skip to main content

Title: Optimizing de novo genome assembly from PCR-amplified metagenomes
Background

Metagenomics has transformed our understanding of microbial diversity across ecosystems, with recent advances enablingde novoassembly of genomes from metagenomes. These metagenome-assembled genomes are critical to provide ecological, evolutionary, and metabolic context for all the microbes and viruses yet to be cultivated. Metagenomes can now be generated from nanogram to subnanogram amounts of DNA. However, these libraries require several rounds of PCR amplification before sequencing, and recent data suggest these typically yield smaller and more fragmented assemblies than regular metagenomes.

Methods

Here we evaluatede novoassembly methods of 169 PCR-amplified metagenomes, including 25 for which an unamplified counterpart is available, to optimize specific assembly approaches for PCR-amplified libraries. We first evaluated coverage bias by mapping reads from PCR-amplified metagenomes onto reference contigs obtained from unamplified metagenomes of the same samples. Then, we compared different assembly pipelines in terms of assembly size (number of bp in contigs ≥ 10 kb) and error rates to evaluate which are the best suited for PCR-amplified metagenomes.

Results

Read mapping analyses revealed that the depth of coverage within individual genomes is significantly more uneven in PCR-amplified datasets versus unamplified metagenomes, with regions of high depth of coverage enriched in short inserts. This enrichment scales with the number of PCR more » cycles performed, and is presumably due to preferential amplification of short inserts. Standard assembly pipelines are confounded by this type of coverage unevenness, so we evaluated other assembly options to mitigate these issues. We found that a pipeline combining read deduplication and an assembly algorithm originally designed to recover genomes from libraries generated after whole genome amplification (single-cell SPAdes) frequently improved assembly of contigs ≥10 kb by 10 to 100-fold for low input metagenomes.

Conclusions

PCR-amplified metagenomes have enabled scientists to explore communities traditionally challenging to describe, including some with extremely low biomass or from which DNA is particularly difficult to extract. Here we show that a modified assembly pipeline can lead to an improvedde novogenome assembly from PCR-amplified datasets, and enables a better genome recovery from low input metagenomes.

« less
Authors:
 ;  ;  ;  ;  ;  ;  ;  ;  ;  ;  ;  ;  ;  ;  
Publication Date:
NSF-PAR ID:
10103815
Journal Name:
PeerJ
Volume:
7
Page Range or eLocation-ID:
Article No. e6902
ISSN:
2167-8359
Publisher:
PeerJ
Sponsoring Org:
National Science Foundation
More Like this
  1. Abstract Motivation

    Whole-genome sequencing of uncultured eukaryotic genomes is complicated by difficulties in acquiring sufficient amounts of tissue. Single-cell genomics (SCG) by multiple displacement amplification provides a technical workaround, yielding whole-genome libraries which can be assembled de novo. Downsides of multiple displacement amplification include coverage biases and exacerbation of contamination. These factors affect assembly continuity and fidelity, complicating discrimination of genomes from contamination and noise by available tools. Uncultured eukaryotes and their relatives are often underrepresented in large sequence data repositories, further impairing identification and separation.

    Results

    We compare the ability of filtering approaches to remove contamination and resolve eukaryotic draft genomes from SCG metagenomes, finding significant variation in outcomes. To address these inconsistencies, we introduce a consensus approach that is codified in the SCGid software package. SCGid parallelly filters assemblies using different approaches, yielding three intermediate drafts from which consensus is drawn. Using genuine and mock SCG metagenomes, we show that our approach corrects for variation among draft genomes predicted by individual approaches and outperforms them in recapitulating published drafts in a fast and repeatable way, providing a useful alternative to available methods and manual curation.

    Availability and implementation

    The SCGid package is implemented in python and R. Source code is availablemore »at http://www.github.com/amsesk/SCGid under the GNU GPL 3.0 license.

    Supplementary information

    Supplementary data are available at Bioinformatics online.

    « less
  2. Abstract Background

    Systems-level analyses, such as differential gene expression analysis, co-expression analysis, and metabolic pathway reconstruction, depend on the accuracy of the transcriptome. Multiple tools exist to perform transcriptome assembly from RNAseq data. However, assembling high quality transcriptomes is still not a trivial problem. This is especially the case for non-model organisms where adequate reference genomes are often not available. Different methods produce different transcriptome models and there is no easy way to determine which are more accurate. Furthermore, having alternative-splicing events exacerbates such difficult assembly problems. While benchmarking transcriptome assemblies is critical, this is also not trivial due to the general lack of true reference transcriptomes.

