Comparison of different assembly and annotation tools on analysis of simulated viral metagenomic communities in the gut

SuRankCo: supervised ranking of contigs in de novo assemblies

Background

Evaluating the quality and reliability of a de novo assembly and of single contigs in particular is challenging since commonly a ground truth is not readily available and numerous factors may influence results. Currently available procedures provide assembly scores but lack a comparative quality ranking of contigs within an assembly.

Results

We present SuRankCo, which relies on a machine learning approach to predict quality scores for contigs and to enable the ranking of contigs within an assembly. The result is a sorted contig set which allows selective contig usage in downstream analysis. Benchmarking on datasets with known ground truth shows promising sensitivity and specificity and favorable comparison to existing methodology.

Conclusions

SuRankCo analyzes the reliability of de novo assemblies on the contig level and thereby allows quality control and ranking prior to further downstream and validation experiments.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0644-7) contains supplementary material, which is available to authorized users.

Combining metagenomics, metatranscriptomics and viromics to explore novel microbial interactions: towards a systems-level understanding of human microbiome

The advances in experimental methods and the development of high performance bioinformatic tools have substantially improved our understanding of microbial communities associated with human niches. Many studies have documented that changes in microbial abundance and composition of the human microbiome is associated with human health and diseased state. The majority of research on human microbiome is typically focused in the analysis of one level of biological information, i.e., metagenomics or metatranscriptomics. In this review, we describe some of the different experimental and bioinformatic strategies applied to analyze the 16S rRNA gene profiling and shotgun sequencing data of the human microbiome. We also discuss how some of the recent insights in the combination of metagenomics, metatranscriptomics and viromics can provide more detailed description on the interactions between microorganisms and viruses in oral and gut microbiomes. Recent studies on viromics have begun to gain importance due to the potential involvement of viruses in microbial dysbiosis. In addition, metatranscriptomic combined with metagenomic analysis have shown that a substantial fraction of microbial transcripts can be differentially regulated relative to their microbial genomic abundances. Thus, understanding the molecular interactions in the microbiome using the combination of metagenomics, metatranscriptomics and viromics is one of the main challenges towards a system level understanding of human microbiome.

Genomic characteristics and environmental distributions of the uncultivated Far-T4 phages

Viral metagenomics (viromics) is a tremendous tool to reveal viral taxonomic and functional diversity across ecosystems ranging from the human gut to the world's oceans. As with microbes however, there appear vast swaths of “dark matter” yet to be documented for viruses, even among relatively well-studied viral types. Here, we use viromics to explore the “Far-T4 phages” sequence space, a neighbor clade from the well-studied T4-like phages that was first detected through PCR study in seawater and subsequently identified in freshwater lakes through 454-sequenced viromes. To advance the description of these viruses beyond this single marker gene, we explore Far-T4 genome fragments assembled from two deeply-sequenced freshwater viromes. Single gene phylogenetic trees confirm that the Far-T4 phages are divergent from the T4-like phages, genome fragments reveal largely collinear genome organizations, and both data led to the delineation of five Far-T4 clades. Three-dimensional models of major capsid proteins are consistent with a T4-like structure, and highlight a highly conserved core flanked by variable insertions. Finally, we contextualize these now better characterized Far-T4 phages by re-analyzing 196 previously published viromes. These suggest that Far-T4 are common in freshwater and seawater as only four of 82 aquatic viromes lacked Far-T4-like sequences. Variability in representation across the five newly identified clades suggests clade-specific niche differentiation may be occurring across the different biomes, though the underlying mechanism remains unidentified. While complete genome assembly from complex communities and the lack of host linkage information still bottleneck virus discovery through viromes, these findings exemplify the power of metagenomics approaches to assess the diversity, evolutionary history, and genomic characteristics of novel uncultivated phages.

