Assembly Graph Research Articles

Geometric deep learning framework for de novo genome assembly.

The critical stage of every de novo genome assembler is identifying paths in assembly graphs that correspond to the reconstructed genomic sequences. The existing algorithmic methods struggle with this, primarily due to repetitive regions causing complex graph tangles, leading to fragmented assemblies. Here, we introduce GNNome, a framework for path identification based on geometric deep learning that enables training models on assembly graphs without relying on existing assembly strategies. By leveraging only the symmetries inherent to the problem, GNNome reconstructs assemblies from PacBio HiFi reads with contiguity and quality comparable to those of the state-of-the-art tools across several species. With every new genome assembled telomere-to-telomere, the amount of reliable training data at our disposal increases. Combining the straightforward generation of abundant simulated data for diverse genomic structures with the AI approach makes the proposed framework a plausible cornerstone for future work on reconstructing complex genomes with different ploidy and aneuploidy degrees. To facilitate such developments, we make the framework and the best-performing model publicly available, provided as a tool that can directly be used to assemble new haploid genomes.

GTasm: a genome assembly method using graph transformers and HiFi reads.

Genome assembly aims to reconstruct the whole chromosome-scale genome sequence. Obtaining accurate and complete chromosome-scale genome sequence serve as an indispensable foundation for downstream genomics analyses. Due to the complex repeat regions contained in genome sequence, the assembly results commonly are fragmented. Long reads with high accuracy rate can greatly enhance the integrity of genome assembly results. Here we introduce GTasm, an assembly method that uses graph transformer network to find optimal assembly results based on assembly graphs. Based on assembly graph, GTasm first extracts features about vertices and edges. Then, GTasm scores the edges by graph transformer model, and adopt a heuristic algorithm to find optimal paths in the assembly graph, each path corresponding to a contig. The graph transformer model is trained using simulated HiFi reads from CHM13, and GTasm is compared with other assembly methods using real HIFI read set. Through experimental result, GTasm can produce well assembly results, and achieve good performance on NA50 and NGA50 evaluation indicators. Applying deep learning models to genome assembly can improve the continuity and accuracy of assembly results. The code is available from https://github.com/chu-xuezhe/GTasm.

Flowtigs: Safety in flow decompositions for assembly graphs

Flowtigs: Safety in flow decompositions for assembly graphs

Telomere-to-telomere assembly by preserving contained reads.

Automated telomere-to-telomere (T2T) de novo assembly of diploid and polyploid genomes remains a formidable task. A string graph is a commonly used assembly graph representation in the assembly algorithms. The string graph formulation employs graph simplification heuristics, which drastically reduce the count of vertices and edges. One of these heuristics involves removing the reads contained in longer reads. In practice, this heuristic occasionally introduces gaps in the assembly by removing all reads that cover one or more genome intervals. The factors contributing to such gaps remain poorly understood. In this work, we mathematically derived the frequency of observing a gap near a germline and a somatic heterozygous variant locus. Our analysis shows that (1) an assembly gap due to contained read deletion is an order of magnitude more frequent in Oxford Nanopore Technologies (ONT) reads than Pacific Biosciences high-fidelity (PacBio HiFi) reads due to differences in their read-length distributions, and (2) this frequency decreases with an increase in the sequencing depth. Drawing cues from these observations, we addressed the weakness of the string graph formulation by developing the repeat-aware fragmenting tool (RAFT) assembly algorithm. RAFT addresses the issue of contained reads by fragmenting reads and producing a more uniform read-length distribution. The algorithm retains spanned repeats in the reads during the fragmentation. We empirically demonstrate that RAFT significantly reduces the number of gaps using simulated data sets. Using real ONT and PacBio HiFi data sets of the HG002 human genome, we achieved a twofold increase in the contig NG50 and the number of haplotype-resolved T2T contigs compared to hifiasm.

Graph-based self-supervised learning for repeat detection in metagenomic assembly.

