De Bruijn Graph Approach Research Articles

Genome-scale de novo assembly using ALGA.

MotivationThere are very few methods for de novo genome assembly based on the overlap graph approach. It is considered as giving more exact results than the so-called de Bruijn graph approach but in much greater time and of much higher memory usage. It is not uncommon that assembly methods involving the overlap graph model are not able to successfully compute greater datasets, mainly due to memory limitation of a computer. This was the reason for developing in last decades mainly de Bruijn-based assembly methods, fast and fairly accurate. However, the latter methods can fail for longer or more repetitive genomes, as they decompose reads to shorter fragments and lose a part of information. An efficient assembler for processing big datasets and using the overlap graph model is still looked out.ResultsWe propose a new genome-scale de novo assembler based on the overlap graph approach, designed for short-read sequencing data. The method, ALGA, incorporates several new ideas resulting in more exact contigs produced in short time. Among these ideas, we have creation of a sparse but quite informative graph, reduction of the graph including a procedure referring to the problem of minimum spanning tree of a local subgraph, and graph traversal connected with simultaneous analysis of contigs stored so far. What is rare in genome assembly, the algorithm is almost parameter-free, with only one optional parameter to be set by a user. ALGA was compared with nine state-of-the-art assemblers in tests on genome-scale sequencing data obtained from real experiments on six organisms, differing in size, coverage, GC content and repetition rate. ALGA produced best results in the sense of overall quality of genome reconstruction, understood as a good balance between genome coverage, accuracy and length of resulting sequences. The algorithm is one of tools involved in processing data in currently realized national project Genomic Map of Poland.Availability and implementationALGA is available at http://alga.put.poznan.pl.Supplementary information Supplementary data are available at Bioinformatics online.

HAssembler: A hybrid de novo genome assembly approach for large genomes

Genome assembly is a process where large contigs and scaffolds are constructed from raw reads generated by sequencing machines. Based on the size of the generated reads they can be primarily categorized into short reads and long reads. Modern genome assemblers follow De Bruijn Graph (DBG) approach for assembly of short reads, whereas Overlap Layout Consensus (OLC) approach for assembly of long reads. For de novo genome assembly, DBG based assemblers are very efficient at repeat resolution but are computation intensive and sensitive to sequencing errors. On the other hand, OLC based assemblers are intuitive and very time efficient but not efficient at resolving repeat regions. Here, we developed an hAssembler, which leverages the advantages of both DBG and OLC approaches and compared its performance with the existing hybrid assemblers. It uses both long reads and short reads and run OLC and DBG in parallel. By using both the long and short reads, the time complexity of hAssembler was reduced considerably. The results showed that hAssembler outperformed the existing hybrid assemblers in terms of time and performance (N50) while assembling the large genomes.

TraRECo: a greedy approach based de novo transcriptome assembler with read error correction using consensus matrix

BackgroundThe challenges when developing a good de novo transcriptome assembler include how to deal with read errors and sequence repeats. Almost all de novo assemblers utilize a de Bruijn graph, with which complexity grows linearly with data size while suffering from errors and repeats. Although one can correct the errors by inspecting the topological structure of the graph, this is not an easy task when there are too many branches. Two research directions are to improve either the graph reliability or the path search precision, and in this study, we focused on the former.ResultsWe present TraRECo, a greedy approach to de novo assembly employing error-aware graph construction. In the proposed approach, we built contigs by direct read alignment within a distance margin and performed a junction search to construct splicing graphs. While doing so, a contig of length l was represented by a 4 × l matrix (called a consensus matrix), in which each element was the base count of the aligned reads so far. A representative sequence was obtained by taking the majority in each column of the consensus matrix to be used for further read alignment. Once the splicing graphs had been obtained, we used IsoLasso to find paths with a noticeable read depth. The experiments using real and simulated reads show that the method provided considerable improvement in sensitivity and moderately better performance when comparing sensitivity and precision. This was achieved by the error-aware graph construction using the consensus matrix, with which the reads having errors were made usable for the graph construction (otherwise, they might have been eventually discarded). This improved the quality of the coverage depth information used in the subsequent path search step and finally the reliability of the graph.ConclusionsDe novo assembly is mainly used to explore undiscovered isoforms and must be able to represent as many reads as possible in an efficient way. In this sense, TraRECo provides us with a potential alternative for improving graph reliability even though the computational burden is much higher than the single k-mer in the de Bruijn graph approach.

Innovative assembly strategy contributes to understanding the evolution and conservation genetics of the endangered Solenodon paradoxus from the island of Hispaniola.

