Biology subjects

SHAO, B.

Publications and source records attributed to SHAO, B..

4 recordsLinked to original sources

bioRxiv2024

Predicting microbial transcriptome using genome sequence

We present TXpredict, a transformer-based framework for predicting microbial transcriptomes using annotated genome sequences. By leveraging information learned from a large protein language model, TXpredict achieves an average Spearman correlation of 0.53 and 0.62 in predicting gene expression for new bacterial and fungal genomes. We further extend this framework to predict transcriptomes for 2, 685 additional microbial genomes spanning 1, 744 genera, 82% of which remain uncharacterized at the transcriptional level. Our analysis highlights conserved and divergent transcriptional programs across understudied genera, providing a powerful resource for uncovering microbial adaptation strategies and metabolic potential across the tree of life. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=103 SRC="FIGDIR/small/630741v4_ufig1.gif" ALT="Figure 1"> View larger version (38K): org.highwire.dtl.DTLVardef@d1cad1org.highwire.dtl.DTLVardef@15a9206org.highwire.dtl.DTLVardef@128e65borg.highwire.dtl.DTLVardef@2b7ca7_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2024

A generative deep learning approach for global species distribution prediction

Anthropogenic pressures on biodiversity necessitate efficient and scalable methods to predict global species distributions. Current species distribution models (SDMs) face limitations with large-scale datasets, complex interspecies interactions, and data quality. Here, we introduce EcoVAE, an autoencoder-based generative model that integrates bioclimatic variables with georeferenced occurrences. The model is trained separately for plants, butterflies, and mammals to predict global distributions at both genus and species levels. EcoVAE achieves high precision and speed, outperforming traditional SDMs in spatial block cross-validation. Through unsupervised learning, it captures underlying distribution patterns and reveals species associations that align with known prey-predator relationships. Additionally, it evaluates global sampling efforts and interpolates distributions in data-limited regions, offering new applications for biodiversity exploration and monitoring.

ecology↗

bioRxiv2024

PlasmidGPT: a generative framework for plasmid design and annotation

We introduce PlasmidGPT, a generative language model pretrained on 153k engineered plasmid sequences from Addgene. PlasmidGPT generates de novo sequences that share similar characteristics with engineered plasmids but show low sequence identity to the training data. We demonstrate its ability to generate plasmids in a controlled manner based on the input sequence or specific design constraint. Moreover, our model learns informative embeddings of both engineered and natural plasmids, allowing for efficient prediction of a wide range of sequence-related attributes.

bioinformatics↗

bioRxiv2023

Riboformer: A Deep Learning Framework for Predicting Context-Dependent Translation Dynamics

Translation elongation is essential for maintaining cellular proteostasis, and alterations in the translational landscape are associated with a range of diseases. Ribosome profiling allows detailed measurement of translation at genome scale. However, it remains unclear how to disentangle biological variations from technical artifacts and identify sequence determinant of translation dysregulation. Here we present Riboformer, a deep learning-based framework for modeling context-dependent changes in translation dynamics. Riboformer leverages the transformer architecture to accurately predict ribosome densities at codon resolution. It corrects experimental artifacts in previously unseen datasets, reveals subtle differences in synonymous codon translation and uncovers a bottleneck in protein synthesis. Further, we show that Riboformer can be combined with in silico mutagenesis analysis to identify sequence motifs that contribute to ribosome stalling across various biological contexts, including aging and viral infection. Our tool offers a context-aware and interpretable approach for standardizing ribosome profiling datasets and elucidating the regulatory basis of translation kinetics.

bioinformatics↗