Repetitive DNA (repeats) poses significant challenges for accurate and efficient genome assembly and sequence alignment. This is particularly true for metagenomic data, in which genome dynamics such as horizontal gene transfer, gene duplication, and gene loss/gain complicate accurate genome assembly from metagenomic communities. Detecting repeats is a crucial first step in overcoming these challenges. To address this issue, we propose GraSSRep, a novel approach that leverages the assembly graph's structure through graph neural networks (GNNs) within a self-supervised learning framework to classify DNA sequences into repetitive and nonrepetitive categories. Specifically, we frame this problem as a node classification task within a metagenomic assembly graph. In a self-supervised fashion, we rely on a high-precision (but low-recall) heuristic to generate pseudolabels for a small proportion of the nodes. We then use those pseudolabels to train a GNN embedding and a random forest classifier to propagate the labels to the remaining nodes. In this way, GraSSRep combines sequencing features with predefined and learned graph features to achieve state-of-the-art performance in repeat detection. We evaluate our method using simulated and synthetic metagenomic data sets. The results on the simulated data highlight GraSSRep's robustness to repeat attributes, demonstrating its effectiveness in handling the complexity of repeated sequences. Additionally, experiments with synthetic metagenomic data sets reveal that incorporating the graph structure and the GNN enhances the detection performance. Finally, in comparative analyses, GraSSRep outperforms existing repeat detection tools with respect to precision and recall.

Graphasing: phasing diploid genome assembly graphs with single-cell strand sequencing

Haplotype information is crucial for biomedical and population genetics research. However, current strategies to produce de novo haplotype-resolved assemblies often require either difficult-to-acquire parental data or an intermediate haplotype-collapsed assembly. Here, we present Graphasing, a workflow which synthesizes the global phase signal of Strand-seq with assembly graph topology to produce chromosome-scale de novo haplotypes for diploid genomes. Graphasing readily integrates with any assembly workflow that both outputs an assembly graph and has a haplotype assembly mode. Graphasing performs comparably to trio phasing in contiguity, phasing accuracy, and assembly quality, outperforms Hi-C in phasing accuracy, and generates human assemblies with over 18 chromosome-spanning haplotypes.

Strain-resolved de-novo metagenomic assembly of viral genomes and microbial 16S rRNAs

BackgroundMetagenomics is a powerful approach to study environmental and human-associated microbial communities and, in particular, the role of viruses in shaping them. Viral genomes are challenging to assemble from metagenomic samples due to their genomic diversity caused by high mutation rates. In the standard de Bruijn graph assemblers, this genomic diversity leads to complex k-mer assembly graphs with a plethora of loops and bulges that are challenging to resolve into strains or haplotypes because variants more than the k-mer size apart cannot be phased. In contrast, overlap assemblers can phase variants as long as they are covered by a single read.ResultsHere, we present PenguiN, a software for strain resolved assembly of viral DNA and RNA genomes and bacterial 16S rRNA from shotgun metagenomics. Its exhaustive detection of all read overlaps in linear time combined with a Bayesian model to select strain-resolved extensions allow it to assemble longer viral contigs, less fragmented genomes, and more strains than existing assembly tools, on both real and simulated datasets. We show a 3–40-fold increase in complete viral genomes and a 6-fold increase in bacterial 16S rRNA genes.ConclusionPenguiN is the first overlap-based assembler for viral genome and 16S rRNA assembly from large and complex metagenomic datasets, which we hope will facilitate studying the key roles of viruses in microbial communities.C22dm7AjjRZTtiuc33W1snVideo

4CAC: 4-class classifier of metagenome contigs using machine learning and assembly graphs.

