency does not differ significantly from the sequencing error rate observed among our synthetic internal control sequences spiked into library preparations (<0.5%). We also searched for A-to-I editing events at specific sites in individual miRNAs. Using a criterion of >5% mismatch frequency, only tgu-miR-24 exhibited significant A-to-G change at the 15th position of the mature sequence. This change occurred in all tissues, but had the highest frequency in brain (~9%, Additional file11). We next examined all miRNAs that had any internal nucleotide change. Thirty-two miRNAs displayed internal nucleotide changes with a frequency >5%. Nucleotide changes at the U position within a GGU motif were the most frequent substitution (25 out of the 32), and it appeared that substitution with any of the other three nucleotides was permissible (Figure4C). For several miRNAs in this group, the GGU motifs occurred within the seed sequence (Table2); thus, substitution of the U nucleotide could potentially alter miRNA targeting specificity (Table2).

Table 2 miRNAs having substitutions at the GGU motif

Male-biased expression and targeting of miR-2954

miR-2954 is a recently identified avian miRNA, which has a single genomic locus on the zebra finch Z chromosome[32, 35]. We did not detect it in the chicken genome assembly (version galGal3) or in the genomes of other animal species (Figure2). However, we found the mature miR-2954 sequence among chicken ESTs, and its expression in chicken embryo is detected by Northern blot analysis[50]. Tgu-miR-2954 was expressed in a sex-biased manner in all four examined tissues; and its expression was significantly higher in male tissues than in female tissues (Additional file2). We validated this expression pattern by Northern blot analysis and quantitative real-time PCR (qRT-PCR) (Figure5A, B, and C). Our original sequencing samples did not include ovary and testis; however, using qRT-PCR, we found that the expression of miR-2954 was 3-fold higher in testis than in ovary (Figure5C). In addition to miR-2954, six other miRNAs (miR-23b, miR-24, miR-27b, miR-122, miR-2973, and miR-2992) were encoded solely by the Z chromosome, and most of them showed slightly higher expression in male tissues than in female tissues (Figure5D). The one exception was miR-122, which was expressed at a higher level in heart in females than in males. Several miRNAs (miR-7, miR-9, miR-101, and miR-204) that are encoded by both the Z and the A chromosomes did not exhibit consistent sex-biased expression (Additional file12).

Figure 5
figure 5

Expression analysis of miR-2954. (A) Northern blot analysis shows that miR-2954 was expressed in brain and liver tissues of male zebra finches, but was not detectable in mouse or human tissues. (B) Northern blot shows expression of miR-2954 and miR-9 in the brain tissues of female and male zebra finches. (C) Sex-biased expression of miR-2954 in the brain, heart, liver, muscle, ovary, and testis of male and female zebra finches validated by qRT-PCR. (D) Expression ratios of Z chromosome-encoded miRNAs between male and female zebra finch tissues based on sequence reads. Note, miR-2973, another Z chromosome-encoded miRNA, was not included here because its total combined reads were < 100.

We next performed target prediction analysis using the TargetScan software package[51]. Target analysis predicted approximately 100 putative tgu-miR-2954 target genes, with a strong bias towards Z chromosome-encoded genes (27 of 100, P < 1e-15, Fisher′s exact test) (Figure6A). A similar pattern was also observed for chicken miR-2954, for which 70 of the 403 putative targets were Z chromosome encoded (17.4%, P < 2.2e-16, Fisher′s exact test) (Figure6B), suggesting that the functional preference of miR-2954 for Z-linked genes is conserved among avians. A significantly larger number of miR-2954 target genes were predicted in chickens (WASHUC2) than in zebra finches, probably reflecting the incomplete annotation of the 3'-UTR sequences in the current zebra finch genome assembly (taeGut3.2.4). In contrast, no enrichment of Z chromosome-encoded target genes was found for other zebra finch Z chromosome-encoded miRNAs such as miR-27b and miR-122 (Figure6C). Among the putative tgu-miR-2954 targets were several Z-linked genes encoding proteins with particular functions in the nervous system, including Ca2+/calmodulin-dependent protein kinase IV (CaMKIV), SCAMP1, and SMARCA2. Genes encoding proteins in the guanine nucleotide exchange signaling pathways (e.g., RICTOR, DelGEF-interacting protein 1, Brefeldin A-inhibited guanine nucleotide-exchange protein 3, RAB40C, developmentally-regulated GTP-binding protein 2, and MCF2) were prominently represented as well. In addition, many putative miR-2954 targets were key regulators of transcription, including TLE4, GPBP1, PSIP1, SMARCA2, HMGB1, TNIP3, ZCCHC4, EAPP, RQCD1, MLF1, STAT3, ATF6, EZH1, and DEDD (Additional file13).

Figure 6
figure 6

Z chromosome distribution bias of predicted targets for miR-2954. (A) Distribution of predicted targets for miR-2954 among zebra finch chromosomes. (B) Distribution of predicted targets for miR-2954 among chicken chromosomes. Note, in both zebra finches and chickens, miR-2954 targets exhibited a strong bias towards Z chromosome-encoded genes. (C) Distribution of predicted targets for miR-27b and miR-122, two other Z chromosome-encoded miRNAs, among zebra finch chromosomes. The distributions of protein coding genes among zebra finch and chicken chromosomes are shown in blue color.


