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The FAST4 format was invented as a derivative of the FASTQ format where each of the 4 bases (A,C,G,T) had separate probabilities stored. It was part of the Swift basecaller, an open source package for primary data analysis on next-gen sequence data "from images to basecalls". The FAST5 format was invented as an extension of the FAST4 format.
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]
Salmon is a software tool for computing transcript abundance from RNA-seq data using either an alignment-free (based directly on the raw reads) or an alignment-based (based on pre-computed alignments) approach. It uses an online stochastic optimization approach to maximize the likelihood of the transcript abundances under the observed data.
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Healthcare Cost and Utilization Project (HCUP) is the largest collection of hospital care data in the United States. It includes hundreds of millions of inpatient, outpatient, and emergency records. LEXAS curates descriptions of biological experiments from PMC articles. Bovine Metabolome Database is a free web database that lists known bovine ...
Currently RNA-Seq relies on copying RNA molecules into cDNA molecules prior to sequencing; therefore, the subsequent platforms are the same for transcriptomic and genomic data. Consequently, the development of DNA sequencing technologies has been a defining feature of RNA-Seq. [ 78 ] [ 80 ] [ 81 ] Direct sequencing of RNA using nanopore ...
Gene Expression Omnibus (GEO) is a database for gene expression profiling and RNA methylation profiling managed by the National Center for Biotechnology Information (NCBI). [1] These high-throughput screening genomics data are derived from microarray or RNA-Seq experimental data. [2]
For a clustering example, suppose that five taxa (to ) have been clustered by UPGMA based on a matrix of genetic distances.The hierarchical clustering dendrogram would show a column of five nodes representing the initial data (here individual taxa), and the remaining nodes represent the clusters to which the data belong, with the arrows representing the distance (dissimilarity).