An ensemble strategy that significantly improves de novo assembly of microbial genomes from metagenomic next-generation sequencing data

Next-generation sequencing (NGS) approaches rapidly produce millions to billions of short reads, which allow pathogen detection and discovery in human clinical, animal and environmental samples. A major limitation of sequence homology-based identification for highly divergent microorganisms is the short length of reads generated by most highly parallel sequencing technologies. Short reads require a high level of sequence similarities to annotated genes to confidently predict gene function or homology. Such recognition of highly divergent homologues can be improved by reference-free (de novo) assembly of short overlapping sequence reads into larger contigs. We describe an ensemble strategy that integrates the sequential use of various de Bruijn graph and overlap-layout-consensus assemblers with a novel partitioned sub-assembly approach. We also proposed new quality metrics that are suitable for evaluating metagenome de novo assembly. We demonstrate that this new ensemble strategy tested using in silico spike-in, clinical and environmental NGS datasets achieved significantly better contigs than current approaches.

Direct sequencing of human gut virome fractions obtained by flow cytometry

The sequence assembly of the human gut virome encounters several difficulties. A high proportion of human and bacterial matches is detected in purified viral samples. Viral DNA extraction results in a low DNA concentration, which does not reach the minimal limit required for sequencing library preparation. Therefore, the viromes are usually enriched by whole genome amplification (WGA), which is, however, prone to the development of chimeras and amplification bias. In addition, as there is a very wide diversity of gut viral species, very extensive sequencing efforts must be made for the assembling of whole viral genomes. We present an approach to improve human gut virome assembly by employing a more precise preparation of a viral sample before sequencing. Particles present in a virome previously filtered through 0.2 μm pores were further divided into groups in accordance with their size and DNA content by fluorescence activated cell sorting (FACS). One selected viral fraction was sequenced excluding the WGA step, so that unbiased sequences with high reliability were obtained. The DNA extracted from the 314 viral particles of the selected fraction was assembled into 34 contigs longer than 1,000 bp. This represents an increase to the number of assembled long contigs per sequenced Gb in comparison with other studies where non-fractioned viromes are sequenced. Seven of these contigs contained open reading frames (ORFs) with explicit matches to proteins related to bacteriophages. The remaining contigs also possessed uncharacterized ORFs with bacteriophage-related domains. When the particles that are present in the filtered viromes are sorted into smaller groups by FACS, large pieces of viral genomes can be recovered easily. This approach has several advantages over the conventional sequencing of non-fractioned viromes: non-viral contamination is reduced and the sequencing efforts required for viral assembly are minimized.

Evaluation of viral genome assembly and diversity estimation in deep metagenomes

Background

Viruses have unique properties, small genome and regions of high similarity, whose effects on metagenomic assemblies have not been characterized so far. This study uses diverse in silico simulated viromes to evaluate how extensively genomes can be assembled using different sequencing platforms and assemblers. Further, it investigates the suitability of different methods to estimate viral diversity in metagenomes.

Results

We created in silico metagenomes mimicking various platforms at different sequencing depths. The CLC assembler revealed subpar compared to IDBA_UD and CAMERA , which are metagenomic-specific. Up to a saturation point, Illumina platforms proved more capable of reconstructing large portions of viral genomes compared to 454. Read length was an important factor for limiting chimericity, while scaffolding marginally improved contig length and accuracy. The genome length of the various viruses in the metagenomes did not significantly affect genome reconstruction, but the co-existence of highly similar genomes was detrimental. When evaluating diversity estimation tools, we found that PHACCS results were more accurate than those from CatchAll and clustering, which were both orders of magnitude above expected.

Conclusions

Assemblers designed specifically for the analysis of metagenomes should be used to facilitate the creation of high-quality long contigs. Despite the high coverage possible, scientists should not expect to always obtain complete genomes, because their reconstruction may be hindered by co-existing species bearing highly similar genomic regions. Further development of metagenomics-oriented assemblers may help bypass these limitations in future studies. Meanwhile, the lack of fully reconstructed communities keeps methods to estimate viral diversity relevant. While none of the three methods tested had absolute precision, only PHACCS was deemed suitable for comparative studies.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-989) contains supplementary material, which is available to authorized users.