Here we report a comprehensive annotation and analysis of 193 zebra finch miRNAs expressed in four different tissues of both sexes. While the majority of the 193 identified miRNAs are evolutionarily conserved, relatively large fractions of them are zebra finch-specific (10%) or avian-specific (15%). The list of zebra finch specific miRNAs may be even longer, as approximately 40% of the total sequence reads obtained from our small RNA libraries failed to map to the genome, probably due to gaps in the current genome assembly. Conversely, genomes of other species that we searched may have gaps as well, thus we cannot rule out the possibility that some avian and/or zebra finch specific miRNAs described here may have undiscovered homologs in other species. Several zebra finch specific miRNAs showed tissue specific expression. For example, miR-2963 was detected in the brain, miR-2997 in liver, and the novel miRNA 3 in heart (Additional file14). However, most of the zebra finch specific miRNAs were expressed at low levels, indicating that their expression is restricted to specific cell types or that they have not yet been widely incorporated into gene regulation networks. Further investigation will be needed to determine if these avian or zebra finch specific miRNAs play roles in zebra finch specific features.

Recently, Gunaratne et al. reported the annotation of 155 miRNAs expressed in the auditory forebrain region of zebra finches[35]. Among these miRNAs, 140 are present in our set of miRNAs. Our dataset contains an additional 53 miRNAs, many of which show tissue specific expression in heart, liver, and muscle (Additional files2 and15). This is not unexpected as we sequenced four tissues, whereas only auditory forebrain was sequenced by Gunaratne et al. Although the source materials used in the two studies were different and the criteria for miRNA annotation were slightly different, the large overlap between the two datasets provides additional confidence with respect to the identification of these miRNAs. Combining the two sets of data brings the total number of zebra finch miRNAs to 208. Many of these miRNAs show enriched expression in the brain, including several (miR-25, miR-192, miR-124, miR-129, and miR-92) that are regulated in the auditory forebrain by song exposure[35], indicating that miRNAs may play important roles in song behavior and neural plasticity.

miRNA sequence heterogeneity

We did not observe significant A-to-I editing in mature zebra finch miRNAs except for miR-24. Notably, several mammalian miRNAs with well-characterized A-to-I editing sites (e.g., miR-376a, miR-376c, miR-379, miR-381, miR-411, miR-421, and miR-589,[12, 48, 49]) do not appear to have homologs in zebra finches, hinting that A-to-I editing might be more restricted to mammals. Instead, we observed internal nucleotide changes at the uridine of a GGU motif occurring at a relative high frequency. Our average base call sequencing error rate was <0.5% (calculated based on spiked-in synthetic internal controls), comparable to sequencing error rates observed by others[49]. The rate of observed nucleotide changes at the GGU motif was as high as 14% (in miR-22). Thus it is unlikely that these changes were due to sequencing errors. We also examined each genomic locus for miRNAs known to have multiple genomic loci, and ruled out the possibility that they were due to genetic variations. We cannot rule out the possibility that these changes were due to single nucleotide polymorphisms (SNPs), as we currently know very little about SNPs in zebra finches. However, we would expect that the rate of nucleotide changes generated by SNPs would be much higher than we observed. To our knowledge, internal nucleotide changes at the uridine of a GGU motif has not been reported previously. Some of the GGU motifs are within seed sequences of mature miRNAs (e.g., members of the let-7 family). Nucleotide changes at these sites can change miRNA binding specificity, and subsequently, impact gene expression and related cellular processes. Further analysis will bring better understandings of biosynthesis mechanisms and potential biological functions of this type of internal nucleotide change.

Other types of sequence heterogeneity including untemplated 3' terminal extensions and length variations are far more prevalent than internal nucleotide changes. miRNA isoforms with 3' untemplated extensions account for ~15% of all miRNA reads. miRNA 3' untemplated A or U extensions have been widely reported by others in multiple animal species[12, 13, 5256]. These events seem to be miRNA specific, as some miRNAs are more frequently extended than others. Interestingly, many of the zebra finch miRNAs showing high frequencies of 3' untemplated extensions are also frequently extended in other species (Additional file10,[11, 57, 58]), hinting that mechanisms governing the extension events and their potential functions are likely to be conserved. Although investigation of 3' untemplated extension is still at an early stage, 3' extension appears to be biologically regulated[11] and to affect biological function. Emerging evidence suggests that 3' untemplated extensions provide a post-transcriptional mechanism to regulate miRNA stability and efficiency of target repression[5759].

miR-2954 as a sex specific gene regulator in avian species

Despite extensive searching, we did not find sequence homologs of miR-2954 outside of the avian taxon. An extensive search among transcripts of crocodile and 11 bird species conducted by Gunaratne et al. found miR-2954 in several bird species but not in crocodile[35]. Given that the genomes and transcriptomes searched in these studies are not complete, we cannot rule out the possibility that miR-2954 has as yet unidentified homologs in other species. However, its expression in chickens and zebra finches is now supported by three independent studies including ours[32, 35, 50]. The male-biased expression of miR-2954 in chicken embryos[50] and in various zebra finch tissues suggests that it plays a role in sexually dimorphic animal development and function in avian species.

Animals adapt different dosage compensation mechanisms to balance the expression of sex chromosome genes between the two sexes and to balance the expression between sex chromosomal genes and autosomal genes[26, 60]. In avian species, females are heterogametic with one Z and one W chromosome, and males are homogametic with two Z chromosomes. Both the chicken and zebra finch lack a chromosome-wide dosage compensation mechanism, and many Z chromosome genes exhibit higher expression in males than in females[6167]. While the male to female (M:F) expression ratios of most Z-linked miRNAs are close to 2, reflecting the copy number relationship, miR-2954 exhibits M:F expression ratios ranging from 6 to 20 (based on read counts) or 6 to 40 (based on qRT-PCR) in various tissues, which cannot be explained by the 2:1 difference in gene copy numbers. Clearly, additional male-biased factors contribute to the regulation of miR-2954 expression in various tissues.

