Summary: The recovery of genomes from metagenomic datasets is a critical step to defining the functional roles of the underlying uncultivated populations. We previously developed MaxBin, an automated binning approach for high-throughput recovery of microbial genomes from metagenomes. Here we present an expanded binning algorithm, MaxBin 2.0, which recovers genomes from co-assembly of a collection of metagenomic datasets. Tests on simulated datasets revealed that MaxBin 2.0 is highly accurate in recovering individual genomes, and the application of MaxBin 2.0 to several metagenomes from environmental samples demonstrated that it could achieve two complementary goals: recovering more bacterial genomes compared to binning a single sample as well as comparing the microbial community composition between different sampling environments.

The Critical Assessment of Metagenome Interpretation (CAMI) community initiative presents results from its first challenge, a rigorous benchmarking of software for metagenome assembly, binning and taxonomic profiling. Methods for assembly, taxonomic profiling and binning are key to interpreting metagenome data, but a lack of consensus about benchmarking complicates performance assessment. The Critical Assessment of Metagenome Interpretation (CAMI) challenge has engaged the global developer community to benchmark their programs on highly complex and realistic data sets, generated from ∼700 newly sequenced microorganisms and ∼600 novel viruses and plasmids and representing common experimental setups. Assembly and genome binning programs performed well for species represented by individual genomes but were substantially affected by the presence of related strains. Taxonomic profiling and binning programs were proficient at high taxonomic ranks, with a notable performance decrease below family level. Parameter settings markedly affected performance, underscoring their importance for program reproducibility. The CAMI results highlight current challenges but also provide a roadmap for software selection to answer specific research questions.

Recovering individual genomes from metagenomic datasets allows access to uncultivated microbial populations that may have important roles in natural and engineered ecosystems. Understanding the roles of these uncultivated populations has broad application in ecology, evolution, biotechnology and medicine. Accurate binning of assembled metagenomic sequences is an essential step in recovering the genomes and understanding microbial functions. We have developed a binning algorithm, MaxBin, which automates the binning of assembled metagenomic scaffolds using an expectation-maximization algorithm after the assembly of metagenomic sequencing reads. Binning of simulated metagenomic datasets demonstrated that MaxBin had high levels of accuracy in binning microbial genomes. MaxBin was used to recover genomes from metagenomic data obtained through the Human Microbiome Project, which demonstrated its ability to recover genomes from real metagenomic datasets with variable sequencing coverages. Application of MaxBin to metagenomes obtained from microbial consortia adapted to grow on cellulose allowed genomic analysis of new, uncultivated, cellulolytic bacterial populations, including an abundant myxobacterial population distantly related to Sorangium cellulosum that possessed a much smaller genome (5 MB versus 13 to 14 MB) but has a more extensive set of genes for biomass deconstruction. For the cellulolytic consortia, the MaxBin results were compared to binning using emergent self-organizing maps (ESOMs) and differential coverage binning, demonstrating that it performed comparably to these methods but had distinct advantages in automation, resolution of related genomes and sensitivity. The automatic binning software that we developed successfully classifies assembled sequences in metagenomic datasets into recovered individual genomes. The isolation of dozens of species in cellulolytic microbial consortia, including a novel species of myxobacteria that has the smallest genome among all sequenced aerobic myxobacteria, was easily achieved using the binning software. This work demonstrates that the processes required for recovering genomes from assembled metagenomic datasets can be readily automated, an important advance in understanding the metabolic potential of microbes in natural environments. MaxBin is available at https://sourceforge.net/projects/maxbin/ .