    Results

    In this study, we first provide a pipeline to generate a set of the simulated benchmark transcriptome and corresponding RNAseq data. Using the simulated benchmarking datasets, we compared the performance of various transcriptome assembly approaches including both de novo and genome-guided methods. The results showed that the assembly performance deteriorates significantly when alternative transcripts (isoforms) exist or for genome-guided methods when the reference is not available from the same genome. To improve the transcriptome assembly performance, leveraging the overlapping predictions between different assemblies, we present a new consensus-based ensemble transcriptome assembly approach, ConSemble.

    Conclusions

    Without usingmore »a reference genome, ConSemble using four de novo assemblers achieved an accuracy up to twice as high as any de novo assemblers we compared. When a reference genome is available, ConSemble using four genome-guided assemblies removed many incorrectly assembled contigs with minimal impact on correctly assembled contigs, achieving higher precision and accuracy than individual genome-guided methods. Furthermore, ConSemble using de novo assemblers matched or exceeded the best performing genome-guided assemblers even when the transcriptomes included isoforms. We thus demonstrated that the ConSemble consensus strategy both for de novo and genome-guided assemblers can improve transcriptome assembly. The RNAseq simulation pipeline, the benchmark transcriptome datasets, and the script to perform the ConSemble assembly are all freely available from:http://bioinfolab.unl.edu/emlab/consemble/.

    « less
  3. Abstract Motivation

    De novo transcriptome analysis using RNA-seq offers a promising means to study gene expression in non-model organisms. Yet, the difficulty of transcriptome assembly means that the contigs provided by the assembler often represent a fractured and incomplete view of the transcriptome, complicating downstream analysis. We introduce Grouper, a new method for clustering contigs from de novo assemblies that are likely to belong to the same transcripts and genes; these groups can subsequently be analyzed more robustly. When provided with access to the genome of a related organism, Grouper can transfer annotations to the de novo assembly, further improving the clustering.

    Results

    On de novo assemblies from four different species, we show that Grouper is able to accurately cluster a larger number of contigs than the existing state-of-the-art method. The Grouper pipeline is able to map greater than 10% more reads against the contigs, leading to accurate downstream differential expression analyses. The labeling module, in the presence of a closely related annotated genome, can efficiently transfer annotations to the contigs and use this information to further improve clustering. Overall, Grouper provides a complete and efficient pipeline for processing de novo transcriptomic assemblies.

    Availability and implementation

    The Grouper software is freely available atmore »https://github.com/COMBINE-lab/grouper under the 2-clause BSD license.

    Supplementary information

    Supplementary data are available at Bioinformatics online.

    « less
  4. Abstract

    The PacBio®HiFi sequencing method yields highly accurate long-read sequencing datasets with read lengths averaging 10–25 kb and accuracies greater than 99.5%. These accurate long reads can be used to improve results for complex applications such as single nucleotide and structural variant detection, genome assembly, assembly of difficult polyploid or highly repetitive genomes, and assembly of metagenomes. Currently, there is a need for sample data sets to both evaluate the benefits of these long accurate reads as well as for development of bioinformatic tools including genome assemblers, variant callers, and haplotyping algorithms. We present deep coverage HiFi datasets for five complex samples including the two inbred model genomesMus musculusandZea mays, as well as two complex genomes, octoploidFragaria × ananassaand the diploid anuranRana muscosa. Additionally, we release sequence data from a mock metagenome community. The datasets reported here can be used without restriction to develop new algorithms and explore complex genome structure and evolution. Data were generated on the PacBio Sequel II System.

  5. Abstract Taro (Colocasia esculenta) is a food staple widely cultivated in the humid tropics of Asia, Africa, Pacific and the Caribbean. One of the greatest threats to taro production is Taro Leaf Blight caused by the oomycete pathogen Phytophthora colocasiae. Here we describe a de novo taro genome assembly and use it to analyze sequence data from a Taro Leaf Blight resistant mapping population. The genome was assembled from linked-read sequences (10x Genomics; ∼60x coverage) and gap-filled and scaffolded with contigs assembled from Oxford Nanopore Technology long-reads and linkage map results. The haploid assembly was 2.45 Gb total, with a maximum contig length of 38 Mb and scaffold N50 of 317,420 bp. A comparison of family-level (Araceae) genome features reveals the repeat content of taro to be 82%, >3.5x greater than in great duckweed (Spirodela polyrhiza), 23%. Both genomes recovered a similar percent of Benchmarking Universal Single-copy Orthologs, 80% and 84%, based on a 3,236 gene database for monocot plants. A greater number of nucleotide-binding leucine-rich repeat disease resistance genes were present in genomes of taro than the duckweed, ∼391 vs. ∼70 (∼182 and ∼46 complete). The mapping population data revealed 16 major linkage groups with 520 markers, and 10more »quantitative trait loci (QTL) significantly associated with Taro Leaf Blight disease resistance. The genome sequence of taro enhances our understanding of resistance to TLB, and provides markers that may accelerate breeding programs. This genome project may provide a template for developing genomic resources in other understudied plant species.« less