Biology subjects

Chattopadhyay, A.

Publications and source records attributed to Chattopadhyay, A..

3 recordsLinked to original sources

bioRxiv2019

Hierarchical Discovery of Large-scale and Focal Copy Number Alterations in Low-coverage Cancer Genomes

MotivationDetection of copy number alterations (CNA) is critical to understand genetic diversity, genome evolution and pathological conditions such as cancer. Cancer genomes are plagued with widespread multi-level structural aberrations of chromosomes that pose challenges to discover CNAs of different length scales with distinct biological origin and function. Although several tools are available to identify CNAs using read depth (RD) of coverage, they fail to distinguish between large-scale and focal alterations due to inaccurate modeling of the RD signal of cancer genomes. These tools are also affected by RD signal variations, pronounced in low-coverage data, which significantly inflate false detection of change points and inaccurate CNA calling.\n\nResultsWe have developed CNAtra to hierarchically discover and classify large-scale and focal copy number gain/loss from whole-genome sequencing (WGS) data. CNAtra provides an analytical and visualization framework for CNV profiling using single sequencing sample. CNAtra first utilizes multimodal distribution to estimate the copy number (CN) reference from the complex RD profile of the cancer genome. We utilized Savitzy-Golay filter and Modified Varri segmentation to capture the change points. We then developed a CN state-driven merging algorithm to identify the large segments with distinct copy number. Next, focal alterations were identified in each large segment using coverage-based thresholding to mitigate the adverse effects of signal variations. We tested CNAtra calls using experimentally verified segmental aneuploidies and focal alterations which confirmed CNAtras ability to detect and distinguish the two alteration phenomena. We used realistic simulated data for benchmarking the performance of CNAtra against other detection tools where we artificially spiked-in CNAs in the original cancer profiles. We found that CNAtra is superior in terms of precision, recall, and f-measure. CNAtra shows the highest sensitivity of 93% and 97% for detecting focal and large-scale alterations respectively. Visual inspection of CNAs showed that CNAtra is the most robust detection tool for low-coverage cancer data.\n\nAvailability and implementationCNAtra is an open source software implemented in MATLAB, and is available at https://github.com/AISKhalil/CNAtra

bioinformatics↗

bioRxiv2019

Parsers, Data Structures and Algorithms for Macromolecular Analysis Toolkit (MAT): Design and Implementation

The structural information of biological macromolecules are stored in .pdb, .mm-cif and lately mmtf files and thus it requires accurate and efficient biological tools for various utilities. Here, we describe Macromolecular Analysis Toolkit (MAT) that parses .pdb, .mmcif and .mmtf files; and builds data structures from the input. This original program is written in C++ programming language to ensure efficiency and consistency to organize structural information in an integral way. The novelty of the program lies in the addition of new structure-based biological algorithms and applications. This package also stands out from other similar libraries by being 1) faster and 2) accurate. We also provide detailed comparison of available parsers on the whole PDB database. The parser of MAT is designed in such a way that it allows quick extraction and organized loading of the core data structure. The same data structure is extended to accommodate information from the .mmcif and .mmtf file parsers. Tokenization of the data allows the extraction of information from disordered text, making it compatible for accurate identification of the entities present in the .pdb file. Additionally, we add a new approach of performance optimization by creating a few derived data structures, namely kD-Tree, Octree and graphs, for certain applications that need spatial coordinate calculations. MAT provides advanced data structure which is time efficient and is designed to avail reusability and consistency in a systematic framework. MAT parser can be accessed online through bitbucket at https://bitbucket.org/gazalk/pdb_parser/.

bioinformatics↗

bioRxiv2019

Molecular Biology Information Service: An innovative medical library-based bioinformatics support service for biomedical researchers

Biomedical researchers are increasingly reliant on obtaining bioinformatics training in order to conduct their research. Here we present a model that academic institutions may follow to provide such training for their researchers, based on the Molecular Biology Information Service (MBIS) of the Health Sciences Library System, University of Pittsburgh. The MBIS runs a four-facet service with the following goals: (1) identify, procure, and implement commercially-licensed bioinformatics software, (2) teach hands-on workshops using bioinformatics tools to solve research questions, (3) provide in-person and email consultations on software/databases, and (4) maintain a web portal providing overall guidance on the access and use of bioinformatics resources and MBIS-created webtools. This paper describes these facets of MBIS activities from 2006-2018, including outcomes from a survey measuring attitudes of University of Pittsburgh researchers about MBIS service and performance.

bioinformatics↗