Christopher Quince

UCHIME improves sensitivity and speed of chimera detection

Motivation: Chimeric DNA sequences often form during polymerase chain reaction amplification, especially when sequencing single regions (e.g. 16S rRNA or fungal Internal Transcribed Spacer) to assess diversity or compare populations. Undetected chimeras may be misinterpreted as novel species, causing inflated estimates of diversity and spurious inferences of differences between populations. Detection and removal of chimeras is therefore of critical importance in such experiments. Results: We describe UCHIME, a new program that detects chimeric sequences with two or more segments. UCHIME either uses a database of chimera-free sequences or detects chimeras de novo by exploiting abundance data. UCHIME has better sensitivity than ChimeraSlayer (previously the most sensitive database method), especially with short, noisy sequences. In testing on artificial bacterial communities with known composition, UCHIME de novo sensitivity is shown to be comparable to Perseus. UCHIME is >100× faster than Perseus and >1000× faster than ChimeraSlayer. Contact: robert@drive5.com Availability: Source, binaries and data: http://drive5.com/uchime. Supplementary information: Supplementary data are available at Bioinformatics online.

Accurate determination of microbial diversity from 454 pyrosequencing data.

We present an algorithm, PyroNoise, that clusters the flowgrams of 454 pyrosequencing reads using a distance measure that models sequencing noise. This infers the true sequences in a collection of amplicons. We pyrosequenced a known mixture of microbial 16S rDNA sequences extracted from a lake and found that without noise reduction the number of operational taxonomic units is overestimated but using PyroNoise it can be accurately calculated.

VSEARCH: a versatile open source tool for metagenomics

Background VSEARCH is an open source and free of charge multithreaded 64-bit tool for processing and preparing metagenomics, genomics and population genomics nucleotide sequence data. It is designed as an alternative to the widely used USEARCH tool (Edgar, 2010) for which the source code is not publicly available, algorithm details are only rudimentarily described, and only a memory-confined 32-bit version is freely available for academic use. Methods When searching nucleotide sequences, VSEARCH uses a fast heuristic based on words shared by the query and target sequences in order to quickly identify similar sequences, a similar strategy is probably used in USEARCH. VSEARCH then performs optimal global sequence alignment of the query against potential target sequences, using full dynamic programming instead of the seed-and-extend heuristic used by USEARCH. Pairwise alignments are computed in parallel using vectorisation and multiple threads. Results VSEARCH includes most commands for analysing nucleotide sequences available in USEARCH version 7 and several of those available in USEARCH version 8, including searching (exact or based on global alignment), clustering by similarity (using length pre-sorting, abundance pre-sorting or a user-defined order), chimera detection (reference-based or de novo), dereplication (full length or prefix), pairwise alignment, reverse complementation, sorting, and subsampling. VSEARCH also includes commands for FASTQ file processing, i.e., format detection, filtering, read quality statistics, and merging of paired reads. Furthermore, VSEARCH extends functionality with several new commands and improvements, including shuffling, rereplication, masking of low-complexity sequences with the well-known DUST algorithm, a choice among different similarity definitions, and FASTQ file format conversion. VSEARCH is here shown to be more accurate than USEARCH when performing searching, clustering, chimera detection and subsampling, while on a par with USEARCH for paired-ends read merging. VSEARCH is slower than USEARCH when performing clustering and chimera detection, but significantly faster when performing paired-end reads merging and dereplication. VSEARCH is available at https://github.com/torognes/vsearch under either the BSD 2-clause license or the GNU General Public License version 3.0. Discussion VSEARCH has been shown to be a fast, accurate and full-fledged alternative to USEARCH. A free and open-source versatile tool for sequence analysis is now available to the metagenomics community.

