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Sequencing smart: De novo sequencing and assembly approaches for a non-model mammal.

ABSTRACT: BACKGROUND:Whilst much sequencing effort has focused on key mammalian model organisms such as mouse and human, little is known about the relationship between genome sequencing techniques for non-model mammals and genome assembly quality. This is especially relevant to non-model mammals, where the samples to be sequenced are often degraded and of low quality. A key aspect when planning a genome project is the choice of sequencing data to generate. This decision is driven by several factors, including the biological questions being asked, the quality of DNA available, and the availability of funds. Cutting-edge sequencing technologies now make it possible to achieve highly contiguous, chromosome-level genome assemblies, but rely on high-quality high molecular weight DNA. However, funding is often insufficient for many independent research groups to use these techniques. Here we use a range of different genomic technologies generated from a roadkill European polecat (Mustela putorius) to assess various assembly techniques on this low-quality sample. We evaluated different approaches for de novo assemblies and discuss their value in relation to biological analyses. RESULTS:Generally, assemblies containing more data types achieved better scores in our ranking system. However, when accounting for misassemblies, this was not always the case for Bionano and low-coverage 10x Genomics (for scaffolding only). We also find that the extra cost associated with combining multiple data types is not necessarily associated with better genome assemblies. CONCLUSIONS:The high degree of variability between each de novo assembly method (assessed from the 7 key metrics) highlights the importance of carefully devising the sequencing strategy to be able to carry out the desired analysis. Adding more data to genome assemblies does not always result in better assemblies, so it is important to understand the nuances of genomic data integration explained here, in order to obtain cost-effective value for money when sequencing genomes.

SUBMITTER: Etherington GJ

PROVIDER: S-EPMC7216774 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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Sequencing smart: De novo sequencing and assembly approaches for a non-model mammal.

Etherington Graham J GJ Heavens Darren D Baker David D Lister Ashleigh A McNelly Rose R Garcia Gonzalo G Clavijo Bernardo B Macaulay Iain I Haerty Wilfried W Di Palma Federica F

GigaScience 20200501 5

<h4>Background</h4>Whilst much sequencing effort has focused on key mammalian model organisms such as mouse and human, little is known about the relationship between genome sequencing techniques for non-model mammals and genome assembly quality. This is especially relevant to non-model mammals, where the samples to be sequenced are often degraded and of low quality. A key aspect when planning a genome project is the choice of sequencing data to generate. This decision is driven by several factor ...[more]

PMID: 32396200

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Project description:The k-mer hash length is a key factor affecting the output of de novo transcriptome assembly packages using de Bruijn graph algorithms. Assemblies constructed with varying single k-mer choices might result in the loss of unique contiguous sequences (contigs) and relevant biological information. A common solution to this problem is the clustering of single k-mer assemblies. Even though annotation is one of the primary goals of a transcriptome assembly, the success of assembly strategies does not consider the impact of k-mer selection on the annotation output. This study provides an in-depth k-mer selection analysis that is focused on the degree of functional annotation achieved for a non-model organism where no reference genome information is available. Individual k-mers and clustered assemblies (CA) were considered using three representative software packages. Pair-wise comparison analyses (between individual k-mers and CAs) were produced to reveal missing Kyoto Encyclopedia of Genes and Genomes (KEGG) ortholog identifiers (KOIs), and to determine a strategy that maximizes the recovery of biological information in a de novo transcriptome assembly.Analyses of single k-mer assemblies resulted in the generation of various quantities of contigs and functional annotations within the selection window of k-mers (k-19 to k-63). For each k-mer in this window, generated assemblies contained certain unique contigs and KOIs that were not present in the other k-mer assemblies. Producing a non-redundant CA of k-mers 19 to 63 resulted in a more complete functional annotation than any single k-mer assembly. However, a fraction of unique annotations remained (~0.19 to 0.27% of total KOIs) in the assemblies of individual k-mers (k-19 to k-63) that were not present in the non-redundant CA. A workflow to recover these unique annotations is presented.This study demonstrated that different k-mer choices result in various quantities of unique contigs per single k-mer assembly which affects biological information that is retrievable from the transcriptome. This undesirable effect can be minimized, but not eliminated, with clustering of multi-k assemblies with redundancy removal. The complete extraction of biological information in de novo transcriptomics studies requires both the production of a CA and efforts to identify unique contigs that are present in individual k-mer assemblies but not in the CA.

Dataset Information

Sequencing smart: De novo sequencing and assembly approaches for a non-model mammal.

Publications

Sequencing smart: De novo sequencing and assembly approaches for a non-model mammal.

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