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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
Nature. Author manuscript; available in PMC 2011 October 5.
Published in final edited form as:
PMCID: PMC3187626

The genome of a songbird


The zebra finch is an important model organism in several fields1,2 with unique relevance to human neuroscience3,4. Like other songbirds, the zebra finch communicates through learned vocalizations, an ability otherwise documented only in humans and a few other animals and lacking in the chicken5—the only bird with a sequenced genome until now6. Here we present a structural, functional and comparative analysis of the genome sequence of the zebra finch (Taeniopygia guttata), which is a songbird belonging to the large avian order Passeriformes7. We find that the overall structures of the genomes are similar in zebra finch and chicken, but they differ in many intrachromosomal rearrangements, lineage-specific gene family expansions, the number of long-terminal-repeat-based retrotransposons, and mechanisms of sex chromosome dosage compensation. We show that song behaviour engages gene regulatory networks in the zebra finch brain, altering the expression of long non-coding RNAs, microRNAs, transcription factors and their targets. We also show evidence for rapid molecular evolution in the songbird lineage of genes that are regulated during song experience. These results indicate an active involvement of the genome in neural processes underlying vocal communication and identify potential genetic substrates for the evolution and regulation of this behaviour.

As in all songbirds, singing in the zebra finch is under the control of a discrete neural circuit that includes several dedicated centres in the forebrain termed the ‘song control nuclei’ (for an extensive series of reviews see ref. 8). Neurophysiological studies in these nuclei during singing have yielded some of the most illuminating examples of how vocalizations are encoded in the motor system of a vertebrate brain9,10. In the zebra finch, these nuclei develop more fully in the male than in the female (who does not sing), and they change markedly in size and organization during the juvenile period when the male learns to sing11. Analysis of the underlying cellular mechanisms of plasticity led to the unexpected discovery of neurogenesis in adult songbirds and life-long replacement of neurons12. Sex steroid hormones also contribute to songbird neural plasticity, in part by influencing the survival of new neurons13. Some of these effects are probably caused by oestrogen and/or testosterone synthesized within the brain itself rather than just in the gonads14.

Song perception and memory also involve auditory centres that are present in both sexes, and the mere experience of hearing a song activates gene expression in these auditory centres15. The gene response itself changes as a song becomes familiar over the course of a day16 or as the context of the experience changes17. The act of singing induces gene expression in the male song control nuclei, and these patterns of gene activation also vary with the context of the experience18. The function of this changing genomic activity is not yet understood, but it may support or suppress learning and help integrate information over periods of hours to days19.

The chicken genome is the only other bird genome analysed to date6. The chicken and zebra finch lineages diverged about 100 million years ago near the base of the avian radiation7. By comparing their genomes we can now discern features that are shared (and thus generally characteristic of birds), and features that are most conspicuously different between the two lineages—some of which will be related to the distinctive neural and behavioural traits of songbirds.

We sequenced and assembled a male zebra finch genome using methods described previously6,20. A male (the homogametic sex in birds) was chosen to maximize coverage of the Z chromosome. Of the 1.2 gigabase (Gb) draft assembly, 1.0 Gb has been assigned to 33 chromosomes and three linkage groups, by using zebra finch genetic linkage21 and bacterial artificial chromosome (BAC) fingerprint maps. The genome assembly is of sufficient quality for the analysis presented here (see Supplementary Note 1 and Supplementary Table 1). A total of 17,475 protein-coding genes were predicted from the zebra finch genome assembly using the Ensembl pipeline supplemented by Gpipe gene models (Supplementary Note 1). To extend further the characterization of genes relevant to brain and behaviour, we also sequenced complementary DNAs from the forebrain of zebra finches at 50 (juvenile, during the critical song learning period) and 850 (adult) days post-hatch, mapping these reads (Illumina GA2) to the protein-coding models (Supplementary Note 1). Of the 17,475 protein-coding gene models we find 9,872 (56%) and 10,106 (57%) genes expressed in the forebrain at these two ages (90.7% overlap), respectively. In addition to evidence for developmental regulation, these reads show further splice forms, new exons and untranslated sequences (Supplementary Figs 1 and 2).

