Reads Binning Improves the Assembly of Viral Genome Sequences From Metagenomic Samples.
Ontology highlight
ABSTRACT:
SUBMITTER: Song K
PROVIDER: S-EPMC8175635 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:PacBio long reads sequencing presents several potential advantages for DNA assembly, including being able to provide more complete gene profiling of metagenomic samples. However, lower single-pass accuracy can make gene discovery and assembly for low-abundance organisms difficult. To evaluate the application and performance of PacBio long reads and Illumina HiSeq short reads in metagenomic analyses, we directly compared various assemblies involving PacBio and Illumina sequencing reads based on two anaerobic digestion microbiome samples from a biogas fermenter. Using a PacBio platform, 1.58 million long reads (19.6 Gb) were produced with an average length of 7,604 bp. Using an Illumina HiSeq platform, 151.2 million read pairs (45.4 Gb) were produced. Hybrid assemblies using PacBio long reads and HiSeq contigs produced improvements in assembly statistics, including an increase in the average contig length, contig N50 size, and number of large contigs. Interestingly, depth-based hybrid assemblies generated a higher percentage of complete genes (98.86%) compared to those based on HiSeq contigs only (40.29%), because the PacBio reads were long enough to cover many repeating short elements and capture multiple genes in a single read. Additionally, the incorporation of PacBio long reads led to considerable advantages regarding reducing contig numbers and increasing the completeness of the genome reconstruction, which was poorly assembled and binned when using HiSeq data alone. From this comparison of PacBio long reads with Illumina HiSeq short reads related to complex microbiome samples, we conclude that PacBio long reads can produce longer contigs, more complete genes, and better genome binning, thereby offering more information about metagenomic samples.
| S-EPMC7506068 | biostudies-literature
Project description:Comparing metagenomic samples is a critical step in understanding the relationships among microbial communities. Recently, next-generation sequencing (NGS) technologies have produced a massive amount of short reads data for microbial communities from different environments. The assembly of these short reads can, however, be time-consuming and challenging. In addition, alignment-based methods for metagenome comparison are limited by incomplete genome and/or pathway databases. In contrast, alignment-free methods for metagenome comparison do not depend on the completeness of genome or pathway databases. Still, the existing alignment-free methods, d2S and d2* , which model k-tuple patterns using only one Markov chain for each sample, neglect the heterogeneity within metagenomic data wherein potentially thousands of types of microorganisms are sequenced. To address this imperfection in d2S and d2* , we organized NGS sequences into different reads bins and constructed several corresponding Markov models. Next, we modified the definition of our previous alignment-free methods, d2S and d2* , to make them more compatible with a scheme of analysis which uses the proposed reads bins. We then used two simulated and three real metagenomic datasets to test the effect of the k-tuple size and Markov orders of background sequences on the performance of these de novo alignment-free methods. For dependable comparison of metagenomic samples, our newly developed alignment-free methods with reads binning outperformed alignment-free methods without reads binning in detecting the relationship among microbial communities, including whether they form groups or change according to some environmental gradients.
| S-EPMC6881972 | biostudies-literature
Project description:MotivationDespite recent advancements in sequencing technologies and assembly methods, obtaining high-quality microbial genomes from metagenomic samples is still not a trivial task. Current metagenomic binners do not take full advantage of assembly graphs and are not optimized for long-read assemblies. Deep graph learning algorithms have been proposed in other fields to deal with complex graph data structures. The graph structure generated during the assembly process could be integrated with contig features to obtain better bins with deep learning.ResultsWe propose GraphMB, which uses graph neural networks to incorporate the assembly graph into the binning process. We test GraphMB on long-read datasets of different complexities, and compare the performance with other binners in terms of the number of High Quality (HQ) genome bins obtained. With our approach, we were able to obtain unique bins on all real datasets, and obtain more bins on most datasets. In particular, we obtained on average 17.5% more HQ bins when compared with state-of-the-art binners and 13.7% when aggregating the results of our binner with the others. These results indicate that a deep learning model can integrate contig-specific and graph-structure information to improve metagenomic binning.Availability and implementationGraphMB is available from https://github.com/MicrobialDarkMatter/GraphMB.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC9525014 | biostudies-literature
Project description:Sequencing technologies are capturing longer-range genomic information at lower error rates, enabling alignment to genomic regions that are inaccessible with short reads. However, many methods are unable to align reads to much of the genome, recognized as important in disease, and thus report erroneous results in downstream analyses. We introduce EMA, a novel two-tiered statistical binning model for barcoded read alignment, that first probabilistically maps reads to potentially multiple "read clouds" and then within clouds by newly exploiting the non-uniform read densities characteristic of barcoded read sequencing. EMA substantially improves downstream accuracy over existing methods, including phasing and genotyping on 10x data, with fewer false variant calls in nearly half the time. EMA effectively resolves particularly challenging alignments in genomic regions that contain nearby homologous elements, uncovering variants in the pharmacogenomically important CYP2D region, and clinically important genes C4 (schizophrenia) and AMY1A (obesity), which go undetected by existing methods. Our work provides a framework for future generation sequencing.
