Niranjan Nagarajan

This study investigates how menopause alters Malassezia fungal populations on skin and their impact on inflammation and skin health in Asian women.

This paper introduces HOMES-seq, a spatial transcriptomics workflow for detecting microbes and host transcripts in colorectal tumours, improving sensitivity and reducing costs.

Skiver estimates error rates and quality scores in metagenomic datasets using (k, v)-mer sketches without reference genomes or alignment.

The paper investigates how individual-specific microbial functions and host-microbe interactions influence skin recovery after barrier disruption in a human cohort.

The paper reprocesses SARS-CoV-2 genomic data to correct systematic errors in phylogenies using a novel assembly tool, resulting in a cleaner global tree of 4.47 million samples.

The paper identifies GuFi phages as prevalent gut viral families using long-read sequencing and shows their broad host ranges and underappreciated impact on the microbiome.

This paper evaluates a high-throughput single-cell dispensing platform for isolating Bifidobacterium strains from fecal samples, demonstrating its effectiveness in recovering diverse, novel bacterial isolates.

This paper introduces Bidirectional Beam Search for efficient trace reconstruction in DNA storage systems, achieving high accuracy and speed on real datasets.

This study shows Bacteroides species inhibit C. difficile toxin production through cross-feeding proline in mixed biofilms, revealing a mechanism for microbiota-mediated control of CDI.

The paper develops a skin metatranscriptomics workflow to analyze microbial gene activity across five skin sites in 27 healthy adults, revealing discrepancies between transcriptomic and genomic abundances and identifying key microbial pathways and antimicrobial genes.

The paper uses single-molecule RNA structure probing to study how RNA structural heterogeneity affects the heat shock response in C. albicans.

This study identifies bacteriocin genes co-occurring with antibiotic resistance and virulence factors in gut E. coli, highlighting their role in microbial dynamics and resistance dissemination.

The paper identifies prevalent skin microbiome configurations (dermotypes) across body sites and links them to host factors and disease risk.

This paper demonstrates direct high-throughput sequencing of non-canonical bases using nanopore sequencing and bootstrapped learning, achieving high accuracy and specificity in basecalling.

The paper analyzes gut metagenomes of Asian octogenarians to identify age-related microbial shifts and their associations with aging phenotypes.

Floria rapidly and accurately recovers strain haplotypes from metagenomic data, outperforming existing methods in speed and strain content recovery.

MetageNN improves taxonomic classification of long-read sequencing data by using a neural network that is robust to sequencing errors and missing genomes, outperforming existing methods in sensitivity and speed.

The paper benchmarks metagenomic classification tools for long-read sequencing data, finding that general-purpose mappers outperform classification tools in accuracy but are slower, while kmer-based tools are faster but less accurate.

The paper uses long-read and Hi-C technologies to analyze gut microbiomes from Southeast Asians, revealing novel species, improved references, and uncharacterized biosynthetic gene clusters.

The paper characterizes the ecological and evolutionary dynamics of gut microbiomes colonized by carbapenemase-producing Enterobacteriaceae (CPE) using longitudinal metagenomics and whole genome sequencing.

The paper evaluates metagenomic software tools using realistic datasets, highlighting improvements in assembly and taxon profiling while identifying remaining challenges in virus/archaea detection and reproducibility.

The paper uses single-cell RNA-seq data to predict drug responses and combinations by analyzing intra-tumor transcriptomic heterogeneity in cancer cell lines.

BEEM-Static infers ecological interactions from metagenomic data, outperforming existing methods in detecting interaction directionality and robustness to non-steady-state samples.

TUGDA reduces negative transfer in drug response prediction by adapting in vitro models to in vivo settings through uncertainty-guided domain adaptation.

Graphmap2 improves transcript detection and exon boundary recognition using long-read RNA-seq data, outperforming existing tools in accuracy and recall.

This study maps the structural interactions of dengue and Zika viruses, identifying functional long-range interactions and their role in viral replication and attenuation.

This paper introduces a hybrid metagenomic assembler that improves assembly quality and enables high-resolution analysis of resistance determinants and mobile elements in the human gut microbiome.

BEEM improves ecological modeling of microbial communities using longitudinal metagenome data by integrating biomass estimation and model inference without requiring additional experimental data.

The paper identifies specific bacterial species associated with gut microbiome recovery after antibiotic use and shows their role in promoting microbial diversity and preventing pathogen colonization.

This paper introduces a recommender system approach to predict cancer drug responses using pharmacogenomic data and demonstrates its effectiveness across multiple datasets.