To address issues of large-scale genome structure and evolution, we compared the chromosomes of zebra finch and chicken using both sequence alignment and fluorescent in situ hybridization. These analyses showed overall conservation of synteny and karyotype in the two species, although the rate of intrachromosomal rearrangement was high (Supplementary Note 2). We were also surprised to see genes of the major histocompatibility complex (MHC) dispersed across several chromosomes in the zebra finch, in contrast to the syntenic organization of both chicken and human MHCs (Supplementary Note 2).

We assessed specific gene losses and expansions in the zebra finch lineage by constructing phylogenies of genes present in the last common ancestor of birds and mammals (Supplementary Note 2 and Supplementary Fig. 3). Both the zebra finch and the chicken genome assemblies lack genes encoding vomeronasal receptors, casein milk proteins, salivary-associated proteins and enamel proteins—not surprisingly, as birds lack vomeronasal organs, mammary glands and teeth. Unexpectedly, both species lack the gene for the neuronal protein synapsin 1 (SYN1); comparative analyses suggest that the loss of SYN1 and flanking genes probably occurred in an ancestor to modern birds, possibly within the dinosaur lineage (Supplementary Note 2, Supplementary Table 2 and Supplementary Fig. 4). Both zebra finch and chicken have extensive repertoires of olfactory receptor-like sequences (Supplementary Note 2 and Supplementary Fig. 5), proteases (Supplementary Table 3), and a rich repertoire of neuropeptide and pro-hormone genes.

Compared to mammals, zebra finch has duplications of genes encoding several proteins with known neural functions, including growth hormone, (Supplementary Fig. 3), caspase-3 and β-secretase (Supplementary Table 3). Two large expansions of gene families expressed in the brain seem to have occurred in the zebra finch lineage after the split from mammals. One involves a family related to the PAK3 (p21-activated kinase) gene. Thirty-one uninterrupted PAK3-like sequences have been identified in the zebra finch genome, of which 29 are expressed in testis and/or brain (Supplementary Note 2). The second involves the PHF7 gene, which encodes a zinc-finger-containing transcriptional control protein. Humans only have a single PHF7 gene, but remarkably the gene has been duplicated independently, many times in both the zebra finch and chicken lineages to form species-specific clades of 17 and 18 genes, respectively (Supplementary Fig. 6). In the zebra finch these genes are expressed in the brain (Supplementary Note 2).

An intriguing puzzle in avian genomics has been the evident lack of a chromosome-wide dosage compensation mechanism to balance the expression of genes on the Z sex chromosome, which is present in two copies in males but only one in females22,23. The chicken has been suspected of exerting dosage compensation on a more local level, by the non-coding RNA MHM (male hypermethylated)24,25, to cause a characteristic variation of gene expression along the Z chromosome. The zebra finch genome assembly, however, lacks an MHM sequence, and genes adjacent to the comparable MHM chromosomal position show no special cluster of dosage compensation (Fig. 1 and Supplementary Note 2). Thus, the putative MHM-mediated mechanism of restricted Z-chromosome dosage compensation is not common to all birds. Chromosomal sex differences in the brain could have a direct role in the sex differences so evident in zebra finch neuroanatomy and singing behaviour.

Figure 1
Divergent patterns of dosage compensation in birds

In mammals, as much as half of their genomes represent interspersed repeats derived from mobile elements, whereas the interspersed repeat content of the chicken genome is only 8.5%. We find that the zebra finch genome also has a low overall interspersed repeat content (7.7%), containing a little over 200,000 mobile elements (Supplementary Tables 4 and 5). The zebra finch, however, has about three times as many retrovirus-derived long terminal repeat (LTR) element copies as the chicken, and a low copy number of short interspersed elements (SINEs), which the chicken lacks altogether. Expressed sequence tag (EST) analysis shows that mobile elements are present in about 4% of the transcripts expressed in the zebra finch brain, and some of these transcripts are regulated by song exposure (next section, Table 1). Figure 2 shows an example of an RNA that was identified in a microarray screening for genes specifically enriched in song control nuclei26 and now seems to represent a long non-coding RNA (ncRNA) containing a CR1-like mobile element. These results indicate that further experiments investigating a possible role of mobile-element-derived repeated sequences in vocal communication are warranted.

