Half-Million UK Biobank WGS ?? Synthetic Enhancer Design for Cell Types ?? Mouse Brain Cell Atlas ?? Single-Nucleus Barcoding with Slide-Tags ??
Bioinformer Weekly Roundup
Stay Updated with the Latest in Bioinformatics!
Issue: 17 | Date: 15 December 2023
?? Welcome to the Bioinformer Weekly Roundup!
In this newsletter, we curate and bring you the most captivating stories, developments, and breakthroughs from the world of bioinformatics. Whether you're a seasoned researcher, a student, or simply curious about the intersection of biology and data science, we've got you covered. Subscribe now to stay ahead in the exciting realm of bioinformatics!
?? In The Spotlight
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?? Featured Research
This study reports the whole-genome sequencing of 490,640 UK Biobank participants, enhancing understanding of genetics in disease biology and providing novel insights into genomic associations with health traits across different ancestries.
This study benchmarks three commercial imaging spatial transcriptomics platforms using tissue microarrays containing various tumor and normal tissues, finding differences in transcript counts, specificity, and cell typing accuracy.
This study demonstrates the use of deep learning to design synthetic cell type-specific enhancers from random sequences, enabling high-resolution tracing of enhancer features and successful application in both fruit fly and human cells.
?? The first author, Ibrahim Taskiran, has posted a Twitter thread highlighting the results of the research.
This research uses deep and transfer learning to design tissue-specific enhancers for five different tissues in the Drosophila embryo, achieving a high success rate in targeted activity, demonstrating a broadly applicable strategy for designing enhancers specific to tissue, cell type, and state.
?? The corresponding author, Alexander Stark, has posted a Twitter thread describing the results and impact of the research.
The study introduces Slide-tags, a technique for spatially localising single nuclei in tissue sections with high precision, enabling the integration of spatial information into various single-nucleus profiling assays, as demonstrated in mouse hippocampus and human tissues including brain, tonsil, and melanoma.
?? The second author, Jackson Weir, has posted a Twitter thread introducing the technology and applications.
This research presents a new method to obtain high-quality single-nucleus transcriptomic data from formalin-fixed paraffin-embedded samples, overcoming previous limitations related to DNA and RNA quality, thus enhancing the utility of these samples in single-cell genomics.
This study creates a comprehensive cell atlas of the adult mouse brain by integrating advanced spatially resolved single-cell transcriptomics and fluorescence in situ hybridization, identifying over 5,000 cell clusters and more than 300 cell types, and enabling detailed analysis of cell-type compositions, spatial organization, and cell-cell interactions in the brain.
This perspective explores the unique challenges of analyzing spatial transcriptomic data, suggesting the adaptation of methods from geographical sciences to fully harness the spatial dimension in RNA sequencing, thereby enhancing the interpretation and utility of these high-resolution, spatially-informed datasets.
??? Latest Tools
The paper introduces a novel method for detecting viral sequences in transcriptomics data, capable of identifying up to 1.012 trillion virus species with single-cell resolution, which was successfully used to identify new viruses in rhesus macaque cells, highlighting its potential for understanding virus-host interactions and predicting epidemics.
?? The first author, Laura Luebbert, has posted a Twitter thread detailing the method and results.
The paper presents a method to address read-to-transcript ambiguity in RNA-seq data by using a quasi-Poisson model for bootstrapped transcript counts, improving the accuracy and efficiency of differential transcript expression analysis with tools like edgeR, as demonstrated through extensive simulations and tests.
The paper introduces a tool that refines gene annotations using 3'-end single-cell transcriptomics data, demonstrated to enhance gene expression quantification in non-model species and facilitate better biological interpretation and cross-species comparison of cell type expression atlases.
?? The first author, Gregory Zolotarov, has posted a Twitter thread describing the motivation and results.
领英推荐
The paper introduces CellCharter, a versatile algorithmic framework for analyzing cellular niches in spatial omics datasets, demonstrating its superiority over existing methods by identifying distinct cellular environments in various human lung cancer samples and linking them with clinical outcomes.
The study introduces USPNet, a deep learning method for signal peptide classification and cleavage-site prediction, which outperforms existing methods by 10% and successfully identifies novel signal peptides from metagenomic data.
This study introduces GASTON, an unsupervised deep learning algorithm that creates a topographic map of tissue slices to analyze spatial gene expression patterns, effectively identifying distinct cell types and expression gradients, as demonstrated in brain and tumor samples.
?? The corresponding author, Benjamin Raphael, has posted a Twitter thread describing the motivation and overview of the model.
?? Community News
The 'Featured resource' series highlights essential yet often overlooked scientific resources, with this post focusing on the Xenbase team's introduction of their database and how the community can contribute to it.
The Genomics Landscape is a monthly newsletter from NHGRI Director Eric Green, M.D., Ph.D., featuring activities and accomplishments from the institute and the field of genomics.
This episode of the Channels podcast explores 2023's Nextflow advancements, including nf-core's community growth, governance evolution, new pipeline releases, infrastructure enhancements, community initiatives, and upcoming 2024 events.
This episode of The Bioinformatics Lab podcast addresses public health responses to emerging infectious diseases in the post-COVID era, covering topics like evolving disease patterns, lab and bioinformatics in outbreak response.
?? Upcoming Events
The Lorne Genome conference offers an opportunity for the gene regulation community to convene, covering diverse topics and including social events, local sightseeing, and networking opportunities.
The Festival of Genomics & Biodata is a community gathering offering inspirational talks, the latest research, technology showcases, and networking opportunities, primarily free to attendees, to enhance their impact on patient care.
?? Educational Corner
The repository contains teaching material aimed at introducing proteomics from a data analysis perspective, addressing the gap in university education where proteomics is often taught from a chemistry viewpoint without interactive data exploration, thus attracting bioinformatics and computational scientists to the field.
The primer discusses the importance of correctly defining the biological entity (N) in experiments, emphasizing that inaccuracies in this definition can lead to inflated sample sizes and an increase in both false-positive and false-negative results.
?? Connect with Us
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Editor: James Ashmore | Contact: [email protected]
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