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1. Optimizing de novo genome assembly from PCR-amplified metagenomes

   https://doi.org/10.7717/peerj.6902  

   Roux, Simon ; Trubl, Gareth ; Goudeau, Danielle ; Nath, Nandita ; Couradeau, Estelle ; Ahlgren, Nathan A. ; Zhan, Yuanchao ; Marsan, David ; Chen, Feng ; Fuhrman, Jed A. ; et al ( May 2019 , PeerJ)

   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.

   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.

   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 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.

   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.

    

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2. Towards quantitative viromics for both double-stranded and single-stranded DNA viruses

   https://doi.org/10.7717/peerj.2777  

   Roux, Simon ; Solonenko, Natalie E. ; Dang, Vinh T. ; Poulos, Bonnie T. ; Schwenck, Sarah M. ; Goldsmith, Dawn B. ; Coleman, Maureen L. ; Breitbart, Mya ; Sullivan, Matthew B. ( December 2016 , PeerJ)

   Viruses strongly influence microbial population dynamics and ecosystem functions. However, our ability to quantitatively evaluate those viral impacts is limited to the few cultivated viruses and double-stranded DNA (dsDNA) viral genomes captured in quantitative viral metagenomes (viromes). This leaves the ecology of non-dsDNA viruses nearly unknown, including single-stranded DNA (ssDNA) viruses that have been frequently observed in viromes, but not quantified due to amplification biases in sequencing library preparations (Multiple Displacement Amplification, Linker Amplification or Tagmentation).

   Here we designed mock viral communities including both ssDNA and dsDNA viruses to evaluate the capability of a sequencing library preparation approach including an Adaptase step prior to Linker Amplification for quantitative amplification of both dsDNA and ssDNA templates. We then surveyed aquatic samples to provide first estimates of the abundance of ssDNA viruses.

   Mock community experiments confirmed the biased nature of existing library preparation methods for ssDNA templates (either largely enriched or selected against) and showed that the protocol using Adaptase plus Linker Amplification yielded viromes that were ±1.8-fold quantitative for ssDNA and dsDNA viruses. Application of this protocol to community virus DNA from three freshwater and three marine samples revealed that ssDNA viruses as a whole represent only a minor fraction (<5%) of DNA virus communities, though individual ssDNA genomes, both eukaryote-infecting Circular Rep-Encoding Single-Stranded DNA (CRESS-DNA) viruses and bacteriophages from theMicroviridaefamily, can be among the most abundant viral genomes in a sample.

   Together these findings provide empirical data for a new virome library preparation protocol, and a first estimate of ssDNA virus abundance in aquatic systems.

    

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3. HolistIC: leveraging Hi–C and whole genome shotgun sequencing for double minute chromosome discovery

   https://doi.org/10.1093/bioinformatics/btab816  

   Hayes, Matthew ; Nguyen, Angela ; Islam, Rahib ; Butler, Caryn ; Tran, Ethan ; Mullins, Derrick ; Hicks, Chindo ; Birol, ed., Inanc ( December 2021 , Bioinformatics)

   Double minute (DM) chromosomes are acentric extrachromosomal DNA artifacts that are frequently observed in the cells of numerous cancers. They are highly amplified and contain oncogenes and drug-resistance genes, making their presence a challenge for effective cancer treatment. Algorithmic discovery of DM can potentially improve bench-derived therapies for cancer treatment. A hindrance to this task is that DMs evolve, yielding circular chromatin that shares segments from progenitor DMs. This creates DMs with overlapping amplicon coordinates. Existing DM discovery algorithms use whole genome shotgun sequencing (WGS) in isolation, which can potentially incorrectly classify DMs that share overlapping coordinates.

   In this study, we describe an algorithm called ‘HolistIC’ that can predict DMs in tumor genomes by integrating WGS and Hi–C sequencing data. The consolidation of these sources of information resolves ambiguity in DM amplicon prediction that exists in DM prediction with WGS data used in isolation. We implemented and tested our algorithm on the tandem Hi–C and WGS datasets of three cancer datasets and a simulated dataset. Results on the cancer datasets demonstrated HolistIC’s ability to predict DMs from Hi–C and WGS data in tandem. The results on the simulated data showed the HolistIC can accurately distinguish DMs that have overlapping amplicon coordinates, an advance over methods that predict extrachromosomal amplification using WGS data in isolation.

