Normalization software tools | RNA sequencing data analysis
The expected expression level of each transcript is limited by the sequencing depth or total number of reads, which is pre-determined by the experimental design and budget before sequencing. Since the expression level of the transcripts within the sample is dependent upon the other transcripts present, given a fixed total read count, higher expressed transcripts will have a greater proportion of total reads. Furthermore, longer transcripts have more reads mapping to them compared with shorter transcripts of a similar expression level. Therefore, a number of normalization methods for RNA-seq data have been proposed to correct for library size bias as well as length and GC-content bias.
Adjusting batch effects in microarray expression data using Empirical Bayes methods. The modified ComBat (M-Combat) is designed specifically in the context of meta-analysis and batch effect adjustment for use with predictive models that are validated and fixed on historical data from a ‘gold-standard’ batch.
Assembles transcripts, estimates their abundances, and tests for differential expression and regulation in RNA-Seq samples. Cufflinks assembles individual transcripts from RNA-seq reads that have been aligned to the genome. This software is able to infer the splicing structure of each gene because reads from multiple splice variants for a given gene can be found in a sample. Quantification of transcript abundances is also possible by preferring a reference annotation to assembling the reads.
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.
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.
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.
Offers an intuitive user interface and built-in workflows for a variety of genomic applications that guide researchers though every step of the analysis process. Partek Genomics Suite gives biologists, bioinformaticists, and statisticians a single, integrated solution for trustworthy results with a user-friendly interface, comprehensive workflows, and ability to support all next generation sequencing, microarray, and qPCR platforms.
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 can be used for quantifying de novo transcriptome assemblies.