Assessment of de novoassemblers for draft genomes: a case study with fungal genomes

Abbas, Mostafa M.; Malluhi, Qutaibah M.; Balakrishnan, P.

doi:10.1186/1471-2164-15-s9-s10

Cited by 30 publications

(16 citation statements)

References 45 publications

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“…In addition to providing basic statistics like N50 (the largest contig/scaffold size wherein half of the total assembly size is contained in contigs/scaffolds no shorter than this value), QUAST compares each assembly against the reference genome (in the case of simulations) and generates a number of highly informative quality matrices, such as misassemblies, assembled sequences not present in the reference (and vice versa), and genes recovered in the assembly if the reference genome is annotated. At the end, iWGS ranks all assemblies based on selected matrices in the QUAST report using a previously described weighting strategy (Abbas et al 2014). This ranking, along with the detailed QUAST report, helps users to identify the best overall assembly, as well as the corresponding combination of sequencing strategy and assembly protocol.…”

Section: Resultsmentioning

confidence: 99%

In Silico Whole Genome Sequencer and Analyzer (iWGS): a Computational Pipeline to Guide the Design and Analysis of de novo Genome Sequencing Studies

Zhou

Peris

Kominek

et al. 2016

G3 Genes|Genomes|Genetics

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The availability of genomes across the tree of life is highly biased toward vertebrates, pathogens, human disease models, and organisms with relatively small and simple genomes. Recent progress in genomics has enabled the de novo decoding of the genome of virtually any organism, greatly expanding its potential for understanding the biology and evolution of the full spectrum of biodiversity. The increasing diversity of sequencing technologies, assays, and de novo assembly algorithms have augmented the complexity of de novo genome sequencing projects in nonmodel organisms. To reduce the costs and challenges in de novo genome sequencing projects and streamline their experimental design and analysis, we developed iWGS (in silico Whole Genome Sequencer and Analyzer), an automated pipeline for guiding the choice of appropriate sequencing strategy and assembly protocols. iWGS seamlessly integrates the four key steps of a de novo genome sequencing project: data generation (through simulation), data quality control, de novo assembly, and assembly evaluation and validation. The last three steps can also be applied to the analysis of real data. iWGS is designed to enable the user to have great flexibility in testing the range of experimental designs available for genome sequencing projects, and supports all major sequencing technologies and popular assembly tools. Three case studies illustrate how iWGS can guide the design of de novo genome sequencing projects, and evaluate the performance of a wide variety of user-specified sequencing strategies and assembly protocols on genomes of differing architectures. iWGS, along with a detailed documentation, is freely available at https://github.com/zhouxiaofan1983/iWGS.

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Section: Resultsmentioning

confidence: 99%

In Silico Whole Genome Sequencer and Analyzer (iWGS): a Computational Pipeline to Guide the Design and Analysis of de novo Genome Sequencing Studies

Zhou

Peris

Kominek

et al. 2016

G3 Genes|Genomes|Genetics

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“…Next-generation sequencing (NGS) techniques constitute a fast and cost-effective way of obtaining whole genome sequences, particularly in eukaryotic organisms with small genomes such as pathogenic fungi (Faino and Bart, 2014). De novo genome assemblies can quickly be achieved with assemblers such as ABySS (Simpson et al, 2009;Gallo et al, 2014;Abbas et al, 2014) (see below:…”

Section: Module 1: Genomic Resourcesmentioning

confidence: 99%

Peer Review #1 of "Genome-Enhanced Detection and Identification (GEDI) of plant pathogens (v0.1)"

