High-coverage sequencing and annotated assemblies of the budgerigar genome
- Ganeshkumar Ganapathy†1,
- Jason T Howard†1,
- James M Ward2,
- Jianwen Li3,
- Bo Li3,
- Yingrui Li3,
- Yingqi Xiong3,
- Yong Zhang3,
- Shiguo Zhou4,
- David C Schwartz4,
- Michael Schatz5,
- Robert Aboukhalil5,
- Olivier Fedrigo6,
- Lisa Bukovnik6, 13,
- Ty Wang2,
- Greg Wray7,
- Isabelle Rasolonjatovo8,
- Roger Winer9,
- James R Knight9,
- Sergey Koren10, 12,
- Wesley C Warren11,
- Guojie Zhang3Email author,
- Adam M Phillippy10, 12Email author and
- Erich D Jarvis1Email author
© Ganapathy et al.; licensee BioMed Central Ltd. 2014
Received: 7 October 2013
Accepted: 3 June 2014
Published: 8 July 2014
Parrots belong to a group of behaviorally advanced vertebrates and have an advanced ability of vocal learning relative to other vocal-learning birds. They can imitate human speech, synchronize their body movements to a rhythmic beat, and understand complex concepts of referential meaning to sounds. However, little is known about the genetics of these traits. Elucidating the genetic bases would require whole genome sequencing and a robust assembly of a parrot genome.
We present a genomic resource for the budgerigar, an Australian Parakeet (Melopsittacus undulatus) -- the most widely studied parrot species in neuroscience and behavior. We present genomic sequence data that includes over 300× raw read coverage from multiple sequencing technologies and chromosome optical maps from a single male animal. The reads and optical maps were used to create three hybrid assemblies representing some of the largest genomic scaffolds to date for a bird; two of which were annotated based on similarities to reference sets of non-redundant human, zebra finch and chicken proteins, and budgerigar transcriptome sequence assemblies. The sequence reads for this project were in part generated and used for both the Assemblathon 2 competition and the first de novo assembly of a giga-scale vertebrate genome utilizing PacBio single-molecule sequencing.
Across several quality metrics, these budgerigar assemblies are comparable to or better than the chicken and zebra finch genome assemblies built from traditional Sanger sequencing reads, and are sufficient to analyze regions that are difficult to sequence and assemble, including those not yet assembled in prior bird genomes, and promoter regions of genes differentially regulated in vocal learning brain regions. This work provides valuable data and material for genome technology development and for investigating the genomics of complex behavioral traits.
KeywordsMelopsittacus undulatus Budgerigar Parakeet Next-generation sequencing Hybrid assemblies Optical maps Vocal learning
Raw genome DNA sequence reads
Summary of genomic reads
Total BP (Mb)
Coverage (assuming 1.23 Gbp genome size)
Shotgun, 3 kb, 8 kb, 20 kb mate pair
220, 230, 500, 400–600, 800, 2 kb, 5 kb, 10 kb, 20 kb, 40 kb paired end
7.5Kb, 13 kb
To validate the assemblies in the Assemblathon 2 competition, a fosmid library was created from sheared genomic DNA (35–40 Kbp) of Mr. B . Ten pools of clones were generated and sequenced using Illumina as described in . Each pool of reads was individually assembled using Velvet . The fosmid assemblies have been deposited at GigaDB ).
454 FLX transcriptome reads were generated from brain RNA isolated from two males, neither of whom was Mr. B. An initial set of sequencing runs of both males was conducted at Washington University at St. Louis, producing 89.2 Mb of transcriptome sequence as reported in  (NCBI accession numbers SRR029329–30) and were assembled using Newbler  into 19,198 contigs. An additional 21× coverage (run label GK0K2XF01) was generated at Duke University from one of the males.
