Biology subjects

Bhattacharya, N.

Publications and source records attributed to Bhattacharya, N..

At least 19 recordsLinked to original sources

bioRxiv2026

Impaired value estimation over space explains turn-induced freezing of gait in Parkinson's disease: A cortico-basal ganglia-inspired reinforcement learning model

Parkinson's disease (PD) gait is characterized by reduced walking speed, short shuffling steps, increased double stance time, and increased step time variability. The most debilitating symptom in PD is freezing of gait (FOG), a context-dependent locomotion arrest, mostly occurring while encountering turns, obstacles, and narrow pathways. FOG is more pronounced during turning, a complex task that challenges motor planning and execution. Here, we simulate this phenomenon using a computational model inspired by the cortico-basal ganglia circuitry, based on reinforcement learning (RL) principles. In the model, the agent was trained to learn a value profile while navigating a path that involved turning. To simulate PD conditions, we clamped the temporal difference (TD) error, typically associated with dopaminergic signaling. Our model results show that compared to healthy controls (HC), PD non-freezers and PD freezers exhibit a reduction in turning velocity and step length, alongside an attenuated compensatory step-width widening at the sharpest turn, more pronounced in PD freezers, who additionally show increased step time variability. These kinematic impairments worsen during tighter turns. Ultimately, this model implicates FOG as a problem of impaired estimation of value over space and could potentially represent a generalized deficit in gait impairments in PD.

neuroscience↗

bioRxiv2026

Deterministic retrieval recovers biomedical associations lost by language models

Biomedical research increasingly relies on expert-curated databases to connect diseases, genes, variants, phenotypes, pathways and therapeutics. Incomplete or irreproducible retrieval can distort interpretation, mechanistic inference, variant assessment or therapeutic prioritization despite an unchanged evidence base. Agentic natural-language-to-SQL (NL2SQL) systems and Model Context Protocol-enabled agents can autonomously retrieve evidence from biomedical databases, including Open Targets and the Highly Confident Drug-Target Database, in response to natural-language questions. However, they may truncate large result sets, miss records when terminology differs from database vocabulary and return different outputs across runs. We systematically quantify these failures and introduce BioChirp, a goal-directed autonomous system for reliable retrieval from structured biomedical databases, built on interpretation-execution separation. A coordinated language-model layer interprets intent and resolves database-field assignments, after which entity resolution maps user terms to database terminology. A deterministic Steiner-tree planner and executor then retrieve matching records without further language-model involvement. Across curated biomedical databases spanning more than one million associations, BioChirp retrieved thousands of records for exhaustive queries, whereas baseline agents returned at most a few hundred or failed. Across 70 queries, five runs and three BioChirp database backends, BioChirp achieved a median cross-run Jaccard similarity of 1.0. Across 910 database-question evaluations derived from expert-written BioASQ questions and spanning ten databases, BioChirp retrieved at least one database record in 85.4% of cases and produced a fully correct answer in 51.1% of cases, compared with 44.6% and 17.4% for agentic NL2SQL. BioChirp is publicly available at https://biochirp.iiitd.edu.in.

bioinformatics↗

bioRxiv2026

Irrational groups: decoy placement, not group size, shapes collective shoal-size preference in adult zebrafish

Group living often improves decision accuracy in animals, yet whether increasing group size buffers against context-dependent biases remains unexplored. One such bias is the decoy effect, where the presence of a third option shifts preferences between two alternatives. Here, we tested whether introducing a decoy shoal influences collective preference for the larger female shoal in adult male zebrafish (Danio rerio), and whether the strength of this effect depends on group size. Groups of two, three, or four males were presented with female shoals under two choice contexts: a dichotomous contrast (four vs. two fish) and a trichotomous contrast including an additional alternative of one, three, or five fish. The order of presentation (dichotomous-first or trichotomous-first) was counterbalanced, and multi-animal tracking was used to quantify group-level shoal-size preference (time spent near shoals), inter-individual distance (IID), polarization, and swimming speed. In the dichotomous-first order, across all group sizes, subject shoals consistently showed a baseline preference for the larger shoal. Adding a third decoy option altered this preference only when the decoy was extreme (one or five fish relative to the 4 vs. 2 alternatives), reducing relative preference toward indifference. Group IIDs were associated with context-dependent shifts in relative preference in the dichotomous-first order, whereas polarization and swimming speed were not. In the trichotomous-first order, groups showed no preference for the larger shoal, with or without a decoy shoal. Our results demonstrate that context-dependent biases, shape collective shoal choice, with effects driven by choice structure and order of presentation of options than by group size.

