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queryable-rna-seq-database Formally known as the Queryable RNA-Seq Database, this system is designed to simplify the process of RNA-seq analysis by providing the ability upload the result data from RNA-Seq analysis into a database, store it, and query it in many different ways.
Data analysis usually requires a combination of bioinformatics software tools (see also List of RNA-Seq bioinformatics tools) that vary according to the experimental design and goals. The process can be broken down into four stages: quality control, alignment, quantification, and differential expression. [ 105 ]
Whereas high sequence coverage for a genome may indicate the presence of repetitive sequences (and thus be masked), for a transcriptome, they may indicate abundance. In addition, unlike genome sequencing, transcriptome sequencing can be strand-specific, due to the possibility of both sense and antisense transcripts. Finally, it can be difficult ...
RNA-Seq (named as an abbreviation of RNA sequencing) is a technique that uses next-generation sequencing to reveal the presence and quantity of RNA molecules in a biological sample, providing a snapshot of gene expression in the sample, also known as transcriptome. [2] [3]
DESeq2 is a software package in the field of bioinformatics and computational biology for the statistical programming language R. It is primarily employed for the analysis of high-throughput RNA sequencing (RNA-seq) data to identify differentially expressed genes between different experimental conditions.
Computational genomics refers to the use of computational and statistical analysis to decipher biology from genome sequences and related data, [1] including both DNA and RNA sequence as well as other "post-genomic" data (i.e., experimental data obtained with technologies that require the genome sequence, such as genomic DNA microarrays).
Automated software package to annotate eukaryotic genes from RNA-Seq data and associated protein sequences Eukaryotes [1] FragGeneScan: Predicting genes in complete genomes and sequencing Reads: Prokaryotes, Metagenomes [2] ATGpr: Identifies translational initiation sites in cDNA sequences: Human [3] Prodigal
It is the first step in sequence analysis to limit wrong conclusions due to poor quality data. The tools used at this stage depend on the sequencing platform. For instance, FastQC checks the quality of short reads (including RNA sequences), Nanoplot or PycoQC are used for long read sequences (e.g. Nanopore sequence reads), and MultiQC ...