Welcome to the 23rd Web Server Issue of Nucleic Acids Research with 72 web servers from and for different scientific and medical fields.
The trend toward using large-language models (LLMs) has reached scientific web servers, and consequently, we received a number of proposals describing LLM-based web servers. Surprisingly, none of those that employ an LLM to interact with the user made it into this Issue. Some of them were already filtered out in the first step, e.g. because they did not give better answers than existing general-purpose LLMs or came without any documentation at all—refusing even to provide it when prompted. Others did not pass our thorough in-house technical review or external expert evaluations, often due to unreliable output or “hallucinations.”
Chatbot-style interfaces also demand comprehensive documentation—not only for the interaction mechanisms but also for the underlying data and, most importantly, the limitations of the system. This includes clearly outlining the circumstances under which the model might generate incorrect or misleading results.
Another trend is the use of cloud-based solutions. These pose a great challenge for the Web Server Issue because they usually require users to log in—and that is something that we do not allow (unless for sensitive data such as human medical or genetic data). An additional concern is the potential need for payment or at least credit card registration when using commercial cloud platforms, which conflicts with our fundamental principle of providing free and open-access web servers. We do, on the other hand, recognize that the provision of IT infrastructure does not come for free and that many groups are simply not able to cover the cost for high-performance computing or large data storage from their own budgets. While our yearly editors’ meeting takes place after this editorial’s deadline, I encourage all future submitters to review our updated submission guidelines at https://academic.oup.com/nar/pages/Submission_Webserver, which will be revised by August.
You may also notice a lower number of single-cell analysis web servers in this year’s Issue. One reason is that many of the suggested web servers were too specialized (e.g. aimed at only one specific tissue) or did not provide clear advantages over existing solutions. However, a main problem here was that many were web versions of existing R packages and would require to use R to prepare the input data and sometimes even for further downstream analysis, limiting accessibility for non-bioinformaticians.
One clear aim of the Web Server Issue is to facilitate data analysis or lab tasks for researchers who do not want to use the command line. A web server is of limited use if it demands extensive pre-processing before it can be used or if it only provides plain text files as output, but does not provide contextual information (e.g. gene annotation data or external links) and options for filtering and sorting the results.
As in the last year, I tried to group this year’s web servers into thematic categories. This categorization is not always straightforward because, for example, a web server about protein–RNA interactions would obviously fit into either group. So, I encourage you to browse the whole list and, if something raises your interest, to follow the hyperlinks to the article (under the short title) or to the software itself. Please find the complete list at the bottom.
| Cancer/medicine/cheminformatics | ||
| AllergyPred | Allergen prediction | https://allergypred.charite.de/AllergyPred/ |
| CellHit | Predicting and analyzing patients’ drug responsiveness | https://cellhit.bioinfolab.sns.it/ |
| GEPIA3 | Drug sensitivity and interaction network analysis for cancer research | https://gepia3.bioinfoliu.com/ |
| HAb-Convergent | Antibody engineering | https://nmdc.cn/zoe/ |
| Onkopus | Biological and clinical interpretation of DNA variants in cancer | https://mtb.bioinf.med.uni-goettingen.de/onkopus |
| TIMER3 | Tumor immune analysis | https://compbio.cn/timer3/ |
| Lab work | ||
| BEscreen | Toolkit to design base editing libraries | https://bescreen.ostendorflab.org/ |
| CRISPR-BEasy | Designing sgRNA tiling libraries for base editing screens | https://crispr-beasy.cerc-genomic-medicine.ca/ |
