Project description:ChIP-Seq is a technique used to analyse protein-DNA interactions. The protein-DNA complex is pulled down using a protein antibody, after which sequencing and analysis of the bound DNA fragments is performed. A key bioinformatics analysis step is “peak” calling - identifying regions of enrichment. Benchmarking studies have consistently shown that no optimal peak caller exists. Peak callers have distinct selectivity and specificity characteristics which are often not additive and seldom completely overlap in many scenarios. In the absence of a universal peak caller, we rationalized one ought to utilize multiple peak-callers to 1) gauge peak confidence as determined through detection by multiple algorithms, and 2) more thoroughly survey the protein-bound landscape by capturing peaks not detected by individual peak callers owing to algorithmic limitations and biases. We therefore developed an integrated ChIP-Seq Analysis Pipeline (ChIP AP) which performs all analysis steps from raw fastq files to final result, and utilizes four commonly used peak callers to more thoroughly and comprehensively analyse datasets. Results are integrated and presented in a single file enabling users to apply selectivity and sensitivity thresholds to select the consensus peak set, the union peak set, or any sub-set in-between to more confidently and comprehensively explore the protein bound landscape. (https://github.com/JSuryatenggara/ChIP-AP).
Project description:We report a novel modular pipeline (iMir) for comprehensive analysis of miRNA-Seq data, from linker removal and sequence quality check to differential expression and biological target prediction, integrating multiple open source modules and resources linker together in an automated flow. Development of an integrated pipeline (iMir) for comprehensive analysis of miRNA-Seq experiment.
Project description:With the recent advancements in genome editing, next generation sequencing (NGS), and scalable cloning techniques, scientists can now conduct genetic screens at unprecedented levels of scale and precision. With such a multitude of technologies, there is a need for a simple yet comprehensive pipeline to enable systematic mammalian genetic screening. In this study, we develop novel algorithms for target identi fication and a toxin-less Gateway cloning tool, termed MegaGate, for library cloning which, when combined with existing genetic perturbation methods and NGS-coupled readouts, enable versatile engineering of relevant mammalian cell lines. Our integrated pipeline for Sequencing-based Target Ascertainment and Modular Perturbation Screening (STAMPScreen) can thus be utilized for a host of cell state engineering applications.
Project description:Here we present a microfluidics-based affinity purification-mass spectrometry (AP-MS) pipeline to identify protein-protein interactions (PPI) using minute amounts of input material. The use of this automated platform allowed us to identify the human cohesin and CCC complex from only 4 micrograms of input lysate, representing a ~50-100 fold downscaling compared to regular microcentrifuge tubes-based workflows. As such, our method holds great promise for future AP-MS applications in which sample amounts are limited.