This is a list of software tools developed at Zhong lab.
MARIO (Mapping RNA interactome in vivo) is a technology to massively reveal RNA–RNA interactions from unperturbed cells (Nat Comm, 2016, 7:12023). MARIO tools is a suite of bioinformatic tools to analyse and visualize MARIO data. MARIO tools automated the analysis steps, including removing PCR duplicates, splitting multiplexed samples, identifying the linker sequence, splitting junction reads, calling interacting RNAs, performing statistical assessments, categorizing RNA interaction types, calling interacting sites and analysing RNA structure.
This web portal is the first entry point for biomedical researchers and scientists to the 4D Nucleome (4DN) network. 4DN resources, data, and protocols can be retrieved through this web portal.
A web-based search engine for functional genomic data. GeNemo searches user-input data against online functional genomic datasets, including the entire collection of ENCODE and mouse ENCODE datasets. Unlike text-based search engines, GeNemo's searches are based on pattern matching of functional genomic regions. This distinguishes GeNemo from text or DNA sequence searches. The user can input any complete or partial functional genomic dataset, for example, a binding intensity file (bigWig) or a peak file. GeNemo reports any genomic regions, ranging from hundred bases to hundred thousand bases, from any of the online ENCODE datasets that share similar functional (binding, modification, accessibility) patterns.
Single cell browser organizes and visualizes single cell RNA-seq datasets. Single cell data of mouse oocytes and preimplantation embryos are pre-loaded in this browser.
APEG implements a quantitative model for TF-DNA binding in a given epigenomic context. This model can be used to predict the binding intensity of a TF in any genomic region in any cell type, using the genomic sequence and the epigenomic modifications (cell-type-specific data).
This web-based comparative epigenome browser enables easy comparison of epigenomic modifications across species.
GATE implements a probabilistic model to annotate the genome. This model detects similar but not necessarily synchronous epigenomic changes in different genomic segments.
perEditor changes the reference human genome (NCBI36/hg18) into an individual genome, taking into account single nucleotide polymorphisms (SNPs), insertions and deletions, copy number variation, and chromosomal rearrangements. perEditor outputs two alleles (maternal, paternal) of the individual genome that is ready for mapping ChIP-seq and RNA-seq reads, and enabling the analyses of allele specific binding, chromatin structure, and gene expression.
SCSC clustering two species gene expression data, and identifies shared as well as species-specific co-expression modules.
Mapping of sequence reads of ChIP-seq, RNA-seq and MethylC-seq experiments. Key feature: fast and accurate mapping of MethylC-seq reads.
Cluster time-course data, with a Dirichlet Process that automatically chooses cluster numbers. Compare time-course patterns between two conditions, taking co-expression information to increase sensitivity and robustness
Predict transcription factor and DNA interaction affinity and the DNA sequence, considering combinatorial interactions of multiple transcription factors. STAP can be applied to analyze ChIP-seq data.
Multispecies de novo identification of cis-regulatory motifs and modules.
An R package for selection of the differentially expressed genes from microarray data. Unlike other programs, it considers the inter-laboratory measurement variability in the selection procedure and aims to deliver more reproducible results.
Windows-based software for searching and visualizing Gene Ontology terms that are enriched in gene sets obtained by microarray studies. It can be activated from dChip software to perform downstream analysis. See application example in gene expression analysis of bipolar disorder.