[{"scopus_import":"1","pmid":1,"_id":"13263","issue":"Supplement_1","author":[{"full_name":"Trinh, Van Giang","first_name":"Van Giang","last_name":"Trinh"},{"full_name":"Benhamou, Belaid","last_name":"Benhamou","first_name":"Belaid"},{"last_name":"Henzinger","first_name":"Thomas A","full_name":"Henzinger, Thomas A","orcid":"0000-0002-2985-7724","id":"40876CD8-F248-11E8-B48F-1D18A9856A87"},{"id":"07c5ea74-f61c-11ec-a664-aa7c5d957b2b","full_name":"Pastva, Samuel","orcid":"0000-0003-1993-0331","last_name":"Pastva","first_name":"Samuel"}],"department":[{"_id":"ToHe"}],"date_created":"2023-07-23T22:01:12Z","article_processing_charge":"Yes","publication_status":"published","intvolume":"        39","title":"Trap spaces of multi-valued networks: Definition, computation, and applications","quality_controlled":"1","ec_funded":1,"page":"i513-i522","file_date_updated":"2023-07-31T11:09:05Z","publisher":"Oxford Academic","article_type":"original","year":"2023","citation":{"ieee":"V. G. Trinh, B. Benhamou, T. A. Henzinger, and S. Pastva, “Trap spaces of multi-valued networks: Definition, computation, and applications,” <i>Bioinformatics</i>, vol. 39, no. Supplement_1. Oxford Academic, pp. i513–i522, 2023.","chicago":"Trinh, Van Giang, Belaid Benhamou, Thomas A Henzinger, and Samuel Pastva. “Trap Spaces of Multi-Valued Networks: Definition, Computation, and Applications.” <i>Bioinformatics</i>. Oxford Academic, 2023. <a href=\"https://doi.org/10.1093/bioinformatics/btad262\">https://doi.org/10.1093/bioinformatics/btad262</a>.","apa":"Trinh, V. G., Benhamou, B., Henzinger, T. A., &#38; Pastva, S. (2023). Trap spaces of multi-valued networks: Definition, computation, and applications. <i>Bioinformatics</i>. Oxford Academic. <a href=\"https://doi.org/10.1093/bioinformatics/btad262\">https://doi.org/10.1093/bioinformatics/btad262</a>","ama":"Trinh VG, Benhamou B, Henzinger TA, Pastva S. Trap spaces of multi-valued networks: Definition, computation, and applications. <i>Bioinformatics</i>. 2023;39(Supplement_1):i513-i522. doi:<a href=\"https://doi.org/10.1093/bioinformatics/btad262\">10.1093/bioinformatics/btad262</a>","ista":"Trinh VG, Benhamou B, Henzinger TA, Pastva S. 2023. Trap spaces of multi-valued networks: Definition, computation, and applications. Bioinformatics. 39(Supplement_1), i513–i522.","short":"V.G. Trinh, B. Benhamou, T.A. Henzinger, S. Pastva, Bioinformatics 39 (2023) i513–i522.","mla":"Trinh, Van Giang, et al. “Trap Spaces of Multi-Valued Networks: Definition, Computation, and Applications.” <i>Bioinformatics</i>, vol. 39, no. Supplement_1, Oxford Academic, 2023, pp. i513–22, doi:<a href=\"https://doi.org/10.1093/bioinformatics/btad262\">10.1093/bioinformatics/btad262</a>."},"date_updated":"2023-12-13T11:41:52Z","external_id":{"pmid":["37387165"],"isi":["001027457000060"]},"isi":1,"day":"30","doi":"10.1093/bioinformatics/btad262","abstract":[{"text":"Motivation: Boolean networks are simple but efficient mathematical formalism for modelling complex biological systems. However, having only two levels of activation is sometimes not enough to fully capture the dynamics of real-world biological systems. Hence, the need for multi-valued networks (MVNs), a generalization of Boolean networks. Despite the importance of MVNs for modelling biological systems, only limited progress has been made on developing theories, analysis methods, and tools that can support them. In particular, the recent use of trap spaces in Boolean networks made a great impact on the field of systems biology, but there has been no similar concept defined and studied for MVNs to date.