Biology subjects

Nahid, A. A.

Publications and source records attributed to Nahid, A. A..

4 recordsLinked to original sources

bioRxiv2025

traceax: a JAX-based framework for stochastic trace estimation

In many applications, from statistical inference to machine learning, calculating the trace of a matrix is a fundamental operation, yet may be infeasible due to memory constraints. Stochastic trace estimation offers a practical solution by using randomized matrix-vector products to obtain accurate, unbiased estimates without constructing the full matrix in memory. Here, we present traceax, a Python framework for scalable trace estimation that leverages efficient linear operator representations of matrices while supporting automatic differentiation and hardware acceleration. traceax supports state-of-the-art trace estimators and through simulations we recapitulate results demonstrating their high accuracy while significantly reducing runtime and memory usage when compared with direct trace computation. As a proof of concept, we implemented a stochastic heritability estimator using traceax requiring only several lines of code. Overall, traceax provides a versatile tool for stochastic trace estimation that can be easily integrated into existing inferential pipelines. traceax is freely available at: https://github.com/mancusolab/traceax

bioinformatics↗

bioRxiv2023

The tidyomics ecosystem: Enhancing omic data analyses

The growth of omic data presents evolving challenges in data manipulation, analysis, and integration. Addressing these challenges, Bioconductor1 provides an extensive community-driven biological data analysis platform. Meanwhile, tidy R programming2 offers a revolutionary standard for data organisation and manipulation. Here, we present the tidyomics software ecosystem, bridging Bioconductor to the tidy R paradigm. This ecosystem aims to streamline omic analysis, ease learning, and encourage cross-disciplinary collaborations. We demonstrate the effectiveness of tidyomics by analysing 7.5 million peripheral blood mononuclear cells from the Human Cell Atlas3, spanning six data frameworks and ten analysis tools.

bioinformatics↗

bioRxiv2021

Investigating the possible origin and transmission routes of SARS-CoV-2 genomes and variants of concern in Bangladesh

The COVID-19 pandemic induced by the SARS-CoV-2 virus and its variants has ravaged most countries around the world including Bangladesh. We have analyzed publicly available genomic data to understand the current COVID-19 outbreak scenario as well as the evolutionary origin and transmission routes of SARS-CoV-2 isolates in Bangladesh. All the early isolates as well as recent B.1.1.7 and B.1.351 variants had already spread across the major divisional cities of Bangladesh. A sex biasness towards male COVID-19 patient samples sequencing has observed over female in all age-group, that could be the trend in infection rate. Phylogenetic analysis indicated a total of 13 estimated countries, including Italy, India, United Kingdom, Saudi Arabia, United Arab Emirates, Germany, Australia, New Zealand, South Africa, Democratic Republic of the Congo, United States, Russia, and Denmark, could be the possible origin introduced SARS-CoV-2 isolates in Bangladesh due to regional and intercontinental travel. Recent, B.1.1.7 variant could be imported from a total of 7 estimated countries including UK, India, Nigeria, Spain, Ireland, Australia, and Indonesia, while South Africa and the United States are the most likely sources of B.1351 variant in Bangladesh. Based on these findings, public health strategies could be designed and implemented to reduce the local transmission of the virus.

genomics↗

bioRxiv2021

VirusTaxo: Taxonomic classification of virus genome using multi-class hierarchical classification by k-mer enrichment

Taxonomic classification of viruses is a multi-class hierarchical classification problem, as taxonomic ranks (e.g., order, family and genus) of viruses are hierarchically structured and have multiple classes in each rank. Classification of biological sequences which are hierarchically structured with multiple classes is challenging. Here we developed a machine learning architecture, VirusTaxo, using a multi-class hierarchical classification by k-mer enrichment. VirusTaxo classifies DNA and RNA viruses to their taxonomic ranks using genome sequence. To assign taxonomic ranks, VirusTaxo extracts k-mers from genome sequence and creates bag-of-k-mers for each class in a rank. VirusTaxo uses a top-down hierarchical classification approach and accurately assigns the order, family and genus of a virus from the genome sequence. The average accuracies of VirusTaxo for DNA viruses are 99% (order), 98% (family) and 95% (genus) and for RNA viruses 97% (order), 96% (family) and 82% (genus). VirusTaxo can be used to detect taxonomy of novel viruses using full length genome or contig sequences. AvailabilityOnline version of VirusTaxo is available at https://omics-lab.com/virustaxo/.

bioinformatics↗