?? CD9 in Glioma, ??Docker AI for Code Fix, ??Lentinan for GC/COVID-19, ??Castanet: Multi-Pathogen Data Analysis??
Bioinformer Weekly Roundup
Stay Updated with the Latest in Bioinformatics!
Issue: 59 | Date: 25 October 2024
?? 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!
?? Featured Research
The study investigates how RNA stem-loops can regulate translation to reduce gene expression noise. By analysing a structured model of gene expression, the researchers found that mRNA molecules can exist in multiple states, transitioning between them. This transition can influence the steady-state protein distribution, potentially reducing noise.
The study introduces PreMLS, a novel undersampling technique based on ClusterCentroids, designed to predict multiple lysine modification sites on proteins. By addressing class imbalance in datasets, the model uses a convolutional neural network (CNN) to accurately forecast modifications such as methylation, acetylation, crotonylation, and succinylation.
This study examines the role of the transcription factor IRF4 in regulating immune responses. Using single-cell RNA sequencing and chromatin accessibility assays, the researchers discovered that IRF4 is crucial for the differentiation and function of various immune cells, including T cells and macrophages.
The study explores how sequencing depth and false positives impact the recovery of metagenome-assembled genomes (MAGs) in microbial communities. By simulating 69 microbial communities, the researchers found that sequencing depth significantly affects the accuracy of MAG recovery, with different pipelines showing varying levels of performance.
This study investigates the role of CD9 in gliomas, focusing on its prognostic value and molecular mechanisms. Using data from TCGA, CGGA, and GEO databases, the researchers found that high CD9 expression is associated with lower survival rates in glioma patients. The study also highlights CD9’s involvement in immune responses and its potential impact on immunotherapy effectiveness.
METASPACE-ML is a machine learning approach designed to improve the assignment of metabolites in imaging mass spectrometry data. It uses new scoring methods and efficient False Discovery Rate estimation. The model was trained and evaluated on 1710 datasets from 159 researchers across 47 labs, covering various spatial metabolomics contexts.
Researchers created a Mouse Placentation Spatiotemporal Transcriptomic Atlas (MPSTA) from embryonic day (E) 7.5 to E14.5 using Stereo-seq. This atlas includes previously uncaptured large trophoblast cells and maps trophoblast differentiation. Key modulators for trophoblast development and placental angiogenesis were identified, with paternally expressed genes enriched in the placenta. Maternal high-fat diet exposure was found to increase inflammation and disrupt angiogenesis.
Patients with gastric cancer (GC) are more vulnerable to COVID-19, worsening their prognosis. Lentinan, known for its antiviral and antitumor properties, was hypothesized to benefit these patients. A prognostic model for GC/COVID-19 patients was created, using network pharmacology to explore lentinan’s biological functions, drug targets, and mechanisms.
This study examines type-A response regulator (ARR) genes in four rosids and one monocot. In Populus deltoides, PdRR8 and PdFERR were identified as dispersed duplicates from a common ancestor. PdRR8 is highly expressed in leaves, while PdFERR is specific to female floral buds. Cold represses PdRR8, and 6-BA inhibits PdFERR. Overexpressing PdRR8 in Arabidopsis mutants restores hydrotropic response, unlike PdFERR.
Researchers analysed protein sequences of SnRK1, hexokinase, and TORC1 kinases from seven sugarcane datasets, identifying domains, phylogenetic relationships, and expression levels. They found 11 TOR, 23 RAPTOR, 55 LST8, 95 SnRK1α, 98 HXK, and 14 HXK-like sequences. HXK showed the highest enzymatic activity in culms during the first month, crucial for early plant development.
??? Latest Tools
DAVID?is an integrative tool designed for functional interpretation of large gene or protein lists generated from high-throughput assays. The new feature, DAVID Ortholog, enables the conversion of gene lists between species using ortholog data from OMA and Ensembl Compara. This allows researchers to analyse genes in the context of model organisms, providing insights when gene information for a specific species is limited. DAVID Ortholog integrates this functionality into the existing DAVID platform, supporting more comprehensive functional analysis through orthologs.?
DNASimCLR?is an unsupervised framework designed to efficiently extract features from microbial gene sequence data using convolutional neural networks and the?SimCLR?framework. Pre-trained on large?unlabelled?datasets, it was fine-tuned for classification tasks, outperforming state-of-the-art CNN-based methods and demonstrating adaptability.
Xenomake?is a standalone pipeline that automates the processing, alignment, and sorting of spatially resolved transcriptomics-derived xenograft reads, integrating with downstream spatial analysis packages. It accurately assigns organism-specific reads, increases gene counts, and maintains biological relevance, aiding scientists in studying the tumour microenvironment and drug response in xenograft models.
The source code is available?here.?
NeoDisc?is an end-to-end clinical proteogenomic pipeline designed to identify, predict, and prioritize tumour-specific and immunogenic antigens for personalized cancer immunotherapies. It integrates mass spectrometry immunopeptidomics data with genomics and transcriptomics, using both rule-based and machine-learning approaches.?
The pipeline usage details can be found?here.?
