Biology subjects

Akeson, S.

Publications and source records attributed to Akeson, S..

11 recordsLinked to original sources

bioRxiv2026

tRNAZAP: Deep learning toolkit for nanopore tRNA signal analysis

tRNA are short, ~76 nucleotide-long, modification-rich molecules that play an important role in protein translation. Nanopore sequencing is the only platform capable of directly sequencing full-length, native tRNA molecules. This capability is enabled by splinted ligation of tRNA-specific adapters. As a result, the measured ionic current signal reflects a composition of both adapter and tRNA sequence features. Despite these advantages, analysis of nanopore tRNA sequencing data poses distinct computational challenges. Existing ionic current-based signal-to-sequence decoding (basecalling) models are optimized for mRNA and perform suboptimally on short, modification-dense RNA such as tRNA. Furthermore, alignment algorithms for reference-guided analysis of short-length reads are not optimized for nanopore reads with lower accuracy than Next-generation sequencing reads. To overcome these limitations, domain-specific computational approaches are required. We present tRNAZAP, a nanopore ionic current signal analysis toolkit designed to enable precise reference-guided sequence alignment. tRNAZAP operates directly on raw ionic current signals and decomposes the analysis into two complementary tasks: 1) segmentation of adapter and tRNA regions, and 2) tRNA classification, both implemented using transformer-based models. The segmentation component isolates the tRNA signal portion and extracts the corresponding nucleotide sequence as the alignment query, while the classification component identifies the tRNA type as the alignment reference without requiring sequence-level information. Together, these outputs allow direct optimal pairwise alignment between the tRNA subsequence of the full query and reference sequences. Trained on in vitro transcribed RNA data and evaluated on yeast tRNA, these models achieved ~98% classification accuracy and intersection-over-union of ~0.98 for segmentation.

bioinformatics↗

bioRxiv2026

A strategy for ionic current analysis of nanopore protein readouts

BackgroundNanopore-based protein reading holds promise for single-molecule proteomics, but analyzing the resulting ionic current signals presents substantial computational challenges. Unlike nucleic acids, peptide-associated ionic signals exhibit complex patterns with subtle transitions between amino acids that can be obscured by noise. We developed an integrated computational strategy for processing nanopore protein ionic current signals from synthetic protein constructs containing all 20 amino acids in controlled sequence contexts. We started with denoising to remove artifacts, followed by change point detection using variable-number and fixed-number segmentation methods. ResultsWe employed dynamic time warping barycenter averaging to generate amino acid-specific reference templates. In parallel, we performed classification of amino acid regions using bidirectional LSTM networks trained on segment-level statistical features. Accuracy for 20-way amino acid classification remained limited, while charge-based binary classification reached 93.3% accuracy. Pairwise classification revealed that certain amino acids, particularly negatively charged residues, generate distinctive signatures. We used a profile hidden Markov model to capture sequential structure of protein translocation. ConclusionThis approach provides computational strategies for automated segmentation, classification, and modeling of nanopore peptide translocation ionic current data.

bioinformatics↗

bioRxiv2025

tRNA isodecoder analysis using Nanopore ionic current signals and deep learning

tRNA are short non-coding RNA characterized by their distinct tertiary structure and abundant chemical modifications. Conventional analysis strategies do not fully characterize tRNA isodecoders. We demonstrate that this limitation can be resolved for tRNA using nanopore ionic current data. We developed tRNAZAP, a deep learning strategy that uses nanopore ionic current signal information to classify native tRNA strands at isodecoder-level resolution without relying on sequence information. Additionally, the ionic current level classification allows for pairwise alignment of read sequences to reference sequences, producing optimal tRNA alignments. We applied tRNAZAP to direct tRNA sequencing data from Escherichia coli and Saccharomyces cerevisiae, and recovered 2.6% and 13.1% more aligned reads than BWA-MEM, respectively. tRNAZAP resolved these reads at an isodecoder-level and with consistently higher alignment identity. tRNAZAP is a powerful complement to sequence-based profiling and can contribute towards resolving the isodecoder landscape in more complex organisms including humans.

genomics↗

bioRxiv2025

Cytoplasmic localization of PUS7 facilitates a pseudouridine-dependent enhancement of cellular stress tolerance

Pseudouridine ({Psi}) is an abundant post-transcriptional modification found across all classes of RNA. It has been widely speculated that {Psi} inclusion in mRNAs might provide an avenue for cells to control gene expression post-transcriptionally. Here we demonstrate that one of the principal mRNA pseudouridylating enzymes, pseudouridine synthase 7 (PUS7), exhibits a stress-induced accumulation in the cytoplasm of yeast and human epithelial lung cells. Stress-induced and cytoplasmic localization of PUS7 promote {Psi}-incorporation into hundreds of mRNA targets. Furthermore, engineered PUS7 cytoplasmic localization increases cellular fitness under ROS and divalent metal ion stress. Consistent with this, transcripts modified upon PUS7 cytoplasmic localization are enriched within mRNAs encoding proteins involved in divalent metal metabolism and ROS stress pathways. In contrast, tRNA sites modified by PUS7 ({Psi}13 and {Psi}35 are unperturbed). Quantitative proteomics reveal a reshaping of the proteome upon PUS7 relocalization under stress, with proteins involved in metal and ROS homeostasis being particularly sensitive to PUS7 localization. Collectively, our data demonstrate that PUS7 localization alters mRNA pseudouridylation patterns to modulate protein production and enhance cellular fitness.

