1. the complete title of one (or more) paper(s) published in the open literature describing the work that the author claims describes a human-competitive result; Design of Specific Primer Sets for SARS-CoV-2 Variants Using Evolutionary Algorithms Design of Specific Primer Sets for the Detection of SARS-CoV-2 Variants of Concern B.1.1.7, B.1.351, P.1, B.1.617.2 using Artificial Intelligence 2. the name, complete physical mailing address, e-mail address, and phone number of EACH author of EACH paper(s); Eric Claassen; De Boelelaan 1105, 1081 HV Amsterdam, The Netherlands; h.j.h.m.claassen@vu.nl; +31205987031 Etienne Coz; 6 rue Danton, 92120, Montrouge, France; etiennecoz@gmail.com; +33 7 69 54 46 72 Johan Garssen; Universiteitsweg 99, Kamer 0, 3584 CG Utrecht, The Netherlands; J.Garssen@uu.nl; 030 253 7357 Aletta D. Kraneveld; Universiteitsweg 99, Kamer 0, 3584 CG Utrecht, The Netherlands; A.D.Kraneveld@uu.nl; 030 253 4509 Alejandro Lopez Rincon; Universiteitsweg 99, Kamer 0, 3584 CG Utrecht, The Netherlands; a.lopezrincon@uu.nl; +33 6 64 89 93 58 Lucero Mendoza Maldonado; Hospital Civil de Guadalajara "Dr. Juan I. Menchaca", Salvador Quevedo y Zubieta 750, Independencia Oriente, 44340 Guadalajara, Jal, Mexico; +52 6871178638 Carmina A. Perez Romero; UNICEQ, Av. 5 de Febrero 1602, San Pablo, 76130 Santiago de Querétaro, Qro., Mexico; +52 442 210 2710 Patrick Tabeling; Institut Pierre Gilles de Gennes (IPGG), 6 Rue Jean Calvin, 75005 Paris, France; ptabeling30@gmail.com; +33 1 40 79 59 63 Alberto Tonda; UMR 518 MIA-PS, 22 place de l'Agriculture, 91200 Palaiseau, France; alberto.tonda@inrae.fr; +33 6 95 24 52 93 Jessica Vanhomwegen; Institut Pasteur, 25-28 Rue du Dr Roux, 75015 Paris; jessica.vanhomwegen@pasteur.fr; +33 1 45 68 80 00 3. the name of the corresponding author (i.e., the author to whom notices will be sent concerning the competition); Alberto Tonda 4. the abstract of the paper(s); Design of Specific Primer Sets for SARS-CoV-2 Variants Using Evolutionary Algorithms Primer sets are short DNA sequences of 18-22 base pairs, that can be used to verify the presence of a virus, and designed to attach to a specific part of a viral DNA. Designing a primer set requires choosing a region of DNA, avoiding the possibility of hybridization to a similar sequence, as well as considering its GC content and Tm (melting temperature). Coronaviruses, such as SARS-CoV-2, have a considerably large genome (around 30 thousand nucleotides) when compared to other viruses. With the rapid rise and spread of SARS-CoV-2 variants, it has become a priority to breach our lack of specific primers available for diagnosis of this new variants. Here, we propose an evolutionary-based approach to primer design, able to rapidly deliver a high-quality primer set for a target sequence of the virus variant. Starting from viral sequences collected from open repositories, the proposed approach is proven able to uncover a specific primer set for the B.1.1.7 SARS-CoV-2 variant. Only recently identified, B.1.1.7 is already considered potentially dangerous, as it presents a considerably higher transmissibility when compared to other variants. Design of Specific Primer Sets for the Detection of SARS-CoV-2 Variants of Concern B.1.1.7, B.1.351, P.1, B.1.617.2 using Artificial Intelligence As the COVID-19 pandemic continues, new SARS-CoV-2 variants with potentially dangerous features have been identified by the scientific community. Variant B.1.1.7 lineage clade GR from Global Initiative on Sharing All Influenza Data (GISAID) was first detected in the UK, and it appears to possess an increased transmissibility. At the same time, South African authorities reported variant B.1.351, that shares several mutations with B.1.1.7, and might also present high transmissibility. Earlier this year, a variant labelled P.1 with 17 non-synonymous mutations was detected in Brazil. Recently the World Health Organization has raised concern for the variants B.1.617.2 mainly detected in India but now exported worldwide. It is paramount to rapidly develop specific molecular tests to uniquely identify new variants. Using a completely automated pipeline built around deep learning and evolutionary algorithms techniques, we designed primer sets specific to variants B.1.1.7, B.1.351, P.1 and respectively. Starting from sequences openly available in the GISAID repository, our pipeline was able to deliver the primer sets for each variant. In-silico tests show that the sequences in the primer sets present high accuracy and are based on 2 mutations or more. In addition, we present an analysis of key mutations for SARS-CoV-2 variants. Finally, we tested the designed primers for B.1.1.7 using RT-PCR. The presented methodology can be exploited to swiftly obtain primer sets for each new variant, that can later be a part of a multiplexed approach for the initial diagnosis of COVID-19 patients. 