Microbial communities usually harbor a mix of bacteria, archaea, plasmids, virusesand microeukaryotes. Within these communities, viruses, plasmids, and microeukaryotes coexist in relatively low abundance, yet they engage in intricate interactions with bacteria. Moreover, viruses and plasmids, as mobile genetic elements, play important roles in horizontal gene transfer and the development of antibiotic resistance within microbial populations. However, due to the difficulty of identifying viruses, plasmids, and microeukaryotes in microbial communities, our understanding of these minor classes lags behind that of bacteria and archaea. Recently, several classifiers have been developed to separate one or more minor classes from bacteria and archaea in metagenome assemblies. However, these classifiers often overlook the issue of class imbalance, leading to low precision in identifying the minor classes. Here, we developed a classifier called 4CAC that is able to identify viruses, plasmids, microeukaryotes, and prokaryotes simultaneously from metagenome assemblies. 4CAC generates an initial four-way classification using several sequence length-adjusted XGBoost models and further improves the classification using the assembly graph. Evaluation on simulated and real metagenome datasets demonstrates that 4CAC substantially outperforms existing classifiers and combinations thereof on short reads. On long reads, it also shows an advantage unless the abundance of the minor classes is very low. 4CAC runs 1-2 orders of magnitude faster than the other classifiers. The 4CAC software is available at https://github.com/Shamir-Lab/4CAC.

Assessing Assembly Errors in Immunoglobulin Loci: A Comprehensive Evaluation of Long-read Genome Assemblies Across Vertebrates.

Long-read sequencing technologies have revolutionized genome assembly producing near-complete chromosome assemblies for numerous organisms, which are invaluable to research in many fields. However, regions with complex repetitive structure continue to represent a challenge for genome assembly algorithms, particularly in areas with high heterozygosity. Robust and comprehensive solutions for the assessment of assembly accuracy and completeness in these regions do not exist. In this study we focus on the assembly of biomedically important antibody-encoding immunoglobulin (IG) loci, which are characterized by complex duplications and repeat structures. High-quality full-length assemblies for these loci are critical for resolving haplotype-level annotations of IG genes, without which, functional and evolutionary studies of antibody immunity across vertebrates are not tractable. To address these challenges, we developed a pipeline, "CloseRead", that generates multiple assembly verification metrics for analysis and visualization. These metrics expand upon those of existing quality assessment tools and specifically target complex and highly heterozygous regions. Using CloseRead, we systematically assessed the accuracy and completeness of IG loci in publicly available assemblies of 74 vertebrate species, identifying problematic regions. We also demonstrated that inspecting assembly graphs for problematic regions can both identify the root cause of assembly errors and illuminate solutions for improving erroneous assemblies. For a subset of species, we were able to correct assembly errors through targeted reassembly. Together, our analysis demonstrated the utility of assembly assessment in improving the completeness and accuracy of IG loci across species.

Label-guided seed-chain-extend alignment on annotated De Bruijn graphs.

Exponential growth in sequencing databases has motivated scalable De Bruijn graph-based (DBG) indexing for searching these data, using annotations to label nodes with sample IDs. Low-depth sequencing samples correspond to fragmented subgraphs, complicating finding the long contiguous walks required for alignment queries. Aligners that target single-labelled subgraphs reduce alignment lengths due to fragmentation, leading to low recall for long reads. While some (e.g. label-free) aligners partially overcome fragmentation by combining information from multiple samples, biologically irrelevant combinations in such approaches can inflate the search space or reduce accuracy. We introduce a new scoring model, 'multi-label alignment' (MLA), for annotated DBGs. MLA leverages two new operations: To promote biologically relevant sample combinations, 'Label Change' incorporates more informative global sample similarity into local scores. To improve connectivity, 'Node Length Change' dynamically adjusts the DBG node length during traversal. Our fast, approximate, yet accurate MLA implementation has two key steps: a single-label seed-chain-extend aligner (SCA) and a multi-label chainer (MLC). SCA uses a traditional scoring model adapting recent chaining improvements to assembly graphs and provides a curated pool of alignments. MLC extracts seed anchors from SCAs alignments, produces multi-label chains using MLA scoring, then finally forms multi-label alignments. We show via substantial improvements in taxonomic classification accuracy that MLA produces biologically relevant alignments, decreasing average weighted UniFrac errors by 63.1%-66.8% and covering 45.5%-47.4% (median) more long-read query characters than state-of-the-art aligners. MLAs runtimes are competitive with label-combining alignment and substantially faster than single-label alignment. The data, scripts, and instructions for generating our results are available at https://github.com/ratschlab/mla.