animal behavior and cognition↗

bioRxiv2024

Innate Immune Receptor NLRX1: Potential Modulator of Glioblastoma Pathophysiology

Gliomas are primary brain tumors that develop from glial cells within the central nervous system and are among the deadliest human cancers. Glioblastoma (GBM) is the most malignant form of glioma. NLRX1 is an innate immune pattern recognition receptor that exhibits tumor-suppressive and tumor-promoting effects that may be cancer or cell-type, context-dependent, aided by differences in the microenvironment. Here, we report that NLRX1 is differentially expressed in microglia, astrocytes, GBM cell lines, and glioma patient tissues. siRNA-mediated silencing of Nlrx1 decreases the ability of the GBM cell line, LN-229, to proliferate and migrate. Nlrx1-/- GBM cells exhibit attenuated ability to generate 3D spheroids and enhanced capability to form tunneling nanotubes. Moreover, Nlrx1-/- GBM cells show decreased expression of autophagy markers, suggesting that NLRX1 plays a role in maintaining autophagy in GBM. In summary, our findings indicate that NLRX1 may modulate GBM pathophysiology by regulating GBM cell proliferation, migration, and metabolism. We believe our understanding of NLRX1 in GBM pathophysiology paves the potential development of GBM-targeting therapeutics that may delay disease progression and/or improve survival.

cancer biology↗

bioRxiv2023

Non-canonical NF-κB signaling in dendritic cells promotes intestinal inflammation by restraining the tolerogenic β-catenin-Raldh2 axis

Dendritic cell (DC) dysfunctions exacerbate intestinal pathologies. However, the mechanisms compromising DC-mediated immune controls remain unclear. We found that intestinal DCs from mice subjected to experimental colitis possessed heightened non-canonical NF-{kappa}B signaling, which activates the RelB:p52 heterodimer. Genetic inactivation of this pathway in DCs alleviated inflammation in colitogenic mice. Unexpectedly, RelB:p52 deficiency diminished the transcription of Axin1, a critical component of the {beta}-catenin destruction complex. This reinforced {beta}-catenin-driven expression of Raldh2, which imparts tolerogenic DC attributes by promoting retinoic acid (RA) synthesis. Indeed, DC-specific non-canonical NF-{kappa}B impairment improved the colonic frequency of Tregs and IgA+ B cells, which fostered luminal IgA and eubiosis. Introducing {beta}-catenin haploinsufficiency in non-canonical NF-{kappa}B-deficient DCs moderated Raldh2 activity, reinstating colitogenic sensitivity in mice. Finally, IBD patients displayed a deleterious non-canonical NF-{kappa}B signature in intestinal DCs. In sum, we establish a DC network that integrates non-canonical NF-{kappa}B signaling to subvert RA metabolic pathway in fueling intestinal inflammation. Significance (100)Distorted dendritic cell (DC) functions have been implicated in aberrant intestinal inflammation; however, the underlying mechanism remains obscure. We discovered that the non-canonical NF-{kappa}B pathway exacerbates inflammation in the colitogenic gut by downmodulating {beta}-catenin-driven synthesis of Raldh2 in DCs. Raldh2 represents a key enzyme involved in the production of tolerogenic retinoic acid in intestinal DCs. Beyond regulating immune genes, therefore, non-canonical NF-{kappa}B signaling appears to instruct retinoic acid-mediated control of gut health. While we illustrate a DC network integrating immune signaling and micronutrient metabolic pathways in the intestine, our finding may have broad relevance for nutritional interventions in inflammatory ailments. eToCDeka and Kumar et al. illustrate a DC-circuitry that exacerbates intestinal inflammation in IBD patients and colitogenic mice. Non-canonical NF-{kappa}B signaling restrains {beta}-catenin in DCs to downmodulate Raldh2, which promotes tolerogenic RA synthesis, leading to diminished Treg and IgA+ cell frequencies in the gut. HighlightsO_LIAberrant intestinal inflammation is associated with and exacerbated by non-canonical NF-{kappa}B signaling in DCs. C_LIO_LINon-canonical signaling restrains the tolerogenic {beta}-catenin-Raldh2 axis in DCs by upregulating Axin1. C_LIO_LIDC-specific RelB:p52 impairment promotes {beta}-catenin-dependent Treg accumulation in the gut. C_LIO_LIA DC defect of non-canonical signaling causes {beta}-catenin-dependent increase in luminal sIgA, fostering the gut microbiome. C_LI One sentenceThe non-canonical NF-{kappa}B pathway fuels intestinal inflammation by waning the tolerogenic {beta}-catenin-Raldh2-retinoic acid axis in DCs.