| S-EPMC6214366 | biostudies-literature
Project description:Metagenomics has enabled sequencing of viral communities from a myriad of different environments. Viral metagenomic studies routinely uncover sequences with no recognizable homology to known coding regions or genomes. Nevertheless, complete viral genomes have been constructed directly from complex community metagenomes, often through tedious manual curation. To address this, we developed the software tool virMine to identify viral genomes from raw reads representative of viral or mixed (viral and bacterial) communities. virMine automates sequence read quality control, assembly, and annotation. Researchers can easily refine their search for a specific study system and/or feature(s) of interest. In contrast to other viral genome detection tools that often rely on the recognition of viral signature sequences, virMine is not restricted by the insufficient representation of viral diversity in public data repositories. Rather, viral genomes are identified through an iterative approach, first omitting non-viral sequences. Thus, both relatives of previously characterized viruses and novel species can be detected, including both eukaryotic viruses and bacteriophages. Here we present virMine and its analysis of synthetic communities as well as metagenomic data sets from three distinctly different environments: the gut microbiota, the urinary microbiota, and freshwater viromes. Several new viral genomes were identified and annotated, thus contributing to our understanding of viral genetic diversity in these three environments.
| S-EPMC6462185 | biostudies-literature
Project description:Metagenomics is the study of microbial communities sampled directly from their natural environment, without prior culturing. Among the computational tools recently developed for metagenomic sequence analysis, binning tools attempt to classify the sequences in a metagenomic dataset into different bins (i.e., species), based on various DNA composition patterns (e.g., the tetramer frequencies) of various genomes. Composition-based binning methods, however, cannot be used to classify very short fragments, because of the substantial variation of DNA composition patterns within a single genome. We developed a novel approach (AbundanceBin) for metagenomics binning by utilizing the different abundances of species living in the same environment. AbundanceBin is an application of the Lander-Waterman model to metagenomics, which is based on the l-tuple content of the reads. AbundanceBin achieved accurate, unsupervised, clustering of metagenomic sequences into different bins, such that the reads classified in a bin belong to species of identical or very similar abundances in the sample. In addition, AbundanceBin gave accurate estimations of species abundances, as well as their genome sizes-two important parameters for characterizing a microbial community. We also show that AbundanceBin performed well when the sequence lengths are very short (e.g., 75 bp) or have sequencing errors. By combining AbundanceBin and a composition-based method (MetaCluster), we can achieve even higher binning accuracy. Supplementary Material is available at www.liebertonline.com/cmb .
| S-EPMC3123841 | biostudies-other
Project description:BackgroundMetagenomic sequencing allows us to study the structure, diversity and ecology in microbial communities without the necessity of obtaining pure cultures. In many metagenomics studies, the reads obtained from metagenomics sequencing are first assembled into longer contigs and these contigs are then binned into clusters of contigs where contigs in a cluster are expected to come from the same species. As different species may share common sequences in their genomes, one assembled contig may belong to multiple species. However, existing tools for binning contigs only support non-overlapped binning, i.e., each contig is assigned to at most one bin (species).ResultsIn this paper, we introduce GraphBin2 which refines the binning results obtained from existing tools and, more importantly, is able to assign contigs to multiple bins. GraphBin2 uses the connectivity and coverage information from assembly graphs to adjust existing binning results on contigs and to infer contigs shared by multiple species. Experimental results on both simulated and real datasets demonstrate that GraphBin2 not only improves binning results of existing tools but also supports to assign contigs to multiple bins.ConclusionGraphBin2 incorporates the coverage information into the assembly graph to refine the binning results obtained from existing binning tools. GraphBin2 also enables the detection of contigs that may belong to multiple species. We show that GraphBin2 outperforms its predecessor GraphBin on both simulated and real datasets. GraphBin2 is freely available at https://github.com/Vini2/GraphBin2 .