In both chickens and zebra finches, dosage compensation can occur locally in a gene-specific manner[6567]. In chickens, this is exemplified by dosage compensation mediated by the MHM (male hypermethylated) locus on nearby genes on the Z chromosome[26, 68, 69]. However, the zebra finch genome does not appear to contain the MHM locus[66], suggesting the existence of other sex specific dosage compensation mechanisms. The male-biased expression of miR-2954 and its preferential targeting of Z-chromosome genes may provide a novel dosage compensation mechanism at the post-transcriptional level. A miRNA-mediated gene-specific mechanism for sex chromosome gene regulation would offer flexibility in response to specific developmental and functional needs.

Both song behavior and the underlying neural circuit are highly sexually dimorphic in zebra finches. The song system nuclei HVC[70] and the robust nucleus of the arcopallium (RA), which control motor patterns of song, are considerably larger in males than in females, and Area-X, a forebrain nucleus required for song learning, is a large nucleus in adult males but is almost invisible in adult females[27, 71]. The gene regulation network underlying this structural and functional sexual dimorphism is not clear. Among the putative targets of miR-2954, SCAMP1, a Z chromosome encoded synaptic vesicle associated protein gene, is known to be expressed at higher levels in HVC and RA in male zebra finches than in females[72]. Recently, Gunaratne et al. reported that miR-2954 is regulated by hearing songs in the auditory forebrain of zebra finches[35], suggesting that in the context of song behavior this miRNA plays important roles in relaying physiological changes to changes in gene expression. Further analysis of miR-2954 expression in the song control circuits, and validation of its target genes will provide a better understanding of its roles in the sexually dimorphic structural and functional development of the zebra finch brain.


Our results provide a comprehensive miRNA expression atlas of brain, heart, liver, and muscle tissues of both male and female zebra finches. These data significantly enlarge the zebra finch miRNA repertoire, and will serve as a valuable resource for comparative and functional studies for the scientific community. In addition, we report a GGU motif as a potential site for miRNA internal substitution. We also describe male-biased expression of tgu-miR-2954, as well as its Z chromosome biased target relationship, which may point to a novel avian specific dosage compensation mechanism.


Library preparation and sequencing

Four tissues (heart, liver, muscle, and whole brain) from adult male and female zebra finches were collected, and total RNA was isolated using the Trizol method. The RNAs were used for library construction. Briefly, in a total reaction volume of 20 μl, 2 μg total RNA was ligated to 100 pmol adenylated 3′ adapter containing a unique pentamer barcode (App-(Barcode)TCGTATGCCGTCTTCTGCTTGT), 1 μg Rnl2(1–249)K227Q (plasmid for expression of recombinant ligase is available at in 50 mM Tris–HCl, pH 7.6; 10 mM MgCl2; 10 mM 2-mercaptoethanol; 0.1 mg/ml acetylated BSA (Sigma, St. Louis, MO), and 15% DMSO for 16 hours on ice. Following 3′ adapter ligation, 20 bar-coded samples were pooled and products were purified on a 15% denaturing polyacrylamide gel. Small RNAs, measuring 45–50 nt in length, were excised from the gel, eluted, and ligated to 100 pmol 5′ oligoribonucleotide adapter (GUUCAGAGUUCUACAGUCCGACGAUC) in a 20 μl reaction volume using 1 μg Rnl1 RNA Ligase in 50 mM Tris–HCl, pH 7.6; 10 mM MgCl2; 10 mM 2-mercaptoethanol; 0.2 mg/ml acetylated BSA; 0.2 mM ATP, and 15% DMSO for 1 h at 37°C. Ligated small RNAs were purified on a 12% polyacrylamide gel, reverse transcribed using SuperScript III Reverse Transcriptase (Invitrogen, Carlsbad, CA), and amplified by PCR using appropriate primers (forward primer: AATGATACGGCGACCACCGACAGGTTCAGAGTTCTACAGTCCGA; RT and reverse primer: CAAGCAGAAGACGGCATACGA). A total of eight libraries were barcoded and sequenced in one lane using Illumina GAII.

Sequence analysis and miRNA identification

After adapter trimming and removal of orphan reads, reads of 18–32 nt in length were kept for further analysis. For miRNA annotations, we compared sequences to known miRNAs (miRBase 17.0, 04/2011), and identical sequences were identified as homology miRNAs. Sequences were mapped to the zebra finch genome (Ensembl, taeGut3.2.4) permitting no mismatches. Sequences with more than 100 genomic loci were excluded from further analysis. Sequences homologous (with at most one mismatch) to known tRNA/rRNA/ncRNA sequences collected from the NCBI GenBank database were classified as tRNAs/rRNAs/ncRNAs. Small RNAs derived from repeat region/transposable elements were identified by screening the zebra finch genome using the RepeatMasker software. The remaining sequences were used for new miRNA candidate prediction. Flanking genomic sequences of various lengths (60, 80, 100, and 120 nt) of each small RNA mapping locus were extracted and subjected to analysis by the mFold program ([36] to predict secondary structures. miRNA candidates were identified using the following criteria: (1) presence of hairpin-shaped precursor structures, (2) presence of >10 sequence reads, (3) presence of star sequences originated from the opposite stem of the hairpin structure, and (4) precise 5’ ends among all sequence variants. In addition, sequences homologous to known miRNAs in miRBase, without the star sequence were also accepted. Because the incompleteness of the current genome precluded unambiguous analysis, 16 miRNAs which did not meet criterion 1 but were homologous to known miRNAs were also accepted.