immunology↗

bioRxiv2023

SecDATA: Secure Data Access and de novo TranscriptAssembly protocol - To meet the challenge of reliableNGS data analysis

Recent developments in sequencing technologies have created new opportunities to generate high-throughput biological data at an affordable price. Such high-throughput data needs immense computational resources for performing transcript assembly. Further, a high-end storage facility is needed to store the analyzed data and raw data. Here comes the need for centralized repositories to store such mountains of raw and analyzed data. Hence, it is of utmost importance to ensure data privacy for storing the data while performing transcript assembly. In this paper, we have developed a protocol named SecDATA which performs de novo transcript assembly ensuring data security. It consists of two modules. The first module deals with a framework for secured access and storage of data. The novelty of the first module lies in the employment of distributed ledger technology for data storage that ensures the privacy of the data. The second module deals with the development of an optimized graph-based method for de novo transcript assembly. We have compared our results with the state-of-art method de Bruijn graph and the popular pipeline Trinity, for transcript reconstruction, and our protocol outperforms them.

bioinformatics↗

bioRxiv2023

scSPARKL: Apache Spark based parallel analytical framework for the downstream analysis of scRNA-seq data.

As the field of single-cell genomics continues to develop, the generation of large-scale scRNA-seq datasets has become more prevalent. While these datasets offer tremendous potential for shedding light on the complex biology of individual cells, the sheer volume of data presents significant challenges for management and analysis. To address these challenges, a new discipline, known as "big single-cell data science," has emerged. Within this field, a variety of computational tools have been developed to facilitate the processing and interpretation of scRNA-seq data. In this paper, we present a novel parallel analytical framework, scSPARKL, that leverages the power of Apache Spark to enable the efficient analysis of single-cell transcriptomic data. Our methodology incorporates six key operations for dealing with single-cell Big Data, including data reshaping, data preprocessing, cell/gene filtering, data normalization, dimensionality reduction, and clustering. By utilizing Sparks unlimited scalability, fault tolerance, and parallelism, scSPARKL enables researchers to rapidly and accurately analyze scRNA-seq datasets of any size. We demonstrate the utility of our framework through a series of experiments on simulated and real-world scRNA-seq data. Overall, our results suggest that scSPARKL represents a powerful and flexible tool for the analysis of single-cell transcriptomic data, with broad applications across the fields of biology and medicine.

bioinformatics↗

bioRxiv2022

Latent representation of single-cell transcriptomes enables algebraic operations on cellular phenotypes

Single-cell RNA-sequencing (scRNA-seq) coupled with robust computational analysis facilitates the characterization of phenotypic heterogeneity within tumors. Current scRNA-seq analysis pipelines are capable of identifying a myriad of malignant and non-malignant cell subtypes from single-cell profiling of tumors. However, given the extent of intra-tumoral heterogeneity, it is challenging to assess the risk associated with individual cell subpopulations, primarily due to the complexity of the cancer phenotype space and the lack of clinical annotations associated with tumor scRNA-seq studies. To this end, we introduce SCellBOW, a scRNA-seq analysis framework inspired by document embedding techniques from the domain of Natural Language Processing (NLP). SCellBOW is a novel computational approach that facilitates effective identification and high-quality visualization of single-cell subpopulations. We compared SCellBOW with existing best practice methods for its ability to precisely represent phenotypically divergent cell types across multiple scRNA-seq datasets, including our in-house generated human splenocyte and matched peripheral blood mononuclear cell (PBMC) dataset. For tumor cells, SCellBOW estimates the relative risk associated with each cluster and stratifies them based on their aggressiveness. This is achieved by simulating how the presence or absence of a specific cell subpopulation influences disease prognosis. Using SCellBOW, we identified a hitherto unknown and pervasive AR-/NElow (androgen-receptor-negative, neuroendocrine-low) malignant subpopulation in metastatic prostate cancer with conspicuously high aggressiveness. Overall, the risk-stratification capabilities of SCellBOW hold promise for formulating tailored therapeutic interventions by identifying clinically relevant tumor subpopulations and their impact on prognosis.

