CD-HIT was originally a protein clustering program. The main advantage of this program is its ultra-fast speed. It can be hundreds of times faster than other clustering programs, for example, BLASTCLUST. Therefore it can handle very large databases, like NR. The 1st version of this program, CD-HI, was published and released in 2001. The 2nd version, called CD-HIT, was published in 2002 with significant improvements. Since 2004, CD-HIT has been hosted at bioinformatics.org as an open source project. Current CD-HIT package can perform various jobs like clustering a protein database, clustering a DNA/RNA database, comparing two databases (protein or DNA/RNA), generating protein families, and many others.

In summary, extracting information from PDB files and visualizing it with PyVista enables a deep and interactive exploration of biomolecular structures, which is vital for various scientific and medical research purposes. This approach harnesses the power of computational tools to augment our understanding of complex biological systems.

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.

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.

scRNA-Seq: makers explore

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.

Behavioral research is a vast field that offers insights into the complexities of behavior and provides a foundation for making predictions and interventions in various sectors of life and society.

IdTrackerAI is an automated tracking software that uses deep learning algorithms to track individual animals in videos, even in challenging conditions such as occlusions and interactions between animals. The software can be used to extract a variety of metrics, including animal trajectories, activity levels, and social behavior, making it a useful tool for behavioral research in fields such as ecology, neuroscience, and psychology. Who sad this?

Cellpose is a deep learning-based software that automates cell segmentation and classification from fluorescence microscopy images. It provides a user-friendly interface and can process a large number of images in a short time, making it a valuable tool for biologists and biomedical researchers studying cell morphology and behavior. Who sad this?

TCGAbiolinks is an R package that provides an easy-to-use interface to access and analyze data from The Cancer Genome Atlas (TCGA) project. It allows users to download TCGA data, perform quality control, differential expression analysis, and data visualization. TCGAbiolinks has contributed to a better understanding of the molecular basis of cancer and identified new potential biomarkers and therapeutic targets. Who sad this?