Deconvolute individual genomes from metagenome sequences through short read clustering.
Ontology highlight
ABSTRACT:
SUBMITTER: Li K
PROVIDER: S-EPMC7150542 | biostudies-literature | 2020
REPOSITORIES: biostudies-literature
Publications
Deconvolute individual genomes from metagenome sequences through short read clustering.
Li Kexue K Lu Yakang Y Deng Li L Wang Lili L Shi Lizhen L Wang Zhong Z
PeerJ 20200408
Metagenome assembly from short next-generation sequencing data is a challenging process due to its large scale and computational complexity. Clustering short reads by species before assembly offers a unique opportunity for parallel downstream assembly of genomes with individualized optimization. However, current read clustering methods suffer either false negative (under-clustering) or false positive (over-clustering) problems. Here we extended our previous read clustering software, SpaRC, by ex ...[more]
Similar Datasets
Project description:The impacts of climate change on the Arctic Ocean are manifesting throughout the ecosystem at an unprecedented rate. Of global importance are the impacts on heat and freshwater exchange between the Arctic and North Atlantic Oceans. An expanding Atlantic influence in the Arctic has accelerated sea-ice decline, weakened water column stability and supported the northward shift of temperate species. The only deep-water gateway connecting the Arctic and North Atlantic and thus, fundamental for these exchange processes is the Fram Strait. Previous research in this region is extensive, however, data on the ecology of microbial communities is limited, reflecting the wider bias towards temperate and tropical latitudes. Therefore, we present 14 metagenomes, 11 short-read from Illumina and three long-read from PacBio Sequel II, of the 0.2-3 µm fraction to help alleviate such biases and support future analyses on changing ecological patterns. Additionally, we provide 136 species-representative, manually refined metagenome-assembled genomes which can be used for comparative genomics analyses and addressing questions regarding functionality or distribution of taxa.
| S-EPMC8254474 | biostudies-literature
Project description:UnlabelledBackgroundThe metagenomics approach allows the simultaneous sequencing of all genomes in an environmental sample. This results in high complexity datasets, where in addition to repeats and sequencing errors, the number of genomes and their abundance ratios are unknown. Recently developed next-generation sequencing (NGS) technologies significantly improve the sequencing efficiency and cost. On the other hand, they result in shorter reads, which makes the separation of reads from different species harder. Among the existing computational tools for metagenomic analysis, there are similarity-based methods that use reference databases to align reads and composition-based methods that use composition patterns (i.e., frequencies of short words or l-mers) to cluster reads. Similarity-based methods are unable to classify reads from unknown species without close references (which constitute the majority of reads). Since composition patterns are preserved only in significantly large fragments, composition-based tools cannot be used for very short reads, which becomes a significant limitation with the development of NGS. A recently proposed algorithm, AbundanceBin, introduced another method that bins reads based on predicted abundances of the genomes sequenced. However, it does not separate reads from genomes of similar abundance levels.ResultsIn this work, we present a two-phase heuristic algorithm for separating short paired-end reads from different genomes in a metagenomic dataset. We use the observation that most of the l-mers belong to unique genomes when l is sufficiently large. The first phase of the algorithm results in clusters of l-mers each of which belongs to one genome. During the second phase, clusters are merged based on l-mer repeat information. These final clusters are used to assign reads. The algorithm could handle very short reads and sequencing errors. It is initially designed for genomes with similar abundance levels and then extended to handle arbitrary abundance ratios. The software can be download for free at http://www.cs.ucr.edu/∼tanaseio/toss.htm.ConclusionsOur tests on a large number of simulated metagenomic datasets concerning species at various phylogenetic distances demonstrate that genomes can be separated if the number of common repeats is smaller than the number of genome-specific repeats. For such genomes, our method can separate NGS reads with a high precision and sensitivity.
| S-EPMC3537596 | biostudies-literature
Project description:Metagenome community analyses, driven by the continued development in sequencing technology, is rapidly providing insights in many aspects of microbiology and becoming a cornerstone tool. Illumina, Oxford Nanopore Technologies (ONT) and Pacific Biosciences (PacBio) are the leading technologies, each with their own advantages and drawbacks. Illumina provides accurate reads at a low cost, but their length is too short to close bacterial genomes. Long reads overcome this limitation, but these technologies produce reads with lower accuracy (ONT) or with lower throughput (PacBio high-fidelity reads). In a critical first analysis step, reads are assembled to reconstruct genomes or individual genes within the community. However, to date, the performance of existing assemblers has never been challenged with a complex mock metagenome. Here, we evaluate the performance of current assemblers that use short, long or both read types on a complex mock metagenome consisting of 227 bacterial strains with varying degrees of relatedness. We show that many of the current assemblers are not suited to handle such a complex metagenome. In addition, hybrid assemblies do not fulfil their potential. We conclude that ONT reads assembled with CANU and Illumina reads assembled with SPAdes offer the best value for reconstructing genomes and individual genes of complex metagenomes, respectively.