Solenodons are insectivores that live in Hispaniola and Cuba. They form an isolated branch in the tree of placental mammals that are highly divergent from other eulipothyplan insectivores The history, unique biology, and adaptations of these enigmatic venomous species could be illuminated by the availability of genome data. However, a whole genome assembly for solenodons has not been previously performed, partially due to the difficulty in obtaining samples from the field. Island isolation and reduced numbers have likely resulted in high homozygosity within the Hispaniolan solenodon (Solenodon paradoxus). Thus, we tested the performance of several assembly strategies on the genome of this genetically impoverished species. The string graph–based assembly strategy seemed a better choice compared to the conventional de Bruijn graph approach due to the high levels of homozygosity, which is often a hallmark of endemic or endangered species. A consensus reference genome was assembled from sequences of 5 individuals from the southern subspecies (S. p. woodi). In addition, we obtained an additional sequence from 1 sample of the northern subspecies (S. p. paradoxus). The resulting genome assemblies were compared to each other and annotated for genes, with an emphasis on venom genes, repeats, variable microsatellite loci, and other genomic variants. Phylogenetic positioning and selection signatures were inferred based on 4,416 single-copy orthologs from 10 other mammals. We estimated that solenodons diverged from other extant mammals 73.6 million years ago. Patterns of single-nucleotide polymorphism variation allowed us to infer population demography, which supported a subspecies split within the Hispaniolan solenodon at least 300 thousand years ago.

De novo assembly of transcriptome from next‐generation sequencing data

Reconstruction of transcriptome by de novo assembly from next generation sequencing (NGS) short‐sequence reads provides an essential mean to catalog expressed genes, identify splicing isoforms, and capture the expression detail of transcripts for organisms with no reference genome available. De novo transcriptome assembly faces many unique challenges, including alternative splicing, variable expression level covering a dynamic range of several orders of magnitude, artifacts introduced by reverse transcription, etc. In the current review, we illustrate the grand strategy in applying De Bruijn Graph (DBG) approach in de novo transcriptome assembly. We further analyze many parameters proven critical in transcriptome assembly using DBG. Among them, k‐mer length, coverage depth of reads, genome complexity, performance of different programs are addressed in greater details. A multi‐k‐mer strategy balancing efficiency and sensitivity is discussed and highly recommended for de novo transcriptome assembly. Future direction points to the combination of NGS and third generation sequencing technology that would greatly enhance the power of de novo transcriptomics study.

De novo assembly methods for next generation sequencing data

The recent breakthroughs in next-generation sequencing technologies, such as those of Roche 454, Illumina/Solexa, and ABI SOLID, have dramatically reduced the cost of producing short reads of the genome of new species. The huge volume of reads, along with short read length, high coverage, and sequencing errors, poses a great challenge to de novo genome assembly. However, the paired-end information provides a new solution to these problems. In this paper, we review and compare some current assembly tools, including Newbler, CAP3, Velvet, SOAPdenovo, AllPaths, Abyss, IDBA, PE-Assembly, and Telescoper. In general, we compare the seed extension and graph-based methods that use the overlap/lapout/consensus approach and the de Bruijn graph approach for assembly. At the end of the paper, we summarize these methods and discuss the future directions of genome assembly.

Assembler for de novo assembly of large genomes

Assembling a large genome using next generation sequencing reads requires large computer memory and a long execution time. To reduce these requirements, we propose an extension-based assembler, called JR-Assembler, where J and R stand for "jumping" extension and read "remapping." First, it uses the read count to select good quality reads as seeds. Second, it extends each seed by a whole-read extension process, which expedites the extension process and can jump over short repeats. Third, it uses a dynamic back trimming process to avoid extension termination due to sequencing errors. Fourth, it remaps reads to each assembled sequence, and if an assembly error occurs by the presence of a repeat, it breaks the contig at the repeat boundaries. Fifth, it applies a less stringent extension criterion to connect low-coverage regions. Finally, it merges contigs by unused reads. An extensive comparison of JR-Assembler with current assemblers using datasets from small, medium, and large genomes shows that JR-Assembler achieves a better or comparable overall assembly quality and requires lower memory use and less central processing unit time, especially for large genomes. Finally, a simulation study shows that JR-Assembler achieves a superior performance on memory use and central processing unit time than most current assemblers when the read length is 150 bp or longer, indicating that the advantages of JR-Assembler over current assemblers will increase as the read length increases with advances in next generation sequencing technology.

Pathset Graphs: A Novel Approach for Comprehensive Utilization of Paired Reads in Genome Assembly

One of the key advances in genome assembly that has led to a significant improvement in contig lengths has been improved algorithms for utilization of paired reads (mate-pairs). While in most assemblers, mate-pair information is used in a post-processing step, the recently proposed Paired de Bruijn Graph (PDBG) approach incorporates the mate-pair information directly in the assembly graph structure. However, the PDBG approach faces difficulties when the variation in the insert sizes is high. To address this problem, we first transform mate-pairs into edge-pair histograms that allow one to better estimate the distance between edges in the assembly graph that represent regions linked by multiple mate-pairs. Further, we combine the ideas of mate-pair transformation and PDBGs to construct new data structures for genome assembly: pathsets and pathset graphs.