\r\n\r\nResults: In this work, we generalize the concept of trap spaces in Boolean networks to that in MVNs. We then develop the theory and the analysis methods for trap spaces in MVNs. In particular, we implement all proposed methods in a Python package called trapmvn. Not only showing the applicability of our approach via a realistic case study, we also evaluate the time efficiency of the method on a large collection of real-world models. The experimental results confirm the time efficiency, which we believe enables more accurate analysis on larger and more complex multi-valued models.","lang":"eng"}],"acknowledgement":"This work was supported by L’Institut Carnot STAR, Marseille, France, and by the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Grant Agreement No. [101034413].","volume":39,"ddc":["000"],"has_accepted_license":"1","publication":"Bioinformatics","project":[{"_id":"fc2ed2f7-9c52-11eb-aca3-c01059dda49c","call_identifier":"H2020","grant_number":"101034413","name":"IST-BRIDGE: International postdoctoral program"}],"oa_version":"Published Version","month":"06","language":[{"iso":"eng"}],"tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"type":"journal_article","date_published":"2023-06-30T00:00:00Z","publication_identifier":{"issn":["1367-4803"],"eissn":["1367-4811"]},"oa":1,"file":[{"content_type":"application/pdf","file_name":"2023_Bioinformatics_Trinh.pdf","date_updated":"2023-07-31T11:09:05Z","file_size":641736,"checksum":"ba3abe1171df1958413b7c7f957f5486","date_created":"2023-07-31T11:09:05Z","creator":"dernst","file_id":"13335","access_level":"open_access","success":1,"relation":"main_file"}],"status":"public","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","related_material":{"link":[{"url":"https://github.com/giang-trinh/trap-mvn","relation":"software"}]}},{"year":"2022","citation":{"ista":"Zabelkin A, Yakovleva Y, Bochkareva O, Alexeev N. 2022. PaReBrick: PArallel REarrangements and BReaks identification toolkit. Bioinformatics. 38(2), 357–363.","mla":"Zabelkin, Alexey, et al. “PaReBrick: PArallel REarrangements and BReaks Identification Toolkit.” <i>Bioinformatics</i>, vol. 38, no. 2, Oxford Academic, 2022, pp. 357–63, doi:<a href=\"https://doi.org/10.1093/bioinformatics/btab691\">10.1093/bioinformatics/btab691</a>.","short":"A. Zabelkin, Y. Yakovleva, O. Bochkareva, N. Alexeev, Bioinformatics 38 (2022) 357–363.","ieee":"A. Zabelkin, Y. Yakovleva, O. Bochkareva, and N. Alexeev, “PaReBrick: PArallel REarrangements and BReaks identification toolkit,” <i>Bioinformatics</i>, vol. 38, no. 2. Oxford Academic, pp. 357–363, 2022.","chicago":"Zabelkin, Alexey, Yulia Yakovleva, Olga Bochkareva, and Nikita Alexeev. “PaReBrick: PArallel REarrangements and BReaks Identification Toolkit.” <i>Bioinformatics</i>. Oxford Academic, 2022. <a href=\"https://doi.org/10.1093/bioinformatics/btab691\">https://doi.org/10.1093/bioinformatics/btab691</a>.","ama":"Zabelkin A, Yakovleva Y, Bochkareva O, Alexeev N. PaReBrick: PArallel REarrangements and BReaks identification toolkit. <i>Bioinformatics</i>. 2022;38(2):357-363. doi:<a href=\"https://doi.org/10.1093/bioinformatics/btab691\">10.1093/bioinformatics/btab691</a>","apa":"Zabelkin, A., Yakovleva, Y., Bochkareva, O., &#38; Alexeev, N. (2022). PaReBrick: PArallel REarrangements and BReaks identification toolkit. <i>Bioinformatics</i>. Oxford Academic. <a href=\"https://doi.org/10.1093/bioinformatics/btab691\">https://doi.org/10.1093/bioinformatics/btab691</a>"},"date_updated":"2023-08-03T06:21:46Z","external_id":{"isi":["000743380100008"]},"isi":1,"day":"15","doi":"10.1093/bioinformatics/btab691","abstract":[{"lang":"eng","text":"Motivation\r\nHigh plasticity of bacterial genomes is provided by numerous mechanisms including horizontal gene transfer and recombination via numerous flanking repeats. Genome rearrangements such as inversions, deletions, insertions and duplications may independently occur in different strains, providing parallel adaptation or phenotypic diversity. Specifically, such rearrangements might be responsible for virulence, antibiotic resistance and antigenic variation. However, identification of such events requires laborious manual inspection and verification of phyletic pattern consistency.