Evaluating the Benefits and Limits of Multiple Displacement Amplification with Whole-Genome Oxford Nanopore Sequencing

Dadzie, F. A.; Beaudry, M. S.; Deyanov, A.; Slanis, H.; Duong, M. Q.; Turner, R.; Khan, A.; Arias, C. A.; Kissinger, J. C.; Glenn, T. C.; de Paula Baptista, R.

doi:10.1101/2024.02.09.579537

bioRxiv · 10.1101/2024.02.09.579537

Evaluating the Benefits and Limits of Multiple Displacement Amplification with Whole-Genome Oxford Nanopore Sequencing

Dadzie, F. A.·Beaudry, M. S.·Deyanov, A.·Slanis, H.·Duong, M. Q.·Turner, R.·Khan, A.·Arias, C. A.·Kissinger, J. C.·Glenn, T. C.·de Paula Baptista, R.

View original source ↗Export metadata ↓

Abstract

Multiple Displacement Amplification (MDA) outperforms conventional PCR in long fragment and whole genome amplification which makes it attractive to couple with long-read sequencing of samples with limited quantities of DNA to obtain improved genome assemblies. Here, we explore the efficacy and limits of MDA for genome sequence assembly using Oxford Nanopore Technologies (ONT) rapid library preparations and minION sequencing. We successfully generated almost complete genome sequences for all organisms examined, including Cryptosporidium meleagridis, Staphylococcus aureus, Enterococcus faecium, and Escherichia coli, with the ability to generate high-quality data from samples starting with only 0.025 ng of total DNA. Controlled sheared DNA samples exhibited a distinct pattern of size-increase after MDA, which may be associated with the amplification of long, low-abundance fragments present in the assay, as well as generating concatemeric sequences during amplification. To address concatemers, we developed a computational pipeline (CADECT: Concatemer Detection Tool) to identify and remove putative concatemeric sequences. This study highlights the efficacy of MDA in generating high-quality genome assemblies from limited amounts of input DNA. Also, the CADECT pipeline effectively mitigated the impact of concatemeric sequences, enabling the assembly of contiguous sequences even in cases where the input genomic DNA was degraded. These results have significant implications for the study of organisms that are challenging to culture in vitro, such as Cryptosporidium, and for expediting critical results in clinical settings with limited quantities of available genomic DNA.

Explore related subjects

bioinformatics Preprint

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Dadzie, F. A., Beaudry, M. S., Deyanov, A., Slanis, H., Duong, M. Q., Turner, R., Khan, A., Arias, C. A., Kissinger, J. C., Glenn, T. C., de Paula Baptista, R.. 2024-02-12. Evaluating the Benefits and Limits of Multiple Displacement Amplification with Whole-Genome Oxford Nanopore Sequencing. https://doi.org/10.1101/2024.02.09.579537

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

Targeted finetuning enables co-folding models to learn ligand-induced protein conformational states

Advances in protein structure prediction have enabled all-atom protein-ligand co-folding models that predict bound conformations directly from sequence and small-molecule structure. However, these models often fail to generalize to novel binding sites or alternative protein conformational states, limiting their utility for chemical biology and drug discovery. Here we show this limitation reflects training data bias rather than architectural constraints and can be overcome through targeted finetuning. Using ten previously unseen X-ray structures of Werner (WRN) helicase from a drug discovery program, we finetune Boltz-1 to learn both an allosteric binding site and a large conformational change locking the enzyme in an inactive state, while preserving accuracy on the ATP-bound state. The finetuned model generalizes to different chemical series and transfers the conformational logic across RecQ-family helicases in a binding-site sequence-dependent manner. This approach provides a blueprint for adapting foundation models as new structural and mechanistic data emerge, enabling co-folding networks to capture ligand-induced conformational switches and binding poses absent from their training data but central to biological regulation and therapeutic intervention.

bioinformatics↗

bioRxiv2026

Benchmarking single-cell foundation models for aging biology

Single cell foundation models (scFMs) provide representations of cellular states, but their utility across biological questions in aging research remains unclear. We established a benchmark of cellular representations for aging research, evaluating ten general-purpose scFMs, three aging-specific models and conventional methods across five biological questions using more than 2.5 million single cell transcriptomes. Using frozen pretrained representations, Geneformer performed best among scFMs for chronological age prediction and age pseudotime concordance, although 2,000 highly variable genes achieved higher mean performance. Several scFMs captured positive molecular age shifts across three disease contexts, consistent with reported aging-associated changes. SCimilarity performed well for rare cellular state identification across out-of-distribution datasets, exceeding aging specific models and conventional baselines. At the gene level, scGPT showed the highest recovery of reference TF target interactions, including aging-related regulatory hubs. Overall, scFMs supported diverse aging analyses, but performance depended on the biological question, highlighting their utility for rare cellular state identification and regulatory analysis.

bioinformatics↗

bioRxiv2026

CryoMV: Structure-Prior-Guided Modeling and Real-Particle Validation of Continuous Conformational Transitions in Cryo-EM

Continuous protein conformations are essential for understanding fundamental biological processes and supporting drug discovery. Although cryo-EM can resolve individual states at high resolution, recovering continuous heterogeneity from 2D particle images remains challenging. High noise, motion blur, and limited structural priors make it difficult to accurately generate and validate high-resolution continuous conformations using raw particle data. Here, we introduce cryoMV, a framework that integrates structure-prior-guided modeling with real-particle validation for continuous conformational transitions. CryoMV uses reference density maps to establish structural anchors and motion priors, models candidate transition paths between selected conformations, and transfers the learned representation to raw 2D cryo-EM particle images. Each candidate conformation is subsequently evaluated using the estimated particle poses and contrast transfer functions. Supported conformations are reconstructed through raw particle back-projection and assessed using canonical half-maps and Fourier shell correlation. On EMPIAR-10516 and EMPIAR-10345, cryoMV achieves excellent performance in terms of robustness, verifiability, and reconstruction resolution. By incorporating structure-prior modeling and evidence from the raw particles, cryoMV offers an explicit mechanism for assessing whether generated conformations are supported by experimental data and provides a practical approach to reducing model-induced artifacts in continuous cryo-EM heterogeneity analysis.

bioinformatics↗