| S-EPMC11261854 | biostudies-literature
Project description:Assembling individual genomes from complex community metagenomic data remains a challenging issue for environmental studies. We evaluated the quality of genome assemblies from community short read data (Illumina 100 bp pair-ended sequences) using datasets recovered from freshwater and soil microbial communities as well as in silico simulations. Our analyses revealed that the genome of a single genotype (or species) can be accurately assembled from a complex metagenome when it shows at least about 20 × coverage. At lower coverage, however, the derived assemblies contained a substantial fraction of non-target sequences (chimeras), which explains, at least in part, the higher number of hypothetical genes recovered in metagenomic relative to genomic projects. We also provide examples of how to detect intrapopulation structure in metagenomic datasets and estimate the type and frequency of errors in assembled genes and contigs from datasets of varied species complexity.
| S-EPMC3309356 | biostudies-literature
Project description:MotivationStoring, transmitting and archiving data produced by next-generation sequencing is a significant computational burden. New compression techniques tailored to short-read sequence data are needed.ResultsWe present here an approach to compression that reduces the difficulty of managing large-scale sequencing data. Our novel approach sits between pure reference-based compression and reference-free compression and combines much of the benefit of reference-based approaches with the flexibility of de novo encoding. Our method, called path encoding, draws a connection between storing paths in de Bruijn graphs and context-dependent arithmetic coding. Supporting this method is a system to compactly store sets of kmers that is of independent interest. We are able to encode RNA-seq reads using 3-11% of the space of the sequence in raw FASTA files, which is on average more than 34% smaller than competing approaches. We also show that even if the reference is very poorly matched to the reads that are being encoded, good compression can still be achieved.Availability and implementationSource code and binaries freely available for download at http://www.cs.cmu.edu/?ckingsf/software/pathenc/, implemented in Go and supported on Linux and Mac OS X.
| S-EPMC4481695 | biostudies-literature
Project description:BackgroundNext-generation sequencing technologies have given rise to the explosive increase in DNA sequencing throughput, and have promoted the recent development of de novo short read assemblers. However, existing assemblers require high execution times and a large amount of compute resources to assemble large genomes from quantities of short reads.ResultsWe present PASHA, a parallelized short read assembler using de Bruijn graphs, which takes advantage of hybrid computing architectures consisting of both shared-memory multi-core CPUs and distributed-memory compute clusters to gain efficiency and scalability. Evaluation using three small-scale real paired-end datasets shows that PASHA is able to produce more contiguous high-quality assemblies in shorter time compared to three leading assemblers: Velvet, ABySS and SOAPdenovo. PASHA's scalability for large genome datasets is demonstrated with human genome assembly. Compared to ABySS, PASHA achieves competitive assembly quality with faster execution speed on the same compute resources, yielding an NG50 contig size of 503 with the longest correct contig size of 18,252, and an NG50 scaffold size of 2,294. Moreover, the human assembly is completed in about 21 hours with only modest compute resources.ConclusionsDeveloping parallel assemblers for large genomes has been garnering significant research efforts due to the explosive size growth of high-throughput short read datasets. By employing hybrid parallelism consisting of multi-threading on multi-core CPUs and message passing on compute clusters, PASHA is able to assemble the human genome with high quality and in reasonable time using modest compute resources.
