RFGD: Rice Fingerprint & Golden SNPs Database

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rsgcc: Gini methodology-based correlation and clustering analysis of microarray and RNA-Seq gene expression data
Developed by Chuang Ma in 2011

This package provides functions for calculating associations between two genes with five correlation methods(e.g., the Gini correlation coefficient [GCC], the Pearson's product moment correlation coefficient [PCC], the Kendall tau rank correlation coefficient [KCC], the Spearman's rank correlation coefficient [SCC] and the Tukey's biweight correlation coefficient [BiWt], and three non-correlation methods (e.g., mutual information [MI] and the maximal information-based nonparametric exploration [MINE], and the euclidean distance [ED]). It can also been implemented to perform the correlation and clustering analysis of transcriptomic data profiled by microarray and RNA-Seq technologies. Additionally, this package can be further applied to construct gene co-expression networks (GCNs).


mlDNA: Machine Learning-based Differential Network Analysis of Transcriptome Data
Developed by Chuang Ma in 2013

Machine learning (ML) is an intelligent data mining technique that builds a prediction model based on the learning of prior knowledge to recognize patterns in large-scale data sets. We present an ML-based methodology for transcriptome analysis via comparison of gene coexpression networks, implemented as an R package called machine learning-based differential network analysis (mlDNA) and apply this method to reanalyze a set of abiotic stress expression data in Arabidopsis thaliana. The mlDNA first used a ML-based filtering process to remove nonexpressed, constitutively expressed, or non-stress-responsive "noninformative" genes prior to network construction, through learning the patterns of 32 expression characteristics of known stress-related genes. The retained "informative" genes were subsequently analyzed by ML-based network comparison to predict candidate stress-related genes showing expression and network differences between control and stress networks, based on 33 network topological characteristics. Comparative evaluation of the network-centric and gene-centric analytic methods showed that mlDNA substantially outperformed traditional statistical testing-based differential expression analysis at identifying stress-related genes, with markedly improved prediction accuracy.


CrusView: A Java-based visualization platform for comparative genomics analyses in Brassicaceae species.
Developed by Hao Chen in 2013

CrusView is a Java-based bioinformatic application utilizing Standard Widget Toolkit/Swing graphics libraries and a SQLite database for performing visualized analyses of comparative genomics data in Brassicaceae (crucifer) plants. Compared with similar software and databases, one of the unique features of CrusView is its integration of karyotype information when comparing two genomes. This feature allows users to perform karyotype-based genome assembly and karyotype-assisted genome synteny analyses with preset karyotype patterns of the Brassicaceae genomes. Additionally, CrusView is a local program, which gives its users high flexibility when analyzing unpublished genomes and allows users to upload self-defined genomic information so that they can visually study the associations between genome structural variations and genetic elements, including chromosomal rearrangements, genomic macrosynteny, gene families, high-frequency recombination sites, and tandem and segmental duplications between related species. This tool will greatly facilitate karyotype, chromosome, and genome evolution studies using visualized comparative genomics approaches in Brassicaceae species.


KGBassembler: A karyotype-based genome assembler for Brassicaceae species
Developed by Chuang Ma in 2011

KGBassembler is a tool used to assemble full chromosomes from scaffolds if a karyotype is provided. The KGBassembler is featured with a user-friendly graphical user interface (GUI), allowing users to use automatic assembling of chromosomes based on CCP-based karyotypes and/or to manually edit the layouts of contigs according to in silico generated karyotypes. KGBassembler runs in three steps (Phases I–III). Phase I (Contigs2Blocks) is to identify the 24 blocks in contigs via analyzing BLAT alignments using two algorithms: local and global search. KGBassembler was implemented in C++ with a GUI built with the open-source QT toolbox.

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