Biology subjects

Stanojevic, D.

Publications and source records attributed to Stanojevic, D..

5 recordsLinked to original sources

bioRxiv2026

Fermentation of the Edible Brown Seaweed Alaria esculenta by Lactiplantibacillus plantarum affects nutritional prfile and the content of potentially toxic elements.

Edible seaweed has the potential to become a valuable marine resource for food applications due to its potential health benefits and ecological sustainability. The brown seaweed Alaria esculenta is rich in essential minerals, vitamins, and dietary fibers, making it a nutritious food source. Fermentation, as a traditional preservation method, can enhance seaweed shelf-life and be useful for the development of new foods/ beverages. In this study, the effects of fermentation of A. esculenta, by the lactic acid bacterium (LAB) Lactiplantibacillus plantarum, on the nutritional profile, and the content of potentially toxic elements, was investigated. L. plantarum was successfully cultivated on A. esculenta using two modes of operation, submerged (SmF) and solid-state fermentation (SSF), resulting in production of cells and lactic acid, and reduction of the pH to below 4.3 within 3 days, which was not achieved in parallel spontaneous fermentations using indigenous seaweed microbiota. A. esculenta s macro-nutritional profile was altered, reducing mannitol but increasing fucose and glucose content (after acid hydrolysis) while also concentrating the protein content. LAB fermentation significantly increased the concentration of antioxidant phenolic compounds, such as phloroglucinol, syringic acid, and epicatechin, compared to untreated samples. However, lipophilic compounds like carotenoids decreased after both spontaneous and LAB-fermentation. A reduction in total mineral content was observed after LAB fermentation and water soaking, and SmF with L. plantarum effectively reduced arsenic and iodine levels. Overall, fermentation using L. plantarum showed potential as a bio-preservation method for the edible brown seaweed, A. esculenta, improving its nutritional profile and enhancing food safety.

microbiology↗

bioRxiv2026

Metaxa: A Transformer-Based Deep Learning Model for Taxonomic Classification of Long Nanopore Reads

A significant fraction of the microbial diversity remains unclassified, hindering our understanding of microbial roles in health and ecosystems. State-of-the-art methods like Kraken 2 perform well for taxa that are present in the database. However, their accuracy drops significantly when classifying taxa that are not included. While deep learning has advanced many fields, its applications in metagenomics remain limited, and its full potential has yet to be realized. Here, we present Metaxa, a transformer-based deep learning model designed for the taxonomic classification of long-read Nanopore sequences. Metaxa leverages the sequential context of Nanopore reads, enabling robust classification beyond fixed k-mer profiles. Our results show that Metaxa matches Kraken 2 on in-sample data at both the species and genus levels, and significantly outperforms both Kraken 2 and MetageNN at the genus level on out-of-sample datasets where the species genome is absent from the reference database but a different species from the same genus is present. Furthermore, Metaxa demonstrates strong generalization across different Nanopore chemistries (R9.4.1 and R10.4.1). This work highlights the potential of deep learning models to improve metagenomic classification accuracy, especially in complex or underexplored environments where traditional tools fall short.

bioinformatics↗

bioRxiv2024

Telomere-to-telomere phased genome assembly using error-corrected Simplex nanopore reads

Telomere-to-telomere phased assemblies have become the norm in genomics. To achieve these for diploid and even polyploid genomes, the contemporary approach involves a combination of two long-read sequencing technologies: high-accuracy long reads, e.g. Pacific Biosciences (PacBio) HiFi or Oxford Nanopore (ONT) Duplex reads, and ultra-long ONT Simplex reads. Using two different technologies increases the cost and the required amount of genomic DNA. Here, we show that comparable results are possible using error correction of ultra-long ONT Simplex reads and then assembling them using state-of-the-art de novo assembly methods. To achieve this, we have developed the deep learning-based HERRO framework, which corrects ONT Simplex reads while carefully preserving differences in related genomic sequences. Taking into account informative positions that differentiate the haplotypes or genomic repeat copies, HERRO achieves an increase of read accuracy of up to 100-fold for diploid human genomes. By combining HERRO with Verkko assembler, we achieve high contiguity on several human genomes by reconstructing many chromosomes telomere-to-telomere, including chromosomes X and Y. HERRO supports both R9.4.1 and R10.4.1 ONT Simplex reads and generalizes well to other species. These results provide an opportunity to reduce the cost of genome sequencing and use corrected ONT reads to analyze more complex genomes with different levels of ploidy or even aneuploidy.

bioinformatics↗

bioRxiv2022

Rockfish: A Transformer-based Model for Accurate 5-Methylcytosine Prediction from Nanopore Sequencing

DNA methylation plays a crucial role in various biological processes, including cell differentiation, ageing, and cancer development. The most important methylation in mammals is 5-methylcytosine (5mC) which is present in the context of CpG dinucleotides. Sequencing methods such as whole-genome bisulfite sequencing (WGBS) successfully detect 5mC DNA modifications. However, they suffer from the serious drawbacks of short read lengths and might introduce an amplification bias. Here we present Rockfish, a deep learning algorithm that significantly improves read-level 5mC detection by using Nanopore sequencing. Compared to other methods based on Nanopore sequencing, there is an increase in the single-base accuracy and the F1 measure of up to 5% and 12%, respectively. Furthermore, Rockfish shows a high correlation with WGBS and requires lower read depth while being computationally efficient. We deem that Rockfish is broadly applicable to study 5mC methylation in diverse organisms and disease systems to yield biological insights.

bioinformatics↗

bioRxiv2021

A systematic benchmark of Nanopore long read RNA sequencing for transcript level analysis in human cell lines

The human genome contains more than 200,000 gene isoforms. However, different isoforms can be highly similar, and with an average length of 1.5kb remain difficult to study with short read sequencing. To systematically evaluate the ability to study the transcriptome at a resolution of individual isoforms we profiled 5 human cell lines with short read cDNA sequencing and Nanopore long read direct RNA, amplification-free direct cDNA, PCR-cDNA sequencing. The long read protocols showed a high level of consistency, with amplification-free RNA and cDNA sequencing being most similar. While short and long reads generated comparable gene expression estimates, they differed substantially for individual isoforms. We find that increased read length improves read-to-transcript assignment, identifies interactions between alternative promoters and splicing, enables the discovery of novel transcripts from repetitive regions, facilitates the quantification of full-length fusion isoforms and enables the simultaneous profiling of m6A RNA modifications when RNA is sequenced directly. Our study demonstrates the advantage of long read RNA sequencing and provides a comprehensive resource that will enable the development and benchmarking of computational methods for profiling complex transcriptional events at isoform-level resolution.

genomics↗