   Our software, named ‘HolistIC’, is available at http://www.github.com/mhayes20/HolistIC.

   Supplementary data are available at Bioinformatics online.

    

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4. VEBA: a modular end-to-end suite for in silico recovery, clustering, and analysis of prokaryotic, microeukaryotic, and viral genomes from metagenomes

   https://doi.org/10.1186/s12859-022-04973-8  

   Espinoza, Josh L. ; Dupont, Chris L. ( October 2022 , BMC Bioinformatics)

   With the advent of metagenomics, the importance of microorganisms and how their interactions are relevant to ecosystem resilience, sustainability, and human health has become evident. Cataloging and preserving biodiversity is paramount not only for the Earth’s natural systems but also for discovering solutions to challenges that we face as a growing civilization. Metagenomics pertains to the in silico study of all microorganisms within an ecological community in situ,however, many software suites recover only prokaryotes and have limited to no support for viruses and eukaryotes.

   In this study, we introduce theViral Eukaryotic Bacterial Archaeal(VEBA) open-source software suite developed to recover genomes from all domains. To our knowledge,VEBAis the first end-to-end metagenomics suite that can directly recover, quality assess, and classify prokaryotic, eukaryotic, and viral genomes from metagenomes.VEBAimplements a novel iterative binning procedure and hybrid sample-specific/multi-sample framework that yields more genomes than any existing methodology alone.VEBAincludes a consensus microeukaryotic database containing proteins from existing databases to optimize microeukaryotic gene modeling and taxonomic classification.VEBAalso provides a unique clustering-based dereplication strategy allowing for sample-specific genomes and genes to be directly compared across non-overlapping biological samples. Finally,VEBAis the only pipeline that automates the detection of candidate phyla radiation bacteria and implements the appropriate genome quality assessments.VEBA’s capabilities are demonstrated by reanalyzing 3 existing public datasets which recovered a total of 948 MAGs (458 prokaryotic, 8 eukaryotic, and 482 viral) including several uncharacterized organisms and organisms with no public genome representatives.

   TheVEBAsoftware suite allows for the in silico recovery of microorganisms from all domains of life by integrating cutting edge algorithms in novel ways.VEBAfully integrates both end-to-end and task-specific metagenomic analysis in a modular architecture that minimizes dependencies and maximizes productivity. The contributions ofVEBAto the metagenomics community includes seamless end-to-end metagenomics analysis but also provides users with the flexibility to perform specific analytical tasks.VEBAallows for the automation of several metagenomics steps and shows that new information can be recovered from existing datasets.

    

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5. Haplotype phasing in single-cell DNA-sequencing data

   https://doi.org/10.1093/bioinformatics/bty286  

   Satas, Gryte ; Raphael, Benjamin J. ( June 2018 , Bioinformatics)

   Current technologies for single-cell DNA sequencing require whole-genome amplification (WGA), as a single cell contains too little DNA for direct sequencing. Unfortunately, WGA introduces biases in the resulting sequencing data, including non-uniformity in genome coverage and high rates of allele dropout. These biases complicate many downstream analyses, including the detection of genomic variants.

   We show that amplification biases have a potential upside: long-range correlations in rates of allele dropout provide a signal for phasing haplotypes at the lengths of amplicons from WGA, lengths which are generally longer than than individual sequence reads. We describe a statistical test to measure concurrent allele dropout between single-nucleotide polymorphisms (SNPs) across multiple sequenced single cells. We use results of this test to perform haplotype assembly across a collection of single cells. We demonstrate that the algorithm predicts phasing between pairs of SNPs with higher accuracy than phasing from reads alone. Using whole-genome sequencing data from only seven neural cells, we obtain haplotype blocks that are orders of magnitude longer than with sequence reads alone (median length 10.2 kb versus 312 bp), with error rates <2%. We demonstrate similar advantages on whole-exome data from 16 cells, where we obtain haplotype blocks with median length 9.2 kb—comparable to typical gene lengths—compared with median lengths of 41 bp with sequence reads alone, with error rates <4%. Our algorithm will be useful for haplotyping of rare alleles and studies of allele-specific somatic aberrations.

   Source code is available at https://www.github.com/raphael-group.

   Supplementary data are available at Bioinformatics online.

    

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