Biology subjects

Pages-Gallego, M.

Publications and source records attributed to Pages-Gallego, M..

3 recordsLinked to original sources

bioRxiv2024

Direct detection of 8-oxo-dG using nanopore sequencing

Genomic DNA is constantly subjected to oxidative damage, which is thought to be one of the major drivers of cancer and age-dependent decline. The most prominent consequence is the modification of guanine into 8-hydroxyguanine (8-oxo-dG), which has important mutagenic potential and plays a role in methylation-mediated gene regulation. Methods to simultaneously detect and quantify 8-oxo-dG within its genomic context have been lacking; mainly because these methods rely on indirect detection or are based on hydrolysis of the DNA. Nanopore sequencing has been deployed for the direct detection of base-modifications like cytosine methylation during sequencing. However, currently there is no model to detect 8-oxo-dG by nanopore sequencing due to the lack of training data. Here, we developed a strategy based on synthetic oligos to create long DNA molecules with context variability for effective deep learning and nanopore sequencing. Moreover, we showcase a training approach suitable to deal with the extreme scarceness of 8-oxo-dG compared to canonical G to enable specific 8-oxo-dG detection. Applied to an inducible tissue culture system for oxidative DNA damage, our approach reveals variable 8-oxo-dG distribution across the genome, a dissimilar context pattern to C>A mutations, and concurrent 5-mC depletion within a 2-kilobase window surrounding 8-oxo-dG sites. These findings not only underscore the potential of nanopore sequencing in epigenetic research, but also shed light on 8-oxo-dGs role in genomic regulation. By simultaneously measuring 5-mC and 8-oxo-dG at single molecule resolution, our study provides insights into the functional interplay between these DNA modifications. Moreover, our approach using synthetic oligos to generate a ground truth from machine learning modification calling could be applied to any other DNA modification. Overall, our work contributes to advancing the field of epigenetics and highlights nanopore sequencing as a powerful tool for studying DNA modifications.

bioinformatics↗

bioRxiv2024

Nanopore-based consensus sequencing enables accurate multimodal tumor cell-free DNA profiling

Shallow genome-wide cell-free DNA (cfDNA) sequencing holds great promise for non-invasive cancer monitoring by providing reliable copy number alteration (CNA) and fragmentomic profiles. Single nucleotide variations (SNVs) are, however, much harder to identify with low sequencing depth due to sequencing errors. Here we present Nanopore Rolling Circle Amplification (RCA)-enhanced Consensus Sequencing (NanoRCS), which leverages RCA and consensus calling based on genome-wide long-read nanopore sequencing to enable simultaneous multimodal tumor fraction estimation through SNVs, CNAs, and fragmentomics. Efficacy of NanoRCS is tested on 18 cancer patient samples and 3 healthy controls, demonstrating its ability to reliably detect tumor fractions as low as 0.24%. In vitro experiments confirm that SNV measurements are essential for detecting tumor fractions below 2%. NanoRCS provides the opportunity for cost-effective and rapid processing, which aligns well with clinical needs, particularly in settings where quick and accurate cancer monitoring is essential for personalized treatment strategies.

genomics↗

bioRxiv2022

Comprehensive and standardized benchmarking of deep learning architectures for basecalling nanopore sequencing data

AO_SCPLOWBSTRACTC_SCPLOWNanopore-based DNA sequencing relies on basecalling the electric current signal. Basecalling requires neural networks to achieve competitive accuracies. To improve sequencing accuracy further, new models are continuously proposed. However, benchmarking is currently not standardized, and evaluation metrics and datasets used are defined on a per publication basis, impeding progress in the field. To standardize the process of benchmarking, we unified existing benchmarking datasets and defined a rigorous set of evaluation metrics. We benchmarked the latest seven basecaller models and analyzed their deep learning architectures. Our results show that overall Bonito has the best architecture for basecalling. We find, however, that species bias in training can have a large impact on performance. Our comprehensive evaluation of 90 novel architecture demonstrates that different models excel at reducing different types of errors and using RNNs (LSTM) and a CRF decoder are the main drivers of high performing models.

bioinformatics↗