bioinformatics↗

bioRxiv2022

Deep self-supervised learning for biosynthetic gene cluster detection and product classification

Natural products are chemical compounds that form the basis of many therapeutics used in the pharmaceutical industry. In microbes, natural products are synthesized by groups of colocalized genes called biosynthetic gene clusters (BGCs). With advances in high-throughput sequencing, there has been an increase of complete microbial isolate genomes and metagenomes, from which a vast number of BGCs are undiscovered. Here, we introduce a self-supervised learning approach designed to identify and characterize BGCs from such data. To do this, we represent BGCs as chains of functional protein domains and train a masked language model on these domains. We assess the ability of our approach to detect BGCs and characterize BGC properties in bacterial genomes. We also demonstrate that our model can learn meaningful representations of BGCs and their constituent domains, detect BGCs in microbial genomes, and predict BGC product classes. These results highlight self-supervised neural networks as a promising framework for improving BGC prediction and classification. Author summaryBiosynthetic gene clusters (BGCs) encode for natural products of diverse chemical structures and function, but they are often difficult to discover and characterize. Many bioinformatic and deep learning approaches have leveraged the abundance of genomic data to recognize BGCs in bacterial genomes. However, the characterization of BGC properties remains the main bottleneck in identifying novel BGCs and their natural products. In this paper, we present a self-supervised masked language model that learns meaningful representations of BGCs with improved downstream detection and classification.

bioinformatics↗

bioRxiv2022

Randomized gates eliminate bias in sort-seq assays

Sort-seq assays are a staple of the biological engineering toolkit, allowing researchers to profile many groups of cells based on any characteristic that can be tied to fluorescence. However, current approaches, which segregate cells into bins deterministically based on their measured fluorescence, introduce systematic bias. We describe a surprising result: one can obtain unbiased estimates by incorporating randomness into sorting. We validate this approach in simulation and experimentally, and describe extensions for both estimating group level variances and for using multi-bin sorters.

bioengineering↗

bioRxiv2021

Marker-free characterization of single live circulating tumor cell full-length transcriptomes

The identification and characterization of circulating tumor cells (CTCs) are important for gaining insights into the biology of metastatic cancers, monitoring disease progression, and medical management of the disease. The limiting factor that hinders enrichment of purified CTC populations is their sparse availability, heterogeneity, and altered phenotypic traits relative to the tumor of origin. Intensive research both at the technical and molecular fronts led to the development of assays that ease CTC detection and identification from the peripheral blood. Most CTC detection methods use a mix of size selection, immune marker based white blood cells (WBC) depletion, and positive enrichment antibodies targeting tumor-associated antigens. However, the majority of these methods either miss out on atypical CTCs or suffer from WBC contamination. Single-cell RNA sequencing (scRNA-Seq) of CTCs provides a wealth of information about their tumors of origin as well as their fate and is a potent method of enabling unbiased identification of CTCs. We present unCTC, an R package for unbiased identification and characterization of CTCs from single-cell transcriptomic data. unCTC features many standard and novel computational and statistical modules for various analysis tasks. These include a novel method of scRNA-Seq clustering, named Deep Dictionary Learning using K-means clustering cost (DDLK), expression based copy number variation (CNV) inference, and combinatorial, marker-based verification of the malignant phenotypes. DDLK enables robust segregation of CTCs and WBCs in the pathway space, as opposed to the gene expression space. We validated the utility of unCTC on scRNA-Seq profiles of breast CTCs from six patients, captured and profiled using an integrated ClearCell(R) FX and PolarisTM workflow that works by the principles of size-based separation of CTCs and marker based WBC depletion.

bioinformatics↗

bioRxiv2021

Gene expression based inference of drug resistance in cancer

Inter and intra-tumoral heterogeneity are major stumbling blocks in the treatment of cancer and are responsible for imparting differential drug responses in cancer patients. Recently, the availability of large-scale drug screening datasets has provided an opportunity for predicting appropriate patient-tailored therapies by employing machine learning approaches. In this study, we report a predictive modeling approach to infer treatment response in cancers using gene expression data. In particular, we demonstrate the benefits of considering integrated chemogenomics approach, utilizing the molecular drug descriptors and pathway activity information as opposed to gene expression levels. We performed extensive validation of our approach on tissue-derived single-cell and bulk expression data. Further, we constructed several prostate cancer cell lines and xenografts, exposed to differential treatment conditions to assess the predictability of the outcomes. Our approach was further assessed on pan-cancer RNA-sequencing data from The Cancer Genome Atlas (TCGA) archives, as well as an independent clinical trial study describing the treatment journey of three melanoma patients. To summarise, we benchmarked the proposed approach on cancer RNA-seq data, obtained from cell lines, xenografts, as well as humans. We concluded that pathway-activity patterns in cancer cells are reasonably indicative of drug resistance, and therefore can be leveraged in personalized treatment recommendations.

