Biology subjects

Niknejad, A.

Publications and source records attributed to Niknejad, A..

3 recordsLinked to original sources

bioRxiv2025

StereoMapper: Clarifying Metabolite Identity Through Stereochemically Aware Relationship Assignment

Inaccuracies in metabolite cross-mapping frequently introduce errors into genome-scale metabolic models (GEMs). A key challenge lies in distinguishing truly identical metabolites from closely related structural variants, such as stereoisomers, across biochemical databases. StereoMapper addresses this issue by establishing stereochemically aware relationships that clarify metabolite identity. It operates entirely on structural information, using molecular structures to infer equivalence and define relationships. StereoMapper was implemented using RDKit (2025.3.3) and Open Babel-wheel (3.1.1.22) which provides the core functionality of the Open Babel software, and benchmarked across four curated control datasets to evaluate performance in relationship classification (enantiomer, diastereomer, stereo-resolution pairs, and protomer). The pipeline was subsequently applied to 1.3 million molecular structures from multiple metabolic databases to generate equivalent isomeric sets (groups of structures identified as equivalent by StereoMapper). These sets were compared against the human subset of MetaNetX, a context-aware reference, to assess concordance and investigate disagreements. On curated positive control sets containing only positive examples across multiple relationship classes, StereoMapper achieved mean precision, recall, and F1-scores of 92.6, 98.0, and 95.3, respectively; following optimisation, these improved to 98.9, 98.2, and 98.5. Application to the 1.3 million structures produced over 339,000 stereochemically defined relationships between molecular structures. Comparison with the human subset of MetaNetX assignments showed an overall grouping concordance of 93.5%. The remaining 6.5% of differences were mainly associated with protomer relationships (52.3%) and stereo-resolution pairs (37.4%), while 21.6% involved complex or ambiguous cases. Scientific contribution statementStereoMapper provides a stereochemically aware pipeline for metabolite relationship assignment that shows high performance across multiple benchmark datasets, resolving key ambiguities in metabolite identity and providing a complementary approach to existing context-aware frameworks. Its integration will enhance reaction-level cross-mapping and improve the accuracy of future GEM reconstructions.

systems biology↗

bioRxiv2023

Multicolor on-chip fluorescence microscopy for real-time surgical guidance

In curative-intent cancer surgery, intraoperative fluorescence imaging of both diseased and healthy tissue can help to ensure successful removal of all gross and microscopic disease with minimal damage to neighboring critical structures, such as nerves. Current fluorescence-guided surgery (FGS) systems, however, rely on bulky and rigid optics that incur performance-limiting trade-offs between sensitivity and maneuverability. Moreover, many FGS systems are incapable of multiplexed imaging. As a result, clinical FGS is currently limited to millimeter-scale detection of a single fluorescent target. Here we present a scalable, lens-less fluorescence imaging chip, VISION, capable of sensitive and multiplexed detection within a compact form factor. Central to VISION is a novel optical frontend design combining a low-numerical-aperture fiber optic plate (LNA-FOP) and a multi-bandpass interference filter, which is affixed to a custom CMOS image sensor. The LNA-FOP acts as a planar collimator to improve resolution and compensate for the angle-sensitivity of the interference filter, enabling high-resolution and multiplexed fluorescence imaging without lenses. We show VISION is capable of detecting tumor foci of less than 100 cells at near video framerates and, as proof of principle, can simultaneously visualize both tumor and nerves in ex vivo prostate tissue.

cancer biology↗

bioRxiv2020

The Bgee suite: integrated curated expression atlas and comparative transcriptomics in animals

Bgee is a database to retrieve and compare gene expression patterns in multiple animal species, produced by integrating multiple data types (RNA-Seq, Affymetrix, in situ hybridization, and EST data). It is based exclusively on curated healthy wild-type expression data (e.g., no gene knock-out, no treatment, no disease), to provide a comparable reference of normal gene expression. Curation includes very large datasets such as GTEx (re-annotation of samples as "healthy" or not) as well as many small ones. Data are integrated and made comparable between species thanks to consistent data annotation and processing, and to calls of presence/absence of expression, along with expression scores. As a result, Bgee is capable of detecting the conditions of expression of any single gene, accommodating any data type and species. Bgee provides several tools for analyses, allowing, e.g., automated comparisons of gene expression patterns within and between species, retrieval of the prefered conditions of expression of any gene, or enrichment analyses of conditions with expression of sets of genes. Bgee release 14.1 includes 29 animal species, and is available at https://bgee.org/ and through its Bioconductor R package BgeeDB.

bioinformatics↗