Biology subjects

Golji, J.

Publications and source records attributed to Golji, J..

3 recordsLinked to original sources

bioRxiv2020

Highly multiplexed quantitative phosphosite assay for biology and preclinical studies

Reliable methods to quantify dynamic signaling changes across diverse pathways are needed to better understand the effects of disease and drug-treatment in cells and tissues but are presently lacking. Here we present SigPath, a targeted mass spectrometry (MS) assay that measures 284 phosphosites in 200 phosphoproteins of biological interest. SigPath probes a broad swath of signaling biology with high throughput and quantitative precision. We applied the assay to investigate changes in phospho-signaling in drug-treated cancer cell lines, breast cancer preclinical models and human medulloblastoma tumors. In addition to validating previous findings, SigPath detected and quantified a large number of differentially regulated phosphosites newly associated with disease models and human tumors at baseline or with drug perturbation. Our results highlight the potential of SigPath to monitor phosphoproteomic signaling events and to nominate mechanistic hypotheses regarding oncogenesis, response and resistance to therapy.

cancer biology↗

bioRxiv2020

PISCES: a package for rapid quantitation and quality control of large scale mRNA-seq datasets

PISCES eases processing of large mRNA-seq experiments by encouraging capture of metadata using simple textual file formats, processing samples on either a single machine or in parallel on a high performance computing cluster (HPC), validating sample identity using genetic fingerprinting, and summarizing all outputs in analysis-ready data matrices. PISCES consists of two modules: 1) compute cluster-aware analysis of individual mRNA-seq libraries including species detection, SNP genotyping, library geometry detection, and quantitation using salmon, and 2) gene-level transcript aggregation, transcriptional and read-based QC, TMM normalization and differential expression analysis of multiple libraries to produce data ready for visualization and further analysis. PISCES is implemented as a python3 package and is bundled with all necessary dependencies to enable reproducible analysis and easy deployment. JSON configuration files are used to build and identify transcriptome indices, and CSV files are used to supply sample metadata and to define comparison groups for differential expression analysis using DEseq2. PISCES builds on many existing open-source tools, and releases of PISCES are available on GitHub or the python package index (PyPI).

bioinformatics↗

bioRxiv2017

Correction of copy number induced false positives in CRISPR screens

Cell autonomous cancer dependencies are now routinely identified using CRISPR loss-of-function screens. However, a bias exists that makes it difficult to assess the true essentiality of genes located in amplicons, since the entire amplified region can exhibit lethal scores. These false-positive hits can either be discarded from further analysis, which in cancer models can represent a significant number of hits, or methods can be developed to rescue the true-positives within amplified regions. We propose two methods to rescue true positive hits in amplified regions by correcting for this copy number artefact. The Local Drop Out (LDO) method uses the relative lethality scores within genomic regions to assess true essentiality and does not require additional orthogonal data (e.g. copy number value). LDO is meant to be used in screens covering a dense region of the genome (e.g. a whole chromosome or the whole genome). The General Additive Model (GAM) method models the screening data as a function of the known copy number values and removes the systematic effect from the measured lethality. GAM does not require the same density as LDO, but does require prior knowledge of the copy number values. Both methods have been developed with single sample experiments in mind so that the correction can be applied even in smaller screens. Here we demonstrate the efficacy of both methods at removing the copy number effect and rescuing hits from some of the amplified regions. We estimate a 70-80% decrease of false positive hits in regions of high copy number with either method.

bioinformatics↗