Sequence conservation analysis

We searched miRBase for known miRNA homologs in 9 species: chicken (galGal3), human (hg18), mouse (mm9), platypus (ornAna1), lizard (anoCar2), X. tropicalis (xenTro3), zebrafish (danRer7), drosophila (dm3), and C. elegans (ce10). We also compared zebra finch miRNA sequences to the genomes and ESTs (downloaded from the UCSC genome resources) of the same 9 species to search for homologs with at most 1 mismatch to the query sequence and presence of a hairpin shaped precursor structure. Qualified matches were identified as candidate miRNA homologs in the tested species. According to results based on these criteria, zebra finch miRNAs were classified into groups of zebra finch specific, avian specific, conserved in avian, human, and mouse, conserved in vertebrates, and conserved in all tested species.

Tissue specificity

We normalized the read counts of individual miRNA species to the total miRNA read number in each tissue to obtain an RPM measurement, ″R eads P er M illion reads″. RPM values for each miRNA in all tested tissues were compared. We defined a miRNA as ″highly enriched in one tissue″ if it had a minimum of 100 reads in all tissues, and its expression in one tissue accounted for ≥ 90% of all reads in all tissues combined.

miRNA sequence variant identification

All sequences passing the qualification filter were used for miRNA variant analysis. Length variants were selected among sequences matched perfectly to the zebra finch genome by comparing their lengths with the canonical miRNA sequences. For variants containing terminal modifications and internal substitutions, the NCBI Blastn program was used to compare all qualified sequence reads with identified miRNAs. Sequences matching multiple miRNAs were excluded to avoid ambiguity. The criteria used for accepting an internal nucleotide change variant included i) a requirement that the total number of sequence reads of a miRNA including its variants was > 100, ii) the number of reads with internal changes represented more than 5% of total reads, and iii) the mismatch occurred at positions at least 2 nucleotides from the 5’ and 3’ termini. A plot of the motif analysis (Figure4C) was generated with the WebLogo program ([73]).

Target gene prediction

For target gene prediction, the 3’ UTR sequences of all zebra finch and chicken genes were extracted from the Ensemble database (taeGut3.2.4 and WASHUC2) using the BioMart tool ( Genes with 3’ UTR of less than 10 nt or without 3’ UTR were excluded. Since TargetScan (version 6.0,[51]) does not support zebra finch sequences, we made minor modifications to adapt it for target analysis in the zebra finch. Genes containing target sequences within the 3’ UTRs that match perfectly to the 2–8 nt of a miRNA sequence were accepted as putative targets. Enrichment of miRNA targets on the Z chromosome was evaluated by Fisher′s exact test.

Northern blot and qRT-PCR

Total RNAs were extracted from relevant tissues of adult male and female zebra finches with Trizol reagent (Invitrogen). Total RNAs of chicken, human, and mouse were purchased from Ambion. For Northern blot analysis, 20 μg RNA from each tissue was separated on 20% denaturing gel, transferred to a nitrocellulose membrane, and hybridized to relevant miRNA probes labeled with 32P-dCTP at 65°C overnight. Hybridization signals were detected using a phosphorimager. For qRT-PCR, reverse transcription was performed using a custom-designed miR-2954 specific primer and the TaqMan microRNA reverse transcription kit (Applied Biosystems). qRT-PCR was performed in a 20 μL reaction volume containing 1.33 μL cDNA, 1 μL 20× custom designed miR-2954 Taqman probe, and the Taqman universal PCR master mix (Applied Biosystems) using conditions of 10 min at 95°C, followed by 40 cycles of 15 sec at 90°C, 1 min at 60°C. Relative miR-2954 expression was calculated by the ∆ ∆ Ct method as described[74]. GAPDH was used as an internal control for normalizing miR-2954 expression, as no sex-based differential expression of GAPDH has been detected in zebra finches[65]. All qRT-PCR reactions were run in triplicate.

Additional information

The three novel zebra finch miRNAs have been assigned miRBase IDs as tgu-miR-7643 (novel 1), tgu-miR-7644 (novel 2), and tgu-miR-7645 (novel 3).


  1. Bartel DP: MicroRNAs: target recognition and regulatory functions. Cell. 2009, 136 (2): 215-233. 10.1016/j.cell.2009.01.002.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  2. Lee Y, Ahn C, Han J, Choi H, Kim J, Yim J, Lee J, Provost P, Radmark O, Kim S, et al: The nuclear RNase III drosha initiates microRNA processing. Nature. 2003, 425 (6956): 415-419. 10.1038/nature01957.

    Article  CAS  PubMed  Google Scholar 

  3. Gregory RI, Yan KP, Amuthan G, Chendrimada T, Doratotaj B, Cooch N, Shiekhattar R: The microprocessor complex mediates the genesis of microRNAs. Nature. 2004, 432 (7014): 235-240. 10.1038/nature03120.

    Article  CAS  PubMed  Google Scholar 

  4. Han J, Lee Y, Yeom KH, Kim YK, Jin H, Kim VN: The drosha-DGCR8 complex in primary microRNA processing. Genes Dev. 2004, 18 (24): 3016-3027. 10.1101/gad.1262504.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  5. Winter J, Jung S, Keller S, Gregory RI, Diederichs S: Many roads to maturity: microRNA biogenesis pathways and their regulation. Nat Cell Biol. 2009, 11 (3): 228-234. 10.1038/ncb0309-228.

    Article  CAS  PubMed  Google Scholar 

  6. Lund E, Guttinger S, Calado A, Dahlberg JE, Kutay U: Nuclear export of microRNA precursors. Science. 2004, 303 (5654): 95-98. 10.1126/science.1090599.

    Article  CAS  PubMed  Google Scholar 

  7. Yi R, Qin Y, Macara IG, Cullen BR: Exportin-5 mediates the nuclear export of pre-microRNAs and short hairpin RNAs. Genes Dev. 2003, 17 (24): 3011-3016. 10.1101/gad.1158803.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  8. Krol J, Loedige I, Filipowicz W: The widespread regulation of microRNA biogenesis, function and decay. Nat Rev Genet. 2010, 11 (9): 597-610.