Reference-free structural variant detection in microbiomes via long-read co-assembly graphs.

The study of bacterial genome dynamics is vital for understanding the mechanisms underlying microbial adaptation, growth, and their impact on host phenotype. Structural variants (SVs), genomic alterations of 50 base pairs or more, play a pivotal role in driving evolutionary processes and maintaining genomic heterogeneity within bacterial populations. While SV detection in isolate genomes is relatively straightforward, metagenomes present broader challenges due to the absence of clear reference genomes and the presence of mixed strains. In response, our proposed method rhea, forgoes reference genomes and metagenome-assembled genomes (MAGs) by encompassing all metagenomic samples in a series (time or other metric) into a single co-assembly graph. The log fold change in graph coverage between successive samples is then calculated to call SVs that are thriving or declining. We show rhea to outperform existing methods for SV and horizontal gene transfer (HGT) detection in two simulated mock metagenomes, particularly as the simulated reads diverge from reference genomes and an increase in strain diversity is incorporated. We additionally demonstrate use cases for rhea on series metagenomic data of environmental and fermented food microbiomes to detect specific sequence alterations between successive time and temperature samples, suggesting host advantage. Our approach leverages previous work in assembly graph structural and coverage patterns to provide versatility in studying SVs across diverse and poorly characterized microbial communities for more comprehensive insights into microbial gene flux. rhea is open source and available at: https://github.com/treangenlab/rhea.

Phasing Diploid Genome Assembly Graphs with Single-Cell Strand Sequencing.

Haplotype information is crucial for biomedical and population genetics research. However, current strategies to produce de-novo haplotype-resolved assemblies often require either difficult-to-acquire parental data or an intermediate haplotype-collapsed assembly. Here, we present Graphasing, a workflow which synthesizes the global phase signal of Strand-seq with assembly graph topology to produce chromosome-scale de-novo haplotypes for diploid genomes. Graphasing readily integrates with any assembly workflow that both outputs an assembly graph and has a haplotype assembly mode. Graphasing performs comparably to trio-phasing in contiguity, phasing accuracy, and assembly quality, outperforms Hi-C in phasing accuracy, and generates human assemblies with over 18 chromosome-spanning haplotypes.

Critical component detection in assemblies: a graph centrality approach

AbstractThis study examines the use of graph centrality to identify critical components in assembly models, a method typically dominated by computationally intense analyses. By applying centrality measures to simulated assembly graphs, components were ranked to assess their criticality. These rankings were compared against Monte Carlo sensitivity analysis results. Preliminary findings indicate a promising correlation, suggesting graph centrality as a valuable tool in assembly analysis, enhancing efficiency and insight in critical component identification.

Haplotype-resolved assembly of a tetraploid potato genome using long reads and low-depth offspring data

Potato is one of the world’s major staple crops, and like many important crop plants, it has a polyploid genome. Polyploid haplotype assembly poses a major computational challenge. We introduce a novel strategy for the assembly of polyploid genomes and present an assembly of the autotetraploid potato cultivar Altus. Our method uses low-depth sequencing data from an offspring population to achieve chromosomal clustering and haplotype phasing on the assembly graph. Our approach generates high-quality assemblies of individual chromosomes with haplotype-specific sequence resolution of whole chromosome arms and can be applied in common breeding scenarios where collections of offspring are available.

RFGR: Repeat Finder for Complete and Assembled Whole Genomes and NGS Reads.