5. a list containing one or more of the eight letters (A, B, C, D, E, F, G, or H) that correspond to the criteria (see above) that the author claims that the work satisfies; B, E, F 6. a statement stating why the result satisfies the criteria that the contestant claims (see examples of statements of human-competitiveness as a guide to aid in constructing this part of the submission); B (The result is equal to or better than a result that was accepted as a new scientific result at the time when it was published in a peer-reviewed scientific journal.) - The primers discovered thanks to the evolutionary approach are competitive with results obtained by both humans (World Health Organization) and deep neural networks, see: Lopez-Rincon, A., Tonda, A., Mendoza-Maldonado, L. et al. Classification and specific primer design for accurate detection of SARS-CoV-2 using deep learning. Sci Rep 11, 947 (2021). https://doi.org/10.1038/s41598-020-80363-5 ; in particular, they provide equal or better specificity, while preserving all other favorable qualities (melting temperature in a given range, presence of specific base pairs, etc.) E (The result is equal to or better than the most recent human-created solution to a long-standing problem for which there has been a succession of increasingly better human-created solutions.) - Primers obtained through the evolutionary approach are competitive with human-devised primers, and they have been obtained in the span of just a few days. F (The result is equal to or better than a result that was considered an achievement in its field at the time it was first discovered.) - Due to the urgency of tackling the pandemic, the relatively timely development of human-devised primers to correctly identify SARS-CoV-2 and its variants was considered an achievement. 7. a full citation of the paper (that is, author names; title, publication date; name of journal, conference, or book in which article appeared; name of editors, if applicable, of the journal or edited book; publisher name; publisher city; page numbers, if applicable); Alejandro Lopez Rincon, Carmina A. Perez Romero, Lucero Mendoza Maldonado, Eric Claassen, Johan Garssen, Aletta D. Kraneveld, and Alberto Tonda. 2021. Design of specific primer sets for SARS-CoV-2 variants using evolutionary algorithms. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '21). Association for Computing Machinery, New York, NY, USA, 982–990. https://doi.org/10.1145/3449639.3459359 Design of Specific Primer Sets for the Detection of SARS-CoV-2 Variants of Concern B.1.1.7, B.1.351, P.1, B.1.617.2 using Artificial Intelligence Carmina A. Perez-Romero, Alberto Tonda, Lucero Mendoza-Maldonado, Etienne Coz, Patrick Tabeling, Jessica Vanhomwegen, Eric Claassen, Johan Garssen, Aletta D. Kraneveld, Alejandro Lopez-Rincon, bioRxiv 2021.01.20.427043; doi: https://doi.org/10.1101/2021.01.20.427043 8. a statement either that "any prize money, if any, is to be divided equally among the co-authors" OR a specific percentage breakdown as to how the prize money, if any, is to be divided among the co-authors; Any prize money, if any, is to be divided equally among the co-authors. 9. a statement stating why the authors expect that their entry would be the "best" Primers are short RNA/DNA sequences, used to uniquely identify virus strains. They are necessary for the development of test kits, that in turn are pivotal to identify and contain pandemics. Primer design is a complex procedure, that traditionally requires domain experts to analyze the genome of a target virus and find the most promising areas, that are subsequently tested, in-silico and in-vitro, for suitability as primers. Recent developments proposed to automatize the process resorting to convolutional neural networks (CNNs), but the presented approaches had several limitations, including relatively long training times for the network, and the necessity of post-processing the sequences identified by the CNN through another software that evaluated their suitability as primers. The evolutionary approach we presented is not only faster, but it also makes it possible to evaluate the candidate subsequences' suitability for primers as part of the fitness function, removing the need for post-processing. The primers obtained through our approach were also validated in a laboratory setting. Besides the results published in the GECCO 2021 paper presented for this competition, we also have a biorXiv paper that shows how it was possible to obtain a primer for another SARS-CoV-2 variant ("Omicron") in less than a day from its labeling as a Variant of Concern, thanks to the EA-based primer discovery. Our research line on the automatic discovery of primers also caught the attention of the generalist press, especially in the countries of origin of most of the collaborators (The Netherlands, Mexico) but also abroad (Belgium, India, Pakistan). Links to a few sample press articles: - Press release in the Mexican news site E-Consulta https://www.e-consulta.com/nota/2021-02-14/universidades/egresado-de-la-udlap-trabaja-en-pruebas-para-mutacion-de-covid - Press release in the Indian educational website Education Diary https://indiaeducationdiary.in/utrecht-university-method-can-help-develop-new-corona-tests/ - Divulgative article in the Dutch newspaper NRC https://www.nrc.nl/nieuws/2021/01/07/koopmans-britse-variant-kan-zich-snel-uitbreiden-in-nederland-a4026637 - Divulgation article in the Belgian website EngineeringNet https://engineeringnet.be/belgie/detail_belgie.asp?Id=23817 10. An indication of the general type of genetic or evolutionary computation used, such as GA (genetic algorithms), GP (genetic programming), ES (evolution strategies), EP (evolutionary programming), LCS (learning classifier systems), GI (genetic improvement), GE (grammatical evolution), GEP (gene expression programming), DE (differential evolution), etc. GA 11. The date of publication of each paper. If the date of publication is not on or before the deadline for submission, but instead, the paper has been unconditionally accepted for publication and is “in press” by the deadline for this competition, the entry must include a copy of the documentation establishing that the paper meets the "in press" requirement. Design of specific primer sets for SARS-CoV-2 variants using evolutionary algorithms -> 26 June 2021 Design of Specific Primer Sets for the Detection of SARS-CoV-2 Variants of Concern B.1.1.7, B.1.351, P.1, B.1.617.2 using Artificial Intelligence -> 15 October 2021 (online on biorXiv) ,