Omega: an Overlap-graph<i>de novo</i>Assembler for Metagenomics

Haider, Bahlul; Ahn, Tae-Hyuk; Bushnell, Brian; Chai, Juanjuan; Copeland, Alex; Pan, Chongle

doi:10.1093/bioinformatics/btu395

Cited by 82 publications

(63 citation statements)

References 17 publications

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“…For each dataset and each of the de Bruijn graph assemblers MetaVelvet, Ray meta and SOAPdenovo2 [32,43,47], two separate assemblies were performed at k-mer lengths 21 and 101, in order to demonstrate the effect on sensitivity and scaffold length. Similarly, two different overlap length cutoffs were employed for the string graph assembler Omega [48]. MetaVelvet was run using 8 parallel threads.…”

Section: Methodsmentioning

confidence: 99%

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Comparing and Evaluating Metagenome Assembly Tools from a Microbiologist’s Perspective - Not Only Size Matters!

2017

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With the constant improvement in cost-efficiency and quality of Next Generation Sequencing technologies, shotgun-sequencing approaches -such as metagenomics- have nowadays become the methods of choice for studying and classifying microorganisms from various habitats. The production of data has dramatically increased over the past years and processing and analysis steps are becoming more and more of a bottleneck. Limiting factors are partly the availability of computational resources, but mainly the bioinformatics expertise in establishing and applying appropriate processing and analysis pipelines. Fortunately, a large diversity of specialized software tools is nowadays available. Nevertheless, choosing the most appropriate methods for answering specific biological questions can be rather challenging, especially for non-bioinformaticians. In order to provide a comprehensive overview and guide for the microbiological scientific community, we assessed the most common and freely available metagenome assembly tools with respect to their output statistics, their sensitivity for low abundant community members and variability in resulting community profiles as well as their ease-of-use. In contrast to the highly anticipated "Critical Assessment of Metagenomic Interpretation" (CAMI) challenge, which uses general mock community-based assembler comparison we here tested assemblers on real Illumina metagenome sequencing data from natural communities of varying complexity sampled from forest soil and algal biofilms. Our observations clearly demonstrate that different assembly tools can prove optimal, depending on the sample type, available computational resources and, most importantly, the specific research goal. In addition, we present detailed descriptions of the underlying principles and pitfalls of publically available assembly tools from a microbiologist’s perspective, and provide guidance regarding the user-friendliness, sensitivity and reliability of the resulting phylogenetic profiles.

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Section: Methodsmentioning

confidence: 99%

“…Omega [48] is the only example among the here compared assembly tools which is not a de Bruijn graph based assembler. Instead it utilizes the overlap based string graph approach [49] usually used for assembly of long sequencing read data.…”

Section: Introductionmentioning

confidence: 99%

Comparing and Evaluating Metagenome Assembly Tools from a Microbiologist’s Perspective - Not Only Size Matters!

2017

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“…Isolate reads were assembled using SPAdes 3.6.0 (29). Shotgun-sequenced stool reads were assembled using Omega (14). These metagenomically assembled contigs were filtered for those matching Salmonella by a BLAST search against a Salmonella genome recovered from an isolate from the outbreak and selecting those contigs with 95% or greater identity and 85% or greater percent coverage.…”

Section: Methodsmentioning

confidence: 99%

“…By using a BLAST recruitment method, which measures the coverage depth across an isolate Salmonella genome recovered from the outbreak by those reads matching at a species-specific nucleotide identity (Ͼ95%) (12), we corroborated the presence of a Salmonella serovar Heidelberg strain in the metagenomes and noted sufficiently high coverage to support de novo assembly (7.8ϫ to 120.4ϫ coverage depth) (Table S2). To gauge recovery of Salmonella genomic material in an unbiased fashion, we performed metagenomic de novo assembly of each metagenomic data set, using both IBDA-UD and Omega (13,14), and then extracted those contigs that matched with high identity (Ͼ95%) and coverage (Ͼ80% of query length) to a reference Salmonella genome. We found a large number of long contigs matching Salmonella, indicative of both the abundance and high quality of the Salmonella reads in these stool libraries (total assembled length, 1.5 Mbp to 4.2 Mb, depending on the data set considered; Table S2).…”

Section: Figmentioning

confidence: 99%

Metagenomics of Two Severe Foodborne Outbreaks Provides Diagnostic Signatures and Signs of Coinfection Not Attainable by Traditional Methods

Huang

Luo

Pena‐Gonzalez

et al. 2017

Appl Environ Microbiol

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Diagnostic testing for foodborne pathogens relies on culture-based techniques that are not rapid enough for real-time disease surveillance and do not give a quantitative picture of pathogen abundance or the response of the natural microbiome. Powerful sequence-based culture-independent approaches, such as shotgun metagenomics, could sidestep these limitations and potentially reveal a pathogenspecific signature on the microbiome that would have implications not only for diagnostics but also for better understanding disease progression and pathogen ecology. However, metagenomics have not yet been validated for foodborne pathogen detection. Toward closing these gaps, we applied shotgun metagenomics to stool samples collected from two geographically isolated (Alabama and Colorado) foodborne outbreaks, where the etiologic agents were identified by culture-dependent methods as distinct strains of Salmonella enterica subsp. enterica serovar Heidelberg. Metagenomic investigations were consistent with the culture-based findings and revealed, in addition, the in situ abundance and level of intrapopulation diversity of the pathogen, the possibility of coinfections with Staphylococcus aureus, overgrowth of commensal Escherichia coli, and significant shifts in the gut microbiome during infection relative to reference healthy samples. Additionally, we designed our bioinformatics pipeline to deal with several challenges associated with the analysis of clinical samples, such as the high frequency of coeluting human DNA sequences and assessment of the virulence potential of pathogens. Comparisons of these results to those of other studies revealed that in several, but not all, cases of diarrheal outbreaks, the disease and healthy states of the gut microbial community might be distinguishable, opening new possibilities for diagnostics.IMPORTANCE Diagnostic testing for enteric pathogens has relied for decades on culture-based techniques, but a total of 38.4 million cases of foodborne illness per year cannot be attributed to specific causes. This study describes new cultureindependent metagenomic approaches and the associated bioinformatics pipeline to detect and type the causative agents of microbial disease with unprecedented accuracy, opening new possibilities for the future development of health technologies and diagnostics. Our tools and approaches should be applicable to other microbial diseases in addition to foodborne diarrhea.KEYWORDS Salmonella, diagnostics, diarrhea, human gut, metagenomics D iagnostic testing for enteric pathogens has relied for decades on culture-based techniques, and culture-derived isolates currently form the foundation for public health surveillance. However, the clinical landscape is rapidly changing as culture-

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“…As metagenomic assembly is still a work in progress, there are a plethora of metagenomic assemblers to test utilizing various algorithmic and computational approaches; the Omega [18] assembler utilizes overlap graphs, whereas MEGAHIT [10], IDBA-UD [8], metaSPAdes [9], metaVelvet [11], SOAPdeNovo2 [19], and RayMeta [20] are de Bruijn graph based. Furthermore, RayMeta is implemented using MPI, while other approaches run on standalone Linux system.…”

Section: Introductionmentioning

confidence: 99%

Utilization of defined microbial communities enables effective evaluation of meta-genomic assemblies

et al. 2017

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BackgroundMetagenomics is the study of the microbial genomes isolated from communities found on our bodies or in our environment. By correctly determining the relation between human health and the human associated microbial communities, novel mechanisms of health and disease can be found, thus enabling the development of novel diagnostics and therapeutics. Due to the diversity of the microbial communities, strategies developed for aligning human genomes cannot be utilized, and genomes of the microbial species in the community must be assembled de novo. However, in order to obtain the best metagenomic assemblies, it is important to choose the proper assembler. Due to the rapidly evolving nature of metagenomics, new assemblers are constantly created, and the field has not yet agreed on a standardized process. Furthermore, the truth sets used to compare these methods are either too simple (computationally derived diverse communities) or complex (microbial communities of unknown composition), yielding results that are hard to interpret. In this analysis, we interrogate the strengths and weaknesses of five popular assemblers through the use of defined biological samples of known genomic composition and abundance. We assessed the performance of each assembler on their ability to reassemble genomes, call taxonomic abundances, and recreate open reading frames (ORFs).ResultsWe tested five metagenomic assemblers: Omega, metaSPAdes, IDBA-UD, metaVelvet and MEGAHIT on known and synthetic metagenomic data sets. MetaSPAdes excelled in diverse sets, IDBA-UD performed well all around, metaVelvet had high accuracy in high abundance organisms, and MEGAHIT was able to accurately differentiate similar organisms within a community. At the ORF level, metaSPAdes and MEGAHIT had the least number of missing ORFs within diverse and similar communities respectively.ConclusionsDepending on the metagenomics question asked, the correct assembler for the task at hand will differ. It is important to choose the appropriate assembler, and thus clearly define the biological problem of an experiment, as different assemblers will give different answers to the same question.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-017-3679-5) contains supplementary material, which is available to authorized users.

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Omega: an Overlap-graphde novoAssembler for Metagenomics

Abstract: Implemented in C++ with source code and binaries freely available at http://omega.omicsbio.org.

Cited by 82 publications

References 17 publications

Comparing and Evaluating Metagenome Assembly Tools from a Microbiologist’s Perspective - Not Only Size Matters!

Comparing and Evaluating Metagenome Assembly Tools from a Microbiologist’s Perspective - Not Only Size Matters!

Metagenomics of Two Severe Foodborne Outbreaks Provides Diagnostic Signatures and Signs of Coinfection Not Attainable by Traditional Methods

Utilization of defined microbial communities enables effective evaluation of meta-genomic assemblies

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