Biology subjects

Nadendla, S.

Publications and source records attributed to Nadendla, S..

6 recordsLinked to original sources

bioRxiv2026

A layered standards framework for integrating single-cell and spatial omics data into brain cell atlases

The BRAIN Initiative Cell Atlas Network (BICAN) is generating large-scale multimodal datasets to profile cell types in the human, non-human primate, and mouse brain. The diversity of single-cell and spatial transcriptomic and epigenomic assays, combined with varied experimental contexts, multiple data-generating laboratories and distributed infrastructure, poses substantial challenges for data integration and reuse in BICAN. To address this, we implemented a standards framework that enables layered integration of these data into knowledge-ready products for interoperable brain cell atlases. This framework organizes data based on three progressively structured layers. First, we introduced an assay-agnostic modeling layer that unifies the representation of single-cell and spatial omics data using a common set of biological entities and processes assessed by diverse experimental techniques. Second, we implemented harmonized metadata standards that capture key experimental features linked to biospecimen provenance across heterogeneous tissue sources, species, and preparations, supporting integration and validation while minimizing burden on data contributors. Third, we present an extensible representation for data-driven cell type taxonomies that integrates molecular data with annotations, ontology mappings, and evidence. Together, these contributions represent an end-to-end framework that transforms heterogeneous datasets into structured, interoperable resources that support broad community reuse via mapping algorithms, annotation systems, and visualization platforms. This approach links biospecimen provenance with cell-level outputs and embeds these in a standardized taxonomy format, enabling downstream applications such as cross-dataset integration, reference mapping, and knowledge-driven analysis. More broadly, our work demonstrates a generalizable strategy for enabling an efficient data-to-knowledge pipeline in a large-scale consortium setting.

genomics↗

bioRxiv2024

Exploring putative enteric methanogenesis inhibitors using molecular simulations and a graph neural network

Atmospheric methane (CH4) acts as a key contributor to global warming. As CH4 is a short-lived climate forcer (12 years atmospheric lifespan), its mitigation represents the most promising means to address climate change in the short term. Enteric CH4 (the biosynthesized CH4 from the rumen of ruminants) represents 5.1% of total global greenhouse gas (GHG) emissions, 23% of emissions from agriculture, and 27.2% of global CH4 emissions. Therefore, it is imperative to investigate methanogenesis inhibitors and their underlying modes of action. We hereby elucidate the detailed biophysical and thermodynamic interplay between anti-methanogenic molecules and cofactor F430 of methyl coenzyme M reductase and interpret the stoichiometric ratios and binding affinities of sixteen inhibitor molecules. We leverage this as prior in a graph neural network to first functionally cluster these sixteen known inhibitors among [~]54,000 bovine metabolites. We subsequently demonstrate a protocol to identify precursors to and putative inhibitors for methanogenesis, based on Tanimoto chemical similarity and membrane permeability predictions. This work lays the foundation for computational and de novo design of inhibitor molecules that retain/ reject one or more biochemical properties of known inhibitors discussed in this study. COVER ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=146 SRC="FIGDIR/small/613350v1_ufig1.gif" ALT="Figure 1"> View larger version (49K): org.highwire.dtl.DTLVardef@1fd4518org.highwire.dtl.DTLVardef@c34d87org.highwire.dtl.DTLVardef@16eef9org.highwire.dtl.DTLVardef@1a33512_HPS_FORMAT_FIGEXP M_FIG C_FIG

molecular biology↗

bioRxiv2022

The Neuroscience Multi-Omic Archive: A BRAIN Initiative resource for single-cell transcriptomic and epigenomic data from the mammalian brain

