Michael Imelfort

Anaerobic oxidation of methane coupled to nitrate reduction in a novel archaeal lineage

Anaerobic oxidation of methane (AOM) is critical for controlling the flux of methane from anoxic environments. AOM coupled to iron, manganese and sulphate reduction have been demonstrated in consortia containing anaerobic methanotrophic (ANME) archaea. More recently it has been shown that the bacterium Candidatus ‘Methylomirabilis oxyfera’ can couple AOM to nitrite reduction through an intra-aerobic methane oxidation pathway. Bioreactors capable of AOM coupled to denitrification have resulted in the enrichment of ‘M. oxyfera’ and a novel ANME lineage, ANME-2d. However, as ‘M. oxyfera’ can independently couple AOM to denitrification, the role of ANME-2d in the process is unresolved. Here, a bioreactor fed with nitrate, ammonium and methane was dominated by a single ANME-2d population performing nitrate-driven AOM. Metagenomic, single-cell genomic and metatranscriptomic analyses combined with bioreactor performance and 13C- and 15N-labelling experiments show that ANME-2d is capable of independent AOM through reverse methanogenesis using nitrate as the terminal electron acceptor. Comparative analyses reveal that the genes for nitrate reduction were transferred laterally from a bacterial donor, suggesting selection for this novel process within ANME-2d. Nitrite produced by ANME-2d is reduced to dinitrogen gas through a syntrophic relationship with an anaerobic ammonium-oxidizing bacterium, effectively outcompeting ‘M. oxyfera’ in the system. We propose the name Candidatus ‘Methanoperedens nitroreducens’ for the ANME-2d population and the family Candidatus ‘Methanoperedenaceae’ for the ANME-2d lineage. We predict that ‘M. nitroreducens’ and other members of the ‘Methanoperedenaceae’ have an important role in linking the global carbon and nitrogen cycles in anoxic environments.

Fast, accurate error-correction of amplicon pyrosequences using Acacia

To the Editor: Microbial diversity metrics based on high-throughput amplicon sequencing are compromised by read errors. Roche 454 GS FLX Titanium pyrosequencing is currently the most widely used technology for amplicon-based microbial community studies, despite high homopolymer-associated insertion-deletion error rates1,2. Currently, there are two software packages, AmpliconNoise3 and Denoiser4, that are commonly used to correct amplicon pyrosequencing errors. AmpliconNoise applies an approximate likelihood using empirically derived error distributions to remove pyrosequencing noise from reads. AmpliconNoise is highly effective at noise removal but is computationally intensive3. Denoiser is a faster algorithm that uses frequency-based heuristics rather than statistical modeling to cluster reads. Neither tool modifies individual reads; instead both select an ‘error-free’ read to represent reads in a given cluster. We developed a tool for homopolymer error-correction that has greater scalability than existing tools. We explored whether there was sufficient information in the FASTA files alone to achieve the sensitivity and specificity of AmpliconNoise and Denoiser, which both use raw flowgrams. Our error-correction tool, Acacia, meets these objectives. First, Acacia reduces the number and complexity of alignments. Rather than performing all-against-all alignments in a cluster, each read in the cluster is aligned to a dynamically updated cluster consensus; the alignment algorithm is made more efficient using heuristics that only consider homopolymer overand under-calls. Secondly, Acacia uses a quicker but less sensitive statistical approach to distinguish between error and genuine sequence differences (Supplementary Methods and Supplementary Notes 1–3). We measured the performance of Acacia relative to AmpliconNoise and Denoiser using three synthetic small subunit ribosomal RNA (SSU rRNA) gene amplicon data sets (‘artificial’, ‘divergent’ and ‘titanium’) previously used to benchmark the latter tools3,4. For each data set, we recorded the peak memory usage and CPU run time (Supplementary Table 1). We benchmarked AmpliconNoise using only the smaller artificial data set, which was sufficient to indicate that this software was impractical for analyzing larger data sets. The peak memory used by Acacia was 1–4× higher than that used by Denoiser and ~14× lower than by AmpliconNoise. Acacia ran on all data sets in under 1 minute, was up to ~500× faster than Denoiser for the titanium data set, and more than 2,000× faster than AmpliconNoise for the artificial data set. Acacia processed larger contemporary data sets (200,000 GS FLX Titanium reads) in under 80 CPU minutes. We next benchmarked the error-correction sensitivity and specificity of Acacia. For convenience, we refer to correction of individual reads although corrections are derived from either a cluster consensus (Acacia) or representative read (AmpliconNoise and Denoiser). Despite working with the less precise rounded flow values, Acacia corrected the majority of GS FLX Titanium homopolymer errors corrected by AmpliconNoise and Denoiser (Fig. 1a). As expected, Acacia has less sensitivity than AmpliconNoise and Denoiser for correcting substitution errors because it only attempts to correct homopolymer errors. Acacia did, however, correct ~40% of the AmpliconNoiseand Denoiser-corrected substitutions in the titanium data set (Fig. 1b) because these errors were the consequence of consecutive over-under calls or vice versa. We found that AmpliconNoise and Denoiser introduced a substantial number of errors, most of them non-homopolymer substitutions, during error-correction (Fig. 1c). Notably, Acacia introduced 2× and 12× fewer errors than AmpliconNoise and Denoiser, respectively. Errors