| S-EPMC8097841 | biostudies-literature
Project description:Metagenomics is a next-generation omics field currently impacting postgenomic life sciences and medicine. Binning metagenomic reads is essential for the understanding of microbial function, compositions, and interactions in given environments. Despite the existence of dozens of computational methods for metagenomic read binning, it is still very challenging to bin reads. This is especially true for reads from unknown species, from species with similar abundance, and/or from low-abundance species in environmental samples. In this study, we developed a novel taxonomy-dependent and alignment-free approach called MBMC (Metagenomic Binning by Markov Chains). Different from all existing methods, MBMC bins reads by measuring the similarity of reads to the trained Markov chains for different taxa instead of directly comparing reads with known genomic sequences. By testing on more than 24 simulated and experimental datasets with species of similar abundance, species of low abundance, and/or unknown species, we report here that MBMC reliably grouped reads from different species into separate bins. Compared with four existing approaches, we demonstrated that the performance of MBMC was comparable with existing approaches when binning reads from sequenced species, and superior to existing approaches when binning reads from unknown species. MBMC is a pivotal tool for binning metagenomic reads in the current era of Big Data and postgenomic integrative biology. The MBMC software can be freely downloaded at http://hulab.ucf.edu/research/projects/metagenomics/MBMC.html .
| S-EPMC4982950 | biostudies-literature
Project description:BackgroundNext-generation sequencing (NGS) has greatly facilitated metagenomic analysis but also raised new challenges for metagenomic DNA sequence assembly, owing to its high-throughput nature and extremely short reads generated by sequencers such as Illumina. To date, how to generate a high-quality draft assembly for metagenomic sequencing projects has not been fully addressed.ResultsWe conducted a comprehensive assessment on state-of-the-art de novo assemblers and revealed that the performance of each assembler depends critically on the sequencing depth. To address this problem, we developed a pipeline named InteMAP to integrate three assemblers, ABySS, IDBA-UD and CABOG, which were found to complement each other in assembling metagenomic sequences. Making a decision of which assembling approaches to use according to the sequencing coverage estimation algorithm for each short read, the pipeline presents an automatic platform suitable to assemble real metagenomic NGS data with uneven coverage distribution of sequencing depth. By comparing the performance of InteMAP with current assemblers on both synthetic and real NGS metagenomic data, we demonstrated that InteMAP achieves better performance with a longer total contig length and higher contiguity, and contains more genes than others.ConclusionsWe developed a de novo pipeline, named InteMAP, that integrates existing tools for metagenomics assembly. The pipeline outperforms previous assembly methods on metagenomic assembly by providing a longer total contig length, a higher contiguity and covering more genes. InteMAP, therefore, could potentially be a useful tool for the research community of metagenomics.
| S-EPMC4545859 | biostudies-literature
Project description:Giant viruses have large genomes, often within the size range of cellular organisms. This distinguishes them from most other viruses and demands additional effort for the successful recovery of their genomes from environmental sequence data. Here, we tested the performance of genome-resolved metagenomics on a recently isolated giant virus, Fadolivirus, by spiking it into an environmental sample from which two other giant viruses were isolated. At high spike-in levels, metagenome assembly and binning led to the successful genomic recovery of Fadolivirus from the sample. A complementary survey of the major capsid protein indicated the presence of other giant viruses in the sample matrix but did not detect the two isolated from this sample. Our results indicate that genome-resolved metagenomics is a valid approach for the recovery of near-complete giant virus genomes given that sufficient clonal particles are present. However, our data also underline that a vast majority of giant viruses remain currently undetected, even in an era of terabase-scale metagenomics.IMPORTANCE The discovery of large and giant nucleocytoplasmic large DNA viruses (NCLDV) with genomes in the megabase range and equipped with a wide variety of features typically associated with cellular organisms was one of the most unexpected, intriguing, and spectacular breakthroughs in virology. Recent studies suggest that these viruses are highly abundant in the oceans, freshwater, and soil, impact the biology and ecology of their eukaryotic hosts, and ultimately affect global nutrient cycles. Genome-resolved metagenomics is becoming an increasingly popular tool to assess the diversity and coding potential of giant viruses, but this approach is currently lacking validation.
| S-EPMC7311315 | biostudies-literature