Biology subjects

Bapatdhar, N.

Publications and source records attributed to Bapatdhar, N..

3 recordsLinked to original sources

bioRxiv2024

Precise and scalable metagenomic profiling with sample-tailored minimizer libraries

Reference-based metagenomic profiling requires large genome libraries to maximize detection and minimize false positives. However, as libraries grow, classification accuracy suffers, particularly in k-mer-based tools, as the growing overlap in genomic regions among organisms results in more high-level taxonomic assignments, blunting precision. To address this, we propose sample-tailored minimizer libraries, which improve on the minimizer-LCA (lowest common ancestor) classification algorithm from the widely used Kraken 2 [1]. In this method, an initial filtering step using a large library removes non-resemblance genomes, followed by a refined classification step using a dynamically built smaller minimizer library. This 2-step classification method shows significant performance improvements compared to the state of the art. We develop a new computational tool called Slacken, a distributed and highly scalable platform based on Apache Spark, to implement the 2-step classification method, which improves speed while keeping the cost per sample comparable to Kraken 2. Specifically, in the CAMI2 [2] "strain madness" samples, the fraction of reads classified at species level increased by 3.5x, while for in silico samples it increased by 2.2x. The 2-step method achieves the sensitivity of large genomic libraries and the specificity of smaller ones, unlocking the true potential of large reference libraries for metagenomic read profiling.

genomics↗

bioRxiv2024

IntegrIBS: Towards Building a Robust IBS Classifier with Integrated Microbiome Data

Irritable Bowel Syndrome (IBS) is a condition that is quite complicated and shares its symptoms with other related diseases, making it difficult to diagnose. In this study, we initially trained machine learning models on individual microbiome datasets and tested their performance on other datasets, observing variability and low precision among them. To mitigate this, we hypothesised that integrating multiple publicly available microbiome datasets will capture a wide spectrum of microbiome variations across different geographies and demographics. Utilizing this integrated dataset, the XGBoost model achieved a mean accuracy of 0.75 with a standard deviation of 0.04 in 10-fold cross-validation, demonstrating its potential for robust IBS prediction. Explainability analysis identified key bacterial taxa influencing predictions, aligning with existing literature. However, the models performance declined significantly when using a leave-one-dataset-out approach, where the model was trained on all but one dataset and tested on the excluded dataset. The results highlight the challenges of generalizing across diverse datasets due to biological and technical variability. These findings present a cautionary tale regarding the integration of datasets and interpretation of results, emphasizing the need for more comprehensive approaches to develop reliable diagnostic tools for IBS.

bioinformatics↗

bioRxiv2023

MoMA: Large scale network model of Microbes, Metabolites and Aging hallmarks

The gut microbiome is known to be a driver of age-related health decline. Various studies have shone light on the role of the gut microbiome as a marker as well as modulator of aging processes. However, the mechanisms by which the microbiome affects aging are still unclear. We have developed a Microbiome Metabolite Aging (MMA) fusion network by building upon a metabolic interaction network of gut microbiota to develop associations with the hallmarks of aging. The MMA, consisting of 238 metabolite-aging hallmark interactions serves as a tool to investigate the mammalian (and in particular human) gut microbiome as an effector of aging at a systems-level. The network further identifies 249 microbes that unequivocally affect the hallmarks of aging. The results highlight how the underlying biology of microbial metabolite mediated interactions, in conjunction with the topological properties at a network level, differentially regulate the aging hallmarks. This detailed microbial and metabolite association to the hallmarks of aging provides a foundation which is envisaged to be instrumental in advancing our knowledge of the physiology of aging, and for the development of novel therapeutic tools.

systems biology↗