The article introduces LDAGM, a novel method for predicting lncRNA-disease associations using a Graph Convolutional Autoencoder and Multilayer Perceptron model. By integrating multiple similarity networks and employing deep feature extraction techniques, LDAGM captures complex relationships between?lncRNAs?and diseases.?
The source code is available here.
Castanet: a pipeline for rapid analysis of targeted multi-pathogen genomic data | Oxford Academic? The article presents Castanet, a pipeline for the rapid analysis of targeted multi-pathogen genomic data. Castanet processes short-read data from targeted enrichment strategies to provide accurate genome reconstructions and method-specific metrics.
The source code is available here.
METASPACE-ML is a machine learning-based method designed to improve metabolite annotation in imaging mass spectrometry. It has incorporated new scoring methods and efficient False Discovery Rate estimation. This study says this approach advances metabolite identification for spatial metabolomics, can offer better accuracy and sensitivity.
The source code and details can be found?here.?
?? Community News
In a recent study, researchers found that P-stalk ribosomes (PSRs) are crucial in cytokine-mediated processes, affecting antigen presentation and immune surveillance. The study highlights PSRs' role in immune detection of tumors and their potential to enhance cancer immunotherapies. This mechanism was observed in both in vitro human melanoma cell lines and in vivo murine models, making it generalizable across multiple cancer types.?
In a new study, researchers have developed an AI tool called the 'UK Deceased Donor Kidney Transplant Outcome Prediction' (UK-DTOP) to improve predictions and outcomes for kidney transplant patients. Using data from nearly 30,000 transplant cases over 15 years, the model enhances donor-recipient matching and organ allocation.?
Proteomics Could Go Ion-Free with “Fingerprinted” NEMS-MS | Genetic Engineering & Biotechnology News??
Researchers at Caltech have developed a fingerprint analysis technique using machine learning to enhance nanoelectromechanical systems (NEMS) for mass spectrometry. This method captures vibrational behaviors of NEMS devices to identify molecules without fragmentation. The article says that this method can potentially simplify protein identification and highlights the potential for more accurate single-molecule mass spectrometry without the need for prior calibration.
Mapping Cancer Drug Resistance Mutations and Mechanisms | Genetic Engineering & Biotechnology News???
A recent study by the?Wellcome?Sanger Institute, EMBL-EBI, and Open Targets has mapped cancer drug resistance mutations into four categories. This research helps understand how these mutations impact drug resistance and suggests potential second-line treatments based on genetic profiles. The findings aim to personalize cancer therapies and improve treatment efficacy.
Researchers have found that the deletion of a specific long non-coding RNA (lncRNA) is linked to a rare neurodevelopmental disorder. This study highlights the significant role?lncRNAs?play in brain development. The findings offer new insights into the genetic basis of such disorders and suggest potential areas for further research.
?? Upcoming Events
The?Nextflow?Summit 2024 in Barcelona, from October 28 to November 1, will feature foundational training, a hackathon, and a summit. This event is aimed at bringing together experts and researchers to?showcase the latest approach in workflow management.
EMBL's European Bioinformatics Institute's free online webinar, on October 31st ,?will give an overview on programmatic access using python, to the publicly available protein sequence database,?Uniprot. The webinar aims to cover key aspect of searching, filtering, and bulk downloads of querying proteins and further processing with downloaded targets.
?? Educational Corner
This blog emphasizes the importance of validating R Shiny applications in the pharmaceutical industry to ensure data integrity, regulatory compliance, and patient safety. It discusses the unique challenges of validating dynamic and interactive Shiny apps compared to static R packages. The post covers best practices, guidelines, and tools like Rhino and Teal that support the validation process, ensuring reproducibility, performance, and usability standards in clinical settings.?
This article explores how Docker AI tools can aid the software development process by providing context for better code fixes. It details the integration of linting tools like Pylint with large language models (LLMs) to map out codebases and identify issues more effectively. The process involves containerizing these tools to streamline the workflow, allowing the LLM to access necessary context and fix code issues efficiently.?
This tutorial provides step-by-step instructions for setting up a development environment for AWS. It includes creating an AWS account, configuring user permissions, and optionally setting up the AWS CLI.
This blog post explains the principles of PCA projection and cell label transfer in Seurat, a popular tool for single-cell RNA-seq data analysis. It covers the mathematical foundations of PCA, the process of projecting query datasets into reference datasets, and the use of k-nearest neighbors for cell label transfer. The post includes practical examples and code snippets to illustrate these concepts.?
This blog provides a comprehensive guide for beginners on how to loop through lists in R using both base R functions and the?purrr?package. It includes practical examples and best practices to help users efficiently manipulate and?analyse?complex datasets.?
This blog explores the use of model-based testing (MBT) with?Testcontainers and jqwik?to automate test case generation by?modelling?expected software?behaviour. It demonstrates how to perform regression testing on a simple REST API using the?jqwik?test engine on JUnit 5, and how?Testcontainers?can spin up Docker containers with different application versions to compare results.
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Senior Medical Coder (BCHH-C) - Home Health
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