    CAS  PubMed  Google Scholar 

  9. Yang JS, Phillips MD, Betel D, Mu P, Ventura A, Siepel AC, Chen KC, Lai EC: Widespread regulatory activity of vertebrate microRNA* species. RNA. 2011, 17 (2): 312-326. 10.1261/rna.2537911.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  10. Landgraf P, Rusu M, Sheridan R, Sewer A, Iovino N, Aravin A, Pfeffer S, Rice A, Kamphorst AO, Landthaler M, et al: A mammalian microRNA expression atlas based on small RNA library sequencing. Cell. 2007, 129 (7): 1401-1414. 10.1016/j.cell.2007.04.040.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  11. Wyman SK, Knouf EC, Parkin RK, Fritz BR, Lin DW, Dennis LM, Krouse MA, Webster PJ, Tewari M: Post-transcriptional generation of miRNA variants by multiple nucleotidyl transferases contributes to miRNA transcriptome complexity. Genome Res. 2011, 21 (9): 1450-1461. 10.1101/gr.118059.110.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  12. Chiang HR, Schoenfeld LW, Ruby JG, Auyeung VC, Spies N, Baek D, Johnston WK, Russ C, Luo S, Babiarz JE, et al: Mammalian microRNAs: experimental evaluation of novel and previously annotated genes. Genes Dev. 2010, 24 (10): 992-1009. 10.1101/gad.1884710.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  13. Burroughs AM, Ando Y, de Hoon MJ, Tomaru Y, Nishibu T, Ukekawa R, Funakoshi T, Kurokawa T, Suzuki H, Hayashizaki Y, et al: A comprehensive survey of 3′ animal miRNA modification events and a possible role for 3′ adenylation in modulating miRNA targeting effectiveness. Genome Res. 2010, 20 (10): 1398-1410. 10.1101/gr.106054.110.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  14. Brodersen P, Voinnet O: Revisiting the principles of microRNA target recognition and mode of action. Nat Rev Mol Cell Biol. 2009, 10 (2): 141-148.

    Article  CAS  PubMed  Google Scholar 

  15. Gu S, Jin L, Zhang F, Sarnow P, Kay MA: Biological basis for restriction of microRNA targets to the 3′ untranslated region in mammalian mRNAs. Nat Struct Mol Biol. 2009, 16 (2): 144-150. 10.1038/nsmb.1552.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  16. Hafner M, Landthaler M, Burger L, Khorshid M, Hausser J, Berninger P, Rothballer A, Ascano M, Jungkamp AC, Munschauer M, et al: Transcriptome-wide identification of RNA-binding protein and microRNA target sites by PAR-CLIP. Cell. 2010, 141 (1): 129-141. 10.1016/j.cell.2010.03.009.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  17. Dannemann M, Nickel B, Lizano E, Burbano HA, Kelso J: Annotation of primate miRNAs by high throughput sequencing of small RNA libraries. BMC Genomics. 2012, 13: 116-10.1186/1471-2164-13-116.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  18. Murchison EP, Kheradpour P, Sachidanandam R, Smith C, Hodges E, Xuan Z, Kellis M, Grutzner F, Stark A, Hannon GJ: Conservation of small RNA pathways in platypus. Genome Res. 2008, 18 (6): 995-1004. 10.1101/gr.073056.107.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  19. Rajasethupathy P, Fiumara F, Sheridan R, Betel D, Puthanveettil SV, Russo JJ, Sander C, Tuschl T, Kandel E: Characterization of small RNAs in aplysia reveals a role for miR-124 in constraining synaptic plasticity through CREB. Neuron. 2009, 63 (6): 803-817. 10.1016/j.neuron.2009.05.029.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  20. Wan LC, Zhang H, Lu S, Zhang L, Qiu Z, Zhao Y, Zeng QY, Lin J: Transcriptome-wide identification and characterization of miRNAs from pinus densata. BMC Genomics. 2012, 13: 132-10.1186/1471-2164-13-132.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  21. Kozomara A, Griffiths-Jones S: MiRBase: integrating microRNA annotation and deep-sequencing data. Nucleic Acids Res. 2011, 39 (Database issue): D152-157.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  22. Friedman RC, Farh KK, Burge CB, Bartel DP: Most mammalian mRNAs are conserved targets of microRNAs. Genome Res. 2009, 19 (1): 92-105.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  23. Bartel DP: MicroRNAs: genomics, biogenesis, mechanism, and function. Cell. 2004, 116 (2): 281-297. 10.1016/S0092-8674(04)00045-5.

    Article  CAS  PubMed  Google Scholar 

  24. Doupe AJ, Kuhl PK: Birdsong and human speech: common themes and mechanisms. Annu Rev Neurosci. 1999, 22: 567-631. 10.1146/annurev.neuro.22.1.567.

    Article  CAS  PubMed  Google Scholar 

  25. Nottebohm F: The anatomy and timing of vocal learning in birds. The design of animal communication. Edited by: Hauser MD, Konishi M. 1999, Mass: MIT Press, Cambridge, 63-110.

    Google Scholar 

  26. Arnold AP, Itoh Y, Melamed E: A bird′s-eye view of sex chromosome dosage compensation. Annu Rev Genomics Hum Genet. 2008, 9: 109-127. 10.1146/annurev.genom.9.081307.164220.

    Article  CAS  PubMed  Google Scholar 

  27. Nottebohm F, Arnold AP: Sexual dimorphism in vocal control areas of the songbird brain. Science. 1976, 194 (4261): 211-213. 10.1126/science.959852.