Figure 2
Enriched expression of a CR1-like element in the zebra finch song system
Table 1
Structural features of the song responsive genome

A large portion of the genome is directly engaged by vocal communication. A recent study27 defined distinct sets of RNAs in the auditory forebrain that respond in different ways to song playbacks during the process of song-specific habituation, a form of learning16. We now map each of these song-responsive RNAs to the genome assembly (Table 1 and Supplementary Note 3). Notably, we find evidence that ~40% of transcripts in the unstimulated auditory fore-brain are non-coding and derive from intronic or intergenic loci (Table 1). Among the RNAs that are rapidly suppressed in response to new vocal signals (‘novel down’), two-thirds are ncRNAs.

The robust involvement of ncRNAs in the response to song led us to ask whether song exposure alters the expression of microRNAs—small ncRNAs that regulate gene expression by binding to target messenger RNAs. Indeed we find that miR-124, a conserved microRNA implicated in neurological function in other species28, is rapidly suppressed in response to song playbacks (Fig. 3). We independently measured this effect by direct Illumina sequencing of small RNAs in the auditory forebrain, and also identified other known and new microRNAs, several of which also change in expression after song stimulation (Supplementary Note 2).

Figure 3
miR-124 in the auditory forebrain is suppressed by exposure to new song

A potential site of action for microRNAs was shown by genomic mapping of transcripts that increase rapidly after new song exposure (Table 1, ‘novel up’). Two of the cDNA clones that measured the most robust increases27 align to an unusually long (3 kilobases (kb)) 3′ untranslated region (UTR) in the human gene that encodes the NR4A3 transcription factor protein (Fig. 4a). The entire UTR is similar in humans and zebra finches, with several long segments of >80% identity (Fig. 4b). Within these segments we find conserved predicted binding sites for 11 different microRNAs, including five new microRNAs found by direct sequencing of small RNAs from the zebra finch forebrain (Fig. 4b). These findings indicate that this NR4A3 transcript element may function in both humans and songbirds to integrate many conserved microRNA regulatory pathways.

Figure 4
Conserved NR4A3 3′ UTR is a potential region for microRNA integration

The act of singing also alters gene expression in song control nuclei29, and we used the genome assembly to analyse the transcriptional control structure of this response. Using oligonucleotide microarrays, we identified 807 genes in which expression significantly changed as a result of singing. These were grouped by k-means clustering into 20 distinct expression profile clusters (Fig. 5a and Supplementary Note 3). Gene regulatory sequences (transcription-factor-binding sites) were predicted across the genome using a new motif-scanning approach (Supplementary Note 1), and we observed significant correlation between changes in expression of transcription factor genes and their predicted targets (Fig. 5b and Supplementary Table 6). Thus, the experience of singing and hearing song engages complex gene regulatory networks in the forebrain, altering the expression of microRNAs, transcription factor genes, and their targets, as well as of non-coding RNA elements that may integrate transcriptional and post-transcriptional control systems.

Figure 5
Transcriptional control network in area X engaged by singing

Learned vocal communication is crucial to the reproductive success of a songbird, and this behaviour evolved after divergence of the songbird lineage5. Thus, it seems likely that genes involved in the neurobiology of vocal communication have been influenced by positive selection in songbirds. With this in mind, we examined the intersection of two sets of genes: (1) those that respond to song exposure in the auditory forebrain as discussed in the previous section; and (2) those that contain residues that seem to have been positively selected in the zebra finch lineage, as determined using phylogenetic analysis by maximum likelihood (PAML) (Supplementary Note 4). There are 214 genes that are common to both lists. Of these, 49 are suppressed by song exposure (Supplementary Table 7), and 6 of these 49 are explicitly annotated for ion channel activity (Table 2). This yields a highly significant statistical enrichment for the term ‘ion channel activity’ (P = 0.0016, false discovery rate (FDR) adjusted Fisher's exact test) and other related terms in this subset of genes (Supplementary Tables 8 and 9). Independent evidence has also demonstrated differential anatomical expression of ion channel genes in song control nuclei26,30. Ion channel genes have important roles in many aspects of behaviour, neurological function and disease31. This class of genes is highly likely to be linked to song behaviour and should be a major target for future functional studies.

