Biology subjects

Pandya, R.

Publications and source records attributed to Pandya, R..

3 recordsLinked to original sources

bioRxiv2021

Fuzzy set intersection based paired-end short-read alignment

Much genomic data comes in the form of paired-end reads: two reads that represent genetic material with a small gap between. We present a new algorithm for aligning both reads in a pair simultaneously by fuzzily intersecting the sets of candidate alignment locations for each read. This algorithm is often much faster and produces alignments that result in variant calls having roughly the same concordance as the best competing aligners.

bioinformatics↗

bioRxiv2019

Library of model implementations for sharing deep-learning image segmentation and outcomes models

An open-source library of implementations for deep-learning based image segmentation and outcomes models is presented in this work. As oncology treatment planning becomes increasingly driven by automation, such a library of model implementations is crucial to (i) validate existing models on datasets collected at different institutions, (ii) automate segmentation, (iii) create ensembles for improving performance and (iv) incorporate validated models in the clinical workflow. The library was developed with Computational Environment for Radiological Research (CERR) software platform. CERR is a natural choice to centralize model implementations due to its comprehensiveness, popularity, and ease of use. CERR provides well-validated feature extraction for radiotherapy dosimetry and radiomics with fine control over the calculation settings. This allows users to select the appropriate feature calculation used in the model derivation. Models for automatic image segmentation are distributed via Singularity containers, with seamless i/o to and from CERR. Singularity containers allow for segmentation models to be deployed with a variety of scientific computing architectures. Deployment of models is driven by JSON configuration file, making it convenient to plug-in models. Models from the library can be called programmatically for batch evaluation. The library includes implementations for popular radiotherapy models outlined in the Quantitative Analysis of Normal Tissue Effects in the Clinic effort and recently published literature. Radiomics models include features from Image Biomarker Standardization features found to be important across multiple sites and image modalities. Deep learning-based image segmentation models include state of the art networks such as Deeplab and other problem-specific architectures. The library is distributed as GNU-copyrighted software at https://www.github.com/cerr/CERR.

bioinformatics↗

bioRxiv2017

Project Dhaka: Variational Autoencoder for Unmasking Tumor Heterogeneity from Single Cell Genomic Data

Intra-tumor heterogeneity is one of the key confounding factors in deciphering tumor evolution. Malignant cells will have variations in their gene expression, copy numbers, and mutation even when coming from a single tumor. Single cell sequencing of tumor cells is of paramount importance for unmasking the underlying the tumor heterogeneity. However extracting features from the single cell genomic data coherent with the underlying biology is computationally challenging, given the extremely noisy and sparse nature of the data. Here we are proposing Dhaka a variational autoencoder based single cell analysis tool to transform genomic data to a latent encoded feature space that is more efficient in differentiating between the hidden tumor subpopulations. This technique is generalized across different types of genomic data such as copy number variation from DNA sequencing and gene expression data from RNA sequencing. We have tested the method on two gene expression datasets having 4K to 6K tumor cells and two copy number variation datasets having 250 to 260 tumor cells. Analysis of the encoded feature space revealed sub-populations of cells bearing distinct genomic signatures and the evolutionary relationship between them, which other existing feature transformation methods like t-SNE and PCA fail to do.

genomics↗