Nature Biotechnology Impact Factor 2024-2025 - ResearchHelpDesk - Nature Biotechnology is interested in the best research from across the field of Biotechnology; our broad scope ensures that work published reaches the widest possible audience. All editorial decisions are made by a team of full-time professional editors. Nature Biotechnology is a monthly journal covering the science and business of biotechnology. It publishes new concepts in technology/methodology of relevance to the biological, biomedical, agricultural and environmental sciences as well as covers the commercial, political, ethical, legal, and societal aspects of this research. The first function is fulfilled by the peer-reviewed research section, the second by the expository efforts in the front of the journal. We provide researchers with news about business; we provide the business community with news about research developments. The core areas in which we are actively seeking research papers include: molecular engineering of nucleic acids and proteins; molecular therapy (therapeutics genes, antisense, siRNAs, aptamers, DNAzymes, ribozymes, peptides, proteins); large-scale biology (genomics, functional genomics, proteomics, structural genomics, metabolomics, etc.); computational biology (algorithms and modeling), regenerative medicine (stem cells, tissue engineering, biomaterials); imaging technology; analytical biotechnology (sensors/detectors for analytes/macromolecules), applied immunology (antibody engineering, xenotransplantation, T-cell therapies); food and agricultural biotechnology; and environmental biotechnology. A comprehensive list of areas of interest is shown below. Strategies for controlling gene expression Strategies for manipulating gene structure Strategies for gene containment Technologies for analyzing gene function (e.g., arrays, SAGE) Technologies for analyzing gene structure/organization (e.g., molecular beacons) Chemogenomics or chemical genetics Pharmacogenomics/SNPs Computational analysis Technologies for analyzing/identifying protein structure/function (e.g., 2-D gels, mass spectrometry, yeast two-hybrid, SPR, NMR, arrays and chips) Structural genomics Computational analysis Technologies for analyzing/profiling metabolites (chromatography, mass spectrometry) Computational analysis Bioinformatics; algorithms; data deconvolution Modeling and systems biology: kinetics-based models and constraints-based models Rational approaches for proteins/antibodies/enzymes/drugs Molecular evolution Molecular breeding approaches Genetic manipulation of species of interest to modify or allow the production of a commercially or therapeutically relevant compound Computational analysis Mammalian cells Insect cells Bacteria Fungi Plant cells Targeting strategies Viral and nonviral vector strategies Reporter molecules Imaging approaches/technologies for visualizing whole animals, cells, or single molecules Computational analysis Gene therapy (targeting, expression, integration, immunogenicity) Antisense RNAi DNAzymes and ribozymes Nanomaterials for use in drug delivery or as therapeutics Nanomaterials for use in industrial biotechnology Nanosensors Nanosystems for imaging molecules and cells Antibody engineering T-cell therapies Therapies exploiting innate immunity (e.g. complement) Antigen delivery vectors and approaches Nucleic acid vaccines Computational analysis Stem cells Tissue engineering Therapeutic cloning (somatic cell nuclear transfer) Xenotransplantation Biomaterials Approaches for detecting biological molecules Use of biological systems in detecting analytes Approaches for multiplexing and increasing throughput Selection/screening strategies for gene/proteins/drugs Microfluidics Engineering materials for biological application Molecular imprinting Biomimetics Nanotechnology Crop improvement (resistance to stress, disease, pests) Nutraceuticals Forest biotechnology Plant vaccines Plants as bioreactors Gene-containment strategies Transgenic animals Knockouts Reproductive cloning Biopharmaceutical and enzyme production Transgene targeting and expression strategies Bioremediation Biomining Phytoremediation Monitoring

