?? Protein Mapping Unveils Insights into Aging Brain ??, Enrichment on Steps, Not Genes ??, HIHISIV: Database for HIV & SIV Immune Gene Expression ??
Bioinformer Weekly Roundup
Stay Updated with the Latest in Bioinformatics!
Issue: 30 | Date: 29 March 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 research introduces an Ensemble Learning method for Prediction of Genetic Values (ELPGV), which combines predictions from various existing methods like GBLUP, BayesA, BayesB, and BayesCπ to enhance accuracy. Study claims that through validation with diverse datasets and simulations, ELPGV consistently performs better than individual methods, as indicated by significant p-values. Notably, ELPGV integrates the strengths of each method, offering improved predictive ability without the need for genotype data.
The study enhances pathway enrichment analysis by considering functional relationships between genes and enriching on step-enabling entities within pathways. By weighting gene sets accordingly, the method improves sensitivity towards pathways with interchangeable genes, revealing pathways unidentified by traditional methods. This offers better insights into perturbed pathways in genome-scale experiments, potentially helping in medical research.
The study addresses challenges in somatic variant detection using biological replicates in next-generation sequencing (NGS). By employing replicate-based consensus approaches and machine learning (ML) models trained on biological replicates, the research enhances variant calling performance. Results indicate improved accuracy and efficiency in somatic mutation detection, particularly beneficial for cancer-related studies.
This study investigates mRNA and lncRNA expression in slow transit constipation (STC) using RNA sequencing. It identifies 1420 DE lncRNAs and 1634 DE mRNAs associated with STC, implicating immune-related pathways. WGCNA identifies hub lncRNAs, and PPI analysis highlights significant proteins like IL2 and CD80 in STC development, offering potential biomarkers for diagnosis or therapy.
MIT and Mayo Clinic researchers find remarkable cellular and molecular overlaps between ALS and FTLD, suggesting shared therapeutic targets. They highlight identical vulnerabilities in neurons and gene expression patterns, indicating potential treatments applicable to both disorders. This finding guides the development of effective therapies for ALS and FTLD.
This study examines the transition from ductal carcinoma in situ (DCIS) to invasive breast carcinoma (IBC) in 50 patients. Shared mutations and copy number variations are found, but transcriptional profiles differ significantly, with IBC exhibiting distinct pathways linked to invasion and proliferation. These results suggest additional regulatory mechanisms driving the transition to invasive disease.
??? Latest Tools
The HIHISIV database supports research on lentiviral pathogenesis by integrating transcriptional data from SIV/HIV infections in nonhuman primates and humans. It contains curated microarray and RNA-Seq gene expression data, aiding analysis through six query templates. This resource assists in understanding the immune response and disease progression in lentiviral infections.
The Database is accessible here.
"MintTea" offers an integration-based approach for studying the human gut microbiome, identifying "disease-associated multi-omic modules" with predictive power and cross-omic correlations. In a metabolic syndrome study, it uncovers modules comprising serum metabolites and bacteria linked to insulin resistance. Similarly, in colorectal cancer data, MintTea highlights modules associated with cancer progression, demonstrating its potential in deciphering microbiome-disease interactions.
AmpliDiff is a computational tool for identifying discriminatory genomic regions and designing primers within viral genomes, enabling accurate estimation of SARS-CoV-2 lineage abundances in metagenomic data. It offers better performance for whole genome sequencing and remains robust against incomplete data. AmpliDiff presents a cost-efficient alternative for lineage abundance estimation in viral metagenomes.
PathIntegrate is a pathway-based method for integrating multi-omics datasets, providing interpretable models for complex studies. It ranks pathways by their contribution to outcome prediction, aiding interpretation of biological processes. Demonstrated with COPD and COVID-19 data, PathIntegrate efficiently extracts perturbed multi-omics pathways, showcasing its utility for integration and interpretation.
PathIntegrate is available via the open-source Python package.
scATAnno, a workflow for scATAC-seq data annotation, utilizes reference atlases and uncertainty scores for precise cell type detection without relying on scRNA-seq profiling. It integrates query data with public datasets to identify cell types effectively. Tested on various datasets like PBMC, BCC, and TNBC, scATAnno aids interpretation of complex biological systems by annotating cell types efficiently.
领英推荐
GFPrint? is a streaming algorithm for transforming genetic sequencing data into embedded representations, aiding in exploring disease-related genes and pathways. Tested on cancer datasets like TCGA and the Broad Institute, it identifies gene panels potentially influencing survival and prognosis. Accessible through a secure web portal, GFPrint? is applicable in therapeutic areas where patient genetic profiles may affect disease evolution.
?? Community News
This news discusses how the aging-related decline in brain endothelial cell (BEC) function contributes to neurological diseases like strokes and dementia. While transcriptomes of BECs are mapped, proteomic data is lacking. Martin Dichgans emphasizes the need for proteomic insights to understand protein-level changes driving BEC dysfunction, crucial for addressing these conditions.
Brown University's Warren Alpert Medical School convened the first meeting of the Human RNome Project, mirroring the Human Genome Project's scope. This initiative aims to sequence all human RNA, with potential implications for disease research and treatment development, including mRNA-based vaccines for COVID-19.
?? Educational Corner
Explore this blog to learn how to read the .sf salmon quantification file into R, obtain the tx2gene.txt file, and perform DESeq2 for differential gene expression analysis.
This course covers cancer genomic analysis, including transcriptomics and single-cell tech, for PhD students, post-docs, and industry pros. It explores HTS applications, cancer genomics, CRISPR-Cas9, RNA-seq, and single-cell research. Prior knowledge of HTS, R/Bioconductor, and Unix/Linux is required. It features talks, interactive sessions, and practical exercises, with pre-recorded and live sessions via Zoom.
This blog post discusses the challenges of accurately clustering cells in single-cell RNA sequencing (scRNA-seq) data. In scRNA-seq, individual cells are sequenced, offering insights into gene expression at the single-cell level, revealing cellular identities and states. However, the data's high dimensionality and technical noise pose challenges, such as over-clustering, where biologically similar cells are incorrectly grouped into distinct clusters.
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Student at Vinayaka Mission's Research Foundation - University
7 个月Hello, This is Geethalakshmi currently pursuing 3rd year Biotechnology, I would like to do internship in Zifo.What can I do for get internship?