Biology subjects

Ghosh, D.

Publications and source records attributed to Ghosh, D..

76 records · Page 5Linked to original sources

bioRxiv2018

Kernel machine tests of association between brain networks and phenotypes

Applications of quantitative network analysis to functional brain connectivity have become popular in the last decade due to their ability to describe the general topological principles of brain networks. However, many issues arise when standard statistical analysis techniques are applied to functional magnetic resonance imaging (fMRI) connectivity maps. Frequently, summary measures of these maps, such as global efficiency and clustering coefficients, collapse the changing structures of graph topology from many scales to one. This can result in a loss of whole-brain spatio-temporal pattern information that may be significant in association and prediction analyses. Drawing from the electrical engineering field, the resistance perturbation distance is a quantification of similarity between graphs on the same vertex set that has been shown to identify changes in dynamic graphs, such as those from fMRI, while not being computationally expensive or result in a loss of information. This work proposes a novel kernel-based regression scheme that incorporates the resistance perturbation distance to better understand the association with biological phenotypes from fMRI using both simulated and real datasets.

neuroscience↗

bioRxiv2017

TCIApathfinder: an R client for The Cancer Imaging Archive REST API

SummaryThe Cancer Imaging Archive (TCIA) hosts publicly available de-identified medical images of cancer from over 25 body sites and over 30, 000 patients. Over 400 published studies have utilized freely available TCIA images. Image series and metadata are available for download through a web interface or a REST API. We present TCIApathfinder, an R client for the TCIA REST API. TCIApathfinder wraps API access in user-friendly R functions that can be called within an R session or easily incorporated into scripts. Functions are provided to explore the contents of the large database and to download image files.\n\nAvailability and implementationTCIApathfinder is available under the MIT license as a package on CRAN (https://cran.r-project.org/web/packages/TCIApathfinder/index.html) and at https://github.com/pamelarussell/TCIApathfinder.\n\nContactpamela.russell@ucdenver.edu

bioinformatics↗

bioRxiv2017

A computational framework for detecting signatures of accelerated somatic evolution in cancer genomes

By accumulation of somatic mutations, cancer genomes evolve, diverging away from the genome of the host. It remains unclear to what extent somatic evolutionary divergence is comparable across different regions of the cancer genome versus concentrated in specific genomic elements. We present a novel computational framework, SASE-mapper, to identify genomic regions that show signatures of accelerated somatic evolution (SASE) in a subset of samples in a cohort, marked by accumulation of an excess of somatic mutations compared to that expected based on local, context-aware background mutation rates in the cancer genomes. Analyzing tumor whole genome sequencing data for 365 samples from 5 cohorts we detect recurrent SASE at a genome-wide scale. The SASEs were enriched for genomic elements associated with active chromatin, and regulatory regions of several known cancer genes had SASE in multiple cohorts. Regions with SASE carried specific mutagenic signatures and often co-localized within the 3D nuclear space suggesting their common basis. A subset of SASEs was frequently associated with regulatory changes in key cancer pathways and also poor clinical outcome. While the SASE-associated mutations were not necessarily recurrent at base-pair resolution, the SASEs recurrently targeted same functional regions, with similar consequences. It is likely that regulatory redundancy and plasticity promote prevalence of SASE-like patterns in the cancer genomes.

bioinformatics↗

bioRxiv2017

A toolbox of immunoprecipitation-grade monoclonal antibodies against human transcription factors.

A key component to overcoming the reproducibility crisis in biomedical research is the development of readily available, rigorously validated and renewable protein affinity reagents. As part of the NIH Protein Capture Reagents Program (PCRP), we have generated a collection of 1406 highly validated, immunoprecipitation (IP) and/or immunoblotting (IB) grade, mouse monoclonal antibodies (mAbs) to 736 human transcription factors. We used HuProt human protein microarrays to identify mAbs that recognize their cognate targets with exceptional specificity. Using an integrated production and validation pipeline, we validated these mAbs in multiple experimental applications, and have distributed them to the Developmental Studies Hybridoma Bank (DSHB) and several commercial suppliers. This study allowed us to perform a meta-analysis that identified critical variables that contribute to the generation of high quality mAbs. We find that using full-length antigens for immunization, in combination with HuProt analysis, provides the highest overall success rates. The efficiencies built into this pipeline ensure substantial cost savings compared to current standard practices.

biochemistry↗