GroopM: an automated tool for the recovery of population genomes from related metagenomes

Metagenomic binning methods that leverage differential population abundances in microbial communities (differential coverage) are emerging as a complementary approach to conventional composition-based binning. Here we introduce GroopM, an automated binning tool that primarily uses differential coverage to obtain high fidelity population genomes from related metagenomes. We demonstrate the effectiveness of GroopM using synthetic and real-world metagenomes, and show that GroopM produces results comparable with more time consuming, labor-intensive methods.

Discovering genetic polymorphisms in next-generation sequencing data.

The ongoing revolution in DNA sequencing technology now enables the reading of thousands of millions of nucleotide bases in a single instrument run. However, this data quantity is often compromised by poor confidence in the read quality. The identification of genetic polymorphisms from this data is therefore problematic and, combined with the vast quantity of data, poses a major bioinformatics challenge. However, once these difficulties have been addressed, next-generation sequencing will offer a means to identify and characterize the wealth of genetic polymorphisms underlying the vast phenotypic variation in biological systems. We describe the recent advances in next-generation sequencing technology, together with preliminary approaches that can be applied for single nucleotide polymorphism discovery in plant species.

An Expanded Genomic Representation of the Phylum Cyanobacteria

Molecular surveys of aphotic habitats have indicated the presence of major uncultured lineages phylogenetically classified as members of the Cyanobacteria. One of these lineages has recently been proposed as a nonphotosynthetic sister phylum to the Cyanobacteria, the Melainabacteria, based on recovery of population genomes from human gut and groundwater samples. Here, we expand the phylogenomic representation of the Melainabacteria through sequencing of six diverse population genomes from gut and bioreactor samples supporting the inference that this lineage is nonphotosynthetic, but not the assertion that they are strictly fermentative. We propose that the Melainabacteria is a class within the phylogenetically defined Cyanobacteria based on robust monophyly and shared ancestral traits with photosynthetic representatives. Our findings are consistent with theories that photosynthesis occurred late in the Cyanobacteria and involved extensive lateral gene transfer and extends the recognized functionality of members of this phylum.

Inferring short tandem repeat variation from paired-end short reads

The advances of high-throughput sequencing offer an unprecedented opportunity to study genetic variation. This is challenged by the difficulty of resolving variant calls in repetitive DNA regions. We present a Bayesian method to estimate repeat-length variation from paired-end sequence read data. The method makes variant calls based on deviations in sequence fragment sizes, allowing the analysis of repeats at lengths of relevance to a range of phenotypes. We demonstrate the method’s ability to detect and quantify changes in repeat lengths from short read genomic sequence data across genotypes. We use the method to estimate repeat variation among 12 strains of Arabidopsis thaliana and demonstrate experimentally that our method compares favourably against existing methods. Using this method, we have identified all repeats across the genome, which are likely to be polymorphic. In addition, our predicted polymorphic repeats also included the only known repeat expansion in A. thaliana, suggesting an ability to discover potential unstable repeats.

Future tools for association mapping in crop plants.

