Bioinformatics advances最新文献

Summary: Common approaches for deciphering biological networks involve network embedding algorithms. These approaches strictly focus on clustering the genes' embedding vectors and interpreting such clusters to reveal the hidden information of the networks. However, the difficulty in interpreting the genes' clusters and the limitations of the functional annotations' resources hinder the identification of the currently unknown cell's functioning mechanisms. We propose a new approach that shifts this functional exploration from the embedding vectors of genes in space to the axes of the space itself. Our methodology better disentangles biological information from the embedding space than the classic gene-centric approach. Moreover, it uncovers new data-driven functional interactions that are unregistered in the functional ontologies, but biologically coherent. Furthermore, we exploit these interactions to define new higher-level annotations that we term Axes-Specific Functional Annotations and validate them through literature curation. Finally, we leverage our methodology to discover evolutionary connections between cellular functions and the evolution of species.

Availability and implementation: Data and source code can be accessed at https://gitlab.bsc.es/sdoria/axes-of-biology.git.

Summary: We explore the nuanced temporal and epistemological distinctions among natural sciences, particularly the contrasting treatment of time and the interplay between theory and experimentation. Physics, an exemplar of mature science, relies on theoretical models for predictability and simulations. In contrast, biology, traditionally experimental, is witnessing a computational surge, with data analytics and simulations reshaping its research paradigms. Despite these strides, a unified theoretical framework in biology remains elusive. We propose that contemporary global challenges might usher in a renewed emphasis, presenting an opportunity for the establishment of a novel theoretical underpinning for the life sciences.

Availability and implementation: https://github.com/ouzounis/CLS-emerges Data in Json format, Images in PNG format.

Summary: The increasing number of publicly available bacterial gene expression data sets provides an unprecedented resource for the study of gene regulation in diverse conditions, but emphasizes the need for self-supervised methods for the automated generation of new hypotheses. One approach for inferring coordinated regulation from bacterial expression data is through neural networks known as denoising autoencoders (DAEs) which encode large datasets in a reduced bottleneck layer. We have generalized this application of DAEs to include deep networks and explore the effects of network architecture on gene set inference using deep learning. We developed a DAE-based pipeline to extract gene sets from transcriptomic data in Escherichia coli, validate our method by comparing inferred gene sets with known pathways, and have used this pipeline to explore how the choice of network architecture impacts gene set recovery. We find that increasing network depth leads the DAEs to explain gene expression in terms of fewer, more concisely defined gene sets, and that adjusting the width results in a tradeoff between generalizability and biological inference. Finally, leveraging our understanding of the impact of DAE architecture, we apply our pipeline to an independent uropathogenic E.coli dataset to identify genes uniquely induced during human colonization.

Availability and implementation: https://github.com/BarquistLab/DAE_architecture_exploration.

Motivation: Ribonucleoside monophosphates (rNMPs) are the most abundant non-standard nucleotides embedded in genomic DNA. If the presence of rNMP in DNA cannot be controlled, it can lead to genome instability. The actual regulatory functions of rNMPs in DNA remain mainly unknown. Considering the association between rNMP embedment and various diseases and cancer, the phenomenon of rNMP embedment in DNA has become a prominent area of research in recent years.

Results: We introduce the rNMPID database, which is the first database revealing rNMP-embedment characteristics, strand bias, and preferred incorporation patterns in the genomic DNA of samples from bacterial to human cells of different genetic backgrounds. The rNMPID database uses datasets generated by different rNMP-mapping techniques. It provides the researchers with a solid foundation to explore the features of rNMP embedded in the genomic DNA of multiple sources, and their association with cellular functions, and, in future, disease. It also significantly benefits researchers in the fields of genetics and genomics who aim to integrate their studies with the rNMP-embedment data.

Availability and implementation: rNMPID is freely accessible on the web at https://www.rnmpid.org.