Biology subjects

Krishnakumar, R.

Publications and source records attributed to Krishnakumar, R..

9 recordsLinked to original sources

bioRxiv2026

Benchmarking Large Language Models for Transcriptomic Data Analysis and Visualization: Multi-Model Workflow Evaluation

This study presents a comprehensive benchmarking evaluation of large language models(LLMs) and agentic coding systems for automated analysis and visualization workflows for transcriptomic data, in particular human responses to COVID-19 infection. Multiple models, including ChatGPT 5.5, Claude Sonnet 4.6, Gemini 3.1, and Llama4-Scout, were assessed across a series of scientific prompts designed to test their ability to reproduce workflow components such as dimensionality reduction, clustering, outlier detection, reproducibility, and code generation. The benchmark protocol involved varying prompt specificity and examined both quantitative metrics such as clustering scores and outlier counts as well as qualitative aspects such as code quality and reproducibility. Results indicate that prompt specificity improves code structure and methodological correctness, but quantitative performance depends heavily on preprocessing choices and execution environment. While some models produced strong code and reproducible outputs, alignment with scientific targets like label-based clustering was inconsistent. The findings highlight the promise and limitations of current LLMs for automating scientific data analysis and visualization and suggest directions for future benchmarking and workflow standardization.

bioinformatics↗

bioRxiv2026

Intracellular screening of nanobodies reveals an intrabody that inhibits ITCH E3 ubiquitin ligase

Nanobodies are a class of small, monomeric camelid antibody fragments that can bind target antigens with high affinity and specificity. Their small size, structural simplicity, and limited reliance on disulfide bonding makes them attractive for intracellular expression for labeling and perturbing cellular processes in live cells. However, screening campaigns carried out exclusively in vitro often yield antigen binders that fail to perform well in live cells due to low expression, misfolding, or mistargeting. We demonstrate that traditional in vitro screening of a nanobody library combined with an intracellular bioluminescence resonant energy transfer (BRET) proximity sensor approach for sequence down-selection can yield strong in vitro binders that also perform well as intrabodies, in this case capable of binding to, and inhibiting the enzymatic activity of, ITCH E3 ubiquitin ligase in human cells. This strategy allows a more direct and scalable path toward intrabody discovery.

cell biology↗

bioRxiv2025

Multi-omic and phenotypic analysis of growth and resilience of open raceway pond production of Monoraphidium minutum 26B-AM

BackgroundGreen microalgae, such as Monoraphidium minutum 26B-AM, have garnered significant commercial interest due to their high biomass production and lipid yield, providing promising candidates for various bioprocessing applications. However, the economic viability of large-scale algal cultivation in open raceway ponds is limited by biocontamination and environmental stressors, necessitating deeper understanding of the molecular mechanisms that underpin resilience and productivity in these systems. We hypothesized that the molecular signature associated with the cellular responses of M. minutum to environmental stressors will reveal critical information for the timely prediction of resilience and productivity in algal cultures within open pond systems. ResultsTo test this hypothesis, we conducted a longitudinal multi-omic study, integrating transcriptomics, proteomics, metabolomics, and phenomics, to monitor the acclimation, growth dynamics, and pathogen responses of algal cultures in two 1000 L raceway ponds, before and after the introduction of a pathogen as a stressor. We identified a number of molecular patterns that correlate with changes in the algal environment, and we can track these changes within the ponds per time. Furthermore, we identify scale-up and infection-specific molecular pathways through integrated multi-omics, showing that most patterns are unique to each studied stressor/transition. ConclusionsUltimately, this study demonstrates the utility of multi-omics observations at scale, revealing unique signatures and laying the groundwork for developing molecular detection techniques and predictive models that can improve the sustainability and efficiency of large-scale algae biomass production.

molecular biology↗

bioRxiv2024

A hybrid machine learning model for predicting gene expression from epigenetics across fungal species