    Article  CAS  PubMed  Google Scholar 

  28. Mooney R: Neural mechanisms for learned birdsong. Learn Mem. 2009, 16 (11): 655-669. 10.1101/lm.1065209.

    Article  PubMed  Google Scholar 

  29. Wada K, Howard JT, McConnell P, Whitney O, Lints T, Rivas MV, Horita H, Patterson MA, White SA, Scharff C, et al: A molecular neuroethological approach for identifying and characterizing a cascade of behaviorally regulated genes. Proc Natl Acad Sci USA. 2006, 103 (41): 15212-15217. 10.1073/pnas.0607098103.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  30. Li X, Wang XJ, Tannenhauser J, Podell S, Mukherjee P, Hertel M, Biane J, Masuda S, Nottebohm F, Gaasterland T: Genomic resources for songbird research and their use in characterizing gene expression during brain development. Proc Natl Acad Sci USA. 2007, 104 (16): 6834-6839. 10.1073/pnas.0701619104.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  31. Replogle K, Arnold AP, Ball GF, Band M, Bensch S, Brenowitz EA, Dong S, Drnevich J, Ferris M, George JM, et al: The songbird neurogenomics (SoNG) initiative: community-based tools and strategies for study of brain gene function and evolution. BMC Genomics. 2008, 9: 131-10.1186/1471-2164-9-131.

    Article  PubMed Central  PubMed  Google Scholar 

  32. Warren WC, Clayton DF, Ellegren H, Arnold AP, Hillier LW, Kunstner A, Searle S, White S, Vilella AJ, Fairley S, et al: The genome of a songbird. Nature. 2010, 464 (7289): 757-762. 10.1038/nature08819.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  33. Lovell PV, Clayton DF, Replogle KL, Mello CV: Birdsong ″transcriptomics″: neurochemical specializations of the oscine song system. PLoS One. 2008, 3 (10): e3440-10.1371/journal.pone.0003440.

    Article  PubMed Central  PubMed  Google Scholar 

  34. Hilliard AT, Miller JE, Fraley ER, Horvath S, White SA: Molecular microcircuitry underlies functional specification in a basal ganglia circuit dedicated to vocal learning. Neuron. 2012, 73 (3): 537-552. 10.1016/j.neuron.2012.01.005.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  35. Gunaratne PH, Lin YC, Benham AL, Drnevich J, Coarfa C, Tennakoon JB, Creighton CJ, Kim JH, Milosavljevic A, Watson M, et al: Song exposure regulates known and novel microRNAs in the zebra finch auditory forebrain. BMC Genomics. 2011, 12 (1): 277-10.1186/1471-2164-12-277.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  36. Zuker M: Mfold web server for nucleic acid folding and hybridization prediction. Nucleic Acids Res. 2003, 31 (13): 3406-3415. 10.1093/nar/gkg595.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  37. Griffiths-Jones S: The microRNA registry. Nucleic Acids Res. 2004, 32 (Database issue): D109-111.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  38. Glazov EA, Cottee PA, Barris WC, Moore RJ, Dalrymple BP, Tizard ML: A microRNA catalog of the developing chicken embryo identified by a deep sequencing approach. Genome Res. 2008, 18 (6): 957-964. 10.1101/gr.074740.107.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  39. Okamura K, Hagen JW, Duan H, Tyler DM, Lai EC: The mirtron pathway generates microRNA-class regulatory RNAs in drosophila. Cell. 2007, 130 (1): 89-100. 10.1016/j.cell.2007.06.028.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  40. Ruby JG, Jan CH, Bartel DP: Intronic microRNA precursors that bypass drosha processing. Nature. 2007, 448 (7149): 83-86. 10.1038/nature05983.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  41. Berezikov E, Chung WJ, Willis J, Cuppen E, Lai EC: Mammalian mirtron genes. Mol Cell. 2007, 28 (2): 328-336. 10.1016/j.molcel.2007.09.028.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  42. Yang JS, Lai EC: Dicer-independent, Ago2-mediated microRNA biogenesis in vertebrates. Cell Cycle. 2010, 9 (22): 4455-4460. 10.4161/cc.9.22.13958.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  43. Cifuentes D, Xue H, Taylor DW, Patnode H, Mishima Y, Cheloufi S, Ma E, Mane S, Hannon GJ, Lawson ND, et al: A novel miRNA processing pathway independent of dicer requires Argonaute2 catalytic activity. Science. 2010, 328 (5986): 1694-1698. 10.1126/science.1190809.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  44. Ruby JG, Jan C, Player C, Axtell MJ, Lee W, Nusbaum C, Ge H, Bartel DP: Large-scale sequencing reveals 21U-RNAs and additional microRNAs and endogenous siRNAs in C. Elegans. Cell. 2006, 127 (6): 1193-1207. 10.1016/j.cell.2006.10.040.