| S-EPMC3167803 | biostudies-literature
Project description:BACKGROUND:When compared to Sanger sequencing technology, next-generation sequencing (NGS) technologies are hindered by shorter sequence read length, higher base-call error rate, non-uniform coverage, and platform-specific sequencing artifacts. These characteristics lower the quality of their downstream analyses, e.g. de novo and reference-based assembly, by introducing sequencing artifacts and errors that may contribute to incorrect interpretation of data. Although many tools have been developed for quality control and pre-processing of NGS data, none of them provide flexible and comprehensive trimming options in conjunction with parallel processing to expedite pre-processing of large NGS datasets. METHODS:We developed ngsShoRT (next-generation sequencing Short Reads Trimmer), a flexible and comprehensive open-source software package written in Perl that provides a set of algorithms commonly used for pre-processing NGS short read sequences. We compared the features and performance of ngsShoRT with existing tools: CutAdapt, NGS QC Toolkit and Trimmomatic. We also compared the effects of using pre-processed short read sequences generated by different algorithms on de novo and reference-based assembly for three different genomes: Caenorhabditis elegans, Saccharomyces cerevisiae S288c, and Escherichia coli O157 H7. RESULTS:Several combinations of ngsShoRT algorithms were tested on publicly available Illumina GA II, HiSeq 2000, and MiSeq eukaryotic and bacteria genomic short read sequences with the focus on removing sequencing artifacts and low-quality reads and/or bases. Our results show that across three organisms and three sequencing platforms, trimming improved the mean quality scores of trimmed sequences. Using trimmed sequences for de novo and reference-based assembly improved assembly quality as well as assembler performance. In general, ngsShoRT outperformed comparable trimming tools in terms of trimming speed and improvement of de novo and reference-based assembly as measured by assembly contiguity and correctness. CONCLUSIONS:Trimming of short read sequences can improve the quality of de novo and reference-based assembly and assembler performance. The parallel processing capability of ngsShoRT reduces trimming time and improves the memory efficiency when dealing with large datasets. We recommend combining sequencing artifacts removal, and quality score based read filtering and base trimming as the most consistent method for improving sequence quality and downstream assemblies. ngsShoRT source code, user guide and tutorial are available at http://research.bioinformatics.udel.edu/genomics/ngsShoRT/. ngsShoRT can be incorporated as a pre-processing step in genome and transcriptome assembly projects.
| S-EPMC4064128 | biostudies-literature
Project description:Advances in sequencing technology provide special opportunities for genotyping individuals with speed and thrift, but the lack of software to automate the calling of tens of thousands of genotypes over hundreds of individuals has hindered progress. Stacks is a software system that uses short-read sequence data to identify and genotype loci in a set of individuals either de novo or by comparison to a reference genome. From reduced representation Illumina sequence data, such as RAD-tags, Stacks can recover thousands of single nucleotide polymorphism (SNP) markers useful for the genetic analysis of crosses or populations. Stacks can generate markers for ultra-dense genetic linkage maps, facilitate the examination of population phylogeography, and help in reference genome assembly. We report here the algorithms implemented in Stacks and demonstrate their efficacy by constructing loci from simulated RAD-tags taken from the stickleback reference genome and by recapitulating and improving a genetic map of the zebrafish, Danio rerio.
| S-EPMC3276136 | biostudies-literature
Project description:Next-generation massively parallel DNA sequencing technologies provide ultrahigh throughput at a substantially lower unit data cost; however, the data are very short read length sequences, making de novo assembly extremely challenging. Here, we describe a novel method for de novo assembly of large genomes from short read sequences. We successfully assembled both the Asian and African human genome sequences, achieving an N50 contig size of 7.4 and 5.9 kilobases (kb) and scaffold of 446.3 and 61.9 kb, respectively. The development of this de novo short read assembly method creates new opportunities for building reference sequences and carrying out accurate analyses of unexplored genomes in a cost-effective way.
| S-EPMC2813482 | biostudies-literature
Project description:BackgroundStructural variations play an important role in bacterial genomes. They can mediate genome adaptation quickly in response to the external environment and thus can also play a role in antibiotic resistance. The detection of structural variations in bacteria is challenging, and the recognition of even small rearrangements can be important. Even though most detection tools are aimed at and benchmarked on eukaryotic genomes, they can also be used on prokaryotic genomes. The key features of detection are the ability to detect small rearrangements and support haploid genomes. Because of the limiting performance of a single detection tool, combining the detection abilities of multiple tools can lead to more robust results. There are already available workflows for structural variation detection for long-reads technologies and for the detection of single-nucleotide variation and indels, both aimed at bacteria. Yet we are unaware of structural variations detection workflows for the short-reads sequencing platform. Motivated by this gap we created our workflow. Further, we were interested in increasing the detection performance and providing more robust results.ResultsWe developed an open-source bioinformatics pipeline, ProcaryaSV, for the detection of structural variations in bacterial isolates from paired-end short sequencing reads. Multiple tools, starting with quality control and trimming of sequencing data, alignment to the reference genome, and multiple structural variation detection tools, are integrated. All the partial results are then processed and merged with an in-house merging algorithm. Compared with a single detection approach, ProcaryaSV has improved detection performance and is a reproducible easy-to-use tool.ConclusionsThe ProcaryaSV pipeline provides an integrative approach to structural variation detection from paired-end next-generation sequencing of bacterial samples. It can be easily installed and used on Linux machines. It is publicly available on GitHub at https://github.com/robinjugas/ProcaryaSV .
| S-EPMC11234778 | biostudies-literature