With the completion of the first gymnosperm mitochondrial genome (mtDNA) from Cycas taitungensis and the availability of more mtDNA taxa in the past 5 years, we have conducted a systematic analysis of DNA transfer from chloroplast genomes (cpDNAs) to mtDNAs (mtpts) in 11 plants, including 2 algae, 1 liverwort, 1 moss, 1 gymnosperm, 3 monocots, and 3 eudicots. By using shared gene order and boundaries between different mtpts as the criterion, the timing of cpDNA transfer during plant evolution was estimated from the phylogenetic tree reconstructed independently from concatenated protein-coding genes of 11 available mtDNAs. Several interesting findings emerged. First, frequent DNA transfer from cpDNA to mtDNA occurred at least as far back as the common ancestor of extant gymnosperms and angiosperms, about 300 MYA. The oldest mtpt is trnV(uac)-trnM(cau)-atpE-atpB-rbcL. Three other mtpts—psaA-psaB, rps19-trnH(gug)-rpl2-rpl23, and psbE-psbF—were dated to the common ancestor of extant angiosperms, at least 150 MYA. However, all protein-coding genes of mtpts have degenerated since their first transfer. Therefore, mtpts contribute nothing to the functioning of mtDNA but junk sequences. We discovered that the cpDNA transfers have occurred randomly at any positions of the cpDNAs. We provide strong evidence that the cp-derived tRNA-trnM(cau) is the only mtpt (1 out of 3 cp-derived tRNA shared by seed plants) truly transferred from cpDNA to mtDNA since the time of the common ancestor of extant gymnosperms and angiosperms. Our observations support the proposition of Richly and Leister (2004) that “primary insertions of organellar DNAs are large and then diverge and fragment over evolutionary time.”

The mtDNA of Cycas taitungensis is a circular molecule of 414,903 bp, making it 2- to 6-fold larger than the known mtDNAs of charophytes and bryophytes, but similar to the average of 7 elucidated angiosperm mtDNAs. It is characterized by abundant RNA editing sites (1,084), more than twice the number found in the angiosperm mtDNAs. The A + T content of Cycas mtDNA is 53.1%, the lowest among known land plants. About 5% of the Cycas mtDNA is composed of a novel family of mobile elements, which we designated as “Bpu sequences.” They share a consensus sequence of 36 bp with 2 terminal direct repeats (AAGG) and a recognition site for the Bpu 10I restriction endonuclease (CCTGAAGC). Comparison of the Cycas mtDNA with other plant mtDNAs revealed many new insights into the biology and evolution of land plant mtDNAs. For example, the noncoding sequences in mtDNAs have drastically expanded as land plants have evolved, with abrupt increases appearing in the bryophytes, and then in the seed plants. As a result, the genomic organizations of seed plant mtDNAs are much less compact than in other plants. Also, the Cycas mtDNA appears to have been exempted from the frequent gene loss observed in angiosperm mtDNAs. Similar to the angiosperms, the 3 Cycas genes nad1, nad2, and nad5 are disrupted by 5 group II intron squences, which have brought the genes into trans-splicing arrangements. The evolutionary origin and invasion/duplication mechanism of the Bpu sequences in Cycas mtDNA are hypothesized and discussed.

Metagenomics is the study of microbial communities sampled directly from their natural environment, without prior culturing. Among the computational tools recently developed for metagenomic sequence analysis, binning tools attempt to classify the sequences in a metagenomic dataset into different bins (i.e., species), based on various DNA composition patterns (e.g., the tetramer frequencies) of various genomes. Composition-based binning methods, however, cannot be used to classify very short fragments, because of the substantial variation of DNA composition patterns within a single genome. We developed a novel approach (AbundanceBin) for metagenomics binning by utilizing the different abundances of species living in the same environment. AbundanceBin is an application of the Lander-Waterman model to metagenomics, which is based on the l -tuple content of the reads. AbundanceBin achieved accurate, unsupervised, clustering of metagenomic sequences into different bins, such that the reads classified in a bin belong to species of identical or very similar abundances in the sample. In addition, AbundanceBin gave accurate estimations of species abundances, as well as their genome sizes—two important parameters for characterizing a microbial community. We also show that AbundanceBin performed well when the sequence lengths are very short (e.g., 75 bp) or have sequencing errors. By combining AbundanceBin and a composition-based method (MetaCluster), we can achieve even higher binning accuracy. Supplementary Material is available at www.liebertonline.com/cmb .