Summary of assemblies
Megascaffolds from Budgerigar_v6.3 + Optical Map
Megascaffolds from PBcR + Optical Map
Illumina + 454 SOAPdenovo2
Puerto Rican Parrot
Celera CABOG 
PBcR assembler 
CLC Genomics Workbench
454 FLX, FLX+, Illumina
PacBio corrected with Illumina, 454 FL×, FL×+
454 FLX, FLX+, Illumina, Optical Maps.
PacBio corrected with Illumina, 454 FL×, FL×+, Optical Maps.
Illumina, 454 FL×+
Illumina, 454 FL×+
137.59 Illumina, 6.85 FL×+
Total bases in scaffolds
Number of scaffolds
Avg. scaffold size
N50 scaffold size
Largest scaffold size
Total gaps in scaffolds
Number of Contigs
Avg. contig size
N50 contig size
Largest contig size
Validating sequence assemblies with optical maps
Optical Mapping is a single molecule system for the construction of ordered restriction maps of whole genomes , and it has been used to guide and validate sequence assemblies . An optical map for the budgerirgar genome was created, using a method described in Additional file 1. The optical map contigs ranged in size from 2 Mbp to 74 Mbp and spanned over 900 Mbp with a resolution of 13.94 Kbp (i.e., one non-redundant SwaI every 13.94 Kbp). The contigs were then aligned to in silico restriction maps generated from Budgerigar_v6.3 and PBcR assembly scaffolds in order to validate the scaffolds. An approximate 859.21 Mb of the optical maps aligned to the Budgerigar_v6.3 assembly, in 146 scaffolds with 3 or more SwaI restriction fragments (excluding ends and fragments less than 0.4 Kbp). Of these 146 scaffolds, 43 appeared chimeric (i.e., aligned to two or more optical map contigs). For the PBcR assembly, 796.63 Mbp optical map contigs aligned, in 673 scaffolds. Of the 673 scaffolds, only 51 were chimeric. Thus, while the Budgerigar_v6.3 assembly has a higher N50 scaffold metric and hence longer scaffolds compared to the PBcR assembly, 30% the v6.3 scaffolds are chimeric, whereas only 7.6% of the PBcR assembly are chimeric.
Optical map assisted assemblies
We took both Budgerigar_v6.3 and PBcR assemblies and filtered out alignments that did not extend to the end of either the genomic sequence scaffold or the optical map. The remaining high-quality alignments were then used to identify optical map alignments that bridged scaffolds, such that a single optical map aligned to the ends of at least two sequence scaffolds. We then iteratively extended the megascaffolds beyond pairs of sequence scaffolds, using three heuristics: (1) we limited the overhangs (i.e., the portion of the scaffold sequence that does not align to the optical map) to 2 Mbp total; (2) we bridged two scaffolds together only if the size of the gap separating them is less than 2 Mbp of Ns; and (3) if a sequence scaffold aligned to more than one optical map, we placed it into the largest one it aligns with. The above procedure slightly reduced the number of scaffolds from 25,212 to 25,163 in the Budgerigar_v6.3 assembly, and from 54,668 to 54,138 in the PBcR assembly. This relatively small change in number is expected as our procedure tended to join only sequence scaffolds that were already fairly large into even larger megascaffolds, since it is only possible to confidently align an optical map to a fairly large sequence scaffold bearing numerous SwaI restriction sites. However, this analysis substantially improved the scaffold N50 sizes from 10.6 Mbp to 13.8 Mbp in the Budgerigar_v6.3, and 1.7 Mbp to 7.3 Mbp in the PBcR assemblies, respectively (Table 2). Without limiting the length of the overhangs and gap sizes to 2 Mbp, the increase in N50 scaffold sizes in the Budgerigar_v6.3 is 17.1 Mbp (which we think could be an artifact). We speculate that some of the large gaps in the optical map correspond to centromeres or highly repetitive DNA that are difficult to assemble.