| Cas-OFFinder | Variant-aware identification of off-target site for genome editing | https://crispr.pnucolab.com/ |
| Diffdigester.uni-jena.de | Selecting restriction enzymes for plasmid identification | https://diffdigester.uni-jena.de/ |
| IVA Prime | Primer design for in vivo assembly cloning | https://ivaprime.com |
| Microbiology/virology/phylogeny | ||
| AmrProfiler | Identifying antimicrobial resistance genes and mutations across species | https://dianalab.e-ce.uth.gr/amrprofiler |
| AutoMLST2.0 | Phylogeny and microbial taxonomy | https://automlst2.cs.uni-tuebingen.de/ |
| BASys2 | Annotation of bacterial genomes | https://basys2.ca |
| Bakta Web | Annotation of bacterial genomes | https://bakta.computational.bio |
| CapBuild | Capsid engineering for adeno-associated viruses | https://capbuild.csiro.au/ |
| ClipKIT | Trimming of multiple sequence alignments for phylogenetics | https://clipkit.genomelybio.com/ |
| CocoVax | Codon-based de-optimization of viral genes | https://comics.med.sustech.edu.cn/cocovax/ |
| M1CR0B1AL1Z3R 2.0 | Comparative analysis of bacterial genomes | https://microbializer.tau.ac.il/ |
| MOBHunter | Identification and classification of mobile genetic elements in microbial genomes | https://informatica.utem.cl/mobhunter/ |
| MetaHiCNet | Microbial Hi-C interaction networks | https://metahicnet.com |
| OriV-Finder | Bacterial plasmid replication origin analysis | https://tubic.org/OriV-Finder/ |
| antiSMASH | Secondary metabolite biosynthetic gene clusters | https://antismash.secondarymetabolites.org/ |
| Nucleic acids | ||
| ASOptimizer | Optimizing chemical diversity of antisense oligonucleotides | https://asoptimizer.s-core.ai/ |
| DIGGER 2.0 | Functional impact of differential splicing on human and mouse disorders | https://www.exbio.wzw.tum.de/digger-dev/ |
| MAGNETIC | Find correlations between gene promoters | https://cospi.iiserpune.ac.in/magnetic/ |
| RNAhub | Search and align RNA homologs with secondary structure assessment | https://rnahub.org |
| RRMScorer | Predicting RNA recognition motifs | https://bio2byte.be/rrmscorer/ |
| RegRNA 3.0 | Analysis of regulatory RNAs | http://awi.cuhk.edu.cn/∼RegRNA |
| SIREs 3.0 | Predicting iron-responsive elements | https://www.sires-webserver.eu/ |
| mRNAdesigner | Optimizing mRNA design and protein translation in eukaryotes | https://www.biosino.org/mRNAdesigner/ |
| Proteins | ||
| AlphaLasso | Identifying loop and lasso motifs in 3D structures | https://alphalasso.cent.uw.edu.pl/ |
| BeStSel | Analysis of protein circular dichroism spectra | https://bestsel.elte.hu/ |
| CABS-flex 3.0 web server | Simulating protein structural flexibility | https://lcbio.pl/cabsflex3/ |
| CGeNArateWeb | Atomistic study of chromatin fibers | https://mmb.irbbarcelona.org/webdev/slim/CGeNArate/public/ |
| Caver Web 2.0 | Analysis of tunnels and ligand transport in proteins | https://loschmidt.chemi.muni.cz/caverweb/ |
| ChiraKit | Analysis of circular dichroism spectroscopy data | https://spc.embl-hamburg.de/app/chirakit |
| ClusterONE Web | Discovering and analyzing overlapping protein complexes | https://paccanarolab.org/clusteroneweb/ |
| DEMO-EMol | Modeling protein–nucleic acid complex structures from cryo-EM | https://zhanggroup.org/DEMO-EMol/ |
| DeepMolecules | Predicting enzyme and transporter–small molecule interactions | https://deepmolecules.org |
| DeepUMQA-X Server | Estimation of model accuracy for proteins and protein complexes | http://zhanglab-bioinf.com/DeepUMQA-X |
| Dr. Kinase | Predicting the drug-resistance hotspots of protein kinases | http://modinfor.com/drkinase |
| E3Docker | Discovery of potential E3 binders | https://e3docker.schanglab.org.cn/ |
| FoldScript | Analysis of AI-generated 3D protein models | https://foldscript.ibcp.fr/ |
| HawkDock 2.0 | Predict the structures of protein–protein complexes | http://cadd.zju.edu.cn/hawkdock/ |
| InDeepNet | Predicting functional binding sites in proteins | https://indeep-net.gpu.pasteur.cloud/ |
| LIGYSIS-web | Analysis of protein–ligand binding sites | https://www.compbio.dundee.ac.uk/ligysis/ |
| MolViewSpec | Describing and sharing molecular visualizations | https://molstar.org/mol-view-spec/ |
| MultiFOLD2 and ModFOLDdock2 | Prediction and quality assessment of protein quaternary structure models | https://www.reading.ac.uk/bioinf/MultiFOLD/; https://www.reading.ac.uk/bioinf/ModFOLDdock/ |
