This is a list of software tools developed at Zhong lab.

  • GIVE

    GIVE is an open source programming library that allows anyone with HTML programming experience to build custom genome browser websites or apps. With a few lines of codes, one can add to a personal webpage an interactive genome browser that host custom data. It typically takes less than half a day to build a genome browser website with GIVE. This portable library encapsulates novel data communication and data visualization technologies, including new data structures and new memory management methods that enable efficient data transfer between the data-hosting website and internet browsers. GIVE is the acronym of Genomic Interaction Visualization Engine, although GIVE's utilities have outgrown its original name.

  • MARIO tools

    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.

  • 4D Nucleome web portal

    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.

  • GeNemo search engine

    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 transcriptome browser

    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 (Affinity Prediction by Epigenome and Genome)

    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).

  • CEpBrowser

    This web-based comparative epigenome browser enables easy comparison of epigenomic modifications across species.

  • GATE (Genomic Annotation using Temporal Epigenomic data)

    GATE implements a probabilistic model to annotate the genome. This model detects similar but not necessarily synchronous epigenomic changes in different genomic segments.

  • perEditor

    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 (Cross-species soft clustering)

    SCSC clustering two species gene expression data, and identifies shared as well as species-specific co-expression modules.

  • WMap

    Mapping of sequence reads of ChIP-seq, RNA-seq and MethylC-seq experiments. Key feature: fast and accurate mapping of MethylC-seq reads.

  • NACEP (Network-based comparison of temporal gene expression patterns)

    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

  • STAP (Sequence To Affinity Prediction)

    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.

  • GibbsModule

    Multispecies de novo identification of cis-regulatory motifs and modules.

  • RPS (Reproducibility Probability Score)

    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.

  • GoSurfer

    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.