Biology subjects

Heinis, T.

Publications and source records attributed to Heinis, T..

7 recordsLinked to original sources

bioRxiv2026

DGM Is Dead; Long Live DGM-Star! A Quantization-Theoretic Approach to Gamma Rate Discretization

Modeling substitution rate heterogeneity as a discretized Gamma distribution is standard in phylogenetic inference. Ferretti et al. (2026) have recently demonstrated that the most commonly used equiprobable Gamma rate discretization, known simply as the discrete Gamma model (DGM), substantially overestimates average branch lengths in reconstructed phylogenies when the true rates are drawn from a continuous Gamma distribution. We propose an alternative method to discretize the Gamma rate distribution that considerably reduces the bias in estimated branch lengths and can be used as a drop-in replacement for the standard DGM. The method works especially well on large trees with short branches, yielding up to a 20-fold reduction in bias on simulated phylogenies. It is developed by applying quantization-theoretic tools to the analysis of a simplified star-phylogeny abstraction introduced by Ferretti et al. (2026), where the new method is unbiased by construction. We explain Ferretti et al.'s counterintuitive observation that increasing the number of rate categories can exacerbate the bias of the standard DGM, even though this should in principle make DGM a better approximation to the continuous case, and offer our method as an immediately available alternative.

evolutionary biology↗

bioRxiv2025

Progressive decoding of DNA-stored JPEG data with on-the-fly error correction

DNA storage is a developing field that uses DNA to archive digital data owing to its superior information density and stability. Although DNA storage has been performed on a significant scale, challenges arise from the synthesis and sequencing of data-encoded oligonucleotides. Synthesis of DNA introduces significant noise into the process. Consequently, high-read-quality sequencers are often required, making the process expensive and lack scalability. Error correction codes are used within the DNA storage pipeline to provide resilience to noise at a cost of additional redundancy and decoding complexity. Given such constraints and challenges, the main objective we seek to deliver is a time- and storage-efficient image coding strategy. We introduce a novel DNA-based progressive JPEG decoder with on-the-fly error-correcting & rendering capability. This system can progressively decode an image while also correcting for errors as they occur. It modifies standard JPEG encoding methods to store data in localized chunks and uses adapted Raptor error-correction codes to improve the speed and quality of partial decoding. We optimize and evaluated the method under varying levels of simulated errors, as well as show how different parts of the pipeline improve real-time decoding capability. We also test the pipeline in a real-world wet-lab experiment. We present the first progressive image decoding schema aimed at realizing real-time rendering of DNA-stored images.

bioinformatics↗

bioRxiv2025

Basecalling for DNA Storage

DNA is a promising medium for data storage with its high information density and stability. To retrieve information stored in DNA, sequencing technologies are used to read the encoded bases. The raw signals from sequencing are mapped to a sequence of {A,C,T,G} by machine learning algorithms known as basecallers. Currently, basecallers are optimised mainly on biological DNA instead of focusing on characteristics or constraints unique to data-encoding DNA. Taking advantage of these unique artificial features to fine-tune and adapt the architecture, we demonstrate for the first time that DNA coding scheme constraints can be leveraged to optimise basecallers. Using low-rank adaptation on the basecalling model, we achieve substantial gains with high resource efficiency. Additionally, constraint-aware beam search provides improvements without requiring model retraining.

bioinformatics↗

bioRxiv2025

OligoSeq: Rapid nanopore-sequencing of single-stranded oligonucleotides

Nanopore-based DNA sequencing technology has achieved remarkable success in sequencing increasingly long DNA strands (e.g. over a million nucleotides long) for genomics research and biotechnology applications. However, the same level of progress has not been achieved for DNA oligonucleotides (usually [≤] 300 nucleotides long). Oligonucleotides play a crucial role in genome engineering efforts through oligo library generation and in DNA data storage, where they are used to encode computer information (such as binary code) in DNA libraries. To enable these applications, accurate sequencing of oligonucleotides in a way that allows to assess for sequence variability, quality and length is essential. But sequencing solutions for oligonucleotides -- particularly DNA primers for PCR, oligo DNA libraries used for mutagenesis or cDNA libraries used in gene expression analysis -- remain inadequate. To address this gap, we develop OligoSeq, an innovative approach that integrates two complementary techniques: AmpliSeq (based on PCR) and RevSeq (based on reverse complementation of either sequence-specific or random primers) to facilitate sequencing of single-stranded oligonucleotides using reference sequence anchor matches of more than [≥]90 % identity spanning from about 70 % to 10 % with AmpliSeq or RevSeq with random nonamers, respectively, and resolving the final reference sequence based on the most likely candidate from basecall frequencies, regardless of length and double-stranding method. OligoSeq uses nanopore technology and can be used as a reference for other sequencing platforms requiring double-stranded adapters, offering a practical and scalable alternative for standard quality control in single-stranded oligonucleotide synthesis. The use of nanopore technology, compatible with the double-stranding methods showcased, is shown to be the most cost-effective method for resolving original DNA sequences of different length and quality, and to assess its sequence variability, compared to other methods such as Illumina, PacBio or HPLC/MS.