Thynne¹

2018

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Plant diseases caused by fungi and Oomycetes represent worldwide threats to crops and forest ecosystems. Effective prevention and appropriate management of emerging diseases rely on rapid detection and identification of the causal pathogens. The increase in genomic resources makes it possible to generate novel genome-enhanced DNA detection assays that can exploit whole genomes to discover candidate genes for pathogen detection. A pipeline was developed to identify genome regions that discriminate taxa or groups of taxa and can be converted into PCR assays. The modular pipeline is comprised of four components: 1) selection and genome sequencing of phylogenetically related taxa, 2) identification of clusters of orthologous genes, 3) elimination of false positives by filtering, and 4) assay design. This pipeline was applied to some of the most important plant pathogens across three broad taxonomic groups: Phytophthoras (Stramenopiles, Oomycota), Dothideomycetes (Fungi, Ascomycota) and Pucciniales (Fungi, Basidiomycota). Comparison of 73 fungal and Oomycete genomes led the discovery of 5939 gene clusters that were unique to the targeted taxa and an additional 535 that were common at higher taxonomic levels. Approximately 28% of the 299 tested were converted into qPCR assays that met our set of specificity criteria. This work demonstrates that a genome-wide

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“…From these assemblies, the assembly with the highest N50 is chosen. The N50 is the length L of the longest contig such that half of the nucleotides in the assembly lie in contigs of length at least L. When the pipeline was implemented the Velvet assembler was chosen because it proved to be robust to a wide range of data sets during testing and has a low computational overhead (Abbas et al 2014) compared to SPAdes (Bankevich et al 2012).…”

Section: Theory and Implementationmentioning

confidence: 99%

Robust high throughput prokaryotede novoassembly and improvement pipeline for Illumina data

Page

Silva

Hunt

et al. 2016

Preprint

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The rapidly reducing cost of bacterial genome sequencing has lead to its routine use in large scale microbial analysis. Though mapping approaches can be used to find differences relative to the reference, many bacteria are subject to constant evolutionary pressures resulting in events such as the loss and gain of mobile genetic elements, horizontal gene transfer through recombination and genomic rearrangements. De novo assembly is the reconstruction of the underlying genome sequence, an essential step to understanding bacterial genome diversity. Here we present a high throughput bacterial assembly and improvement pipeline that has been used to generate nearly 20,000 draft genome assemblies in public databases. We demonstrate its performance on a public data set of 9,404 genomes. We find all the genes used in MLST schema present in 99.6% of assembled genomes. When tested on low, neutral and high GC organisms, more than 94% of genes were present and completely intact. The pipeline has proven to be scalable and robust with a wide variety of datasets without requiring human intervention. All of the software is available on GitHub under the GNU GPL open source license.DATA SUMMARYThe assembly pipeline software is available from Github under the GNU GPL open source license; (url - https://github.com/sanger-pathogens/vr-codebase)The assembly improvement software is available from Github under the GNU GPL open source license; (url - https://github.com/sanger-pathogens/assembly_improvement)Accession numbers for 9,404 assemblies are provided in the supplementary material.The Bordetella pertussis sample has sample accession ERS1058649, sequencing reads accession ERR1274624 and assembly accessions FJMX01000001-FJMX01000249.The Salmonella enterica subsp. enterica serovar Pullorum sample has sample accession ERS1058652, sequencing reads accession ERR1274625 and assembly accession FJMV01000001-FJMV01000026.The Staphylococcus aureus sample has sample accession ERS1058648, sequencing reads accession ERR1274626 and assembly accessions FJMW01000001-FJMW01000040.I/We confirm all supporting data, code and protocols have been provided within the article or through supplementary data files.☑IMPACT STATEMENTThe pipeline described in this paper has been used to assemble and annotate 30% of all bacterial genome assemblies in GenBank (18,080 out of 59,536, accessed 16/2/16). The automated generation of de novo assemblies is a critical step to explore bacterial genome diversity. MLST genes are found in 99.6% of cases, making it at least as good as existing typing methods. In the test genomes we present, more than 94% of genes are correctly assembled into intact reading frames.

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Assessment of de novoassemblers for draft genomes: a case study with fungal genomes

Cited by 30 publications

References 45 publications

In Silico Whole Genome Sequencer and Analyzer (iWGS): a Computational Pipeline to Guide the Design and Analysis of de novo Genome Sequencing Studies

In Silico Whole Genome Sequencer and Analyzer (iWGS): a Computational Pipeline to Guide the Design and Analysis of de novo Genome Sequencing Studies

Peer Review #1 of "Genome-Enhanced Detection and Identification (GEDI) of plant pathogens (v0.1)"

Robust high throughput prokaryotede novoassembly and improvement pipeline for Illumina data

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