Nature Reviews Molecular Cell Biology CiteScore 2024-2025 - ResearchHelpDesk - Molecular cell biology is a marriage of two distinct, yet complementary, disciplines. In its traditional sense, the term 'molecular biology' refers to the study of the macromolecules essential to life — nucleic acids and proteins. The field of cell biology is a natural extension of this, integrating what we know at the molecular level into an understanding of processes and interactions at the cellular level. Only by combining both fields can we paint a broad picture of essential biological processes such as how cells divide, grow, communicate and die. Nature Reviews Molecular Cell Biology features Reviews, Perspective articles and Comments on a broad range of topics, and highlights important primary papers and technological progress. Reviews, Perspectives and Comments are commissioned by the editorial team. The scope of the journal includes: Cell signalling (signalling networks, ion channels, gap junctions) Membrane dynamics (membrane organization, endocytosis, exocytosis, organelle biogenesis) Cell adhesion (adhesion molecules, extracellular matrix) Cytoskeletal dynamics (cell motility, molecular motors, actin, microtubules, intermediate filaments) Developmental and stem cell biology Cell growth and division (cell cycle, cytokinesis, cancer) Cell death (apoptosis, necrosis, autophagy, ageing) Cellular microbiology (host–pathogen interactions) Plant cell biology Gene expression (transcription, splicing, RNA stability, translation, RNA interference, circadian rhythms) Nucleic-acid metabolism (DNA repair, recombination and replication, RNA biogenesis) Chromosome biology and nuclear architecture (chromatin, chromosome structure, transposons) Nuclear transport (import and export of molecules to and from the nucleus) Protein structure and metabolism (structure-function relationships, quality control, post-translational modifications, folding, translocation, degradation) Bioenergetics (respiration, photosynthesis, organelle biochemistry) Technology and techniques (imaging, proteomics, systems biology, bioinformatics)

Extracted from this reference http://www.nature.com/nrd/journal/v14/n6/full/nrd4581.html

List of 8383 taxon names with the number of public records in BOLD. The column "type" indicates if the original name was found at BOLD or if an alternative name from TPL (TPLsynonym or TPLaccepted) was found at BOLD.

Bacterial 16S sequence data collected from air in a hospital in Oregon Data collection procedures are described in the paper "Architectural design influences the diversity and structure of the built environment microbiome" by Kembel et al. (2012, ISME Journal). http://www.nature.com/ismej/journal/v6/n8/abs/ismej2011211a.html File list run1.sff.zip and run2.sff.zip - raw 454 sequencing data from two runs in SFF format air454.fastq.zip - sequences from both runs in FASTQ format air454_barcodes.txt - barcode sequences identifying samples