Scalable technologies to sequence the transcriptomes and epigenomes of single cells are transforming our understanding of cell types and cell states. The Brain Research through Advancing Innovative Neurotechnologies (BRAIN) Initiative Cell Census Network (BICCN) is applying these technologies at unprecedented scale to map the cell types in the mammalian brain. In an effort to increase data FAIRness (Findable, Accessible, Interoperable, Reusable), the NIH has established repositories to make data generated by the BICCN and related BRAIN Initiative projects accessible to the broader research community. Here, we describe the Neuroscience Multi-Omic Archive (NeMO Archive; nemoarchive.org), which serves as the primary repository for genomics data from the BRAIN Initiative. Working closely with other BRAIN Initiative researchers, we have organized these data into a continually expanding, curated repository, which contains transcriptomic and epigenomic data from over 50 million brain cells, including single-cell genomic data from all of the major regions of the adult and prenatal human and mouse brains, as well as substantial single-cell genomic data from non-human primates. We make available several tools for accessing these data, including a searchable web portal, a cloud-computing interface for large-scale data processing (implemented on Terra, terra.bio), and a visualization and analysis platform, NeMO Analytics (nemoanalytics.org). KEY POINTSO_LIThe Neuroscience Multi-Omic Archive serves as the genomics data repository for the BRAIN Initiative. C_LIO_LIGenomic data from >50 million cells span all the major regions of the brains of humans and mice. C_LIO_LIWe provide a searchable web portal, a cloud-computing interface, and a data visualization platform. C_LI

genomics↗

bioRxiv2022

Design and development of potent h-ACE2 derived peptide mimetics in SARS-CoV-2 Omicron variant therapeutics

The pandemic of COVID-19 has become the global health challenge due to the emergence of new variants. The Receptor binding domain (RBD) of spike protein that makes direct interaction with ACE-2 has shown unique mutated residues in most of the variants of concern (VOC). Recently WHO declared the Omicron (B.1.1.529) as VOC considering it as a highly mutated variant which includes a total of 60 mutations out of which 15 mutations occurred in RBD region of SARS-CoV-2. Inhibition of Protein-protein (Omicron RBD-h-ACE2) interaction was already proved to inhibit the viral infection. In this study, by using molecular dynamic simulations efforts are made to explore the atomistic details of Omicron RBD-h-ACE2 interaction. Based on MD simulations, h-ACE2 motif is found to be interacting with omicron RBD domain. Interaction analysis had provided key residues interacting with Omicron-RBD that helped to extract h-ACE2 peptide. Here, rational design of the peptides that have resemblance with h-ACE2 is done and the peptide library is subjected for inhibition studies against Omicron-RBD. The current study helped to identify the significant peptides that can inhibit Omicron-RBD. Altogether the performed studies will provide an opportunity to develop potential therapeutic peptidomimetics effective against Omicron variant of SARS-CoV-2.

bioinformatics↗

bioRxiv2021

Making Common Fund data more Findable: Catalyzing a Data Ecosystem

The Common Fund Data Ecosystem (CFDE) has created a flexible system of data federation that enables users to discover datasets from across the U.S. National Institutes of Health Common Fund without requiring that data owners move, reformat, or rehost those data. The CFDEs federation system is centered on a catalog that ingests metadata from individual Common Fund Programs Data Coordination Centers (DCCs) into a uniform metadata model that can then be indexed and searched from a centralized portal. This uniform Crosscut Metadata Model (C2M2) supports the wide variety of data types and metadata terms used by the individual DCCs and is designed to enable easy expansion to accommodate new data types. We describe its use to ingest and index data from ten DCCs.

bioinformatics↗

bioRxiv2018

FDA-ARGOS: A Public Quality-Controlled Genome Database Resource for Infectious Disease Sequencing Diagnostics and Regulatory Science Research

Infectious disease next generation sequencing (ID-NGS) diagnostics are on the cusp of revolutionizing the clinical market. To facilitate this transition, FDA proactively invested in tools to support innovation of emerging technologies. FDA and collaborators established a publicly available database, FDA dAtabase for Regulatory-Grade micrObial Sequences (FDA-ARGOS), as a tool to fill reference database gaps with quality-controlled genomes. This manuscript discusses quality control metrics for the proposed FDA-ARGOS genomic resource and outlines the need for quality-controlled genome gap filling in the public domain. Here, we also present three case studies showcasing potential applications for FDA-ARGOS in infectious disease diagnostics, specifically: assay design, reference database and in silico sequence comparison in combination with representative microbial organism wet lab testing; a novel composite validation strategy for ID-NGS diagnostics. The use of FDA-ARGOS as an in silico comparator tool could reduce the burden for completing ID-NGS clinical trials. In addition, use cases identifying Enterococcus avium and Ebola virus (Zaire ebolavirus variant Makona) demonstrate the utility of FDA-ARGOS as a reference database for independent performance validation of new tests and for documenting how one would use this database as an in silico sequence target comparator tool for ID-NGS validation, respectively.

microbiology↗