    Article  CAS  PubMed  Google Scholar 

  45. Ruby JG, Stark A, Johnston WK, Kellis M, Bartel DP, Lai EC: Evolution, biogenesis, expression, and target predictions of a substantially expanded set of drosophila microRNAs. Genome Res. 2007, 17 (12): 1850-1864. 10.1101/gr.6597907.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  46. Zhou H, Arcila ML, Li Z, Lee EJ, Henzler C, Liu J, Rana TM, Kosik KS: Deep annotation of mouse iso-miR and iso-moR variation. Nucleic Acids Res. 2012, 40 (13): 5864-5875. 10.1093/nar/gks247.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  47. Yang W, Chendrimada TP, Wang Q, Higuchi M, Seeburg PH, Shiekhattar R, Nishikura K: Modulation of microRNA processing and expression through RNA editing by ADAR deaminases. Nat Struct Mol Biol. 2006, 13 (1): 13-21. 10.1038/nsmb1041.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  48. Kawahara Y, Zinshteyn B, Sethupathy P, Iizasa H, Hatzigeorgiou AG, Nishikura K: Redirection of silencing targets by adenosine-to-inosine editing of miRNAs. Science. 2007, 315 (5815): 1137-1140. 10.1126/science.1138050.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  49. Alon S, Mor E, Vigneault F, Church GM, Locatelli F, Galeano F, Gallo A, Shomron N, Eisenberg E: Systematic identification of edited microRNAs in the human brain. Genome Res. 2012, 22 (8): 1533-1540. 10.1101/gr.131573.111.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  50. Zhao D, McBride D, Nandi S, McQueen HA, McGrew MJ, Hocking PM, Lewis PD, Sang HM, Clinton M: Somatic sex identity is cell autonomous in the chicken. Nature. 2010, 464 (7286): 237-242. 10.1038/nature08852.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  51. Lewis BP, Burge CB, Bartel DP: Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. Cell. 2005, 120 (1): 15-20. 10.1016/j.cell.2004.12.035.

    Article  CAS  PubMed  Google Scholar 

  52. Morin RD, O′Connor MD, Griffith M, Kuchenbauer F, Delaney A, Prabhu AL, Zhao Y, McDonald H, Zeng T, Hirst M, et al: Application of massively parallel sequencing to microRNA profiling and discovery in human embryonic stem cells. Genome Res. 2008, 18 (4): 610-621. 10.1101/gr.7179508.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  53. Marti E, Pantano L, Banez-Coronel M, Llorens F, Minones-Moyano E, Porta S, Sumoy L, Ferrer I, Estivill X: A myriad of miRNA variants in control and Huntington′s disease brain regions detected by massively parallel sequencing. Nucleic Acids Res. 2010, 38 (20): 7219-7235. 10.1093/nar/gkq575.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  54. Berezikov E, Robine N, Samsonova A, Westholm JO, Naqvi A, Hung JH, Okamura K, Dai Q, Bortolamiol-Becet D, Martin R, et al: Deep annotation of drosophila melanogaster microRNAs yields insights into their processing, modification, and emergence. Genome Res. 2011, 21 (2): 203-215. 10.1101/gr.116657.110.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  55. Lee LW, Zhang S, Etheridge A, Ma L, Martin D, Galas D, Wang K: Complexity of the microRNA repertoire revealed by next-generation sequencing. RNA. 2010, 16 (11): 2170-2180. 10.1261/rna.2225110.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  56. Fernandez-Valverde SL, Taft RJ, Mattick JS: Dynamic isomiR regulation in drosophila development. RNA. 2010, 16 (10): 1881-1888. 10.1261/rna.2379610.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  57. Jones MR, Quinton LJ, Blahna MT, Neilson JR, Fu S, Ivanov AR, Wolf DA, Mizgerd JP: Zcchc11-Dependent uridylation of microRNA directs cytokine expression. Nat Cell Biol. 2009, 11 (9): 1157-1163. 10.1038/ncb1931.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  58. Katoh T, Sakaguchi Y, Miyauchi K, Suzuki T, Kashiwabara S, Baba T: Selective stabilization of mammalian microRNAs by 3′ adenylation mediated by the cytoplasmic poly(a) polymerase GLD-2. Genes Dev. 2009, 23 (4): 433-438. 10.1101/gad.1761509.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  59. Baccarini A, Chauhan H, Gardner TJ, Jayaprakash AD, Sachidanandam R, Brown BD: Kinetic analysis reveals the fate of a microRNA following target regulation in mammalian cells. Curr Biol. 21 (5): 369-376.

  60. Birchler JA: Dosage compensation for the birds. Heredity (Edinb). 2009, 102 (5): 423-424. 10.1038/hdy.2008.134.

    Article  CAS  Google Scholar 

  61. Baverstock PR, Adams M, Polkinghorne RW, Gelder M: A sex-linked enzyme in birds–Z-chromosome conservation but no dosage compensation. Nature. 1982, 296 (5859): 763-766. 10.1038/296763a0.

    Article  CAS  PubMed  Google Scholar 

  62. Kuroda Y, Arai N, Arita M, Teranishi M, Hori T, Harata M, Mizuno S: Absence of Z-chromosome inactivation for five genes in male chickens. Chromosome Res. 2001, 9 (6): 457-468. 10.1023/A:1011672227256.

    Article  CAS  PubMed  Google Scholar 

  63. McQueen HA, McBride D, Miele G, Bird AP, Clinton M: Dosage compensation in birds. Curr Biol. 2001, 11 (4): 253-257. 10.1016/S0960-9822(01)00070-7.

    Article  CAS  PubMed  Google Scholar 

  64. Ellegren H, Hultin-Rosenberg L, Brunstrom B, Dencker L, Kultima K, Scholz B: Faced with inequality: chicken do not have a general dosage compensation of sex-linked genes. BMC Biol. 2007, 5: 40-10.1186/1741-7007-5-40.

    Article  PubMed Central  PubMed  Google Scholar 

  65. Itoh Y, Melamed E, Yang X, Kampf K, Wang S, Yehya N, Van Nas A, Replogle K, Band MR, Clayton DF, et al: Dosage compensation is less effective in birds than in mammals. J Biol. 2007, 6 (1): 2-10.1186/jbiol53.

    Article  PubMed Central  PubMed  Google Scholar 

  66. Itoh Y, Replogle K, Kim YH, Wade J, Clayton DF, Arnold AP: Sex bias and dosage compensation in the zebra finch versus chicken genomes: general and specialized patterns among birds. Genome Res. 2010, 20 (4): 512-518. 10.1101/gr.102343.109.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  67. Mank JE, Ellegren H: All dosage compensation is local: gene-by-gene regulation of sex-biased expression on the chicken Z chromosome. Heredity (Edinb). 2009, 102 (3): 312-320. 10.1038/hdy.2008.116.

