Informational articles about algorithms, analyses, and visualizations in computational biology and bioinformatics.
Learn the basics of RNA-seq, including its methodology, applications, experimental design, and data analysis techniques. This 101 overview is perfect for biologists and bioinformaticists investigating transcriptomics for the first time.
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To draw accurate and meaningful conclusions from RNA-seq data, it is essential to consider covariates, which are additional, potentially “hidden” variables that may impact gene expression in your experiment
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Dive into the world of Precision Run-On sequencing (PRO-seq) in this comprehensive blog post. Discover the origins of PRO-seq, how it compares with traditional RNA-seq, and explore a step-by-step guide on conducting a PRO-seq experiment. Uncover how this innovative technology is advancing our understanding of transcriptional dynamics and revolutionizing genomic research.
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RNA sequencing (RNA-Seq) has revolutionized the way we study gene expression. The data deluge it produces, however, presents a critical question: how can we make valid comparisons between different samples or conditions?
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Learn about key file formats in high-throughput research, including FASTQ, FASTA, BAM/SAM, BAI, SAM, VCF, GFF/GTF, BED, BedGraph, BigWig, and PDB. Discover how each format is used, what information it contains, and its significance in analyzing ChIP-seq and ATAC-seq data. Understanding these formats is crucial for efficiently handling and analyzing high-throughput data, enabling valuable biological insights and discoveries.
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In the field of genomics and bioinformatics, research discovery can be slowed down by the time and resources required to analyze and interpret large datasets. Bioinformatics platforms, which are specialized software or online tools that provide a range of analysis functions, can help to streamline the process and accelerate research discovery.
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Curious about CUT&RUN and when you would use it? Learn more in this Pluto blog post about getting started with CUT&RUN.
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This blog post explains the differences between computational biology and bioinformatics. Bioinformatics is concerned with the development and application of computational methods for analyzing and interpreting large biological datasets, while computational biology involves mathematical models and computer simulations to study complex biological systems and processes. The post also describes the different tools and platforms used in each field and how modern SaaS platforms are bringing the two fields together. Finally, a top 10 list summarizes the main differences between computational biology and bioinformatics.
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Kaplan-Meier curves are an essential tool in computational biology research, allowing analysis of survival data and identification of important genes and pathways associated with specific traits or diseases. Learn more about their importance in this blog post. #computationalbiology #KaplanMeier #survivalanalysis
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A mini intro to RSEM, a workhorse in RNA-seq data quantification.
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Unlocking the power of big data in biology with dimensionality reduction techniques. Explore how PCA, t-SNE, and other methods are driving insights in computational biology. #DimensionalityReduction #ComputationalBiology #BigDataInsights
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Discover the power of Integrative Genomics Viewer (IGV) plots - a highly customizable and interactive tool for visualizing and exploring genomic data. Learn how IGV plots can be used in genomic research and the methods for creating data used in these plots. #genomics #bioinformatics.
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Differential expression analysis is a powerful tool used to identify genes or transcripts that are differentially expressed between two or more conditions or sample types. This technique is commonly used in the field of genomics, transcriptomics, and proteomics to study the molecular mechanisms underlying different biological processes, such as disease development, response to treatment, or adaptation to different environments.
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In this blog post we will explain what Z-scores are how they are calculated and how they can be used in RNA-seq analysis.
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qPCR has transformed molecular biology by providing unparalleled accuracy and sensitivity in the detection and quantification of nucleic acid sequences. Despite its potential drawbacks, such as cost and optimization requirements, qPCR has proven invaluable in research, clinical diagnostics, and food safety testing. Recent advancements in qPCR technology, such as Digital PCR and CRISPR-based qPCR, have further expanded its applications. Learn more about the origins and potential of qPCR in our latest blog post.
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ChIP-seq, or chromatin immunoprecipitation sequencing, is a powerful tool used to study protein-DNA interactions in the genome.
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We will explain what these factors are and how they are used to analyze ChIP-seq and ATAC-seq data
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ATAC-seq, or assay for transposase-accessible chromatin sequencing, is a powerful tool used to study the accessibility of the genome.
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Next-generation sequencing (NGS) is a powerful tool used to generate high-throughput DNA sequencing data.
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DESeq2 is a popular and widely used package in the field of bioinformatics for the analysis of RNA-Seq data. It provides a more comprehensive view of gene expression compared to traditional microarray technology.
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Cytokines are small proteins that play a crucial role in regulating the immune system, inflammation, and cell growth.
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SAMtools is a software package for interacting with and processing next-generation sequencing data
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In this blog post, we will walk through the steps of trimming RNA-seq data using popular software tools such as Trimmomatic and Cutadapt.
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Mastering Key Skills and Coding Languages for Effective Analysis of Biological Data: A Guide to Bioinformatics Engineering
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