A de Bruijn Graph Approach to the Quantification of Closely-Related Genomes in a Microbial Community

The wide applications of next-generation sequencing (NGS) technologies in metagenomics have raised many computational challenges. One of the essential problems in metagenomics is to estimate the taxonomic composition of a microbial community, which can be approached by mapping shotgun reads acquired from the community to previously characterized microbial genomes followed by quantity profiling of these species based on the number of mapped reads. This procedure, however, is not as trivial as it appears at first glance. A shotgun metagenomic dataset often contains DNA sequences from many closely-related microbial species (e.g., within the same genus) or strains (e.g., within the same species), thus it is often difficult to determine which species/strain a specific read is sampled from when it can be mapped to a common region shared by multiple genomes at high similarity. Furthermore, high genomic variations are observed among individual genomes within the same species, which are difficult to be differentiated from the inter-species variations during reads mapping. To address these issues, a commonly used approach is to quantify taxonomic distribution only at the genus level, based on the reads mapped to all species belonging to the same genus; alternatively, reads are mapped to a set of representative genomes, each selected to represent a different genus. Here, we introduce a novel approach to the quantity estimation of closely-related species within the same genus by mapping the reads to their genomes represented by a de Bruijn graph, in which the common genomic regions among them are collapsed. Using simulated and real metagenomic datasets, we show the de Bruijn graph approach has several advantages over existing methods, including (1) it avoids redundant mapping of shotgun reads to multiple copies of the common regions in different genomes, and (2) it leads to more accurate quantification for the closely-related species (and even for strains within the same species).

IDBA-UD: a de novo assembler for single-cell and metagenomic sequencing data with highly uneven depth

Next-generation sequencing allows us to sequence reads from a microbial environment using single-cell sequencing or metagenomic sequencing technologies. However, both technologies suffer from the problem that sequencing depth of different regions of a genome or genomes from different species are highly uneven. Most existing genome assemblers usually have an assumption that sequencing depths are even. These assemblers fail to construct correct long contigs. We introduce the IDBA-UD algorithm that is based on the de Bruijn graph approach for assembling reads from single-cell sequencing or metagenomic sequencing technologies with uneven sequencing depths. Several non-trivial techniques have been employed to tackle the problems. Instead of using a simple threshold, we use multiple depthrelative thresholds to remove erroneous k-mers in both low-depth and high-depth regions. The technique of local assembly with paired-end information is used to solve the branch problem of low-depth short repeat regions. To speed up the process, an error correction step is conducted to correct reads of high-depth regions that can be aligned to highconfident contigs. Comparison of the performances of IDBA-UD and existing assemblers (Velvet, Velvet-SC, SOAPdenovo and Meta-IDBA) for different datasets, shows that IDBA-UD can reconstruct longer contigs with higher accuracy. The IDBA-UD toolkit is available at our website http://www.cs.hku.hk/~alse/idba_ud

SparseAssembler2: Sparse k-mer Graph for Memory Efficient Genome Assembly

Motivation: To tackle the problem of huge memory usage associated with de Bruijn graph-based algorithms, upon which some of the most widely used de novo genome assemblers have been built, we released SparseAssembler1. SparseAssembler1 can save as much as 90% memory consumption in comparison with the state-of-art assemblers, but it requires rounds of denoising to accurately assemble genomes. Algorithmetically, we developed an extension of de Bruijn graph structure — 'sparse de Bruijn graphs' — skipping a certain number of intermediate k-mers. In this paper, we introduce a new general model for genome assembly that uses only sparse k-mers. The new model replaces the idea of the de Bruijn graph from the beginning, and achieves similar memory efficiency and much better robustness compared with our previous SparseAssembler1. Results: Based on the sparse k-mers graph model, we develop SparseAssembler2. We demonstrate that the decomposition of reads of all overlapping k-mers, which is used in existing de Bruijn graph genome assemblers, is overly cautious. We introduce a sparse *To whom correspondence should be addressed. k-mer graph structure for saving sparse k-mers, which greatly reduces memory space requirements necessary for de novo genome assembly. In contrast with the de Bruijn graph approach, we devise a simple but powerful strategy, i.e., finding links between the k-mers in the genome and traversing following the links, which can be done by saving only a few k-mers. To implement the strategy, we need to only select some k-mers that may not even be overlapping ones, and build the links between these k-mers indicated by the reads. We can traverse through this sparse k-mer graph to build the contigs, and ultimately complete the genome assembly. Since the new sparse k-mers graph shares almost all advantages of de Bruijn graph, we are able to adapt a Dijkstra-like breadth-first search algorithm, for the new sparse k-mer graph in order to circumvent sequencing errors and resolve polymorphisms. Availability: Programs in both Windows and Linux are available at: https://sites.google.com/site/sparseassembler/. Contact: ma@vandals.uidaho.edu or mpop@umiacs.umd.edu SparseAssembler2: Sparse k-mer Graph for Memory Efficient Genome Assembly

The fragment assembly string graph

We present a concept and formalism, the string graph, which represents all that is inferable about a DNA sequence from a collection of shotgun sequencing reads collected from it. We give time and space efficient algorithms for constructing a string graph given the collection of overlaps between the reads and, in particular, present a novel linear expected time algorithm for transitive reduction in this context. The result demonstrates that the decomposition of reads into kmers employed in the de Bruijn graph approach described earlier is not essential, and exposes its close connection to the unitig approach we developed at Celera. This paper is a preliminary piece giving the basic algorithm and results that demonstrate the efficiency and scalability of the method. These ideas are being used to build a next-generation whole genome assembler called BOA (Berkeley Open Assembler) that will easily scale to mammalian genomes.