biochemistry↗

bioRxiv2025

Haplotype-Resolved DNA Methylation at the APOE Locus identifies Allele-Specific Epigenetic Signatures Relevant to Alzheimer's Disease Risk

The APOE gene encodes a key lipid transport protein and plays a central role in Alzheimers disease (AD) pathogenesis. Three common APOE alleles, {varepsilon}2 (rs7412(C>T), {varepsilon}3 (reference), and {varepsilon}4 (rs429358(T>C)), arise from two coding variants in exon 4 and confer distinct AD risk profiles, with {varepsilon}4 increasing risk and {varepsilon}2 providing protection. The {varepsilon}3-linked APOE variant rs769455[T] has also been associated with elevated AD risk in individuals of African ancestry carrying both rs769455[T] and {varepsilon}4 alleles. These single nucleotide variants (SNVs) reside in a cytosine-phosphate-guanine (CpG) island, which is a region with a higher frequency of CpG sites compared to the rest of the genome. CpG sites are subject to 5-methylcytosine (5mC) methylation by DNA methyltransferases which add a methyl group to the fifth carbon on the cytosine residue of a CpG site. The presence of SNVs can disrupt this process, making these regions prime targets for differential methylation; however, allele-specific methylation patterns in APOE remain poorly resolved due to technical limitations of conventional bisulfite and methylation array based methods, including degraded DNA quality, sparse CpG coverage, and lack of haplotype phasing. Here, we leverage high-accuracy long-read sequencing data to generate haplotype-resolved methylation profiles of the APOE locus in 332 postmortem brain samples from two ancestrally different cohorts. This includes 201 individuals of European ancestry from the North American Brain Expression Consortium (NABEC), comprising 402 haplotypes (48 {varepsilon}2 and 58 {varepsilon}4 alleles), and 131 individuals of African and African admixed ancestry from the Human Brain Core Collection (HBCC), comprising 262 haplotypes (25 {varepsilon}2, 64 {varepsilon}4, and 7 rs769455 alleles). A linear regression analysis identified 18 novel differentially methylated CpG sites (DMCs) associated with APOE {varepsilon}2, {varepsilon}4, and rs769455 within a gene cluster spanning TOMM40, APOE, APOC1, and APOC4-APOC2. This represents the most comprehensive haplotype-resolved methylation study of APOE in human brain tissue to date. Our results uncover distinct allele-specific methylation signatures and demonstrate the power of long-read sequencing for resolving epigenetic variation relevant to AD risk.

genomics↗

bioRxiv2025

Evaluation of Nanopore direct RNA sequencing updates for modification detection

Nanopore technology can directly sequence intact RNA molecules, offering a unique capability to read native modifications. Oxford Nanopore Technologies recently updated its direct RNA sequencing technology from RNA002 to RNA004 chemistry. This update included an improved basecaller (Dorado) for increased sequencing accuracy, and ionic current models for de novo detection of four RNA modifications. Using a single RNA extraction from GM12878 B-lymphocyte cell line, we compared RNA002 and RNA004 sequencing chemistries and evaluated Dorado modification calling accuracy. We computed U-to-C mismatches, previously used to identify putative pseudouridine sites, and ran m6anet for identifying putative N6-methyladenosine sites. Dorado results for each respective modification showed both global and site-specific differences when compared to RNA002 results. We used DRS data from in vitro transcription of GM12878 genomic DNA as well as synthetic oligonucleotides to evaluate Dorado modification calling performance. Dorados pseudouridine model achieved 96-98% for both accuracy and F1-score. Similarly, Dorados N6-methyladenosine model achieved 94-98% accuracy, 96-99% F1-score. Our results demonstrate that Nanopore Direct RNA sequencing could simultaneously detect pseudouridine, N6-methyladenosine, 5-methylcytosine, and inosine modifications on individual mRNA strands. It is critical to validate these calls using orthogonal methods (e.g., Liquid Chromatography with Tandem Mass Spectrometry) for increased confidence. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=81 SRC="FIGDIR/small/651717v1_ufig1.gif" ALT="Figure 1"> View larger version (25K): org.highwire.dtl.DTLVardef@1215534org.highwire.dtl.DTLVardef@160f38forg.highwire.dtl.DTLVardef@164a9corg.highwire.dtl.DTLVardef@17c72e5_HPS_FORMAT_FIGEXP M_FIG C_FIG

genomics↗

bioRxiv2025

Genomic in vitro transcription and Nanopore direct RNA sequencing of a human B-Lymphocyte cell line