cancer biology↗

bioRxiv2021

FLIP: Benchmark tasks in fitness landscape inference for proteins

Machine learning could enable an unprecedented level of control in protein engineering for therapeutic and industrial applications. Critical to its use in designing proteins with desired properties, machine learning models must capture the protein sequence-function relationship, often termed fitness landscape. Existing bench-marks like CASP or CAFA assess structure and function predictions of proteins, respectively, yet they do not target metrics relevant for protein engineering. In this work, we introduce Fitness Landscape Inference for Proteins (FLIP), a benchmark for function prediction to encourage rapid scoring of representation learning for protein engineering. Our curated tasks, baselines, and metrics probe model generalization in settings relevant for protein engineering, e.g. low-resource and extrapolative. Currently, FLIP encompasses experimental data across adeno-associated virus stability for gene therapy, protein domain B1 stability and immunoglobulin binding, and thermostability from multiple protein families. In order to enable ease of use and future expansion to new tasks, all data are presented in a standard format. FLIP scripts and data are freely accessible at https://benchmark.protein.properties.

bioengineering↗

bioRxiv2021

End-to-end learning of multiple sequence alignments with differentiable Smith-Waterman

Multiple Sequence Alignments (MSAs) of homologous sequences contain information on structural and functional constraints and their evolutionary histories. Despite their importance for many downstream tasks, such as structure prediction, MSA generation is often treated as a separate pre-processing step, without any guidance from the application it will be used for. Here, we implement a smooth and differentiable version of the Smith-Waterman pairwise alignment algorithm that enables jointly learning an MSA and a downstream machine learning system in an end-to-end fashion. To demonstrate its utility, we introduce SMURF (Smooth Markov Unaligned Random Field), a new method that jointly learns an alignment and the parameters of a Markov Random Field for unsupervised contact prediction. We find that SMURF learns MSAs that mildly improve contact prediction on a diverse set of protein and RNA families. As a proof of concept, we demonstrate that by connecting our differentiable alignment module to AlphaFold and maximizing predicted confidence, we can learn MSAs that improve structure predictions over the initial MSAs. Interestingly, the alignments that improve AlphaFold predictions are self-inconsistent and can be viewed as adversarial. This work highlights the potential of differentiable dynamic programming to improve neural network pipelines that rely on an alignment and the potential dangers of relying on black-box methods for optimizing predictions of protein sequences.

bioinformatics↗

bioRxiv2020

Single Layers of Attention Suffice to Predict Protein Contacts

AO_SCPLOWBSTRACTC_SCPLOWThe established approach to unsupervised protein contact prediction estimates co-evolving positions using undirected graphical models. This approach trains a Potts model on a Multiple Sequence Alignment, then predicts that the edges with highest weight correspond to contacts in the 3D structure. On the other hand, increasingly large Transformers are being pretrained on protein sequence databases but have demonstrated mixed results for downstream tasks, including contact prediction. This has sparked discussion about the role of scale and attention-based models in unsupervised protein representation learning. We argue that attention is a principled model of protein interactions, grounded in real properties of protein family data. We introduce a simplified attention layer, factored attention, and show that it achieves comparable performance to Potts models, while sharing parameters both within and across families. Further, we extract contacts from the attention maps of a pretrained Transformer and show they perform competitively with the other two approaches. This provides evidence that large-scale pretraining can learn meaningful protein features when presented with unlabeled and unaligned data. We contrast factored attention with the Transformer to indicate that the Transformer leverages hierarchical signal in protein family databases not captured by our single-layer models. This raises the exciting possibility for the development of powerful structured models of protein family databases.1

bioinformatics↗

bioRxiv2020

Transporter genes in biosynthetic gene clusters predict metabolite characteristics and siderophore activity

