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ConPADE: genome assembly ploidy estimation from next-generation sequencing data.

Margarido GR, Heckerman D - PLoS Comput. Biol. (2015)

Bottom Line: As a result of improvements in genome assembly algorithms and the ever decreasing costs of high-throughput sequencing technologies, new high quality draft genome sequences are published at a striking pace.Given the similarity between multiple copies of a basic genome in polyploid individuals, assembly of such data usually results in collapsed contigs that represent a variable number of homoeologous genomic regions.We show that ConPADE may also be used for related applications, such as the identification of duplicated genes in fragmented assemblies, although refinements are needed.

View Article: PubMed Central - PubMed

Affiliation: Microsoft Research, Los Angeles, California, United States of America; Departamento de Genética, Escola Superior de Agricultura ''Luiz de Queiroz", Universidade de São Paulo, Piracicaba, Brazil.

ABSTRACT
As a result of improvements in genome assembly algorithms and the ever decreasing costs of high-throughput sequencing technologies, new high quality draft genome sequences are published at a striking pace. With well-established methodologies, larger and more complex genomes are being tackled, including polyploid plant genomes. Given the similarity between multiple copies of a basic genome in polyploid individuals, assembly of such data usually results in collapsed contigs that represent a variable number of homoeologous genomic regions. Unfortunately, such collapse is often not ideal, as keeping contigs separate can lead both to improved assembly and also insights about how haplotypes influence phenotype. Here, we describe a first step in avoiding inappropriate collapse during assembly. In particular, we describe ConPADE (Contig Ploidy and Allele Dosage Estimation), a probabilistic method that estimates the ploidy of any given contig/scaffold based on its allele proportions. In the process, we report findings regarding errors in sequencing. The method can be used for whole genome shotgun (WGS) sequencing data. We also show applicability of the method for variant calling and allele dosage estimation. Results for simulated and real datasets are discussed and provide evidence that ConPADE performs well as long as enough sequencing coverage is available, or the true contig ploidy is low. We show that ConPADE may also be used for related applications, such as the identification of duplicated genes in fragmented assemblies, although refinements are needed.

No MeSH data available.


Related in: MedlinePlus

Coverage simulation results.Color in each cell indicates the percentage of correct ploidy calls, out of 100 simulations of 200 kb-long contigs for each ploidy level.
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pcbi.1004229.g004: Coverage simulation results.Color in each cell indicates the percentage of correct ploidy calls, out of 100 simulations of 200 kb-long contigs for each ploidy level.

Mentions: These initial results indicate that ploidy estimation is more challenging for higher levels of ploidy, and that there can be random variation leading to errors in estimates. In order to provide estimates of ploidy calling accuracy with varying coverage levels, we simulated 100 sets of 200 kb-long contigs for each ploidy level and evaluated the performance of ConPADE in each situation. Results are shown in Fig 4 and S2 Table, where warmer colors indicate higher ploidy estimation accuracy. For a coverage level of 50X, ploidy estimation with the full error model yielded correct results for all 100 simulated contigs, for all ploidy levels. The accuracy of ploidy estimation decreased with decreasing coverage, particularly for higher ploidies. In that context, we note that the lowest accuracies were 83%, 44% and 19% for coverages of 25X, 15X and 10X, respectively, for a ploidy of 15. It is interesting to note that, whenever ploidy was incorrectly called, the estimated and actual ploidy usually differed by at most two, and never more than three. Specific ploidy calls for coverage of 15X are found in S3 Fig and S3 Table.


ConPADE: genome assembly ploidy estimation from next-generation sequencing data.

Margarido GR, Heckerman D - PLoS Comput. Biol. (2015)

Coverage simulation results.Color in each cell indicates the percentage of correct ploidy calls, out of 100 simulations of 200 kb-long contigs for each ploidy level.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4400156&req=5

pcbi.1004229.g004: Coverage simulation results.Color in each cell indicates the percentage of correct ploidy calls, out of 100 simulations of 200 kb-long contigs for each ploidy level.
Mentions: These initial results indicate that ploidy estimation is more challenging for higher levels of ploidy, and that there can be random variation leading to errors in estimates. In order to provide estimates of ploidy calling accuracy with varying coverage levels, we simulated 100 sets of 200 kb-long contigs for each ploidy level and evaluated the performance of ConPADE in each situation. Results are shown in Fig 4 and S2 Table, where warmer colors indicate higher ploidy estimation accuracy. For a coverage level of 50X, ploidy estimation with the full error model yielded correct results for all 100 simulated contigs, for all ploidy levels. The accuracy of ploidy estimation decreased with decreasing coverage, particularly for higher ploidies. In that context, we note that the lowest accuracies were 83%, 44% and 19% for coverages of 25X, 15X and 10X, respectively, for a ploidy of 15. It is interesting to note that, whenever ploidy was incorrectly called, the estimated and actual ploidy usually differed by at most two, and never more than three. Specific ploidy calls for coverage of 15X are found in S3 Fig and S3 Table.

Bottom Line: As a result of improvements in genome assembly algorithms and the ever decreasing costs of high-throughput sequencing technologies, new high quality draft genome sequences are published at a striking pace.Given the similarity between multiple copies of a basic genome in polyploid individuals, assembly of such data usually results in collapsed contigs that represent a variable number of homoeologous genomic regions.We show that ConPADE may also be used for related applications, such as the identification of duplicated genes in fragmented assemblies, although refinements are needed.

View Article: PubMed Central - PubMed

Affiliation: Microsoft Research, Los Angeles, California, United States of America; Departamento de Genética, Escola Superior de Agricultura ''Luiz de Queiroz", Universidade de São Paulo, Piracicaba, Brazil.

ABSTRACT
As a result of improvements in genome assembly algorithms and the ever decreasing costs of high-throughput sequencing technologies, new high quality draft genome sequences are published at a striking pace. With well-established methodologies, larger and more complex genomes are being tackled, including polyploid plant genomes. Given the similarity between multiple copies of a basic genome in polyploid individuals, assembly of such data usually results in collapsed contigs that represent a variable number of homoeologous genomic regions. Unfortunately, such collapse is often not ideal, as keeping contigs separate can lead both to improved assembly and also insights about how haplotypes influence phenotype. Here, we describe a first step in avoiding inappropriate collapse during assembly. In particular, we describe ConPADE (Contig Ploidy and Allele Dosage Estimation), a probabilistic method that estimates the ploidy of any given contig/scaffold based on its allele proportions. In the process, we report findings regarding errors in sequencing. The method can be used for whole genome shotgun (WGS) sequencing data. We also show applicability of the method for variant calling and allele dosage estimation. Results for simulated and real datasets are discussed and provide evidence that ConPADE performs well as long as enough sequencing coverage is available, or the true contig ploidy is low. We show that ConPADE may also be used for related applications, such as the identification of duplicated genes in fragmented assemblies, although refinements are needed.

No MeSH data available.


Related in: MedlinePlus