Tools

Tools we are developing can be found on our github page:  https://github.com/UcarLab

QUIN : A QUERY TOOL TO BUILD CHROMATIN, ANNOTATE & QUERY CHROMATIN INTERACTION NETWORK 

QuIN: A Web Server for Querying and Visualizing Chromatin Interaction Networks ,Recent studies of the human genome have indicated that regulatory elements (e.g. promoters and enhancers) at distal genomic locations can interact with each other via chromatin folding and affect gene expression levels. Genomic technologies for mapping interactions between DNA regions, e.g., ChIA-PET and HiC, can generate genome-wide maps of interactions between regulatory elements. These interaction datasets are important resources to infer distal gene targets of non-coding regulatory elements and to facilitate prioritization of critical loci for important cellular functions. With the increasing diversity and complexity of genomic information and public ontologies, making sense of these datasets demands integrative and easy-to-use software tools. Moreover, network representation of chromatin interaction maps enables effective data visualization, integration, and mining. Currently, there is no software that can take full advantage of network theory approaches for the analysis of chromatin interaction datasets. To fill this gap, we developed a web-based application, QuIN, which enables: 1) building and visualizing chromatin interaction networks, 2) annotating networks with user-provided private and publicly available functional genomics and interaction datasets, 3) querying network components based on gene name or chromosome location, and 4) utilizing network based measures to identify and prioritize critical regulatory targets and their direct and indirect interactions.

AVAILABILITY : QuIN’s web server is available at http://quin.jax.org QuIN is developed in Java and JavaScript, utilizing an Apache Tomcat web server and MySQL database and the source code is available under the GPLV3 license available on GitHub: https://github.com/UcarLab/QuIN/.


I-ATAC : AN INTERACTIVE ,USER-FRIENDLY,CROSS PLATFORM & OPEN SOURCE DESKTOP APPLICATION FOR EASY PRE-PROCESSING OF ATAC-SEQ DATASETS

I-ATAC: interfacing assay for transposase-accessible chromatin with high-throughput sequencing, The focus of our research is toward the application of a novel epigenetic profiling assay for transposase-accessible chromatin with high throughput sequencing (ATAC-seq) for integrative epigenomic analysis (Buenrostro et al., 2013). ATAC-seq is a new protocol to capture open chromatin sites by performing adaptor ligation and fragmentation of open chromatin regions. Due to its efficiency in requirement of biological sample and in library preparation time, many scientists are generating ATAC-seq libraries to decipher the chromatin landscape of DNA in a given cell type and condition of interest.

ATAC-seq data processing pipeline’s workflow starts with the quality check and adapter trimming, then alignment, shifting, removing duplicates, sorting and peak calling is performed to find significant numbers of mapped reads, indicating the presence of gene regulatory regions. Implementation of ATAC-seq data processing pipeline is a complex task for biologist (without programming skills), as it involves the I/O (input/output) redirectional integration of several non-interactive command-line applications in Unix, Linux and DOS environments.

Here, we present I-ATAC (Interfacing Assay for Transposase-Accessible Chromatin with high-throughput Sequencing) as the interactive, user-friendly, cross platform and open source desktop application, which supports transparent, reproducible and automatic generation of ATAC-data quality check and pre-processing.

AVAILABILITY : You may find the Manuscript of this on https://www.biorxiv.org/content/early/2017/06/18/151217  , the Source code for I-ATAC is available on Github: https://github.com/UcarLab/I-ATAC

 


IA-SVA:  A STATISTICAL FRAMEWORK TO DETECT UNWANTED HETEROGENEITY IN SINGLE-CELL RNA-SEQ DATASETS 

Iteratively Adjusted Surrogate Variable Analysis (IA-SVA) is a statistical framework to uncover hidden sources of variation even when these sources are correlated with the biological variable of interest. IA-SVA provides a flexible methodology to i) identify a hidden factor for unwanted heterogeneity while adjusting for all known factors; ii) test the significance of the putative hidden factor for explaining the variation in the data; and iii), if significant, use the estimated factor as an additional known factor in the next iteration to uncover further hidden factors. 

AVAILABILITY : You may find the Manuscript of this on https://peerj.com/preprints/2942/  , the Source code for  Iteratively Adjusted Surrogate Variable Analysis (IA-SVA) is available on Github: https://github.com/UcarLab/IA-SVA