Table 2
Song-suppressed ion channel genes under positive selection

Passerines represent one of the most successful and complex radiations of terrestrial animals7. Here we present the first, to our knowledge, analysis of the genome of a passerine bird. The zebra finch was chosen because of its well-developed status as a model organism for a number of fields in biology, including neurobiology, ethology, ecology, biogeography and evolution. In the zebra finch as in the chicken, we see a smaller, tighter genome compared to mammals, with a marked reduction of interspersed repeats. The zebra finch presents a picture of greater genomic plasticity than might have been expected from the chicken and other precedents, with a high degree of intrachromosomal rearrangements between the two avian species, gene copy number variations and transcribed mobile elements. Yet we also see an overall similarity to mammals in protein-coding gene content and core transcriptional control systems.

Our analysis suggests several channels through which evolution may have acted to produce the unique neurobiological properties of songbirds compared to the chicken and other animals. These include the management of sex chromosome gene expression, accelerated evolution of neuronal ion transport genes, gene duplications to produce new variants of PHF7, PAK3 and other neurobiologically important genes, and a new arrangement of MHC genes. Most notably, our analyses suggest a large recruitment of the genome during vocal communication, including the extensive involvement of ncRNAs. It has been proposed that ncRNAs have a contributing role in enabling or driving the evolution of greater complexity in humans and other complex eukaryotes32. Seeing that learned vocal communication itself is a phenomenon that has emerged only in some of the most complex organisms, perhaps ncRNAs are a nexus of this phenomenon.

Much work will be needed to establish the actual functional significance of many of these observations and to determine when they arose in avian evolution. This work can now be expedited with the recent development of a method for transgenesis in the zebra finch33. An important general lesson, however, is that dynamic and serendipitous aspects of the genome may have unexpected roles in the elaborate vocal communicative capabilities of songbirds.

Methods Summary

Sequence assembly

Sequenced reads were assembled and attempts were made to assign the largest contiguous blocks of sequence to chromosomes using a genetic linkage map21, fingerprint map and synteny with the chicken genome assembly Gallus_gallus-2.1, a revised version of the original draft6 (Supplementary Note 1).


Gene orthology assignment was performed using the EnsemblCompara GeneTrees pipeline and the OPTIC pipeline (Supplementary Note 1). Orthology rate estimation was performed with PAML (pairwise model = 0, Nssites = 0). In all cases, codon frequencies were estimated from the nucleotide composition at each codon position (F3X4 model).

Gene expression and evolution

Methods for Illumina read counting, in situ hybridization, TaqMan RT–PCR, microarrays, regulatory motif and evolutionary rate analyses are given in Supplementary Notes 1–4.

Supplementary Material



Acknowledgements The sequencing of zebra finch was funded by the National Human Genome Research Institute (NHGRI). Further research support included grants to D.F.C. (NIH RO1 NS045264 and RO1 NS051820), H.E. (Swedish Research Council and Knut and Alice Wallenberg Foundation), E.D.J. (HHMI, NIH Directors Pioneer Award and R01 DC007218), M.A.B. (NIH RO1 GM59290) and J.S. (Biotechnology and Biological Sciences Research Council grant number BBE0175091). Resources for exploring the sequence and annotation data are available on browser displays available at UCSC (, Ensembl (, the NCBI ( and We thank K. Lindblad-Toh for permission to use the green anole lizard genome assembly, the Production Sequencing Group of The Genome Center at Washington University School of Medicine for generating all the sequence reads used for genome assembly, and the Clemson University Genome Institute for the construction of the BAC library. We would like to recognize all the important published work that we were unable to cite owing to space limitations.

Accessory Figure 1

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Predicted motif genome location distribution

(A) Density of predicted motifs (y axis) for different categories of regions (x axis) in terms of location with respect to their nearest genes, shown in blue. The horizontal pink line is the genome-wide average. See text for definitions of motif density and various region categories. (B) P-values of enrichment or depletion of motif occurrence in each category of regions, using one-tailed Fishers exact tests. Negative logarithms are shown.

Accessory Figure 2

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Motif target counts by defined location

Counts of targets windows of individual motifs in different categories of regions are compared to the respective expected values, with colors shown indicating whether the count is greater or less than expectation. Green cells correspond to counts that are higher than the average and red cells correspond to regions with below-average counts.

Accessory Figure 3

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Comparative analysis of marker order on chicken chromosomes 2-8 and Z (GGA2-8, GGAZ) and their zebra finch orthologues (TGU2-8, TGUZ). The central part of each figure was created by aligning whole chromosomal sequences using the program GenAlyzer. Line colour indicates the length of sequences with 100% sequence identity. The tentative chromosomal rearrangements suggested by this analysis were verified using fluorescent in situ hybridization (FISH). Letters indicate the position of chicken and zebra finch BACs with orthologous sequence content in the genome sequences of both species (see accessory file Physical mapping table 2009-09-16.xls for details on the FISH probes used). Red dots on the ideograms illustrate the physical chromosomal position as determined by FISH.