\r\nResults\r\nHere, we define the term ‘parallel rearrangements’ as events that occur independently in phylogenetically distant bacterial strains and present a formalization of the problem of parallel rearrangements calling. We implement an algorithmic solution for the identification of parallel rearrangements in bacterial populations as a tool PaReBrick. The tool takes a collection of strains represented as a sequence of oriented synteny blocks and a phylogenetic tree as input data. It identifies rearrangements, tests them for consistency with a tree, and sorts the events by their parallelism score. The tool provides diagrams of the neighbors for each block of interest, allowing the detection of horizontally transferred blocks or their extra copies and the inversions in which copied blocks are involved. We demonstrated PaReBrick’s efficiency and accuracy and showed its potential to detect genome rearrangements responsible for pathogenicity and adaptation in bacterial genomes."}],"volume":38,"acknowledgement":"The authors thank the 2020 student class of the Bioinformatics Institute, who\r\nused the first versions of the tool and provided many valuable suggestions to\r\nimprove usability. They also thank Louisa Gonzalez Somermeyer for manuscript proofreading\r\nThis work was supported by the National Center for Cognitive Research of\r\nITMO University and JetBrains Research [to A.Z and N.A.]; and the European\r\nUnion’s Horizon 2020 Research and Innovation Programme under the Marie\r\nSkłodowska-Curie [754411 to O.B.].\r\nPaReBrick is written in Python and is available on GitHub: https://github.com/ctlab/parallel-rearrangements.","ddc":["000"],"scopus_import":"1","_id":"10927","issue":"2","author":[{"last_name":"Zabelkin","first_name":"Alexey","full_name":"Zabelkin, Alexey"},{"last_name":"Yakovleva","first_name":"Yulia","full_name":"Yakovleva, Yulia"},{"id":"C4558D3C-6102-11E9-A62E-F418E6697425","full_name":"Bochkareva, Olga","orcid":"0000-0003-1006-6639","last_name":"Bochkareva","first_name":"Olga"},{"last_name":"Alexeev","first_name":"Nikita","full_name":"Alexeev, Nikita"}],"department":[{"_id":"FyKo"}],"date_created":"2022-03-27T22:01:46Z","article_processing_charge":"No","publication_status":"published","intvolume":"        38","title":"PaReBrick: PArallel REarrangements and BReaks identification toolkit","quality_controlled":"1","ec_funded":1,"page":"357-363","file_date_updated":"2022-03-28T08:07:46Z","publisher":"Oxford Academic","article_type":"original","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"type":"journal_article","date_published":"2022-01-15T00:00:00Z","publication_identifier":{"eissn":["1460-2059"],"issn":["1367-4803"]},"oa":1,"file":[{"date_updated":"2022-03-28T08:07:46Z","file_name":"2022_Bioinformatics_Zabelkin.pdf","content_type":"application/pdf","date_created":"2022-03-28T08:07:46Z","checksum":"4b5688ff9ac86180ccdf7f82fa33d926","file_size":3425744,"file_id":"10930","creator":"dernst","access_level":"open_access","success":1,"relation":"main_file"}],"user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","related_material":{"link":[{"url":"https://github.com/ctlab/parallel-rearrangements","relation":"software"}]},"status":"public","has_accepted_license":"1","publication":"Bioinformatics","project":[{"_id":"260C2330-B435-11E9-9278-68D0E5697425","call_identifier":"H2020","name":"ISTplus - Postdoctoral Fellowships","grant_number":"754411"}],"oa_version":"Published Version","month":"01","language":[{"iso":"eng"}]},{"doi":"10.1093/bioinformatics/btz841","day":"15","abstract":[{"text":"Epistasis, the context-dependence of the contribution of an amino acid substitution to fitness, is common in evolution. To detect epistasis, fitness must be measured for at least four genotypes: the reference genotype, two different single mutants and a double mutant with both of the single mutations. For higher-order epistasis of the order n, fitness has to be measured for all 2n genotypes of an n-dimensional hypercube in genotype space forming a ‘combinatorially complete dataset’. So far, only a handful of such datasets have been produced by manual curation. Concurrently, random mutagenesis experiments have produced measurements of fitness and other phenotypes in a high-throughput manner, potentially containing a number of combinatorially complete datasets. We present an effective recursive algorithm for finding all hypercube structures in random mutagenesis experimental data. To test the algorithm, we applied it to the data from a recent HIS3 protein dataset and found all 199 847 053 unique combinatorially complete genotype combinations of dimensionality ranging from 2 to 12. The algorithm may be useful for researchers looking for higher-order epistasis in their high-throughput experimental data.","lang":"eng"}],"date_updated":"2023-08-22T09:57:29Z","citation":{"ama":"Esteban LA, Lonishin LR, Bobrovskiy DM, et al. HypercubeME: Two hundred million combinatorially complete datasets from a single experiment. <i>Bioinformatics</i>. 2020;36(6):1960-1962. doi:<a href=\"https://doi.org/10.1093/bioinformatics/btz841\">10.1093/bioinformatics/btz841</a>","apa":"Esteban, L. A., Lonishin, L. R., Bobrovskiy, D. M., Leleytner, G., Bogatyreva, N. S., Kondrashov, F., &#38; Ivankov, D. N. (2020). HypercubeME: Two hundred million combinatorially complete datasets from a single experiment. <i>Bioinformatics</i>. Oxford Academic. <a href=\"https://doi.org/10.1093/bioinformatics/btz841\">https://doi.org/10.1093/bioinformatics/btz841</a>","ieee":"L. A. Esteban <i>et al.</i>, “HypercubeME: Two hundred million combinatorially complete datasets from a single experiment,” <i>Bioinformatics</i>, vol. 36, no. 6. Oxford Academic, pp. 1960–1962, 2020.","chicago":"Esteban, Laura A, Lyubov R Lonishin, Daniil M Bobrovskiy, Gregory Leleytner, Natalya S Bogatyreva, Fyodor Kondrashov, and Dmitry N  Ivankov. “HypercubeME: Two Hundred Million Combinatorially Complete Datasets from a Single Experiment.” <i>Bioinformatics</i>. Oxford Academic, 2020. <a href=\"https://doi.org/10.1093/bioinformatics/btz841\">https://doi.org/10.1093/bioinformatics/btz841</a>.","short":"L.A. Esteban, L.R. Lonishin, D.M. Bobrovskiy, G. Leleytner, N.S. Bogatyreva, F. Kondrashov, D.N. Ivankov, Bioinformatics 36 (2020) 1960–1962.","mla":"Esteban, Laura A., et al. “HypercubeME: Two Hundred Million Combinatorially Complete Datasets from a Single Experiment.” <i>Bioinformatics</i>, vol. 36, no. 6, Oxford Academic, 2020, pp. 1960–62, doi:<a href=\"https://doi.org/10.1093/bioinformatics/btz841\">10.1093/bioinformatics/btz841</a>.","ista":"Esteban LA, Lonishin LR, Bobrovskiy DM, Leleytner G, Bogatyreva NS, Kondrashov F, Ivankov DN. 2020. HypercubeME: Two hundred million combinatorially complete datasets from a single experiment. Bioinformatics. 36(6), 1960–1962."