    Article  CAS  Google Scholar 

  68. Bisoni L, Batlle-Morera L, Bird AP, Suzuki M, McQueen HA: Female-specific hyperacetylation of histone H4 in the chicken Z chromosome. Chromosome Res. 2005, 13 (2): 205-214. 10.1007/s10577-005-1505-4.

    Article  CAS  PubMed  Google Scholar 

  69. Melamed E, Arnold AP: Regional differences in dosage compensation on the chicken Z chromosome. Genome Biol. 2007, 8 (9): R202-10.1186/gb-2007-8-9-r202.

    Article  PubMed Central  PubMed  Google Scholar 

  70. Reiner A, Laverghetta AV, Meade CA, Cuthbertson SL, Bottjer SW: An immunohistochemical and pathway tracing study of the striatopallidal organization of area X in the male zebra finch. J Comp Neurol. 2004, 469 (2): 239-261. 10.1002/cne.11012.

    Article  PubMed  Google Scholar 

  71. Arnold AP: Sexual differentiation of the zebra finch song system: positive evidence, negative evidence, null hypotheses, and a paradigm shift. J Neurobiol. 1997, 33 (5): 572-584. 10.1002/(SICI)1097-4695(19971105)33:5<572::AID-NEU6>3.0.CO;2-1.

    Article  CAS  PubMed  Google Scholar 

  72. Tang YP, Peabody C, Tomaszycki ML, Wade J: Sexually dimorphic SCAMP1 expression in the forebrain motor pathway for song production of juvenile zebra finches. Dev Neurobiol. 2007, 67 (4): 474-482. 10.1002/dneu.20354.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  73. Crooks GE, Hon G, Chandonia JM, Brenner SE: WebLogo: a sequence logo generator. Genome Res. 2004, 14 (6): 1188-1190. 10.1101/gr.849004.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  74. Livak KJ, Schmittgen TD: Analysis of relative gene expression data using real-time quantitative PCR and the 2(−delta delta C(T)) method. Methods. 2001, 25 (4): 402-408. 10.1006/meth.2001.1262.

    Article  CAS  PubMed  Google Scholar 

Download references


This study was supported by NIH grant MH081254 (XCL) and grants, 973 2011CBA01101 from the National Basic Research Program of China and KSCX2-EW-R-0103 from Chinese Academy of Sciences (XJW). MH was supported by the Charles Revson Jr. foundation. We thank Dr. C. Zhao and Genomic Core of the Rockefeller University for the sequencing work. We thank the Washington University Genome Center for sharing the zebra finch genome sequence prior to publication.

Author information

Authors and Affiliations


Corresponding authors

Correspondence to Xiu-Jie Wang or XiaoChing Li.

Additional information

Competing interests

All authors declare that they have no competing interests.

Authors' contributions

GZL carried out the bioinformatics analysis and participated in writing the paper. MH performed small RNA library construction and sequencing work and participated in writing the paper. MB and GHF helped analyze the data. ZS performed RNA isolation, qRT-PCR, and Northern blot assays. TT participated in designing the work. XCL and XJW designed the study, supervised the experiments and data analysis, and wrote the paper. All authors read and approved the final manuscript.

Guan-Zheng Luo, Markus Hafner contributed equally to this work.

Electronic supplementary material

Additional file 1: A summary of small RNA sequences in 8 libraries.(PDF 96 KB)


Additional file 2: Sequences, genomic loci, and expression information of all identified miRNAs. miRNAs without genomic loci were indicated separately. (XLSX 64 KB)

Additional file 3: Conservation status of all identified miRNAs and their expression reads.(XLSX 21 KB)

Additional file 4: Genomic clusters of zebra finch miRNAs and their conservation status in other species.(XLSX 16 KB)

Additional file 5: miRNA* sequences and their expression information.(XLSX 20 KB)


Additional file 6: Sequences and expression of 3 miR-7*s. Mature miRNAs and corresponding miRNA*s are highlighted by red and blue, respectively. (PDF 109 KB)

Additional file 7: miRNA precursors with both 5p and 3p arms producing high abundant mature miRNAs.(XLSX 11 KB)


Additional file 8: Sequence reads of tgu-miR-451 and precursor structures of the atypically generated miR-451 in zebra finch, mouse, and human.(PDF 112 KB)


Additional file 9: Expression patterns of miRNA clusters in the four tissues. (A) A heatmap was plotted according to the log2 transformed normalized reads in each tissue. (B) The relative expression of three miRNA clusters in 4 tissues. (PDF 53 KB)

Additional file 10: miRNA variants with untemplated 3′ terminal modifications.(XLSX 18 KB)


Additional file 11: Summary of internal nucleotide changes observed among miRNA variants. (A) The total sequence reads of each nucleotide change type detected in all tissues. (B) The relative ratio of each nucleotide change type in each tissue sample. (PDF 23 KB)

Additional file 12: Expression information of Z chromosome-encoded miRNAs.(XLSX 21 KB)

Additional file 13: Predicted target genes of tgu-miR-2954.(XLSX 20 KB)

Additional file 14: Expression information of zebra finch specific miRNAs.(XLSX 10 KB)


Additional file 15: Comparison with the miRNA set from the Gunaratne et al. study. Fifty-three miRNAs unique in this study are listed in the box. (PDF 7 KB)

Authors’ original submitted files for images

Rights and permissions

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

Reprints and permissions

About this article

Cite this article

Luo, GZ., Hafner, M., Shi, Z. et al. Genome-wide annotation and analysis of zebra finch microRNA repertoire reveal sex-biased expression. BMC Genomics 13, 727 (2012).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: