Reference-free comparison of microbial communities via de Bruijn graphs

Microbial communities inhabiting the human body exhibit significant variability across different individuals and tissues and are likely play an important role in health and disease. Serghei Mangul and David Koslicki (Oregon State University) recently published a paper presenting a novel approach for characterizing microbial communities in metatranscriptomics studies. Koslicki developed this tool, which may help scientists explore the role microbiota play in disease development, especially when comparing microbiomes of healthy and disease subjects.

Identifying and characterizing the relative abundance of microbiota in different tissues is essential to better understanding the role of microbial communities in human health. Current approaches use reference databases to identify, classify, and compare microbial communities present in the individual host. However, existing databases are incomplete and rely on a limited compendium of reference genomes. Current reference-based approaches are unable to accurately determine microbial compositions to the extent that could be possible given the high resolution of data produced by today’s high throughput sequencing technology.

Framework of the study. For more information, download our paper.

Ideally, comparison of microbial communities across samples could circumvent this limiting classification step. Mangul and Koslicki recently developed EMDeBruijn, a reference-free approach that uses all available non-host microbial reads, not just those classified in reference databases, to compare microbial communities.

First, EMDeBruijn translates sequencing data to a de Bruijn graph, which represents overlaps between symbols in sequences. De Bruijn graphs are commonly used in de novo assembly of short read sequences to a genome, but have not yet been applied in a reference-free approach. EMDeBruijn then uses properties of the de Bruijn graphs to compare microbiome composition across individuals. This metric is reduced using the Earth Mover’s Distance (EMD), a statistic that can measure the distance between two probability distributions over a region.

In their recent paper, Mangul and Koslicki applied EMDeBruijn to study the composition and abundance levels of the microbial communities present in blood samples from coronary artery calcification (CAC) patients and controls. EMDeBruijn uses candidate microbial reads to differentiate between case (CAC-affected) and control (healthy) samples, and a filtered set of non-host reads are used to determine the composition of the blood microbiome. Hierarchical clustering using the EMDeBruijn metric successfully identifies several large clusters unique to samples from either health or control groups.

This study indicates the presence of the disease-specific microbial community structure in CAC patients, and points to the need for additional investigation of potentially causal relationships between the microbiome and CAC disease.

Using the same data set, Mangul and Koslicki compare the results of EMDeBruijn with those of current approaches. Existing computational methods, including MetaPhlAn and RDP’s NBC, discovered various microbial communities across the health and control samples. However, neither of these methods were able to identify any disease-specific patterns in the microbiome nor discriminate the samples into disease and healthy groups.

EMDeBruijn provides a powerful, species independent way to assess microbial diversity across individuals and subjects. For more information, see our paper, which was published in the Proceedings of the 7th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics: http://dl.acm.org/citation.cfm?id=2975174.

Code implementing this method is available at: https://github.com/dkoslicki/EMDeBruijn.

Visualization of the EMDeBruijn Distance. a) Pictorial representation of 2-mer frequencies for two hypothetical samples, S1 and S2. b) The 2-mer frequencies overlaid the de Bruijn graph B2(A ). c) Representation of the flow used to compute EMD2(S1; S2); dark arrows denote mass moved from the initial node to the terminal node. d) Result of applying the flow to the 2-mer frequencies of S1.

This project was a collaboration that started at the Mathematical and Computational Approaches in High-Throughput Genomics program held in Fall 2011 at the Institute of Pure and Applied Mathematics (IPAM). Our on-going Computational Genomics Summer Institute (CGSI; also co-organized by IPAM) was inspired by the 2011 program. Check out the 2017 CGSI website for a preview of this summer’s programs – the deadline for applications is February 1, 2017!

The full citation to our paper is:

Mangul S, Koslicki D. Reference-free comparison of microbial communities via de Bruijn graphs. In Proceedings of the 7th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics. 2016 Oct 2 (pp. 68-77). Association for Computing Machinery, New York.

Long Single-Molecule Reads Can Resolve the Complexity of the Influenza Virus Composed of Rare, Closely Related Mutant Variants

RNA viruses represent the majority of emerging and re-emerging diseases that pose a significant risk to global health – including influenza, hantaviruses, Ebola virus, and Nipah virus. When compared to DNA viruses, RNA viruses have an especially robust adaptability and evolvability due to their high mutation rates and rapid replication cycles. Development of novel medications for the prevention and treatment of these diseases requires an understanding of the mutant variants that drive an RNA-virus’ resistance mechanisms. The long read length offered by single-molecule sequencing technologies allows each mutant variant to be sequenced in a single pass. However, complete profiling of all viral genomes within a mutant spectrum is not yet possible due to the high error rate embedded in analytical protocols.

In collaboration with Alexander Artyomenko (Georgia State University), Alex Zelikovsky (Georgia State University), Nicholas Wu (The Scripps Research Institute), and Ren Sun (UCLA), Serghei Mangul and Eleazar Eskin developed a novel method for accurately reconstructing viral variants from single-molecule reads. This approach, two Single Nucleotide Variants (2SNV), tolerates the high error rate of the single molecule protocol and uses linkage between single nucleotide variations to efficiently distinguish these mutant variations from read errors.

Overview of the 2SNV method. For more information, see our book chapter.

Any method for reconstructing viral variants from single-molecule reads must overcome low volume and high error rate of sequencing data, combined with very high similarity and very low frequency of viral variants. This challenge is similar to extraction of an extremely weak signal from very noisy background with signal-to-noise ratio approaching zero. However impossible this task may seem, a satisfactory solution can be based on distinguishing randomness of the noise from systematic signal repetition. With a high sensitivity and accuracy, 2SNV is anticipated to facilitate not only viral quasispecies reconstruction, but also other biological questions that require detection of rare haplotypes such as genetic diversity in cancer cell population, and monitoring B-cell and T-cell receptor repertoire.

We present 2SNV in a chapter of conference proceedings from the 2016 RECOMB meeting. To benchmark the sensitivity of 2SNV, we performed a single-molecule sequencing experiment on a sample containing a titrated level of known viral mutant variants. We tested 2SNV on a dataset comprised of PacBio reads from 10 independent clones, ranging from 1 to 13 mutations. These 10 clones were mixed at a geometric ratio with two-fold difference in occurrence frequency for consecutive clones starting with the maximum frequency of 50% and the minimum frequency of 0.1 %. Our method is able to accurately reconstruct clone with frequency of 0.2% and distinguish clones that differed in only two nucleotides distantly located on the genome. 2SNV outperforms existing methods for full-length viral mutant reconstruction.

For more information, see our book chapter, which is available for download through Springer Publications: http://link.springer.com/chapter/10.1007%2F978-3-319-31957-5_12.

In addition, the open source implementation of 2SNV, which was developed by Alexander Artyomenko, is freely available for download at http://alan.cs.gsu.edu/NGS/?q=content/2snv.

The full citation to our paper is: 

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Overview of results using the 2SNV method. (a) 2SNV (orange) outperforms existing haplotype reconstruction tools (blue) in viral variant reconstruction. Using PacBio reads from 10 IAV clones, (b) the pairwise edit distance between clones given in a heat-map and (c) occurring frequency of clone types.

Accurate viral population assembly from ultra-deep sequencing data

Overview of high-fidelity sequencing protocol. (A) DNA material from a viral population is cleaved into sequence fragments using any suitable restriction enzyme. (B) Individual barcode sequences are attached to the fragments. Each tagged fragment is amplified by the polymerase chain reaction (PCR). (C) Amplified fragments are then sequenced. (D) Reads are grouped according to the fragment of origin based on their individual barcode sequence. An error-correction protocol is applied for every read group, correcting the sequencing errors inside the group and producing corrected consensus reads. (E) Error-corrected reads are mapped to the population consensus. (F) SNVs are detected and assembled into individual viral genomes. The ordinary protocol lacks steps (B) and (D)

Overview of high-fidelity sequencing protocol. (A) DNA material from a viral population is cleaved into sequence fragments using any suitable restriction enzyme. (B) Individual barcode sequences are attached to the fragments. Each tagged fragment is amplified by the polymerase chain reaction (PCR). (C) Amplified fragments are then sequenced. (D) Reads are grouped according to the fragment of origin based on their individual barcode sequence. An error-correction protocol is applied for every read group, correcting the sequencing errors inside the group and producing corrected consensus reads. (E) Error-corrected reads are mapped to the population consensus. (F) SNVs are detected and assembled into individual viral genomes. The ordinary protocol lacks steps (B) and (D)

Viral populations change rapidly throughout the course of an infection. Due to this, drugs that initially control an infection can rapidly become ineffective as the viral drug target mutates. For better drug design, however, we first must develop techniques to be able to detect and quantify the presence of various rare viral variants from a given sample.

Currently, next-generation sequencing technologies are employed to better understand and quantify viral population diversity. The existing technologies, however, have difficulty if distinguishing between rare viral variants and sequencing errors even when sequencing with high coverage. To overcome this problem, our lab has proposed a two-step solution in a recent paper by Serghei Mangul.

In his paper, Serghei suggested we first use a high-fidelity protocol known as Safe-SeqS with high coverage. This method employs the use of small individual barcodes that are attached to sequencing fragments before undergoing amplification by polymerase chain reaction (PCR) and being sequenced. By comparing and taking a consensus of amplicons from the same initial sequence fragment, we can easily eliminate some sequencing errors from our data.

These consensus reads then are assembled using an accurate viral assembly method Serghei developed known as the Viral Genome Assembler (VGA). This software uses read overlapping, SNV detection, and a conflict graph to distinguish and reconstruct genome variants in the population. Finally, an expectation-maximization algorithm is used to estimate abundances of assembled viral variants.

In the paper, this approach was applied to both simulated and real data and found to outperform current state-of-the-art methods. Additionally, this viral assembly method is the first of its kind to scale to millions of sequencing reads.

The Viral Genome Assembler tool is freely available here: http://genetics.cs.ucla.edu/vga/

From the paper:
Advances in NGS and the ability to generate deep coverage data in the form of millions of reads provide exceptional resolution for studying the underlying genetic diversity of complex viral populations. However, errors produced by most sequencing protocols complicate distinguishing between true biological mutations and technical artifacts that confound detection of rare mutations and rare individual genome variants. A common approach is to use post-sequencing error correction techniques able to partially correct the sequencing errors. In contrast to clonal samples, the post-sequencing error correction methods are not well suited for mixed viral samples and may lead to filtering out true biological mutations. For this reason, current viral assembly methods are able to detect only highly abundant SNV, thus limiting the discovery of rare viral genomes.

Additional difficulty arises from the genomic architectures of viruses. Long common regions shared across viral population (known as conserved regions) introduce ambiguity in the assembly process. Conserved regions may be due low-diversity population or due to recombination with multiple cross-overs. In contrast to repeats in genome assembly, conserved regions may be phased based on relative abundances of viral variants. Low-diversity viral populations in which all pairs of individual genomes within a viral population have a small genetic distance from each other may represent additional challenges for the assembly procedure.

We apply a high-fidelity sequencing protocol to study viral population structure (Fig. 1). This protocol is able to eliminate errors from sequencing data by attaching individual barcodes during the library preparation step. After the fragments are sequenced, the barcodes identify clusters of reads that originated from the same fragment, thus facilitating error correction. Given that many reads are required to sequence each fragment, we are trading off an increase in sequence coverage for a reduction in error rate. Prior to assembly, we utilize the de novo consensus reconstruction tool, Vicuna (Yang et al., 2012), to produce a linear consensus directly from the sequence data. This approach offers more flexibility for samples that do not have ‘close’ reference sequences available. Traditional assembly methods (Gnerre et al., 2011; Luo et al., 2012; Zerbino and Birney, 2008) aim to reconstruct a linear consensus sequence and are not well-suited for assembling a large number of highly similar but distinct viral genomes. We instead take our ideas from haplotype assembly methods (Bansal and Bafna, 2008; Yang et al., 2013), which aim to reconstruct two closely related haplotypes. However, these methods are not applicable for assembly of a large (a priori unknown) number of individual genomes. Many existing viral assemblers estimate local population diversity and are not well suited for assembling full-length quasi-species variants spanning the entire viral genome. Available genome-wide assemblers able to reconstruct full-length quasi-species variants are originally designed for low throughput and are impractical for high throughput technologies containing millions of sequencing reads.

Overview of VGA. (A) The algorithm takes as input paired-end reads that have been mapped to the population consensus. (B) The first step in the assembly is to determine pairs of conflicting reads that share different SNVs in the overlapping region. Pairs of conflicting reads are connected in the ‘conflict graph’. Each read has a node in the graph, and an edge is placed between each pair of conflicting reads. (C) The graph is colored into a minimal set of colors to distinguish between genome variants in the population. Colors of the graph correspond to independent sets of non-conflicting reads that are assembled into genome variants. In this example, the conflict graph can be minimally colored with four colors (red, green, violet and turquoise), each representing individual viral genomes. (D) Reads of the same color are then assembled into individual viral genomes. Only fully covered viral genomes are reported. (E) Reads are assigned to assembled viral genomes. Read may be shared across two or more viral genomes. VGA infers relative abundances of viral genomes using the expectation–maximization algorithm. (F) Long conserved regions are detected and phased based on expression profiles. In this example red and green viral genome share a long conserved region (colored in black). There is no direct evidence how the viral sub-genomes across the conserved region should be connected. In this example four possible phasing are valid. VGA use the expression information of every sub-genome to resolve ambiguous phasing.

Overview of VGA. (A) The algorithm takes as input paired-end reads that have been mapped to the population consensus. (B) The first step in the assembly is to determine pairs of conflicting reads that share different SNVs in the overlapping region. Pairs of conflicting reads are connected in the ‘conflict graph’. Each read has a node in the graph, and an edge is placed between each pair of conflicting reads. (C) The graph is colored into a minimal set of colors to distinguish between genome variants in the population. Colors of the graph correspond to independent sets of non-conflicting reads that are assembled into genome variants. In this example, the conflict graph can be minimally colored with four colors (red, green, violet and turquoise), each representing individual viral genomes. (D) Reads of the same color are then assembled into individual viral genomes. Only fully covered viral genomes are reported. (E) Reads are assigned to assembled viral genomes. Read may be shared across two or more viral genomes. VGA infers relative abundances of viral genomes using the expectation–maximization algorithm. (F) Long conserved regions are detected and phased based on expression profiles. In this example red and green viral genome share a long conserved region (colored in black). There is no direct evidence how the viral sub-genomes across the conserved region should be connected. In this example four possible phasing are valid. VGA use the expression information of every sub-genome to resolve ambiguous phasing.

We introduce a viral population assembly method (Fig. 2) working on highly accurate sequencing data able to detect rare variants and tolerate conserved regions shared across the population. Our method is coupled with post-assembly procedures able to detect and resolve ambiguity raised from long conserved regions using expression profiles (Fig. 2F). After a consensus has been reconstructed directly from the sequence data, our method detects SNVs from the aligned sequencing reads. Read overlapping is used to link individual SNVs and distinguish between genome variants in the population. The viral population is condensed in a conflict graph built from aligned sequencing data. Two reads are originated from different viral genomes if they share different SNVs in the overlapping region. Viral variants are identified from the graph as independent sets of non-conflicting reads. Non-continuous coverage of rare viral variants may limit assembly capacities, indicating that increase in coverage is required to increase the assembly accuracy. Frequencies of identified variants are then estimated using an expectation–maximization algorithm. Compared with existing approaches, we are able to detect rare population variants while achieving high assembly accuracy.

The full citation of our paper is:

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