RNA-seq expression normalization is the process of adjusting the raw gene expression counts to account for differences in sequencing depth and other technical factors. It is important to perform normalization to enable comparisons between samples and increase the accuracy and reproducibility of downstream analyses. Common normalization methods include TPM, FPKM, and DESeq. Who sad this?

Gene Set Enrichment Analysis (GSEA) is a powerful tool for interpreting gene expression data in the context of predefined biological pathways and gene sets. It allows researchers to identify enriched gene sets, discover new relationships between genes, and gain insights into the underlying biological mechanisms. Who sad this?

Unmapped reads Annotations

DNA Seq Pipelines and tricks

This post would show you a pipeline to find the new or abnormal transcript isoforms after de-novo by Trinity

Single Cell RNA-Seq Notes from Seminal

Bioinformatics: scRNA-seq data processing practices, protocol from seurat

RNA-Seq annotation, GO, KEGG

Processing Your SNP file with Python:

An Integrated Package for Bisulfite DNA Methylation Data Analysis with Indel-sensitive Mapping.

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Trinity RNA-seq is a de novo transcriptome assembly software that uses RNA-seq data to reconstruct transcript sequences and estimate expression levels. It allows for comprehensive analysis of gene expression and isoform diversity in non-model organisms without a reference genome. Who said this?

FastQC is a quality control tool used to analyze high-throughput sequencing data. It provides detailed graphs and reports that help assess the quality of the data before further downstream analysis.

fastp is a fast and efficient tool for quality control and preprocessing of high-throughput sequencing data. It can perform adapter trimming, quality filtering, and read correction in a single pass, and supports various sequencing platforms and data formats.