Repetitive DNA sequences cause genomic instability and are important genetic markers. Identification of repeats is a critical step in genome annotation and analysis. On the other hand, repeats also pose a technical challenge for genome assembly and alignment programs using NGS data. RFGR is a comprehensive tool that can find exact repetitive sequences in complete genomes and assembled genomes, as well as NGS reads of prokaryotes. For complete genomes, RFGR uses a suffix trees to find seed repeats of repetitive sequences of fixed length with indels. For assembled genomes, RFGR uses a modified Bowtie aligner to find seed repeats of exact repetitive sequences in the contigs/ scaffolds, which are then extended to maximal repeats. The repeats are classified and for repeats near a gene, RFGR reports the gene as well. For the control dataset of E. coli UTI89 and E. coli K12, RFGR reports 35,141 and 49,352 repeats, respectively. For NGS reads, RFGR uses the frequency of the repetitive k-mers to determine FASTQ reads containing repetitive sequences and removes them from the dataset. An E. coli K12 NGS dataset pre-processed using RFGR, on comparison with the original dataset, gives an improved assembly. The N50 value improves by 22.86% with a decrease in size of the assembly graph by nearly 50%. Thus, with RFGR, we achieve a better assembly with reduced computation. RFGR can be improved in terms of the length of the minimum repeat found, extending to find approximate repeats and to be applicable to Eukaryotes as well.

GeneMiner: A tool for extracting phylogenetic markers from next-generation sequencing data.

The advancement of next-generation sequencing (NGS) technologies has been revolutionary for the field of evolutionary biology. This technology has led to an abundance of available genomes and transcriptomes for researchers to mine. Specifically, researchers can mine for various types of molecular markers that are vital for phylogenetic, evolutionary and ecological studies. Numerous tools have been developed to extract these molecular markers from NGS data. However, due to an insufficient number of well-annotated reference genomes for non-model organisms, it remains challenging to obtain these markers accurately and efficiently. Here, we present GeneMiner, an improved and expanded version of our previous tool, Easy353. GeneMiner combines the reference-guided de Bruijn graph assembly with seed self-discovery and greedy extension. Additionally, it includes a verification step using a parameter-bootstrap method to reduce the pitfalls associated with using a relatively distant reference. Our results, using both experimental and simulation data, showed GeneMiner can accurately acquire phylogenetic molecular markers for plants using transcriptomic, genomic and other NGS data. GeneMiner is designed to be user-friendly, fast and memory-efficient. Further, it is compatible with Linux, Windows and macOS. All source codes are publicly available on GitHub (https://github.com/sculab/GeneMiner) and Gitee (https://gitee.com/sculab/GeneMiner) for easy accessibility and transparency.

High-quality metagenome assembly from long accurate reads with metaMDBG

We introduce metaMDBG, a metagenomics assembler for PacBio HiFi reads. MetaMDBG combines a de Bruijn graph assembly in a minimizer space with an iterative assembly over sequences of minimizers to address variations in genome coverage depth and an abundance-based filtering strategy to simplify strain complexity. For complex communities, we obtained up to twice as many high-quality circularized prokaryotic metagenome-assembled genomes as existing methods and had better recovery of viruses and plasmids.

AsmMix: an efficient haplotype-resolved hybrid de novo genome assembling pipeline.

Accurate haplotyping facilitates distinguishing allele-specific expression, identifying cis-regulatory elements, and characterizing genomic variations, which enables more precise investigations into the relationship between genotype and phenotype. Recent advances in third-generation single-molecule long read and synthetic co-barcoded read sequencing techniques have harnessed long-range information to simplify the assembly graph and improve assembly genomic sequence. However, it remains methodologically challenging to reconstruct the complete haplotypes due to high sequencing error rates of long reads and limited capturing efficiency of co-barcoded reads. We here present a pipeline, AsmMix, for generating both contiguous and accurate diploid genomes. It first assembles co-barcoded reads to generate accurate haplotype-resolved assemblies that may contain many gaps, while the long-read assembly is contiguous but susceptible to errors. Then two assembly sets are integrated into haplotype-resolved assemblies with reduced misassembles. Through extensive evaluation on multiple synthetic datasets, AsmMix consistently demonstrates high precision and recall rates for haplotyping across diverse sequencing platforms, coverage depths, read lengths, and read accuracies, significantly outperforming other existing tools in the field. Furthermore, we validate the effectiveness of our pipeline using a human whole genome dataset (HG002), and produce highly contiguous, accurate, and haplotype-resolved assemblies. These assemblies are evaluated using the GIAB benchmarks, confirming the accuracy of variant calling. Our results demonstrate that AsmMix offers a straightforward yet highly efficient approach that effectively leverages both long reads and co-barcoded reads for haplotype-resolved assembly.

