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High-coverage sequencing and annotated assemblies of the budgerigar genome.

Ganapathy G, Howard JT, Ward JM, Li J, Li B, Li Y, Xiong Y, Zhang Y, Zhou S, Schwartz DC, Schatz M, Aboukhalil R, Fedrigo O, Bukovnik L, Wang T, Wray G, Rasolonjatovo I, Winer R, Knight JR, Koren S, Warren WC, Zhang G, Phillippy AM, Jarvis ED - Gigascience (2014)

Bottom Line: 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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Neurobiology, Duke University Medical Center, Durham, NC 27710, USA.

ABSTRACT

Background: 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.

Findings: 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.

Conclusions: 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.

No MeSH data available.


Related in: MedlinePlus

The number of genes that are part of a syntenic block between different budgerigar assemblies (A) and between budgerigar and non-budgerigar assemblies (B). The numbers were calculated from CoGE syntenic dotplots (not shown), as the total number of genes represented in syntenic blocks. The y-axis limits have been cut off close to the minimum value in the plot to show a more detailed spread of values.
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Figure 3: The number of genes that are part of a syntenic block between different budgerigar assemblies (A) and between budgerigar and non-budgerigar assemblies (B). The numbers were calculated from CoGE syntenic dotplots (not shown), as the total number of genes represented in syntenic blocks. The y-axis limits have been cut off close to the minimum value in the plot to show a more detailed spread of values.

Mentions: Using the online CoGe tool [22-24], we assessed the structural similarities between the various budgerigar assemblies and other avian assemblies [25-30], by computing the level of coding sequence synteny among assemblies. The highest numbers of genes in synteny were observed, as expected, between a budgerigar assembly and the optical map assisted version of the same assembly (Figure 3A). However, the number of genes in synteny between the Budgerigar_v6.3 and the PBcR assemblies was similar to the number of genes in synteny between budgerigar and falcon (Figure 3A, B). Further, the number of genes in synteny did not strictly reflect phylogenetic relationships, as the zebra finch and budgerigar, close relatives [19], had a lower level of synteny than budgerigar and chicken. In addition, a number of inversions were observed even in the syntenic dotplots between the original budgerigar assemblies and their optical map-assisted assemblies (88 inversions between Budgerigar_v6.3 and Budgerigar_v6.3_mega; 209 inversions between PBcR and PBcR_mega, plots shown in GigaDB [2]). This suggests that synteny based on CoGE syntenic maps is affected by the quality of the assemblies and the characteristics of the synteny algorithm. Thus, the number of genes in synteny computed using the available methods is only a rough measure of the actual structural similarity between the assemblies compared.


High-coverage sequencing and annotated assemblies of the budgerigar genome.

Ganapathy G, Howard JT, Ward JM, Li J, Li B, Li Y, Xiong Y, Zhang Y, Zhou S, Schwartz DC, Schatz M, Aboukhalil R, Fedrigo O, Bukovnik L, Wang T, Wray G, Rasolonjatovo I, Winer R, Knight JR, Koren S, Warren WC, Zhang G, Phillippy AM, Jarvis ED - Gigascience (2014)

The number of genes that are part of a syntenic block between different budgerigar assemblies (A) and between budgerigar and non-budgerigar assemblies (B). The numbers were calculated from CoGE syntenic dotplots (not shown), as the total number of genes represented in syntenic blocks. The y-axis limits have been cut off close to the minimum value in the plot to show a more detailed spread of values.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4109783&req=5

Figure 3: The number of genes that are part of a syntenic block between different budgerigar assemblies (A) and between budgerigar and non-budgerigar assemblies (B). The numbers were calculated from CoGE syntenic dotplots (not shown), as the total number of genes represented in syntenic blocks. The y-axis limits have been cut off close to the minimum value in the plot to show a more detailed spread of values.
Mentions: Using the online CoGe tool [22-24], we assessed the structural similarities between the various budgerigar assemblies and other avian assemblies [25-30], by computing the level of coding sequence synteny among assemblies. The highest numbers of genes in synteny were observed, as expected, between a budgerigar assembly and the optical map assisted version of the same assembly (Figure 3A). However, the number of genes in synteny between the Budgerigar_v6.3 and the PBcR assemblies was similar to the number of genes in synteny between budgerigar and falcon (Figure 3A, B). Further, the number of genes in synteny did not strictly reflect phylogenetic relationships, as the zebra finch and budgerigar, close relatives [19], had a lower level of synteny than budgerigar and chicken. In addition, a number of inversions were observed even in the syntenic dotplots between the original budgerigar assemblies and their optical map-assisted assemblies (88 inversions between Budgerigar_v6.3 and Budgerigar_v6.3_mega; 209 inversions between PBcR and PBcR_mega, plots shown in GigaDB [2]). This suggests that synteny based on CoGE syntenic maps is affected by the quality of the assemblies and the characteristics of the synteny algorithm. Thus, the number of genes in synteny computed using the available methods is only a rough measure of the actual structural similarity between the assemblies compared.

Bottom Line: 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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Neurobiology, Duke University Medical Center, Durham, NC 27710, USA.

ABSTRACT

Background: 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.

Findings: 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.

Conclusions: 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.

No MeSH data available.


Related in: MedlinePlus