Decreased gut microbiota diversity, delayed Bacteroidetes colonisation and reduced Th1 responses in infants delivered by Caesarean section

Objective The early intestinal microbiota exerts important stimuli for immune development, and a reduced microbial exposure as well as caesarean section (CS) has been associated with the development of allergic disease. Here we address how microbiota development in infants is affected by mode of delivery, and relate differences in colonisation patterns to the maturation of a balanced Th1/Th2 immune response. Design The postnatal intestinal colonisation pattern was investigated in 24 infants, born vaginally (15) or by CS (nine). The intestinal microbiota were characterised using pyrosequencing of 16S rRNA genes at 1 week and 1, 3, 6, 12 and 24 months after birth. Venous blood levels of Th1- and Th2-associated chemokines were measured at 6, 12 and 24 months. Results Infants born through CS had lower total microbiota diversity during the first 2 years of life. CS delivered infants also had a lower abundance and diversity of the Bacteroidetes phylum and were less often colonised with the Bacteroidetes phylum. Infants born through CS had significantly lower levels of the Th1-associated chemokines CXCL10 and CXCL11 in blood. Conclusions CS was associated with a lower total microbial diversity, delayed colonisation of the Bacteroidetes phylum and reduced Th1 responses during the first 2 years of life.

Organismal, genetic, and transcriptional variation in the deeply sequenced gut microbiomes of identical twins

We deeply sampled the organismal, genetic, and transcriptional diversity in fecal samples collected from a monozygotic (MZ) twin pair and compared the results to 1,095 communities from the gut and other body habitats of related and unrelated individuals. Using a new scheme for noise reduction in pyrosequencing data, we estimated the total diversity of species-level bacterial phylotypes in the 1.2-1.5 million bacterial 16S rRNA reads obtained from each deeply sampled cotwin to be ~800 (35.9%, 49.1% detected in both). A combined 1.1 million read 16S rRNA dataset representing 281 shallowly sequenced fecal samples from 54 twin pairs and their mothers contained an estimated 4,018 species-level phylotypes, with each sample having a unique species assemblage (53.4 ± 0.6% and 50.3 ± 0.5% overlap with the deeply sampled cotwins). Of the 134 phylotypes with a relative abundance of >0.1% in the combined dataset, only 37 appeared in >50% of the samples, with one phylotype in the Lachnospiraceae family present in 99%. Nongut communities had significantly reduced overlap with the deeply sequenced twins’ fecal microbiota (18.3 ± 0.3%, 15.3 ± 0.3%). The MZ cotwins’ fecal DNA was deeply sequenced (3.8-6.3 Gbp/sample) and assembled reads were assigned to 25 genus-level phylogenetic bins. Only 17% of the genes in these bins were shared between the cotwins. Bins exhibited differences in their degree of sequence variation, gene content including the repertoire of carbohydrate active enzymes present within and between twins (e.g., predicted cellulases, dockerins), and transcriptional activities. These results provide an expanded perspective about features that make each of us unique life forms and directions for future characterization of our gut ecosystems.

Binning metagenomic contigs by coverage and composition

Shotgun sequencing enables the reconstruction of genomes from complex microbial communities, but because assembly does not reconstruct entire genomes, it is necessary to bin genome fragments. Here we present CONCOCT, a new algorithm that combines sequence composition and coverage across multiple samples, to automatically cluster contigs into genomes. We demonstrate high recall and precision on artificial as well as real human gut metagenome data sets.

Insight into biases and sequencing errors for amplicon sequencing with the Illumina MiSeq platform

With read lengths of currently up to 2 × 300 bp, high throughput and low sequencing costs Illuminas MiSeq is becoming one of the most utilized sequencing platforms worldwide. The platform is manageable and affordable even for smaller labs. This enables quick turnaround on a broad range of applications such as targeted gene sequencing, metagenomics, small genome sequencing and clinical molecular diagnostics. However, Illumina error profiles are still poorly understood and programs are therefore not designed for the idiosyncrasies of Illumina data. A better knowledge of the error patterns is essential for sequence analysis and vital if we are to draw valid conclusions. Studying true genetic variation in a population sample is fundamental for understanding diseases, evolution and origin. We conducted a large study on the error patterns for the MiSeq based on 16S rRNA amplicon sequencing data. We tested state-of-the-art library preparation methods for amplicon sequencing and showed that the library preparation method and the choice of primers are the most significant sources of bias and cause distinct error patterns. Furthermore we tested the efficiency of various error correction strategies and identified quality trimming (Sickle) combined with error correction (BayesHammer) followed by read overlapping (PANDAseq) as the most successful approach, reducing substitution error rates on average by 93%.

