Abstract
Metagenomics, the sequence characterization of all genomes within a sample, is widely used as a virus discovery tool as well as a tool to study viral diversity of animals. Metagenomics can be considered to have three main steps; sample collection and preparation, sequencing and finally bioinformatics. Bioinformatic analysis of metagenomic datasets is in itself a complex process, involving few standardized methodologies, thereby hampering comparison of metagenomics studies between research groups. In this publication the new bioinformatics framework MetLab is presented, aimed at providing scientists with an integrated tool for experimental design and analysis of viral metagenomes. MetLab provides support in designing the metagenomics experiment by estimating the sequencing depth needed for the complete coverage of a species. This is achieved by applying a methodology to calculate the probability of coverage using an adaptation of Stevens’ theorem. It also provides scientists with several pipelines aimed at simplifying the analysis of viral metagenomes, including; quality control, assembly and taxonomic binning. We also implement a tool for simulating metagenomics datasets from several sequencing platforms. The overall aim is to provide virologists with an easy to use tool for designing, simulating and analyzing viral metagenomes. The results presented here include a benchmark towards other existing software, with emphasis on detection of viruses as well as speed of applications. This is packaged, as comprehensive software, readily available for Linux and OSX users at https://github.com/norling/metlab.
Highlights
Metagenomics is the study of the combined genomic material of a sample and as such viral metagenomics concentrate on characterization of the viral fraction of the sample.[1,2]
By applying high throughput sequencing (HTS), with selective enrichment of the viral target and reduction of host genome, the virome of an organism or environment is explored in an unbiased way, without the need for culturing or viral isolation
This study aims at producing a bioinformatics framework for design and analysis of viral metagenomics experiments, MetLab
Summary
Metagenomics is the study of the combined genomic material of a sample and as such viral metagenomics concentrate on characterization of the viral fraction of the sample.[1,2]. By applying high throughput sequencing (HTS), with selective enrichment of the viral target and reduction of host genome, the virome of an organism or environment is explored in an unbiased way, without the need for culturing or viral isolation The use of this methodology have increased enormously in the last decade, partially due to the increased availability of high throughput sequencing, and due to development of better tools for analysis and interpretation of datasets [3,4,5]. I), Viruses are small genomic entities within a world of giants; even the smaller bacterial genomes are considerably larger and risk masking out the viral genomes during analysis [10] This is normally solved by either viral enrichment, e.g. DNA amplification or virus isolation, or by increasing the depth of sequencing [11]. This leads to problems in estimating viral abundance in a sample as well as problems related to the availability of target sequences while assigning taxonomic identities to the sequence reads during analysis [5]
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