Biology Methods and Protocols最新文献

We present SEQuence Weighted Alignment for Sorting and Harmonization (Seqwash), an algorithm designed to process sequencing profiles utilizing large language models. Seqwash harmonizes immune cell sequences into a unified representation, empowering LLMs to embed meaningful patterns while eliminating irrelevant information. Evaluations using immune cell sequencing data showcase Seqwash's efficacy in standardizing profiles, leading to improved feature quality and enhanced performance in both supervised and unsupervised downstream tasks for sequencing data.

The integration of data from multiple sources and analytical techniques to obtain novel insights and answer challenging questions is a hallmark of modern science. In arthropods, exocrine secretions may act as pheromones, defensive substances, antibiotics, as well as surface protectants, and as such they play a crucial role in ecology and evolution. Exocrine chemical compounds are frequently characterized by gas chromatography-mass spectrometry. Technological advances of recent years now allow us to routinely characterize the total gene complement transcribed in a particular biological tissue, often in the context of experimental treatment, via RNAseq. We here introduce a novel methodological approach to successfully characterize exocrine secretions and full transcriptomes of one and the same individual of oribatid mites. We found that chemical extraction prior to RNA extraction had only minor effects on the total RNA integrity. De novo transcriptomes obtained from such combined extractions were of comparable quality to those assembled for samples that were subject to RNA extraction only, indicating that combined chemical/RNA extraction is perfectly suitable for phylotranscriptomic studies. However, in-depth analysis of RNA expression analysis indicates that chemical extraction prior to RNAseq may affect transcript degradation rates, similar to the effects reported in previous studies comparing RNA extraction protocols. With this pilot study, we demonstrate that profiling chemical secretions and RNA expression levels from the same individual is methodologically feasible, paving the way for future research to understand the genes and pathways underlying the syntheses of biogenic chemical compounds. Our approach should be applicable broadly to most arachnids, insects, and other arthropods.

Demand for in vitro fertilization (IVF) treatment is growing; however, success rates remain low partly due to difficulty in selecting the best embryo to be transferred. Current manual assessments are subjective and may not take advantage of the most informative moments in embryo development. Here, we apply convolutional neural networks (CNNs) to identify key windows in pre-implantation human development that can be linked to embryo viability and are therefore suitable for the early grading of IVF embryos. We show how machine learning models trained at these developmental time points can be used to refine overall embryo viability assessment. Exploiting the well-known capabilities of transfer learning, we illustrate the performance of CNN models for very limited datasets, paving the way for the use on a clinic-by-clinic basis, catering for local data heterogeneity.

Transformers are a powerful subclass of neural networks catalyzing the development of a growing number of computational methods for RNA structure modeling. Here, we conduct an objective and empirical study of the predictive modeling accuracy of the emerging transformer-based methods for RNA structure prediction. Our study reveals multi-faceted complementarity between the methods and underscores some key aspects that affect the prediction accuracy.

Metabolic rewiring allows cells to adapt their metabolism in response to evolving environmental conditions. Traditional metabolomics techniques, whether targeted or untargeted, often struggle to interpret these adaptive shifts. Here, we introduce MetaboLiteLearner, a lightweight machine learning framework that harnesses the detailed fragmentation patterns from electron ionization (EI) collected in scan mode during gas chromatography/mass spectrometry to predict changes in the metabolite composition of metabolically adapted cells. When tested on breast cancer cells with different preferences to metastasize to specific organs, MetaboLiteLearner predicted the impact of metabolic rewiring on metabolites withheld from the training dataset using only the EI spectra, without metabolite identification or pre-existing knowledge of metabolic networks. Despite its simplicity, the model learned captured shared and unique metabolomic shifts between brain- and lung-homing metastatic lineages, suggesting cellular adaptations associated with metastasis to specific organs. Integrating machine learning and metabolomics paves the way for new insights into complex cellular adaptations.

Biosurfactants have remarkable characteristics, such as environmental friendliness, high safety, and excellent biodegradability. Surfactin is one of the best-known biosurfactants produced by Bacillus subtilis. Because the biosynthetic pathways of biosurfactants, such as surfactin, are complex, mutagenesis is a useful alternative to typical metabolic engineering approaches for developing high-yield strains. Therefore, there is a need for high-throughput and accurate screening methods for high-yield strains derived from mutant libraries. The blood agar lysis method, which takes advantage of the hemolytic activity of biosurfactants, is one way of determining their concentration. This method includes inoculating microbial cells onto blood-containing agar plates, and biosurfactant production is assessed based on the size of the hemolytic zone formed around each colony. Challenges with the blood agar lysis method include low experimental reproducibility and a lack of established protocols for high-throughput screening. Therefore, in this study, we investigated the effects of the inoculation procedure and media composition on the formation of hemolytic zones. We also developed a workflow to evaluate the number of colonies using robotics. The results revealed that by arranging colonies at appropriate intervals and measuring the areas of colonies and hemolytic rings using image analysis software, it was possible to accurately compare the hemolytic activity among several colonies. Although the use of the blood agar lysis method for screening is limited to surfactants exhibiting hemolytic activity, it is believed that by considering the insights gained from this study, it can contribute to the accurate screening of strains with high productivity.