Transcriptomic Data Deep Analysis Service provides systematic interpretation of expression regulation for high-throughput sequencing data such as RNA-seq, single-cell RNA-seq (scRNA-seq), total RNA-seq, polyA RNA-seq, and CAGE-seq. GenScript offers gene expression quantification analysis, alternative splicing event detection, fusion gene identification, and RNA editing analysis. Meanwhile, GenScript deeply explores information including differentially expressed genes (DEGs), co-expression networks (WGCNA), transcription factor regulation (TF analysis), and RNA structural features. Combined with non-coding RNA (lncRNA, circRNA, miRNA) analysis, this service investigates hierarchical gene expression regulation. Additionally, GenScript integrates time-series analysis, gene set enrichment (GO/KEGG) analysis, and regulatory network inference to elucidate key signaling pathways and regulatory factors, providing high-quality data support for functional gene studies, disease mechanism analysis, personalized medicine, and biopharmaceutical research.

Deep analysis services for translatomics data provide systematic interpretation of high-throughput translatomics datasets such as Ribo-seq, 5P-seq, and TRAP-seq, helping researchers comprehensively decipher mechanisms of translational regulation. RiboNext accurately calculates translation efficiency (TE) and ribosome elongation rate, and performs characterization of translation initiation and termination features. In addition, RiboNext can compute translation stalling scores based on ribosome footprinting data, systematically uncovering potential mechanisms and regulatory pathways underlying ribosome stalling. Meanwhile, RiboNext conducts in-depth analyses of sequence and structural features of differentially translated genes to identify key regulatory elements affecting the translation process, providing high-resolution data support for studies on translational control, protein synthesis regulation, and related disease mechanisms.

Protein and complex structure analysis services focus on high-resolution structural data processing and analysis, primarily targeting X-ray crystallography and single-particle cryo-electron microscopy (Cryo-EM) data. RiboNext provides professional services such as data preprocessing, three-dimensional reconstruction, model building and refinement, ensuring the acquisition of high-quality protein and its complex structures. In addition, RiboNext also conducts preliminary structural analysis, including secondary structure features, key binding site identification, conformational change assessment, etc., helping researchers gain a deeper understanding of protein functions and interaction mechanisms, providing reliable structural information support for structural biology research, drug target analysis, and bioengineering optimization.

Structure prediction services are dedicated to accurately predicting the three-dimensional structures of proteins and their complexes using advanced computational modeling and artificial intelligence technologies. RiboNext offers protein structure prediction (based on cutting-edge algorithms such as AlphaFold and Rosetta), protein-protein interaction modeling, protein-RNA/DNA complex structure prediction, and protein-small molecule complex modeling. Key interaction features are further analyzed through methods such as molecular dynamics simulation (MD Simulation) and binding free energy calculations. This service is not only suitable for the structural characterization of unknown proteins, but also supports research on biomolecular interactions, targeted drug design, and bioengineering optimization, providing powerful structural information support for precision medicine, synthetic biology, and biotechnology research.

Drug target analysis services aim to accurately identify potential small-molecule binding sites on proteins through advanced computational simulations and structure prediction methods. Based on high-resolution protein structure data, RiboNext employs molecular docking, molecular dynamics simulation, and machine learning-based target prediction algorithms to predict possible binding sites and interaction patterns between small molecules and proteins. This analysis not only helps screen potential drug binding sites, but also provides binding affinity evaluation of protein-ligand complexes, offering precise guidance for lead compound optimization, structural modification, and targeted drug design in new drug development, thereby accelerating the drug discovery and development process.