A Statistical Approach to Correcting Cross-Annotations in a Metagenomic Functional Profile Generated by Short Reads

Background

Categorizing protein coding sequences into one family, if the proteins they encode perform the same biochemical function, and then tabulating the relative abundances among all the families, is a widely-adopted practice for functional profiling of a metagenomic sample. By homology searching of metagenomic sequencing reads against a protein database, the relative abundance of a family can be represented by the number of reads aligned to its members. However, it has been observed that, for short reads generated by next-generation sequencing platforms, some may be erroneously assigned to the functional families they are not associated to. This commonly occurred phenomenon is termed as cross-annotation. Current methods for functional profiling of a metagenomic sample use empirical cutoff values, to select the alignments and ignore such cross-annotation problem, or employ summarized equation to do a simple adjustment.

Result

By introducing latent variables, we use the Probabilistic Latent Semantic Analysis to model the proportions of reads assigned to functional families in a metagenomic sample. The approach can be applied on a metagenomic sample after the list of the true functional families being obtained or estimated. It was implemented in metagenomic samples functionally characterized by the database of Clusters of Orthologous Groups of proteins, and successfully addressed the cross-annotation issue on both in vitro-simulated, bioinformatics tool simulated metagenomic samples, and a real-world data.

Conclusions

Correcting cross-annotation will increase the accuracy of the functional profiling of a metagenome generated by short reads. It will further benefit differential abundance analysis of metagenomic samples under different conditions.

Pooled assembly of marine metagenomic datasets: enriching annotation through chimerism

MOTIVATION

Despite advances in high-throughput sequencing, marine metagenomic samples remain largely opaque. A typical sample contains billions of microbial organisms from thousands of genomes and quadrillions of DNA base pairs. Its derived metagenomic dataset underrepresents this complexity by orders of magnitude because of the sparseness and shortness of sequencing reads. Read shortness and sequencing errors pose a major challenge to accurate species and functional annotation. This includes distinguishing known from novel species. Often the majority of reads cannot be annotated and thus cannot help our interpretation of the sample.

RESULTS

Here, we demonstrate quantitatively how careful assembly of marine metagenomic reads within, but also across, datasets can alleviate this problem. For 10 simulated datasets, each with species complexity modeled on a real counterpart, chimerism remained within the same species for most contigs (97%). For 42 real pyrosequencing ('454') datasets, assembly increased the proportion of annotated reads, and even more so when datasets were pooled, by on average 1.6% (max 6.6%) for species, 9.0% (max 28.7%) for Pfam protein domains and 9.4% (max 22.9%) for PANTHER gene families. Our results outline exciting prospects for data sharing in the metagenomics community. While chimeric sequences should be avoided in other areas of metagenomics (e.g. biodiversity analyses), conservative pooled assembly is advantageous for annotation specificity and sensitivity. Intriguingly, our experiment also found potential prospects for (low-cost) discovery of new species in 'old' data.

CONTACT

dgerloff@ffame.org

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Metagenomic survey for viruses in Western Arctic caribou, Alaska, through iterative assembly of taxonomic units

Pathogen surveillance in animals does not provide a sufficient level of vigilance because it is generally confined to surveillance of pathogens with known economic impact in domestic animals and practically nonexistent in wildlife species. As most (re-)emerging viral infections originate from animal sources, it is important to obtain insight into viral pathogens present in the wildlife reservoir from a public health perspective. When monitoring living, free-ranging wildlife for viruses, sample collection can be challenging and availability of nucleic acids isolated from samples is often limited. The development of viral metagenomics platforms allows a more comprehensive inventory of viruses present in wildlife. We report a metagenomic viral survey of the Western Arctic herd of barren ground caribou (Rangifer tarandus granti) in Alaska, USA. The presence of mammalian viruses in eye and nose swabs of 39 free-ranging caribou was investigated by random amplification combined with a metagenomic analysis approach that applied exhaustive iterative assembly of sequencing results to define taxonomic units of each metagenome. Through homology search methods we identified the presence of several mammalian viruses, including different papillomaviruses, a novel parvovirus, polyomavirus, and a virus that potentially represents a member of a novel genus in the family Coronaviridae.