The Road to Bioinformatics Via Metagenomics
The data generated by metagenomics experiments are both enormous and inherently noisy, containing fragmented data representing as many as 10,000 species. The sequencing of the cow rumen metagenome generated 279 gigabases, or 279 billion base pairs of nucleotide sequence data, while the human gut microbiome gene catalog identified 3.3 million genes assembled from 567.7 gigabases of sequence data. Collecting, curating, and extracting useful biological information from datasets of this size represent significant computational challenges for researchers.
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Gene prediction
Metagenomic analysis pipelines use two approaches in the annotation of coding regions in the assembled contigs. The first approach is to identify genes based upon homology with genes that are already publicly available in sequence databases, usually by BLAST searches. This type of approach is implemented in the program MEGAN4.
The second, ab initio, uses intrinsic features of the sequence to predict coding regions based upon gene training sets from related organisms. This is the approach taken by programs such as GeneMark and GLIMMER. The main advantage of ab initio prediction is that it enables the detection of coding regions that lack homologs in the sequence databases; however, it is most accurate when there are large regions of contiguous genomic DNA available for comparison.
Shotgun metagenomics
Advances in bioinformatics, refinements of DNA amplification, and the proliferation of computational power have greatly aided the analysis of DNA sequences recovered from environmental samples, allowing the adaptation of shotgun sequencing to metagenomic samples (known also as whole metagenome shotgun or WMGS sequencing).
The approach, used to sequence many cultured microorganisms and the human genome, randomly shears DNA, sequences many short sequences, and reconstructs them into a consensus sequence. Shotgun sequencing reveals genes present in environmental samples.
Historically, clone libraries were used to facilitate this sequencing. However, with advances in high throughput sequencing technologies, the cloning step is no longer necessary and greater yields of sequencing data can be obtained without this labour-intensive bottleneck step. Shotgun metagenomics provides information both about which organisms are present and what metabolic processes are possible in the community.
Because the collection of DNA from an environment is largely uncontrolled, the most abundant organisms in an environmental sample are most highly represented in the resulting sequence data. To achieve the high coverage needed to fully resolve the genomes of under-represented community members, large samples, often prohibitively so, are needed.