Biology subjects

Sablok, G.

Publications and source records attributed to Sablok, G..

3 recordsLinked to original sources

bioRxiv2026

flodl-bio: First Rust data, transformers and model layer for entire deep learning on biological sequences

Python ecosystem dominates the deep learning framework. flodl-bio is a Rust crate that extends flodl, a libtorch-backed deep-learning framework, with deep learning on biological sequences. To our knowledge, flodl-bio is the first Rust crate that provides a comprehensive and practical deep learning for the biological applications, which includes the alphabet-aware sequence encoding, including one-hot and k-mer representations, padded sequences, and channels-first batches. It also includes several FASTA parsing and graph construction utilities for adjacency matrices, GCN-normalised adjacency matrices, graph Laplacians, residue contact maps, and de Bruijn graphs. Among the added functionalities, it includes AUROC and AUPRC with their associated curves; classification metrics, and the export of learned convolutional filters as MEME motifs and seeded splits. Deep biological learning includes a multi-width motif CNN, a dilated residual CNN, bidirectional LSTM and GRU architectures, and transformer encoder. On the five human transcription-factor-binding datasets in the GUE benchmark, a motif CNN with approximately 10,000 parameters, trained from scratch, outperforms all published CNN baselines. It also superseded the 2.5-billion-parameter Nucleotide Transformer on two datasets and DNABERT-2 on one. Given a fixed hardware configuration and software stack, experiments are reproducible at the bit level. See Supplementary Table S2 of Supplementary Material. flodl-bio is written in Rust and released under the MIT licence at https://github.com/sablokg/flodl-bio. The core builds with stable Rust and has no dependencies. The model tier is enabled with the flodl Cargo feature (cuda for NVIDIA GPUs) and requires libtorch and Rust 1.91 or later. The evaluation harness is in the repository's experiments directory, and the complete run outputs are archived at https://doi.org/10.5281/zenodo.23321850

bioinformatics↗

bioRxiv2024

Systemic response to nitrogen starvation in the cyanobacteria hosting hornwort and liverwort provides little evidence for extensive priming to the cyanobiont

Mutualistic plant-microbe symbiotic interactions are thought to have evolved from a loose association between host plants and microbes when nutrients are limited. Therefore, the molecular network enabling intimate mutualistic plant-microbe symbioses may have evolved from a nutrient starvation response shared by all land plants. While the molecular link between nutrient status and symbiotic interaction is well-established, it remains poorly understood in some systems. This is especially true for the symbiotic associations between plants and cyanobacteria. To test the conservation of the starvation network across land plants as well as to investigate the link between nutrient starvation and symbiosis initiation in the plant-cyanobacteria symbiosis, here we explore the transcriptional responses to nutrient starvation in two non-vascular plant species, a hornwort Anthoceros agrestis and a liverwort Blasia pusilla, forming plant-cyanobacteria endophytic symbioses. We observe a deep conservation of the systemic starvation response across land plants. However, very few if any components of the starvation network appear to be specific to cyanobacteria hosting plants, providing little evidence for extensive and specific priming to the cyanobiont. Moreover, we found that some bioactive molecules known to be important in initiating the plant-mycorrhiza and nodule-forming bacteria symbioses, may also have a similar role in plant-cyanobacteria symbioses. HighlightOur results suggest that the most critical step in establishing plant-cyanobacteria interactions using non-host plants is the attraction of the cyanobiont. This finding has significant impact on crop engineering.

evolutionary biology↗

bioRxiv2021

Phylogenomic data reveal hard polytomies across the backbone of the large genus Solanum (Solanaceae)

Premise of the studyEvolutionary studies require solid phylogenetic frameworks, but increased volumes of phylogenomic data have revealed incongruent topologies among gene trees in many organisms both between and within genomes. Some of these incongruences indicate polytomies that may remain impossible to resolve. Here we investigate the degree of gene-tree discordance in Solanum, one of the largest flowering plant genera that includes the cultivated potato, tomato, and eggplant, as well as 24 minor crop plants. MethodsA densely sampled species-level phylogeny of Solanum is built using unpublished and publicly available Sanger sequences comprising 60% of all accepted species (742 spp.) and nine regions (ITS, waxy, and seven plastid markers). The robustness of this topology is tested by examining a full plastome dataset with 140 species and a nuclear target-capture dataset with 39 species of Solanum (Angiosperms353 probe set). Key resultsWhile the taxonomic framework of Solanum remained stable, gene tree conflicts and discordance between phylogenetic trees generated from the target-capture and plastome datasets were observed. The latter correspond to regions with short internodal branches, and network analysis and polytomy tests suggest the backbone is composed of three polytomies found at different evolutionary depths. The strongest area of discordance, near the crown node of Solanum, could potentially represent a hard polytomy. ConclusionsWe argue that incomplete lineage sorting due to rapid diversification is the most likely cause for these polytomies, and that embracing the uncertainty that underlies them is crucial to understand the evolution of large and rapidly radiating lineages.

evolutionary biology↗