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QUBIC / QUery BICluster
Implements a well-cited biclustering algorithm, QUBIC, for the interpretation of gene expression profile data. The unique features of QUBIC include: (i) biclustering is integrated with analyses functions (i.e. data discretization, query-based biclustering, bicluster expanding, biclusters comparison, heatmap visualization and co-expression network elucidation); (ii) the QUBIC source code is optimized and converted to C++, thus has better memory control and is more efficient than the original QUBIC; (iii) on five large-scale datasets, QUBIC performs the best among four popular tools according to the running time. Biclustering algorithms facilitate researchers in identification of co-expressed gene subsets in their gene expression dataset, and has become a useful approach for the interpretation of gene expression profile data.
An easy-to-use application for microarray, RNA-Seq and metabolomics analysis. For splicing sensitive platforms (RNA-Seq or Affymetrix Exon, Gene and Junction arrays), AltAnalyze will assess alternative exon (known and novel) expression along protein isoforms, domain composition and microRNA targeting. In addition to splicing-sensitive platforms, AltAnalyze provides comprehensive methods for the analysis of other data (RMA summarization, batch-effect removal, QC, statistics, annotation, clustering, network creation, lineage characterization, alternative exon visualization, gene-set enrichment and more).
Bio CorEx / Correlation Explanation
Recovers latent factors with Correlation Explanation (CorEx). Bio CorEx consists of python code to build these representations. The principle of Total CorEx has recently been introduced as a way to reconstruct latent factors that are informative about relationships in data. While the methods are domain-agnostic, the version of CorEx was designed to handle challenges inherent in several biomedical problems: missing data, continuous variables, and severely under-sampled data.
Provides a scoring system for hierarchical clustering based on differential expression profiles among multiple conditions. DEclust is a transcriptome analysis method allowing users to search for differentially expressed and statistically overrepresented patterns of gene expression profiles among multiple experimental conditions. The software can be applied to any multi-conditional transcriptome data, and to the results of any differentially expressed genes (DEG) detection tool.
LAS / Large Average Submatrices
Finds large average submatrices within a given real-valued data matrix. LAS operates in an iterative-residual fashion, and is driven by a Bonferroni-based significance score that effectively trades off between submatrix size and average value. It is an effective exploratory tool for the discovery of biologically relevant structures in high dimensional data. The software produced biclusters exhibiting a wide range of gene and sample sizes, and low to moderate overlap during the tests.
Provides an open source RNA-seq processing pipeline that can be used to extract knowledge from any study that profiled gene expression using RNA-seq applied to mammalian cells, comparing two conditions. Zika-RNAseq-Pipeline enables the extraction of knowledge from typical RNA-seq studies by generating interactive principal component analysis (PCA) and hierarchical clustering (HC) plots, performing enrichment analyses against over 90 gene set libraries, and obtaining lists of small molecules that are predicted to either mimic or reverse the observed changes in mRNA expression.
DeBi / Differentially Expressed Biclusters
Discovers maximum size homogeneous biclusters in which each gene is strongly associated with a subset of samples. DeBi is based on a well known data mining approach called frequent item set. The performance of the tool was evaluated on a yeast dataset, on synthetic datasets and on human datasets. It provides functionally more coherent gene sets compared to standard clustering or biclustering algorithms using biological validation measures such as Gene Ontology (GO) term and Transcription Factor Binding Site (TFBS) enrichment. The software is applicable on multiple gene expression datasets coming from different labs or platforms.
Permits clustering contigs from de novo transcriptome assemblies. RapClust is based upon the relationships exposed by multi-mapping sequencing fragments. It is capable of accurately clustering most large de novo transcriptomes in a matter of minutes, while simultaneously providing accurate estimates of expression for the resulting clusters. This tool confers a large benefit in terms of space usage, as it produces only succinct intermediate files even when processing hundreds of millions of reads.
Automatically combines transcriptomes from difference sources, such as assembly and annotation, into a compact and unified reference. necklace is applicable to any species with an incomplete reference genome. It aligns and counts reads in preparation for testing for differential gene expression and differential transcript usage analysis. This tool provides the following steps: initial assembly, clustering transcripts into gene groupings, reassembly to build the superTranscriptome and finally alignment and counting of mapped reads in preparation for differential expression testing.
AMAP / Another Multidimensional Analysis Package
Provides clustering tools. AMAP is a package that implements a component analysis with robust methods, and parallelized functions. It includes standard hierarchical clustering and k-means and offers functions to (i) compute an acp on a contingency table tacking into account weight of rows and columns, to (ii) compute a dissimilarity matrix from a data set and to (iii) computes and returns the distance matrix computed by using the specified distance measure to compute the distances between the rows of a data matrix.
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