Biosynthetic gene clusters (BGCs) are operonic sets of microbial genes that synthesize specialized metabolites with diverse functions, including siderophores and antibiotics, which often require export to the extracellular environment. For this reason, genes for transport across cellular membranes are essential for the production of specialized metabolites, and are often genomically co-localized with BGCs. Here we conducted a comprehensive computational analysis of transporters associated with characterized BGCs. In addition to known exporters, in BGCs we found many importer-specific transmembrane domains that co-occur with substrate binding proteins possibly for uptake of siderophores or metabolic precursors. Machine learning models using transporter gene frequencies were predictive of known siderophore activity, molecular weights, and a measure of lipophilicity (log P) for corresponding BGC-synthesized metabolites. Transporter genes associated with BGCs were often equally or more predictive of metabolite features than biosynthetic genes. Given the importance of siderophores as pathogenicity factors, we used transporters specific for siderophore BGCs to identify both known and uncharacterized siderophore-like BGCs in genomes from metagenomes from the infant and adult gut microbiome. We find that 23% of microbial genomes from the infant gut have siderophore-like BGCs, but only 3% of those assembled from adult gut microbiomes do. While siderophore-like BGCs from the infant gut are predominantly associated with Enterobactericaee and Staphylococcus, siderophore-like BGCs can be identified from taxa in the adult gut microbiome that have rarely been recognized for siderophore production. Taken together, these results show that consideration of BGC-associated transporter genes can inform predictions of specialized metabolite structure and function.

bioinformatics↗

bioRxiv2020

Notch1 regulates breast cancer stem cell function via a non-canonical cleavage-independent pathway

Current treatment of triple negative breast cancer patients is hindered by a high incidence of chemoresistance (30-50%). The prevailing theory is that resistance and subsequent recurrence is driven by cancer stem cells. Unfortunately, the functional characterization of cancer stem cells at the molecular level is still incomplete. We show here, that within the canonical breast cancer stem cell population, a subset of cells characterized by high Notch1 expression possesses the tumor-initiating property associated with cancer stem cells. Moreover, the tumor initiating property of these high Notch1-expressing breast cancer stem cells is mediated by a cleavage independent Notch signaling pathway culminating in the repression of SIRT1. Of note, the Notch1-mediated repression of SIRT1 is required not only for tumor initiation, but also for chemoresistance in breast cancer stem cells. Strikingly, inhibition of SIRT1 obviates the requirement for Notch1, marking the first example of conferring cancer stem cell function by inhibiting the activity of a single protein. We also demonstrate that progenitor-like mammary epithelial cells, which possess both luminal and basal properties, are also characterized by high Notch1 expression and repression of SIRT1 via the non-canonical pathway. These findings provide the first functional mechanistic requirements for tumor initiation by breast cancer stem cells and suggest that activation of the non-canonical Notch1 pathway is hardwired into tumor-initiating progenitor cells and thus a prerequisite for tumor initiation. Statement of SignificanceWe demonstrate that chemoresistant and tumor-initiating properties of breast cancer stem cells are driven by repression of SIRT1 via non-canonical Notch signaling, suggesting a novel therapeutic strategy for triple negative breast cancer.

cancer biology↗

bioRxiv2020

MQ: Tool for HR-AM Based Quantitative Analysis for Direct MS Workflow

There is an increased interest in using high-resolution mass spectrometry (HRMS) in the full scan mode (FS) for simultaneous qualitative and quantitative analysis with accurate mass based extracted ion chromatograms (HRMS-AM) for metabolomic profiling workflows. Herein, the capabilities and features of MQ, a common platform developed to support quantitative analysis from HRMS data are described. Module for qualitative annotation utilizes peak features that include mass accuracy, peak width, and relative abundance of isotopic distribution that offer reliable metabolic confirmation. Additionally, MQ provides multivariate clustering with Principal Component Analysis and a module for high-throughput database query based on user configurable annotated metabolite lists. MQ is capable of handling system-agnostic data formats from both direct HRMS and LC-HRMS analysis. Data processed using MQ for LC-HRMS-AM quantitation of amino acids benchmarked using a commercial tool is presented that validates the quantitative capabilities of the algorithm developed. Datasets representative of three different HRMS-AM based analysis scenarios have been showcased to demonstrate the utility of MQ. Availability and implementationFreely available on web at, http://www.ldims.com/services/software. MQ is implemented in Java and supported on Linux and Microsoft Windows system having Java runtime environment (JRE) pre-installed.

bioinformatics↗