Association mapping currently relies on the identification of genetic markers. Several technologies have been adopted for genetic marker analysis, with single nucleotide polymorphisms (SNPs) being the most popular where a reasonable quantity of genome sequence data are available. We describe several tools we have developed for the discovery, annotation, and visualization of molecular markers for association mapping. These include autoSNPdb for SNP discovery from assembled sequence data; TAGdb for the identification of gene specific paired read Illumina GAII data; CMap3D for the comparison of mapped genetic and physical markers; and BAC and Gene Annotator for the online annotation of genes and genomic sequences.

Capturing the biofuel wellhead and powerhouse: the chloroplast and mitochondrial genomes of the leguminous feedstock tree Pongamia pinnata.

Pongamia pinnata (syn. Millettia pinnata) is a novel, fast-growing arboreal legume that bears prolific quantities of oil-rich seeds suitable for the production of biodiesel and aviation biofuel. Here, we have used Illumina® ‘Second Generation DNA Sequencing (2GS)’ and a new short-read de novo assembler, SaSSY, to assemble and annotate the Pongamia chloroplast (152,968 bp; cpDNA) and mitochondrial (425,718 bp; mtDNA) genomes. We also show that SaSSY can be used to accurately assemble 2GS data, by re-assembling the Lotus japonicus cpDNA and in the process assemble its mtDNA (380,861 bp). The Pongamia cpDNA contains 77 unique protein-coding genes and is almost 60% gene-dense. It contains a 50 kb inversion common to other legumes, as well as a novel 6.5 kb inversion that is responsible for the non-disruptive, re-orientation of five protein-coding genes. Additionally, two copies of an inverted repeat firmly place the species outside the subclade of the Fabaceae lacking the inverted repeat. The Pongamia and L. japonicus mtDNA contain just 33 and 31 unique protein-coding genes, respectively, and like other angiosperm mtDNA, have expanded intergenic and multiple repeat regions. Through comparative analysis with Vigna radiata we measured the average synonymous and non-synonymous divergence of all three legume mitochondrial (1.59% and 2.40%, respectively) and chloroplast (8.37% and 8.99%, respectively) protein-coding genes. Finally, we explored the relatedness of Pongamia within the Fabaceae and showed the utility of the organellar genome sequences by mapping transcriptomic data to identify up- and down-regulated stress-responsive gene candidates and confirm in silico predicted RNA editing sites.

Crass: identification and reconstruction of CRISPR from unassembled metagenomic data

Clustered regularly interspaced short palindromic repeats (CRISPR) constitute a bacterial and archaeal adaptive immune system that protect against bacteriophage (phage). Analysis of CRISPR loci reveals the history of phage infections and provides a direct link between phage and their hosts. All current tools for CRISPR identification have been developed to analyse completed genomes and are not well suited to the analysis of metagenomic data sets, where CRISPR loci are difficult to assemble owing to their repetitive structure and population heterogeneity. Here, we introduce a new algorithm, Crass, which is designed to identify and reconstruct CRISPR loci from raw metagenomic data without the need for assembly or prior knowledge of CRISPR in the data set. CRISPR in assembled data are often fragmented across many contigs/scaffolds and do not fully represent the population heterogeneity of CRISPR loci. Crass identified substantially more CRISPR in metagenomes previously analysed using assembly-based approaches. Using Crass, we were able to detect CRISPR that contained spacers with sequence homology to phage in the system, which would not have been identified using other approaches. The increased sensitivity, specificity and speed of Crass will facilitate comprehensive analysis of CRISPRs in metagenomic data sets, increasing our understanding of phage-host interactions and co-evolution within microbial communities.

Targeted identification of genomic regions using TAGdb

BackgroundThe introduction of second generation sequencing technology has enabled the cost effective sequencing of genomes and the identification of large numbers of genes and gene promoters. However, the assembly of DNA sequences to create a representation of the complete genome sequence remains costly, especially for the larger and more complex plant genomes.ResultsWe have developed an online database, TAGdb, that enables researchers to identify paired read sequences that share identity with a submitted query sequence. These tags can be used to design oligonucleotide primers for the PCR amplification of the region in the target genome.ConclusionsThe ability to produce large numbers of paired read genome tags using second generation sequencing provides a cost effective method for the identification of genes and promoters in large, complex or orphan species without the need for whole genome assembly.