Understanding gene expression is crucial for optimizing biological processes in bioeconomic processes, human health, and environmental regulation. Epigenetic modifications significantly influence gene expression by altering chromatin structure and DNA accessibility. However, knowledge about the conservation of these mechanisms across species, especially in non-model organisms, is limited. This study predicts gene expression levels based on epigenetic modifications across fungal species, facilitating knowledge transfer from well-characterized to less understood species. We developed a deep learning model, MAPLE (Model predictions Across Phylogenetic distances by Learning Expression from Epigenetics), which integrates convolutional layers and multi-head attention to capture dependencies in epigenetic data. MAPLE shows strong cross-species performance in fungi, achieving up to 80% accuracy and 89% AUROC for intra-species validation, and 77% accuracy and 83% AUROC in cross-species tasks, outperforming benchmarks. SHAP analysis reveals key epigenetic features driving gene expression, providing insights for future experimental design. Our findings highlight MAPLEs potential to generalize across fungal species, offering a versatile tool for optimizing gene expression.

systems biology↗

bioRxiv2024

Poplar: A Phylogenetics Pipeline

MotivationGenerating phylogenetic trees from genomic data is essential in understanding biological systems. Each step of this complex process has received extensive attention in the literature, and has been significantly streamlined over the years. Given the volume of publicly available genetic data, obtaining genomes for a wide selection of known species is straightforward. However, analyzing that same data in order to generate a phylogenetic tree is a multi-step process with legitimate scientific and technical challenges, and often requires a significant input from a domain-area scientist. ResultsWe present Poplar, a new, streamlined computational pipeline, to address the computational logistical issues that arise when constructing phylogenetic trees. It provides a framework that runs state-of-the-art software for essential steps in the phylogenetic pipeline, beginning from a genome with or without an annotation, and resulting in a species tree. Running Poplar requires no external databases. In the execution, it enables parallelism for execution for clusters and cloud computing. The trees generated by Poplar match closely with state-of-the-art published trees. The usage and performance of Poplar is far simpler and quicker than manually running a phylogenetic pipeline. Availability and ImplementationFreely available on GitHub at https://github.com/sandialabs/poplar. Implemented using Python and supported on Linux. Supplementary InformationNewick versions of the reference and generated trees.

bioinformatics↗

bioRxiv2021

OperonSEQer: A set of machine-learning algorithms with threshold voting for detection of operon pairs using short-read RNA-sequencing data

Operon prediction in prokaryotes is critical not only for understanding the regulation of endogenous gene expression, but also for exogenous targeting of genes using newly developed tools such as CRISPR-based gene modulation. A number of methods have used transcriptomics data to predict operons, based on the premise that contiguous genes in an operon will be expressed at similar levels. While promising results have been observed using these methods, most of them do not address uncertainty caused by technical variability between experiments, which is especially relevant when the amount of data available is small. In addition, many existing methods do not provide the flexibility to determine whether the stringency with which genes should be evaluated for being in an operon pair. We present OperonSEQer, a set of machine learning algorithms that uses the statistic and p-value from a non-parametric analysis of variance test (Kruskal-Wallis) to determine the likelihood that two adjacent genes are expressed from the same RNA molecule. We implement a voting system to allow users to choose the stringency of operon calls depending on whether your priority is high coverage of operons or high accuracy of the calls. In addition, we provide the code so that users can retrain the algorithm and re-establish hyperparameters based on any data they choose, allowing for this method to be expanded on as additional data is generated and incorporated. We show that our approach detects operon pairs that are missed by current methods by comparing our predictions to publicly available long-read sequencing data. OperonSEQer therefore improves on existing methods in terms of accuracy, flexibility and adaptability. Author SummaryBacteria and archaea, single-cell organisms collectively known as prokaryotes, live in all imaginable environments and comprise the majority of living organisms on this planet. Prokaryotes play a critical role in the homeostasis of multicellular organisms (such as animals and plants) and ecosystems. In addition, bacteria can be pathogenic, and cause a variety of diseases in these same hosts and ecosystems. In short, understanding the biology and molecular functions of bacteria and archaea and devising mechanisms to engineer and optimize their properties are critical scientific endeavors with significant implications in healthcare, agriculture, manufacturing and climate science among others. One major molecular difference between unicellular and multicellular organisms is the way the express genes - rather than making individual RNA molecules like multicellular organisms, prokaryotes express genes in long contiguous RNA molecules known as operons, which are subsequently processed. Understanding which genes exist within operons is critical for elucidating basic biology and for engineering organisms. In this work, we use a combination of statistical and machine learning-based methods to use next-generation sequencing data to predict operon structure across a range of prokaryotes. Our method provides a easily implemented, robust, accurate and flexible way to determine operon structure in an organism-agnosic manner using readily-available data.

