'Metagenomics 2.0'.

I’m not going to bore you with another essay on the promise of this hot new approach called ‘metagenomics’, complete with hand-wringing about enormous datasets and the urgent need for researchers to master advanced bioinformatics tools (that is so 2009 . . .). I would argue that shotgun sequencing of microbial community DNA and cDNA is no longer the hot new thing, but is rapidly becoming a standard component of a microbial ecologist’s tool kit, much like 16S rRNA gene sequencing became standard practice in the late 1990s. We still struggle with storing and managing our data, and can barely keep up with advances in informatics tools or analysis approaches. But the accessibility of nextgeneration sequencing (NGS) and the inevitable full cycle of science fashion trends have put metagenomes and metatranscriptomics in the hands of many, many microbial ecologists. I must confess that over the past 2–3 years, I started to wonder (like many have) whether these approaches were really yielding any new understanding of microbial ecosystems. And I was getting bored looking at all those bar plots summarizing SEED subsystems and COG functional groups, at the whole community level. And yet as I gaze into my crystal ball, I see a fundamental shift occurring towards a more organism-centric approach to community genomics, which I will call ‘Metagenomics 2.0’. And now I’m starting to get excited again! Between ∼ 2003 and ∼ 2010, shotgun metagenomics became wildly popular and analysis relied largely on a gene-centric approach (Tringe et al., 2005) enabled by annotation of raw reads or short contigs. Many researchers were justifiably distrustful of assemblies because the resulting contigs likely represented some mosaic combination of multiple populations (Sharon and Banfield, 2013). However, some groups studying comparably less complex communities, such as acid mine drainage biofilms, succeeded in using such ‘composite genomes’ to infer genome content (Tyson et al., 2004; Garcia Martin et al., 2006) and population-level sequence variation (Allen and Banfield, 2005). Others took a conservative approach with short contigs (Rusch et al., 2007; Yooseph et al., 2007). Most early datasets relied on modest-sized combinations of Sanger and 454 pyrosequencing that did not provide enough coverage depth to assemble contigs from more diverse communities. Thus, read-based (aka ‘gene-centric’) inference of metabolic potential prevailed (Gilbert et al., 2010; Koenig et al., 2011; Fierer et al., 2012), and tool creation focused on this approach (Huson et al., 2007; Meyer et al., 2008). As read depth per sample has increased, more researchers have attempted to assemble shotgun NGS metagenome data to take a more organism-centred view of their ecosystem, e.g. generating draft ‘genomes from metagenomes’ (GFMs) (Qin et al., 2010; Iverson et al., 2012; Wrighton et al., 2012). The breakthroughs that enabled this new approach were (i) deep enough sequencing to obtain ~ 50X coverage of target populations and (ii) coverage-based binning of assembled contigs into ‘population genomes’. The approach depends on both sufficient coverage and the analysis of at least two samples. A time-series approach is even more powerful. In a sense, we are returning to the early days of metagenomics, when large-insert clone libraries allowed analysis of contigs > 10 kbp. However, unlike fosmidbased Sanger sequencing, Illumina-driven coveragebased binning is inexpensive enough to be adopted by the microbial ecology masses. Although we cannot forget about the potential ‘mosaicism’ of GFMs (Sharon and Banfield, 2013), a genome-centred approach to metagenomic data is extraordinarily more appealing than a read-based approach. Genes are expressed within cells, not in a homogenized cytoplasmic soup. It matters a lot whether a complete metabolic pathway is found in a genome versus distributed across multiple distinct genomes (potentially from distinct populations). Furthermore, reference genomes assembled from samples paired with metatranscriptome datasets are especially powerful. De novo read-based annotation of metatranscriptomic reads has been a popular approach, but as time passes it has become clear that much more information can be recovered by mapping these reads against appropriate reference genomes. Indeed, read-based annotation is fraught with pitfalls arising from a lack of suitable references – a limitation that GFMs can directly address (Albertsen et al., 2013a). I didn’t come up with this idea. The usual suspects pioneered the approach (Wrighton et al., 2012; Albertsen bs_bs_banner