Niche specialization of terrestrial archaeal ammonia oxidizers

Soil pH is a major determinant of microbial ecosystem processes and potentially a major driver of evolution, adaptation, and diversity of ammonia oxidizers, which control soil nitrification. Archaea are major components of soil microbial communities and contribute significantly to ammonia oxidation in some soils. To determine whether pH drives evolutionary adaptation and community structure of soil archaeal ammonia oxidizers, sequences of amoA, a key functional gene of ammonia oxidation, were examined in soils at global, regional, and local scales. Globally distributed database sequences clustered into 18 well-supported phylogenetic lineages that dominated specific soil pH ranges classified as acidic (pH <5), acido-neutral (5≤ pH <7), or alkalinophilic (pH ≥7). To determine whether patterns were reproduced at regional and local scales, amoA gene fragments were amplified from DNA extracted from 47 soils in the United Kingdom (pH 3.5–8.7), including a pH-gradient formed by seven soils at a single site (pH 4.5–7.5). High-throughput sequencing and analysis of amoA gene fragments identified an additional, previously undiscovered phylogenetic lineage and revealed similar pH-associated distribution patterns at global, regional, and local scales, which were most evident for the five most abundant clusters. Archaeal amoA abundance and diversity increased with soil pH, which was the only physicochemical characteristic measured that significantly influenced community structure. These results suggest evolution based on specific adaptations to soil pH and niche specialization, resulting in a global distribution of archaeal lineages that have important consequences for soil ecosystem function and nitrogen cycling.

Swarm: robust and fast clustering method for amplicon-based studies

Popular de novo amplicon clustering methods suffer from two fundamental flaws: arbitrary global clustering thresholds, and input-order dependency induced by centroid selection. Swarm was developed to address these issues by first clustering nearly identical amplicons iteratively using a local threshold, and then by using clusters’ internal structure and amplicon abundances to refine its results. This fast, scalable, and input-order independent approach reduces the influence of clustering parameters and produces robust operational taxonomic units.

A Culture-Independent Sequence-Based Metagenomics Approach to the Investigation of an Outbreak of Shiga-Toxigenic Escherichia coli O104:H4

IMPORTANCE Identification of the bacterium responsible for an outbreak can aid in disease management. However, traditional culture-based diagnosis can be difficult, particularly if no specific diagnostic test is available for an outbreak strain. OBJECTIVE To explore the potential of metagenomics, which is the direct sequencing of DNA extracted from microbiologically complex samples, as an open-ended clinical discovery platform capable of identifying and characterizing bacterial strains from an outbreak without laboratory culture. DESIGN, SETTING, AND PATIENTS In a retrospective investigation, 45 samples were selected from fecal specimens obtained from patients with diarrhea during the 2011 outbreak of Shiga-toxigenic Escherichia coli (STEC) O104:H4 in Germany. Samples were subjected to high-throughput sequencing (August-September 2012), followed by a 3-phase analysis (November 2012-February 2013). In phase 1, a de novo assembly approach was developed to obtain a draft genome of the outbreak strain. In phase 2, the depth of coverage of the outbreak strain genome was determined in each sample. In phase 3, sequences from each sample were compared with sequences from known bacteria to identify pathogens other than the outbreak strain. MAIN OUTCOMES AND MEASURES The recovery of genome sequence data for the purposes of identification and characterization of the outbreak strain and other pathogens from fecal samples. RESULTS During phase 1, a draft genome of the STEC outbreak strain was obtained. During phase 2, the outbreak strain genome was recovered from 10 samples at greater than 10-fold coverage and from 26 samples at greater than 1-fold coverage. Sequences from the Shiga-toxin genes were detected in 27 of 40 STEC-positive samples (67%). In phase 3, sequences from Clostridium difficile, Campylobacter jejuni, Campylobacter concisus, and Salmonella enterica were recovered. CONCLUSIONS AND RELEVANCE These results suggest the potential of metagenomics as a culture-independent approach for the identification of bacterial pathogens during an outbreak of diarrheal disease. Challenges include improving diagnostic sensitivity, speeding up and simplifying workflows, and reducing costs.