Accessory Figure 4

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Bayesian phylogenies of zebra finch MHC genes

(A) Class I genes and (B) Class IIB genes were compared to sequences from the chicken MHC-B complex. Putatively functional zebra finch genes with open reading frames are given numerical suffixes and putative pseudogenes are given lettered suffixes. For Class I we also include a chicken sequence from the MHC-Y region (YFV). For zebra finch class I, we show the placement of eight brain ESTs (indicated by their GenBank accession numbers) supporting the expression of MHC Class I genes in the brain. Posterior probabilities are given for well-supported nodes in the tree.

Accessory Figure 5

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General view showing WGAC (>5kb) and WSSD on all chromosomes. Grey above lines is WSSD and red below lines is WGAC. ChrUn was treated as a “distinct” chromosome.

Accessory Figure 6

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(A) Characteristics of co-expressed gene sets from Dong et al. 58 (See S3 supplementary notes). “Gene set”: name of the gene set, as in the original paper. “All genes” refers to the genes on the array in Dong et al. Numbers in parentheses indicate component subsets of a set. “Size”: number of genes in set. “terr_len”: average gene territory length of a gene set. “gene_len”: average coding sequence length. “intergenic_length”: average of (territory length – gene length). “p-value”: statistical significance of enrichment for short (pink cells) or long (green cells) territories, as measured by 2-tailed Wilcoxon Rank Sum tests. (B) Average gene length and intergenic length of a gene set (y axis) versus average gene territory length. Each point corresponds to a gene set.

Accessory Table 1

Zebra finch chromosome nomenclature and length

Chicken (GGA) and finch (TGU) chromosome names suggested by Itoh et al., 2005 (Supplementary Notes 1).


TGUGGAItoh et al., 2005TGU lengthTGU %GCTGU lengthTGU %GC

Abbreviations are as follows TGU – zebra finch, GGU – chicken, ordered presents linkage or other ordering information that allowed unambiguous contig or supercontig chromosomal placement, random – represents partial ordering information that only allows chromosome placement but not order on the chromosome. GC% is the percentage of G and C bases per total bases counted.

Accessory Table 2

Summary of gene orthologs defined from sequence homology, gene trees and conservation of synteny.

Species ComparisonNo Homologs1:1 Orthologs1:M OrthologsM:M OrthologsTotal OrthologsTotal Genes
Zebra finch/Chicken3,86310,1613,56313513,85917,722
Zebra finch/Human4,69510,8612,02814013,02917,724

Accessory Table 3

All (motif, region category) pairs with z-scores greater than 3 or less than -3 are shown.


Accessory Table 4

Comparison of motif – region preferences

Comparisons were made to human (see S1 Supplementary notes; Blanchette et al. 2006). Shown are the z-scores for motifs that were common to the two studies. We note that Blanchette et al. studied only one “FOX” motif, one “GATA” motif and one “RORA” motif, whereas our compendium includes multiple versions (marked in gray, column 1). Columns 2 – 6 are z-scores from this study, columns 7 – 9 indicate overrepresentation (“u”, pink) or depletion (“d”, green) of a motif in a particular region category as reported by Blanchette et al. Cells with bold outlines in columns 7 – 9 are cases where our findings agree with those of Blanchette et al (our z-score is > 2 or < -2). Cells with bold outlines in columns 2 – 6 are cases where our findings disagree with those of Blanchette et al. In all other cases, either of the two studies reports a non-significant enrichment or depletion.

Songbird (this study)Human (Blanchette et al.)

Accessory Table 5

Orthologs predicted with the OPTIC pipeline

Orphaned genes have no ortholog predicted in any of the other species. These often represent rapidly evolving genes or those in large families whole phylogeny may not have been inferred accurately.