This dataset comprises a synthetic construct specifically designed to facilitate the development of primers and probes capable of detecting emerging resistant strains of SARS-CoV-2. It serves as a robust reference for genomic surveillance pipelines and provides valuable insights to guide next-generation antiviral design and therapeutic strategies.The main data contributions come from GVAtlas (tahirhb.com/GVAtlas) platform. It has SARS-CoV-2 genomes processed either fully or processed for specific region of SARS-CoV-2. From that massive dataset all publicly available sequences were extracted then a representative consensus genome was built.A local custom AI model was trained on 10 Million historical genomes and data for Q1 of 2025 simulating remdesivir pressure to predict which mutations are likely to emerge under therapeutic selection.Using the output of this locally trained AI model a synthetic RdRp region was generated incorporating those predicted mutations and was inserted into the consensus backbone.This gave me a model-based remdesivir-resistant candidate genome. It is derived from real-world trends and AI-guided evolution.Summary of Mutation Impacts The RdRp (nsp12) mutations introduced into the consensus genome reflect evolutionary pressures simulated using AI model trained under remdesivir-like conditions. These mutations are located in functionally important regions of the polymerase and may collectively influence the enzyme's interaction with nucleotide analogs and its overall fidelity.The P323S mutation, found in motif F, is a known polymorphic site often linked to increased fitness under treatment pressure. It resides at the interface with nsp7 and nsp8, suggesting potential impacts on complex formation or dynamic movement during RNA synthesis.At position 680 (S680N) , the mutation is in proximity to the active site, and while not directly interacting with the incoming nucleotides, it may influence the structural flexibility of the enzyme, allowing the RdRp to better tolerate nucleotide analogs or maintain activity under drug pressure.A685G replaces an alanine with glycine, increasing local flexibility. While alanine is structurally rigid, glycine can induce turns or bends in secondary structures. If this change occurs within a helical region, it may alter the positioning of nearby residues, possibly reducing the affinity of remdesivir during RNA synthesis.The C861K and N866K mutations are notable for their chemical nature. Cysteine plays a crucial role in maintaining tertiary structure via disulfide bridges, and replacing it with a large, positively charged lysine could disrupt local folding or interactions. Similarly, N866K introduces a bulky residue in a region critical for RNA template binding or enzyme dynamics, potentially influencing elongation efficiency or fidelity .Finally, R897K, though a conservative substitution, could modify protein-protein interactions with cofactors or even affect host factor recruitment, indirectly influencing replication efficiency.Files included:3k_consensus_modified.fasta : Final mutant genomemutations_summary.csv : Mutation table for 8 genesremde_resistance_mutations.csv : RdRp-specific mutationsproteins/*.fasta : Translated protein sequencesRefrences:1- Chen, L., Zhang, Z., Wu, M., & Xiao, J. (2021). Machine learning applications in virus-related research: A review. Briefings in Bioinformatics, 22 (1), 1 14. https://doi.org/10.1093/bib/bbaa104 2- Cock, P. J., Antao, T., Chang, J. T., Chapman, B. A., Cox, C. J., Dalke, A., & de Hoon, M. J. (2009). Biopython: Freely available Python tools for computational molecular biology and bioinformatics. Bioinformatics, 25 (11), 1422 1423. https://doi.org/10.1093/bioinformatics/btp163 3- Elbe, S., & Buckland-Merrett, G. (2017). Data, disease and diplomacy: GISAID’s innovative contribution to global health. Global Challenges, 1 (1), 33 46. https://doi.org/10.1002/gch2.1018 4- GISAID Initiative. (2024). EpiCoV Database. https://www.gisaid.org 5- Grubaugh, N. D., Gangavarapu, K., Quick, J., Matteson, N. L., Debroas, D. H., Moore, A. L., & Andersen, K. G. (2019). An amplicon-based sequencing framework for accurately profiling genomic variation of SARS-CoV-2. Genome Biology, 21 (1), 1 13. https://doi.org/10.1186/s13059-020-02103-z 6- Li, H. (2011). A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation using next-generation sequencing data. Bioinformatics, 27 (21), 2987 2993. https://doi.org/10.1093/bioinformatics/btr509 7- Peters, M. D., Godfrey, C. M., McInerney, P., Khalil, H., Parker, D., & Soares, C. B. (2015). Chapter 11: Systematic reviews of complex evidence: Scoping reviews. In Joanna Briggs Institute Reviewer’s Manual (pp. 1 18). Joanna Briggs Institute.8- Tchesnokov, E. P., Feng, J. Y., Porter, D. P., & Götte, M. (2020). Effect of Remdesivir on the Mutation Rate of SARS-CoV-2. Journal of Virology, 94 (21), e01458-20. https://doi.org/10.1128/JVI.01458-20 9- Wu, F., Zhao, S., Yu, B., Chen, Y. M., Wang, W., Song, J. D., & Tan, W. (2020). A new coronavirus associated with human respiratory disease in China. Nature, 579 (7798), 265 269. https://doi.org/10.1038/s41586-020-2008-3 10- Zou, J., Huss, M., Abid, A., Mohammadi, P., Engreitz, J., & Singh, A. (2021). A primer on deep learning in genomics. Nature Genetics, 53 (1), 1 8. https://doi.org/10.1038/s41588-020-00766-z 11- Agostini, M. L., Pruijssers, A. J., Chappell, J. D., Gribble, J., Lu, X., Andres, S., & Denison, M. R. (2022). Small molecule inhibitors targeting replication enzymes of RNA viruses. Annual Review of Pharmacology and Toxicology, 62 , 253 273. https://doi.org/10.1146/annurev-pharmtox-051221-114943 12- Figshare. (2024). Digital repository for research. https://figshare.com 13- NCBI Resource Coordinators. (2023). GenBank database. National Center for Biotechnology Information . https://www.ncbi.nlm.nih.gov/genbank/ Note:This genome represents a candidate for surveillance and vaccine preparedness.Novel mutations include P323S, A685G, R897K in nsp12 (RdRp).For any query / related data / files i can be contacted at tahirhb@hotmail.com