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Results for « DESeq »

DESeq
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Performs differential gene expression analysis. DEseq is a method that integrates methodological advances with features to facilitate quantitative analysis of comparative RNA-seq data using shrinkage estimators for dispersion and fold change. The software is suitable for small studies with few replicates as well as for large observational studies. Its heuristics for outlier detection assist in recognizing genes for which the modeling assumptions are unsuitable and so avoids type-I errors caused by these.
Corset
A command-line software program to go from a de novo transcriptome assembly to gene-level counts. Corset takes a set of reads that have been multi-mapped to the transcriptome (where multiple alignments per read were reported) and hierarchically clusters the transcripts based on the proportion of shared reads and expression patterns. It will report the clusters and gene-level counts for each sample, which are easily tested for differential expression with count based tools such as edgeR and DESeq.
AbsFilterGSEA
Provides efficient gene-permuting gene-set enrichment analysis (GSEA) methods for small replicate RNA-seq data. AbsFilterGSEA provides three modes of gene-permuting GSEA methods: (i) original two-tailed GSEA, (ii) absolute one-tailed GSEA and (iii) the ordinary GSEA filtered with absolute GSEA results. It accepts a raw read count matrix and normalizes it using a DESeq median method. It also accepts an already normalized dataset. The core GSEA was coded with C++ and the results were cross-checked with those from the original GSEA R-code.
DEAR-O / Differential Expression Analysis based on RNA-seq data – Online
A user-friendly platform for performance evaluation of differential expression analysis based on RNA-seq data. DEAR-O currently includes four of the most popular tools: DESeq, DESeq2, edgeR and Cuffdiff2. Based on the DEAR-O platform, researchers can evaluate the performance of different tools, or the same tool with different versions, with a customised number of biological replicates using already curated RNA-seq datasets. We also initiated an online forum for discussion of RNA-seq differential expression analysis. Through this forum, new useful tools and benchmarking datasets can be introduced.
REDseq
Includes functions to build restriction enzyme cut site (RECS) map, and distributes mapped sequences on the map with five different approaches. REDseq is a Bioconductor package for building genomic map of restriction enzyme sites REmap, assigning sequencing tags to RE sites using five different strategies, visualizing genome-wide distribution of differentially cut regions with the REmap as reference and the distance distribution of sequence tags to corresponding RE sites, generating count table for identifying statistically significant RE sites using edgeR or DEseq.
anamiR
Identify potential interactions of miRNA-target gene interactions from miRNA and mRNA expression data. AnamiR contains functions for statistical test, databases of miRNA-target gene interaction and functional analysis. The anamiR package provides a whole workflow, which contains important functions like “normalization” used to normalize the expression data with one of three methods, including normal, quantile, rank.invariant, “differExp_discrete” used to find the differential genes or miRNAs from given expression data with one of three statistical methods, including t.test, wilcox. test,limma and DESeq or “miR_converter” to convert the older miRNA annotation to the miRBase 21 version.
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Results for « DESeq »

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