SpeciesGenesTranscriptsGenes with orthologsOrphaned genes
H. sapiens20,90746,25918,48588%2,422
M. musculus22,84840,05219,06783%3,781
C. familiaris19,29225,54617,43690%1,856
M. domestica19,45832,54417,02587%2,433
O. anatinus17,93626,82114,87983%3,057
G. gallus16,72322,18114,46586%2,258
T. guttata17,47518,19115,82091%1,655
T. nigroviridis19,58123,09715,37178%4,210

Accessory Table 6

Strict 1:1 orthologs across eight species

Each set contains exactly one gene of each species.

Species(1:1)n ortholog setsSpecies(1:1)n ortholog sets
Human20,907100% Zebrafinch17,475100%
+Mouse15,27273% +Chicken10,86862%
+Dog14,22968% +Platypus8,02446%
+Opossum12,41159% +Opossum7,23841%
+Platypus9,50845% +Dog6,85039%
+Chicken7,88438% +Mouse6,61538%
+Zebrafinch5,26225% +Human6,47437%
+Tetraodon4,34421% +Tetraodon4,34425%

Accessory Table 7

Location of novel transcript models with respect to ENSEMBL gene models

Associated transcript models are within 1kb of an ENSEMBL exon, while intergenic sequences are more than 1kb distant from the closest ENSEMBL exon. Note that the numbers do not sum to 100% as some transcript models contain both intronic and intergenic


Accessory Table 8

Annotation of transcribed loci (TL). Based on the overlap with ENSEMBL exons TL are annotated first into “known”, “novel”, and “ambiguous” sets. This first level annotation is followed by a second level annotation. Known TL are annotated as protein coding (“pc”), pseudogene (“pseudo”), non-protein coding (“npc”) or UTR (“utr”) depending on the kind or part of an ENSEMBL gene with which they overlap. Novel TL are classified by their location as gene-associated (“assoc”), “intronic” or “intergenic” TL. The category “assoc” contains TL that extend at least partially within 1kb of either terminal exon of an ENSEMBL gene.


Accessory Table 9

Novel TL are statistically significantly depleted in intronic sequences. The only exception is the set FastDown.



Supplementary Information is linked to the online version of the paper at

Author Contributions W.C.W., D.F.C., H.E. and A.P.A. comprise the organizing committee of the zebra finch genome sequencing project. Project planning, management and data analysis: W.C.W., D.F.C., H.E. and A.P.A. Assembly annotation and analysis: L.W.H., P.M., S.-P.Y., L.Y., J.N., A.C., S.H., J.Sl., J.St., D.B. and S.-P.Y. Protein coding and non-coding gene prediction: S.S., C.B., P.F., S.W., A.H., C.P.P. and L.K. SNP analysis: P.F. and W.M.M. Orthology prediction and analysis: A.J.V., A.H., C.P.P., S.F. and L.K. Repeat element analysis: M.A.B., A.F.A.S., R.H., M.K.K., J.A.W., W.G. and D.D.P. Segmental duplication and gene duplication analysis: L.C., Z.C., E.E.E., L.K., C.P.P., M.F., C.N.B., R.E., J.G. and S.E.L. Protease annotation and analysis: X.S.P., V.Q., G.V. and C.L.-O. Neuropeptide hormone annotation: J.Sw. and B.S. Small non-coding RNA analysis: Y-C.L., Y.L., P.G., M.W. and X.L. Comparative mapping: D.K.G., M.V. and B.M.S. Singing induced gene network analysis: E.D.J., A.R.P., O.W. and J.H. Z-chromosome analysis: Y.I. and A.P.A. Gene expression and in situ analysis and synapsin synteny/loss analysis: C.V.M., P.L. and T.A.F.V. Adaptive evolution analysis: A.K., K.N., N.B., L.S., B.N. and C.N.B. Gene expression in the brain analysis: C.S., I.A., A.S., H.L., H.R. and M.S. MHC analysis: S.E., C.N.B. and R.E. Olfactory receptor analysis: T.O., D.L. and L.K. Sequencing management: R.K.W., E.R.M. and L.F. Physical map construction: T.G. Zebra finch tissue resources: T.Bu. and T.Bi. Zebra finch cDNA resources: D.F.C., E.D.J. and X.L.

Author Information The Taeniopygia guttata whole-genome shotgun project has been deposited in DDBJ/EMBL/GenBank under the project accession ABQF00000000.

Reprints and permissions information is available at

This paper is distributed under the terms of the Creative Commons Attribution-Non-Commercial-Share Alike licence, and is freely available to all readers at

The authors declare no competing financial interests.


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