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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.
edgeR / empirical analysis of DGE in R
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Allows differential expression analysis of digital gene expression data. edgeR implements a range of statistical methodology based on the negative binomial distributions, including empirical Bayes estimation, exact tests, generalized linear models and quasi likelihood tests. The package and methods are general, and can work on other sources of count data, such as barcoding experiments and peptide counts.
sRNAtoolbox
A collection of small RNA analysis tools. sRNAtoolbox is aimed to provide small RNA researchers with several useful tools including sRNA expression profiling from deep sequencing experiments and several downstream analysis tools. The center piece of sRNAtoolbox is sRNAbench, which allows the expression profiling and prediction of novel microRNAs in deep sequencing experiments. The other tools can be either launched on sRNAbench results, or independently using the appropriate file formats.
ALEXA-Seq
Analyzes parallel RNA sequence data to catalog transcripts and assess differential and alternative expression of known and predicted mRNA isoforms in cells and tissues. ALEXA-Seq comprises several functions: (1) creation of a database of expression and alternative expression sequence ‘features’, (2) mapping of short paired-end sequence reads to these features, (3) identification of features that are expressed above background noise while taking into account locus-by-locus noise, or (4) identification of features that are differentially expressed in samples.
limma / Linear Models for Microarray Data
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Provides an integrated solution for analysing data from gene expression experiments. limma contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. It also contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions: (i) it can perform both differential expression and differential splicing analyses of RNA-seq data; (ii) the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences.
RSEM / RNA-Seq by Expectation-Maximization
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Performs gene and isoform level quantification from RNA-Seq data. RSEM is a software package that quantifies gene and isoform abundances from single-end (SE) or paired-end (PE) RNA-Seq data. The software enables visualization of its output through probabilistically-weighted read alignments and read depth plots. It does not require a reference genome and thus can be useful for quantification with de novo transcriptome assemblies.
BitSeq / Bayesian inference of transcripts from sequencing data
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An application for inferring expression levels of individual transcripts from sequencing (RNA-Seq) data and estimating differential expression (DE) between conditions. An advantage of this approach is the ability to account for both technical uncertainty and intrinsic biological variance in order to avoid false DE calls. The technical contribution to the uncertainty comes both from finite read-depth and the possibly ambiguous mapping of reads to multiple transcripts.
Rockhopper
A comprehensive and user-friendly system for computational analysis of bacterial RNA-seq data. As input, Rockhopper takes RNA sequencing reads output by high-throughput sequencing technology (FASTQ, QSEQ, FASTA, SAM, or BAM files). Rockhopper supports the following tasks: reference based transcript assembly; de novo transcript assembly; normalizing data from different experiments; quantifying transcript abundance; testing for differential gene expression; characterizing operon structures; visualizing results in a genome browser.
Cyber-T
Provides differential analysis tools for high-throughput data. Cyber-T handles many types of data, from DNA and Protein microarrays, to Next Generation Sequencing (NGS), to Quantitative Mass Spectrometry (QMS). It implements a t-test using a Bayesian framework to compute a regularized variance of the measurements associated with each probe under each condition. This approach flexibly addresses problems associated with low replication levels and technology biases, not only for DNA microarrays, but also for other technologies, such as protein arrays, quantitative mass spectrometry and NGS -RNA-seq. This software is available online through the Cyber-T web server and as bayesreg.R code for download.
Babelomics
Serves for the functional analysis of gene expression and genomic data. Babelomics offers the possibility to explore the effects of alteration in gene expression levels or changes in genes sequences within a functional context. It provides user-friendly access to a full range of methods that cover: (1) primary data analysis; (2) a variety of tests for different experimental designs; and (3) different enrichment and network analysis algorithms for the interpretation of the results of such tests in the proper functional context.
stageR
Allows user-friendly automated stage-wise analysis of high-throughput genomic data. stageR implements two-stage testing as a general paradigm for assessing high throughput experiments involving multiple hypotheses that can be aggregated. The procedure was optimized towards RNA-seq applications: differential transcript expression, differential transcript usage and differential gene expression analysis with simple and complex experimental designs. stageR achieves an optimal middle ground between biological resolution and statistical power while providing gene-level false discovery rate (FDR) control, which is beneficial for downstream biological interpretation and validation.
