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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

The graphical model for ploidy estimation and variant calls.Each node represents a variable. Edges represent probabilistic dependencies. Each node is associated with a probability distribution of the corresponding variable conditioned on the variables corresponding to its parents. Variables within the same plate (rectangle) are replicated according to the number of positions in a contig (the “Positions” rectangle) or the number of reads overlapping a given position of a given contig (the “Reads” rectangle). Shaded variables represent the HiSeq error model, which is a component of the ploidy estimation model.
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pcbi.1004229.g003: The graphical model for ploidy estimation and variant calls.Each node represents a variable. Edges represent probabilistic dependencies. Each node is associated with a probability distribution of the corresponding variable conditioned on the variables corresponding to its parents. Variables within the same plate (rectangle) are replicated according to the number of positions in a contig (the “Positions” rectangle) or the number of reads overlapping a given position of a given contig (the “Reads” rectangle). Shaded variables represent the HiSeq error model, which is a component of the ploidy estimation model.

Mentions: In our model, we assume that there are at most two possible alleles at any given position. For a genomic region with any given level of ploidy, herein denoted M, heterozygous sites in the genome can hold varying proportions of these two alleles. As an example, all heterozygous positions in a diploid region will display the two alleles in a 1:1 ratio. Alleles in a triploid region can be present in 2:1 or 1:2 ratios. A tetraploid can display the ratios 3:1, 2:2 and 1:3. In general, the number of heterozygous possibilities is M−1. This structure is captured in the generative model displayed in Fig 3. Because particular contigs or scaffolds can represent a varying number of copies in a polyploid individual, due to collapsing during assembly, this model assumes ploidy is constant along each contig, instead of along the entire genome.


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

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

The graphical model for ploidy estimation and variant calls.Each node represents a variable. Edges represent probabilistic dependencies. Each node is associated with a probability distribution of the corresponding variable conditioned on the variables corresponding to its parents. Variables within the same plate (rectangle) are replicated according to the number of positions in a contig (the “Positions” rectangle) or the number of reads overlapping a given position of a given contig (the “Reads” rectangle). Shaded variables represent the HiSeq error model, which is a component of the ploidy estimation model.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi.1004229.g003: The graphical model for ploidy estimation and variant calls.Each node represents a variable. Edges represent probabilistic dependencies. Each node is associated with a probability distribution of the corresponding variable conditioned on the variables corresponding to its parents. Variables within the same plate (rectangle) are replicated according to the number of positions in a contig (the “Positions” rectangle) or the number of reads overlapping a given position of a given contig (the “Reads” rectangle). Shaded variables represent the HiSeq error model, which is a component of the ploidy estimation model.
Mentions: In our model, we assume that there are at most two possible alleles at any given position. For a genomic region with any given level of ploidy, herein denoted M, heterozygous sites in the genome can hold varying proportions of these two alleles. As an example, all heterozygous positions in a diploid region will display the two alleles in a 1:1 ratio. Alleles in a triploid region can be present in 2:1 or 1:2 ratios. A tetraploid can display the ratios 3:1, 2:2 and 1:3. In general, the number of heterozygous possibilities is M−1. This structure is captured in the generative model displayed in Fig 3. Because particular contigs or scaffolds can represent a varying number of copies in a polyploid individual, due to collapsing during assembly, this model assumes ploidy is constant along each contig, instead of along the entire genome.

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