| PDBCharges | Quantum-mechanical partial atomic charges for PDB structures | https://pdbcharges.biodata.ceitec.cz/ |
| PLIP | Protein–ligand interaction profiles | https://plip-tool.biotec.tu-dresden.de/ |
| PrankWeb 4 | Protein–ligand binding site prediction | https://prankweb.cz/ |
| ProteinsPlus | Protein structure mining | https://proteins.plus/ |
| SHARK | Alignment-free homology assessment for intrinsically disordered proteins | https://bio-shark.org/ |
| StructMAn Web | Structural annotation of protein sequences and mutations | tools.helmholtz-hips.de/structman |
| TmDet | Determining membrane orientation of transmembrane proteins | https://tmdet.unitmp.org/ |
| Web-based GTalign | Protein structure alignment | https://bioinformatics.lt/comer/gtalign |
| Tools/data retrieval | ||
| BioPortal | Sharing, searching, and utilizing biomedical ontologies | https://bioportal.bioontology.org/ |
| Cytoscape Web | The online version of Cytoscape | https://web.cytoscape.org |
| EBI Search | API for accessing EBI’s databases | https://www.ebi.ac.uk/ebisearch/ |
| CORESH | Gene signature-based search engine for gene expression datasets | https://alserglab.wustl.edu/coresh/ |
| Heatmapper2 | Heatmapping | https://heatmapper2.ca |
| LitSense 2.0 | Sentence- and paragraph-level based information retrieval | https://www.ncbi.nlm.nih.gov/research/litsense2/ |
| OntoTiger | Collection of ontology-based tools | https://bio-computing.hrbmu.edu.cn/OntoTiger |
| UniProt REST API | API for accessing UniProt | https://www.uniprot.org/api-documentation |
| WashU Epigenome Browser | Epigenomics data browser | https://epigenomegateway.org/ |
| Others | ||
| CAMI Benchmarking Portal | Online evaluation and ranking of metagenomic software | https://cami-challenge.org/submit/ |
| CausalCCC | Exploring intracellular pathways for cell–cell communication | https://miic.curie.fr/causalCCC.php |
| DIGITtally | Drosophila meta-analysis | https://digittally.org |
| L2S2 | Chemical perturbation and knock-out signature search engine | https://l2s2.maayanlab.cloud/ |
| MMEASE 2.0 | Single-cell metabolomics | https://idrblab.org/mmease2025/ |
| MSTmap | Genetic linkage maps | https://mstmap.org/ |
I would like to thank my fellow editors, Dan Rigden and Janusz Bujnicki, for managing five submissions where I had a potential conflict of interest. And I am of course also grateful to the team at Oxford University Press: Meera Solanki (our new lead for the Web Server Issue in the editorial office), Martine Bernardes Silva, Rhiannon Meaden, Ajit Minj, and the typesetters.
Special thanks go to our technical reviewers. Krzysztof Sulik from Warsaw, Viktoria Wagner from Saarbrücken, and Oliver Küchler from my group have stepped down from the team and Jan-Paul Lerch has joined. He holds a PhD in mathematics and is now studying medicine at the Charité in Berlin.
Our current in-depth review team consists of the following:
Andrea Cappannini Université libre de Bruxelles (ULB), Brussels, Belgium
Jan-Paul Lerch Charité-Universitätsmedizin Berlin, Berlin, Germany
Sunandan Mukherjee Międzynarodowy Instytut Biologii Molekularnej i Komórkowej w Warszawie, Warsaw, Poland
Maja Noack Berliner Institut für Gesundheitsforschung@Charité, Berlin, Germany
Shusruto Rishik Universität des Saarlandes, Saarbrücken, Germany
Georges Pierre Schmartz Universität des Saarlandes, Saarbrücken, Germany
Adam Simpkin University of Liverpool, Liverpool, UK
Dominik Sordyl Międzynarodowy Instytut Biologii Molekularnej i Komórkowej w Warszawie, Warsaw, Poland
My biggest thanks go to our external reviewers. You are spending your free time on this and we are bugging you with our tight deadlines. Thank you for your excellent reports and the many suggestions how to improve the web servers.
Without you, this Issue would not have been possible!
My last words go to our authors: Thank you for all the web servers you have developed—and for continuing to maintain them long after publication. I know how much work (and sometimes money) this requires.
Last, but not least, I apologize for the exceptionally long response times some of you experienced this year. We are actively working to improve our processes and turnaround times.