bioinformatics↗

bioRxiv2020

Augmentation of Antibacterial Activity in Mesenchymal Stromal Cells Through Systems-Level Analysis and CRISPR-mediated Activation of CD14

Mesenchymal stromal cells (MSCs) have broad-ranging therapeutic capabilities, however MSC use is confounded by cell-to-cell heterogeneity, and source-to-source phenotypic inconsistencies. We utilized a systems-based approach to compare MSCs which displayed different capacity for antibacterial activity. Although MSCs from both sources satisfied traditional MSC-defining criteria, comparative transcriptomics and quantitative membrane proteomics demonstrated two unique molecular profiles. The antibacterial MSCs respond rapidly to bacterial lipopolysaccharide (LPS) and have elevated levels of the LPS co-receptor CD14. CRISPR-mediated overexpression of endogenous CD14 in non-antibacterial MSCs resulted in faster LPS response and enhanced antimicrobial activity. Single-cell transcriptome profiling of CD14-activated MSCs revealed uniform enhancement of LPS response kinetics, and a shift in the ground state of these MSCs. Our results demonstrate that systems-level analysis can reveal critical molecular targets to optimize desirable properties in MSCs, and that overexpression of CD14 in these cells can shift their state to be more responsive to future bacterial challenge.

molecular biology↗

bioRxiv2020

New candidates for regulated gene integrity revealed through precise mapping of integrative genetic elements

Integrative genetic elements (IGEs) are mobile multigene DNA units that integrate into and excise from host bacterial chromosomes. Each IGE usually targets a specific site within a conserved host gene, integrating in a manner that preserves target gene function. However, a small number of bacterial genes are known to be inactivated upon IGE integration and reactivated upon excision, regulating phenotypes of virulence, mutation rate, and terminal differentiation in multicellular bacteria. The list of regulated gene integrity (RGI) cases has been slow-growing because IGEs have been challenging to precisely and comprehensively locate in genomes. We present software (TIGER) that maps IGEs with unprecedented precision and without attB site bias. TIGER uses a comparative genomic, ping-pong BLAST approach, based on the principle that the IGE integration module (i.e., its int-attP region) is cohesive. The resultant IGEs, along with integrase phylogenetic analysis and gene inactivation tests, revealed 19 new cases of genes whose integrity is regulated by IGEs (including dut, eccCa1, gntT, hrpB, merA, ompN, prkA, tqsA, traG, yifB, yfaT and ynfE), as well as recovering previously known cases (in sigK, spsM, comK, mlrA, and hlb genes). It also recovered known clades of site-promiscuous integrases and identified possible new ones.

bioinformatics↗

bioRxiv2018

Real-Time Selective Sequencing with RUBRIC: Read Until with Basecall and Reference-Informed Criteria

The Oxford MinION, the first commercial nanopore sequencer, is also the first to implement molecule-by-molecule real-time selective sequencing or "Read Until". As DNA transits a MinION nanopore, real-time pore current data can be accessed and analyzed to provide active feedback to that pore. Fragments of interest are sequenced by default, while DNA deemed non-informative is rejected by reversing the pore bias to eject the strand, providing a novel means of background depletion and/or target enrichment. In contrast to the previously published pattern-matching Read Until approach, our RUBRIC method is the first example of real-time selective sequencing where on-line basecalling enables alignment against conventional nucleic acid references to provide the basis for sequence/reject decisions. We evaluate RUBRIC performance across a range of optimizable parameters, apply it to mixed human/bacteria and CRISPR/Cas9-cut samples, and present a generalized model for estimating real-time selection performance as a function of sample composition and computing configuration.

bioinformatics↗