PoissonSeq
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Assists users in normalization, testing, and false discovery rate (FDR) estimation for RNA-sequencing data. PoissonSeq can be applied to data with quantitative, two-class, or multiple-class outcomes, and the computation is fast even for large data sets. This software is based on a Poisson log-linear model to achieve the sequencing data of general outcome type. The estimation method for FDR eliminates non-null genes from the pooled permutation of the assessment.
derfinder
Improves the annotation-agnostic approach to RNA-seq analysis by: (1) implementing a computationally efficient bump-hunting approach to identify DERs which permits genome-scale analyses in a large number of samples, (2) introducing a flexible statistical modeling framework, including multi-group and time-course analyses and (3) introducing a new set of data visualizations for expressed region analysis. derfinder analysis using expressed region-level and single base-level approaches provides a compromise between full transcript reconstruction and feature-level analysis.
DiffSplice
Detects and visualizes of differential alternative transcription. DiffSplice is an ab initio method to detect alternative splicing isoforms that are differentially expressed under different conditions using high-throughput RNA-seq reads. This software directly localizes where differential splicing occurs, making it easier to identify exons involved in alternative transcription. It estimates the relative proportion of alternative transcription flows in every ASM and calculates the Jensen–Shannon divergence (JSD) to quantify the difference in transcription between samples.
NOISeq
Identifies differentially expressed genes from count data or previously normalized count data. NOISeq empirically models the noise distribution of count changes by contrasting fold-change differences (M) and absolute expression differences (D) for all the features in samples within the same condition. This reference distribution is then used to assess whether the M-D values computed between two conditions for a given gene are likely to be part of the noise or represent a true differential expression.
WoPPER / Webserver fOr Position Related data analysis of gene Expression in Prokaryotes
Integrates transcriptional expression data and genomic annotations to identify groups of physically contiguous genes characterized by regional differential expression in bacterial genomes. WoPPER Is a web app that can analyze any RNA-seq or microarray-based gene expression dataset from any microorganism with a sequenced and annotated genome. This resource provides researchers with novel and informative insights regarding the correlation between gene expression and chromosomal organization in bacterial genomes.
SigFuge
Tests significance of clustering in RNA-seq data. SigFuge allows to identify genomic loci exhibiting differential transcription patterns across many RNA-seq samples. It combines clustering with hypothesis testing to identify genes exhibiting alternative splicing, or differences in isoform expression. SigFuge is presented as a method capable of unsupervised discovery of differential isoform events in RNA-seq. This approach of studying gene expression as per-base expression curves along transcriptome coordinates makes it possible to identify differential events without strictly constraining the analysis to proposed exon or transcript boundaries.
Linnorm
Provides a linear model and normality based transformation method. Linnorm is an R package for the analysis of RNA-seq, scRNA-seq, ChIPseq count data or any large-scale count data. It transforms such datasets for parametric tests. Some pipelines are implemented: (i) library size/batch effect normalization, (ii) cell sub-population analysis and visualization, (iii) differential expression analysis or differential peak detection, (iv) highly variable gene discovery and visualization, (v) gene correlation network analysis and visualization, (vi) stable gene selection for scRNA-seq data and (vii) data imputation.
BBSeq
Simplifies the analysis for researchers less familiar with modelling and construction of design matrices, and issues such as outlier detection are handled automatically. BBSeq is an R package that performs likelihood ratio comparisons for the overall statistical significance of each included factor. This module is designed to make it easy to perform testing for a variety of experimental designs, with modifications for small sample sizes to take advantage of the mean–overdispersion relationship.
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