The flu NA (neuraminidase) protein is crucial because it plays a key role in the influenza virus's ability to infect host cells and spread within the body. It helps the virus to release newly formed viral particles from infected cells, facilitating the spread of the infection. As such, it's a key target for antiviral drugs and vaccine development, helping in controlling and preventing flu outbreaks.

This post provides an in-depth understanding of False Discovery Rate (FDR) and the Benjamini-Hochberg Method, crucial in statistical analysis with large datasets like genomics. It explains FDR's role in identifying false positives in multiple hypothesis testing and the Benjamini-Hochberg Method's effectiveness in controlling FDR. The post compares various p-value adjustment methods, discussing their advantages, limitations, and suitability for different data types. It emphasizes the BH method's balance in statistical power and error control, and its integration in software like R, highlighting its applicability across scientific fields.

The main idea of this work is to use PyMOL for calculating the surface information/mesh structure, and then employ Python to read that information for visualization and other complex/advanced calculations. In other words, it seamlessly bridges PyMOL and Python.

This post provides a detailed exploration of the CR9114 antibody, focusing on its binding properties and effectiveness against various influenza A virus strains. It covers the specific gene segments from which CR9114 is derived, explaining its broad reactivity to both group 1 and group 2 influenza A viruses. The article highlights the significance of the HA stem region in the virus and how CR9114's binding to this region blocks the necessary conformational changes for viral entry into host cells. Additionally, it compares CR9114 to other antibodies like CR6261 and FI6v3, discussing their different binding modes and effectiveness. The content is technical and geared towards readers with a background in biology and immunology.

In the evolving realm of biotechnology, antibodies and phage display stand as two pillars of immense significance. This article delves into the structural intricacies of antibodies, emphasizing their heavy and light chains, along with the pivotal Complementarity-Determining Regions (CDRs). Furthermore, the article illuminates the concept of phage display, a groundbreaking technique that bridges genotypic information with phenotypic expression, offering a high-throughput approach to study protein interactions. Illustrated with detailed visuals, the piece offers readers an in-depth understanding of these subjects, underscoring their paramount importance in modern medicine and research.

The blog post delves into the realm of PacBio sequencing, elucidating its significance in the world of next-generation sequencing. Contrasting PacBio with other sequencing technologies, such as Illumina's short-read and Oxford Nanopore's long-read sequencing, the article highlights the unique advantages and challenges posed by each. The comprehensive PacBio data analysis pipeline is elucidated step by step, from raw data collection to final report generation. A special section is dedicated to comparing tools used in the PacBio pipeline, offering insights into their strengths and limitations.

Gene regulation plays a crucial role in various biological processes, and understanding its mechanisms is essential for advancing our knowledge in life sciences. The Advent of ATAC-seq, a powerful tool for mapping open chromatin regions, has revolutionized the study of gene regulation by providing insight into the regulatory elements that control gene expression. This review aims to provide an overview of the current state of ATAC-seq applications in various fields, including stem cell biology, cancer research, neurobiology, immunology, plant biology, microbiology, drug discovery, personalized medicine, and synthetic biology. We discuss the advantages and limitations of ATAC-seq and highlight its potential for identifying new therapeutic targets and developing personalized therapies. Overall, ATAC-seq has proven to be a valuable tool for unlocking gene regulation and has the potential to lead to significant breakthroughs in many areas of life science research.

Pseudotime analysis provides a transformative lens into cellular dynamics, offering an avenue to chart the developmental journey of individual cells. This primer introduces the novice to the realm of pseudotime and its significance in the intricate landscape of cell differentiation and gene expression. Utilizing Monocle, a pioneering tool in this domain, the article elucidates how cellular trajectories are constructed from single-cell RNA-sequencing data. The comparison of Monocle with its contemporaries highlights its robustness in handling complex trajectories and its unparalleled flexibility. As the biological world delves deeper into cellular intricacies, tools like Monocle stand as indispensable allies in unearthing the secrets of cellular progression. This article serves as a beacon for those navigating the vast ocean of single-cell analysis.

Single-cell sequencing technologies, notably scRNA-Seq and scATAC-Seq, offer unparalleled insights into gene expression and chromatin accessibility at the cellular level. However, integrating these distinct datasets presents a challenge due to their inherent differences. This article delves into the process of transforming scATAC-Seq data from genomic regions to gene-centric information and subsequently integrating it with scRNA-Seq data using shared latent spaces. By leveraging tools and methods that identify underlying patterns across datasets, researchers can achieve a comprehensive view of cellular states, bridging gene expression with chromatin dynamics.

Evaluating the quality of classification

scRNA-Seq: makers explore

This guide illustrates how to visualize the range of available point shapes in the `ggplot2` package of R. By creating a data frame with a sequence of shape numbers and plotting them using `geom_point()`, users can easily identify and select suitable shapes for their data visualization needs. The resulting plot provides a clear representation of each shape, labeled with its respective shape number, allowing for quick and informed decisions in chart design.

Single-cell RNA sequencing (scRNA-seq) offers unparalleled insights into cellular heterogeneity. However, integrating datasets from diverse sources poses challenges, especially for newcomers. This guide provides a concise walkthrough of scRNA-seq data integration using the Seurat package, coupled with essential tips for beginners. From preprocessing to downstream analysis, we cover the key steps to ensure effective data harmonization, aiming to empower researchers to derive meaningful insights from integrated datasets.

In single-cell RNA sequencing (scRNA-seq) analysis, batch effects—non-biological variations from different sample processing—are pervasive challenges. Without correction, they can obscure genuine biological signals. This article elucidates the importance of batch effect removal and presents a comparative overview of three popular correction methods within Seurat: Harmony, fastMNN, and SCTransform. Choosing an apt method ensures accurate and unbiased biological insights, highlighting the significance of vigilant batch correction in scRNA-seq studies.

RNA-Seq stands for RNA sequencing, a revolutionary technique that helps scientists understand the expression of genes within a cell. In traditional RNA-Seq, we study the averaged gene expression of thousands of cells, but this approach has its limitations. It’s like trying to understand the flavor profile of a smoothie by tasting it – you know the overall taste, but you can’t pinpoint the individual fruits that contribute to it.