synthetic biology↗

bioRxiv2025

High Performance protocol for ultra-short DNA sequencing using Oxford Nanopore Technology (ONT)

In recent years, Oxford Nanopore Technologies (ONT) has gained substantial attention across various domains of nucleic acids research, owing to its unique advantages over other sequencing platforms. Originally developed for long-read sequencing, ONT technology has evolved, with recent advancements enhancing its applicability beyond long reads to include short, synthetic DNA-based applications. However, sequencing short DNA fragments with nanopore technology often results in lower data quality, likely due to a lack of protocols optimised for these fragment sizes. To address this challenge, we refined the standard ONT library preparation protocol to improve its performance for ultra-short DNA targets. Utilising the same core reagents required for conventional ONT workflows, we introduced targeted alterations to enhance compatibility with shorter fragment lengths. We then benchmarked these adjustments against libraries prepared using the standard ONT protocol. Here, we present a comprehensive, step-by-step protocol that is accessible to researchers of varied technical expertise, facilitating high-quality sequencing of ultra-short DNA fragments. This protocol represents a significant improvement in sequencing quality for short DNA fragments using ONT technology, broadening the range of possible applications. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=167 SRC="FIGDIR/small/632410v1_ufig1.gif" ALT="Figure 1"> View larger version (40K): org.highwire.dtl.DTLVardef@1c99f4eorg.highwire.dtl.DTLVardef@1f9cf50org.highwire.dtl.DTLVardef@199c8acorg.highwire.dtl.DTLVardef@1459f48_HPS_FORMAT_FIGEXP M_FIG C_FIG

molecular biology↗

bioRxiv2024

Adaptive sampling in nanopore sequencing for PCR-free random access in DNA data storage

Adaptive sampling is a unique feature of nanopore sequencing--it enables the user to selectively enrich the sample (i.e., increase the effectively observed concentration of a subset of DNA in the sample) based on a user-provided set of reference sequences to target (or to avoid). It also enables dynamic switching of random access targets during a sequencing run as well as re-archiving of the sampled aliquot for later sequencing. As we demonstrate experimentally, the advantages of adaptive sampling make it a promising component of random access solutions in DNA data storage. We demonstrate that this approach allows selective data retrieval from a DNA pool without PCR, enabling the remaining aliquot to be re-archived for future retrieval of different target files. Our method helps to limit pool depletion effectively. It also allows dynamic switching between random access targets/data during a sequencing run. Adaptive sampling, however, primarily works for long sequences while for DNA data storage short oligos are typically used. We also show how to work around this limitation.

synthetic biology↗

bioRxiv2022

OligoArchive-DSM: Columnar Design for Error-Tolerant Database Archival using Synthetic DNA

The surge in demand for cost-effective, durable long-term archival media, coupled with density limitations of contemporary magnetic media, has resulted in synthetic DNA emerging as a promising new alternative. Today, the limiting factor for DNA-based data archival is the cost of writing (synthesis) and reading (sequencing) DNA. Newer techniques that reduce the cost often do so at the expense of reliability, as they introduce complex, technology-specific error patterns. In order to deal with such errors, it is important to design efficient pipelines that can carefully use redundancy to mask errors without amplifying overall cost. In this paper, we present OligoArchive-DSM (OA-DSM), an end-to-end DNA archival pipeline that can provide error-tolerant data storage at low read/write costs. Central to OA-DSM is a database-inspired columnar encoding technique that makes it possible to improve efficiency by enabling integrated decoding and consensus calling during data restoration.

bioengineering↗