Bioinformation Newsletter - August 24
GeneSpectrum Life Sciences
Simplifying Genomics and Bioinformatics for a Better World!
Issue: 4 | Date: 20 August 2024
Welcome to Bioinformation, your gateway to the latest updates and insights in the world of bioinformatics and genomics. Our newsletter aims to keep you informed about the cutting-edge technologies, trends, and discoveries that are shaping the future of these fields. Subscribe today to stay at the forefront of the dynamic world of bioinformatics and genomics!
??Research
Recent research highlights that an imbalance in the intratumor microbiome of pancreatic cancer contributes to immunotherapy resistance. A novel approach using Lactobacillus rhamnosus GG probiotics, modified with a gallium-polyphenol network (LGG@Ga-poly), has been shown to enhance the effectiveness of immunotherapy. These probiotics specifically target pancreatic tumors, eliminating harmful bacteria and reducing immunosuppressive signals within the tumor. This leads to better immune cell infiltration and stronger antitumor responses. In preclinical models, LGG@Ga-poly not only slowed tumor growth but also significantly improved the outcomes of immune checkpoint blockade therapies, offering a promising strategy to boost pancreatic cancer treatment.
This study presents "GENESO," a novel framework for pan-cancer classification and marker gene discovery using deep learning and the occlusion method. By training a deep LSTM neural network on RNA-Seq data, the researchers achieved a 96.59% accuracy in identifying cancer origins and statuses. They then introduced a "Symmetrical Occlusion" technique to discover key marker genes, boosting accuracy to 98.30% while using 67% fewer genes. Unlike traditional methods, GENESO effectively identifies crucial marker genes, even those with low expression differences. The approach was further validated using single-cell RNA-Seq data, showcasing its potential for advanced cancer diagnostics.
A recent study from the Qatar Metabolomics Study of Diabetes (QMDiab) used advanced multi-omic techniques to gain deeper insights into human physiology and its connection to diabetes. By analyzing urine, blood, and saliva samples from 391 participants, researchers integrated data from 18 different molecular technologies, encompassing over 6,300 traits, genetic variants, DNA methylation sites, and gene expressions. This extensive dataset led to the creation of "The Molecular Human," a network of over 34,000 molecular links that offers a comprehensive view of how various biological factors interact. The study also explored diabetes subtypes and provided insights into how different omics platforms contribute to traits like age, sex, BMI, and diabetes.
???Tools
Advances in computational signal deconvolution now allow for more detailed analysis of bulk transcriptome data, making it possible to identify cell-type-specific differentially expressed (csDE) genes, which is increasingly valuable in clinical research. However, applying these methods can be challenging, especially in designing experiments. To address this, researchers have developed "cypress," a new tool that assists in experimental design and statistical power analysis for csDE identification. Cypress simulates and evaluates the effects of various factors on bulk RNA-seq data, helping researchers optimize their experimental setups for more accurate and reliable results.
A new tool called SpatialOne has been developed to simplify the complex analysis of spatial transcriptomics data, specifically for 10x Visium. This end-to-end pipeline integrates multiple advanced computational methods to segment, deconvolve, and quantify spatial information, making it easier to analyze data at scale. SpatialOne is available as an open-source tool on GitHub and can be easily implemented via a Docker container, streamlining reproducible spatial data analysis for researchers.
Researchers have developed LocalHGT, a new tool that accurately detects complete horizontal gene transfer (HGT) events from gut microbiome data, with a 99.4% accuracy. Tested on 200 gut microbiome samples, it revealed insights into how HGT events might contribute to diseases like colorectal cancer and acute diarrhea. LocalHGT also shows potential for using HGT patterns as biomarkers for disease prediction, achieving strong predictive performance for colorectal cancer with an AUC of 0.87. This breakthrough could lead to a better understanding of the gut microbiome's role in human health and disease.
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??Learn
In this post by Dave Tang, he introduces how you can use the R package archs4 to check what genes are correlated (by expression pattern) to your gene of interest and how you can generate a heatmap using the list of correlated genes (plus your gene of interest).
As next-generation sequencing (NGS) advances, the identification of single nucleotide variations (SNVs) has surged, presenting both opportunities and challenges in genetic diagnosis. While some SNVs are linked to diseases, others remain uncertain, categorized as "variants of uncertain significance" (VUS). These VUS present a significant challenge for clinicians, as their implications for patient care are unclear due to limited scientific data. Improve your understanding of the strategies to close this knowledge gap in this article.
The microbiota can influence the immune system and modulate response to cancer immunotherapy. In this webinar, the speakers will introduce the role of the microbiome in cancer immunotherapy, discussing recent developments in the field. The presentations will be followed by a panel discussion and Q&A session. The webinar will be hosted by Nature Cancer and Nature Communications editors.
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DrPH Student, ScHARe Research Trainee, BroadStreet Institute Community Data Analyst
6 个月Very informative. Shared ??