Genome Assembly, from Practice to Theory: Safe, Complete and Linear-Time

Genome assembly asks to reconstruct an unknown string from many shorter substrings of it. Even though it is one of the key problems in Bioinformatics, it is generally lacking major theoretical advances. Its hardness stems both from practical issues (size and errors of real data), and from the fact that problem formulations inherently admit multiple solutions. Given these, at their core, most state-of-the-art assemblers are based on finding non-branching paths ( unitigs ) in an assembly graph. While such paths constitute only partial assemblies, they are likely to be correct. More precisely, if one defines a genome assembly solution as a closed arc-covering walk of the graph, then unitigs appear in all solutions, being thus safe partial solutions. Until recently, it was open what are all the safe walks of an assembly graph. Tomescu and Medvedev (RECOMB 2016) characterized all such safe walks ( omnitigs ), thus giving the first safe and complete genome assembly algorithm. Even though maximal omnitig finding was later improved to quadratic time by Cairo et al. (ACM Trans. Algorithms 2019), it remained open whether the crucial linear-time feature of finding unitigs can be attained with omnitigs. We answer this question affirmatively, by describing a surprising O(m) -time algorithm to identify all maximal omnitigs of a graph with n nodes and m arcs, notwithstanding the existence of families of graphs with Θ (mn) total maximal omnitig size. This is based on the discovery of a family of walks ( macrotigs ) with the property that all the non-trivial omnitigs are univocal extensions of subwalks of a macrotig. This has two consequences: (1) A linear-time output-sensitive algorithm enumerating all maximal omnitigs. (2) A compact O(m) representation of all maximal omnitigs, which allows, e.g., for O(m) -time computation of various statistics on them. Our results close a long-standing theoretical question inspired by practical genome assemblers, originating with the use of unitigs in 1995. We envision our results to be at the core of a reverse transfer from theory to practical and complete genome assembly programs, as has been the case for other key Bioinformatics problems.

Semantic knowledge-driven A-GASeq: A dynamic graph learning approach for assembly sequence optimization

In the context of an increasingly automated and personalized manufacturing mode, efficient assembly sequence planning (ASP) has emerged as a critical factor for enhancing production efficiency, ensuring product quality, and satisfying diverse market demands. To address this need, our study first transforms the assembly topology and process into a weighted precedence graph, wherein parts represent nodes, and the assembly interconnections between parts constitute weighted edges. Then, we formulate the quantitative models of semantic knowledge, encompassing three facets: assembly direction changes, assembly stability, and part assembly interference, and thus constructs a heuristic function. We propose a novel dynamic graph learning algorithm, i.e., assembly-oriented graph attention sequence (A-GASeq), utilizing the heuristic information as edge weights of the assembly graph structure to incrementally direct the search towards optimal sequences. The performance of A-GASeq is first evaluated utilizing three key metrics: area under the receiver operation characteristic curve (AUC), precision score, and time consumption. The results reveal the superiority of our model over competing state-of-the-art graph learning models using a real-world dataset. Concurrently, we apply the algorithm to actual industrial products of diverse complexity, thereby demonstrating its broad utility across different complex products and its potential for addressing complex assembly sequence planning problems in the field of smart manufacturing.