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Estimates the relative abundance of circular RNA transcripts from high-throughput RNA-seq data. Sailfish-cir transforms circular transcripts to pseudo-linear transcripts. Comparing to count-based tools, it is superior in estimating the amount of circRNA expression from both simulated and real ribosomal RNA-depleted (rRNAdepleted) RNA-seq datasets. Several factors, such as gene length, amount of expression, and the ratio of circular to linear transcripts, had impacts on quantification performance of the tool.
A tool for quantifying the expression of transcripts using RNA-seq data. Salmon uses new algorithms (specifically, coupling the novel concept of lightweight alignment with a streaming inference algorithm) to provide accurate expression estimates very quickly (i.e. wicked-fast) and while using very little memory. Salmon performs its inference using an expressive and realistic model of RNA-seq data that takes into account the attributes, like position-specific bias, observed in real experimental data.
TGIRT-map / Thermostable group II intron reverse transcriptase mapping
Performs RNA-seq quantification using an iterative genome-mapping procedure. TGIRT-map first filters out trimmed reads and sequentially aligned selected reads to the human genome. Then, it extracts and assigns multiply-mapped reads as uniquely-mapped at loci according three criteria. From these alignments, reads that aligned to tRNA or rRNA loci are extracted and combined with tRNA and rRNA reads from the first step. Lastly, they are realigned to the tRNA and rRNA sequences to generate gene counts.
Allows reproducible RNAseq analysis. Arkas is a cloud computational pipeline that encapsulates Kallisto, automates the construction of composite transcriptomes from multiple sources, quantifies transcript abundances, and implements reproducible rapid differential expression analysis followed by a gene set enrichment analysis over Illumina’s BaseSpace Platform. The server is versionized into Docker containers and publicly deployed within Illumina’s BaseSpace cloud based computational environment.
A python toolkit providing best-practice pipelines for fully automated high throughput sequencing analysis. You write a high level configuration file specifying your inputs and analysis parameters. This input drives a parallel pipeline that handles distributed execution, idempotent processing restarts and safe transactional steps. The goal is to provide a shared community resource that handles the data processing component of sequencing analysis, providing researchers with more time to focus on the downstream biology.
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