Genomic DNA used as a template for in vitro transcription of RNA can serve as a true negative control for benchmarking RNA modification detection by Nanopore direct RNA sequencing (DRS) models. We generated DRS data for in vitro transcribed (IVT) RNA composed of canonical nucleotides using genomic DNA from a human cell line. We applied Dorado modification calling models to these data, and calculated 9-mer specific false-positive rates for eight RNA modifications as a comparison point for future development of RNA modification models. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=119 SRC="FIGDIR/small/650674v2_ufig1.gif" ALT="Figure 1"> View larger version (23K): org.highwire.dtl.DTLVardef@962feorg.highwire.dtl.DTLVardef@4237aborg.highwire.dtl.DTLVardef@154d346org.highwire.dtl.DTLVardef@1fad8f2_HPS_FORMAT_FIGEXP M_FIG C_FIG

genomics↗

bioRxiv2024

Development and extensive sequencing of a broadly-consented Genome in a Bottle matched tumor-normal pair for somatic benchmarks

The Genome in a Bottle Consortium (GIAB), hosted by the National Institute of Standards and Technology (NIST), is developing new matched tumor-normal samples, the first to be explicitly consented for public dissemination of genomic data and cell lines. Here, we describe a comprehensive genomic dataset from the first individual, HG008, including DNA from an adherent, epithelial-like pancreatic ductal adenocarcinoma (PDAC) tumor cell line and matched normal cells from duodenal and pancreatic tissues. Data for the tumor-normal matched samples comes from seventeen distinct state-of-the-art whole genome measurement technologies, including high depth short and long-read bulk whole genome sequencing (WGS), single cell WGS, and Hi-C, and karyotyping. In future publications, these data will be used by the GIAB Consortium to develop matched tumor-normal benchmarks for somatic variant detection. We expect these data to facilitate innovation for whole genome measurement technologies, de novo assembly of tumor and normal genomes, and bioinformatic tools to identify small and structural somatic mutations. This first-of-its-kind broadly consented open-access resource will facilitate further understanding of sequencing methods used for cancer biology.

genomics↗

bioRxiv2024

Probing enzyme-dependent pseudouridylation using direct RNA sequencing to assess neuronal epitranscriptome plasticity

Chemical modifications in mRNAs, such as pseudouridine (psi), can control gene expression. Yet, we know little about how they are regulated, especially in neurons. We applied nanopore direct RNA sequencing to investigate psi dynamics in SH-SY5Y cells in response to two perturbations that model a natural and unnatural cellular state: retinoic-acid-mediated differentiation (healthy) and exposure to the neurotoxicant, lead (unhealthy). We discovered that the expression of some psi writers change significantly in response to physiological conditions. We also found that globally, lead-treated cells have more psi sites but lower relative occupancy than untreated cells and differentiated cells. Interestingly, examples of highly plastic sites were accompanied by constant expression for psi writers, suggesting trans-regulation. Many positions were static throughout all three cellular states, suggestive of a "housekeeping" function. This study enables investigations into mechanisms that control psi modifications in neurons and its possible protective effects in response to cellular stress.

systems biology↗

bioRxiv2024

Assessing methylation detection for primary human tissue using Nanopore sequencing

DNA methylation most commonly occurs as 5-methylcytosine (5-mC) in the human genome and has been associated with human diseases. Recent developments in single-molecule sequencing technologies (Oxford Nanopore Technologies (ONT) and Pacific Biosciences) have enabled readouts of long, native DNA molecules, including cytosine methylation. ONT recently upgraded their Nanopore sequencing chemistry and kits from R9 to the R10 version, which yielded increased accuracy and sequencing throughput. However the effects on methylation detection have not yet been documented. Here we performed a series of computational analyses to characterize differences in Nanopore-based 5mC detection between the ONT R9 and R10 chemistries. We compared 5mC calls in R9 and R10 for three human genome datasets: a cell line, a frontal cortex brain sample, and a blood sample. We performed an in-depth analysis on CpG islands and homopolymer regions, and documented high concordance for methylation detection among sequencing technologies. The strongest correlation was observed between Nanopore R10 and Illumina bisulfite technologies for cell line-derived datasets. Subtle differences in methylation datasets between technologies can impact analysis tools such as differential methylation calling software. Our findings show that comparisons can be drawn between methylation data from different Nanopore chemistries using guided hypotheses. This work will facilitate comparison among Nanopore data cohorts derived using different chemistries from large scale sequencing efforts, such as the NIH CARD Long Read Initiative.

genomics↗

bioRxiv2023

Multicellular, IVT-derived, unmodified human transcriptome for nanopore direct RNA analysis

Nanopore direct RNA sequencing (DRS) enables measurements of RNA modifications. Modification-free transcripts are a practical and targeted control for DRS, providing a baseline measurement for canonical nucleotides within a matched and biologically derived sequence context. However, these controls can be challenging to generate and carry nanopore-specific nuances that can impact analysis. We produced DRS datasets using modification-free transcripts from in vitro transcription (IVT) of cDNA from six immortalized human cell lines. We characterized variation across cell lines and demonstrated how these may be interpreted. These data will serve as a versatile control and resource to the community for RNA modification analysis of human transcripts.

genomics↗