The Budgerigar_v6.3 and PBcR assemblies were annotated at BGI for protein coding genes by first generating a reference set of human, chicken and zebra finch proteins, and then aligning the reference set to the assemblies, and propagating annotations to 30% coverage of the reference at TBlastN, E = 1e−5. For the Budgerigar_v6.3 assembly, the reference set comprised of human proteins from Ensembl 60 and a set of zebra finch and chicken proteins re-annotated based on these human proteins, using a custom BGI pipeline reported on separately (Jarvis et al. in preparation; Zhang et al., in preparation). For the PBcR assembly, the reference set comprised of the Ensembl 60 human, chicken and zebra finch proteins. The propagation of these reference sets to the budgerigar assemblies is described in more detail in Additional file 1. Further, in the PBcR assembly, UTRs were annotated for 6,203 genes using the GK0K2XF01 transcriptome runs with a pipeline similar to the one described in . The assembly annotations were then propagated to the corresponding sets of megascaffolds. No de novo gene annotations were performed.
The annotated Budgerigar assemblies had fewer genes (15,470 and 16,204 genes in the Budgerigar_v6.3 and PBcR assemblies respectively) than the published Zebra Finch (18,618 genes) and Chicken genome assemblies (17,108 genes in the 2011 Galgal4 assembly ). We believe the lower number of annotated genes in budgerigar assemblies is due to the differences in annotation methods rather than assembly completeness, for two reasons: (1) These annotations were produced based on similarities to zebra finch, chicken and human proteins, and hence they cannot contain more genes than the source genome annotations; and (2) The independent GenScan annotation of the Budgerigar_v6.3 assembly at the UCSC Genome Browser contains more genes than in zebra finch and chicken, 24,095 in total.
Comparisons to other avian assemblies
In summary, this study shows that the budgerigar genomic resource we have generated has provided [5, 6] (and is still expected to provide more) valuable data and material for genome technology development and for further investigating complex behavioral traits at the genomics level.
All procedures on live animals were approved by the Institutional Animal Care and Use Committee of Duke University.
Availability and requirements
The genomic sequence reads have been deposited in NCBI’s sequence read archives (SRA) and the EBI’s ENA archive, under the same project accession number ERP002324. The SOAPdenovo2 assembly has been submitted to GigaDB by the Assemblathon 2 team and is available at GigaDB . Other supporting resources that have been deposited in GigaDB  are:
Duke University brain transcriptome reads.
Budgerigar_v6.3, PBcR assemblies (contigs and scaffolds) and optical map assisted megascaffolds based on these two assemblies (two contigs and four scaffolds in total).
The per base sequence quality distribution of the paired end and mate paired libraries. The estimated fragment length distribution of the mate paired libraries. Peptide and coding sequences (CDS) for the Budgerigar_v6.3 and PBcR assemblies.
Gene annotations and Repeat Masker annotations for the scaffolds.
Optical map alignments of Budgerigar_v6.3 and PBcR assemblies in Microsoft Excel and XML formats and software (Gnomspace.rar) to view the XML alignments.
The optical map dataset.
JH, EJ, GZ are members of the Bird 10 K project.
Celera assembler with the best overlap graph
Pac bio corrected reads
Extensible markup language.
We thank Graham Alexander (Duke Institute for Genome Sciences & Policy [IGSP]) for his work with the 454 sequencing, James Furbee (Roche) for his role in coordinating the sequencing of the 454 MP libraries and for assisting in the optimization of the 454 FLX + chemistry, Fangfei Ye and Nicholas Hoang (both from Duke IGSP) for their work with the Illumina sequencing, and Xiaoxia Qin, from Duke IGSP, for her advice on assembling the budgie genome. We are very appreciative of Tin Le (Gentris Corporation) for his efforts on coordinating the low-density Illumina sequencing and his role in optimizing the TruSeq3 approach. We also thank Brian Kelly, Edwin Hauw and Swati Ranade (Pacific Biosciences) for supervising and assisting with the PacBio sequencing. Optical mapping was supported in part by NHGRI R01HG000225 (DCS) and R01HG004348 (1K; subcontract to DCS). We thank Roche, Illumina and Pacific Biosciences corporations for providing sequencing and computational resources. Finally, we thank the G10K group and the Assemblathon2 group for including Budgerigar as one the model genomes in the Assemblathon2 competition.
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