},"year":"2020","isi":1,"external_id":{"isi":["000538696800054"],"pmid":["31742320"]},"volume":36,"acknowledgement":"This work was supported by the European Research Council under the European Union’s Seventh Framework Programme (FP7/2007-2013, ERC grant agreement 335980_EinME) and Startup package to the Ivankov laboratory at Skolkovo Institute of Science and Technology. The work was started at the School of Molecular and Theoretical Biology 2017 supported by the Zimin Foundation. N.S.B. was supported by the Woman Scientists Support Grant in Centre for Genomic Regulation (CRG). ","ddc":["000","570"],"publication_status":"published","date_created":"2020-10-11T22:01:14Z","department":[{"_id":"FyKo"}],"article_processing_charge":"No","title":"HypercubeME: Two hundred million combinatorially complete datasets from a single experiment","intvolume":"        36","pmid":1,"_id":"8645","scopus_import":"1","author":[{"full_name":"Esteban, Laura A","last_name":"Esteban","first_name":"Laura A"},{"first_name":"Lyubov R","last_name":"Lonishin","full_name":"Lonishin, Lyubov R"},{"full_name":"Bobrovskiy, Daniil M","last_name":"Bobrovskiy","first_name":"Daniil M"},{"full_name":"Leleytner, Gregory","first_name":"Gregory","last_name":"Leleytner"},{"first_name":"Natalya S","last_name":"Bogatyreva","full_name":"Bogatyreva, Natalya S"},{"first_name":"Fyodor","last_name":"Kondrashov","orcid":"0000-0001-8243-4694","full_name":"Kondrashov, Fyodor","id":"44FDEF62-F248-11E8-B48F-1D18A9856A87"},{"last_name":"Ivankov","first_name":"Dmitry N ","full_name":"Ivankov, Dmitry N "}],"issue":"6","publisher":"Oxford Academic","article_type":"original","page":"1960-1962","ec_funded":1,"quality_controlled":"1","file_date_updated":"2020-10-12T12:02:09Z","publication_identifier":{"issn":["1367-4803"],"eissn":["1460-2059"]},"oa":1,"tmp":{"name":"Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)","image":"/images/cc_by_nc.png","short":"CC BY-NC (4.0)","legal_code_url":"https://creativecommons.org/licenses/by-nc/4.0/legalcode"},"date_published":"2020-03-15T00:00:00Z","type":"journal_article","file":[{"content_type":"application/pdf","file_name":"2020_Bioinformatics_Esteban.pdf","date_updated":"2020-10-12T12:02:09Z","checksum":"21d6f71839deb3b83e4a356193f72767","file_size":308341,"date_created":"2020-10-12T12:02:09Z","creator":"dernst","file_id":"8649","access_level":"open_access","success":1,"relation":"main_file"}],"status":"public","user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","oa_version":"Published Version","project":[{"grant_number":"335980","name":"Systematic investigation of epistasis in molecular evolution","call_identifier":"FP7","_id":"26120F5C-B435-11E9-9278-68D0E5697425"}],"month":"03","publication":"Bioinformatics","has_accepted_license":"1","language":[{"iso":"eng"}]},{"file":[{"creator":"kschuh","file_id":"5997","access_level":"open_access","relation":"main_file","content_type":"application/pdf","file_name":"2018_Oxford_Usmanova.pdf","date_updated":"2020-07-14T12:47:15Z","file_size":291969,"checksum":"7e0495153f44211479674601d7f6ee03","date_created":"2019-02-14T13:00:55Z"}],"user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","status":"public","publication_identifier":{"issn":["1367-4803","1460-2059"]},"oa":1,"tmp":{"name":"Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)","image":"/images/cc_by_nc.png","short":"CC BY-NC (4.0)","legal_code_url":"https://creativecommons.org/licenses/by-nc/4.0/legalcode"},"type":"journal_article","date_published":"2018-11-01T00:00:00Z","language":[{"iso":"eng"}],"project":[{"grant_number":"335980","name":"Systematic investigation of epistasis in molecular evolution","call_identifier":"FP7","_id":"26120F5C-B435-11E9-9278-68D0E5697425"}],"oa_version":"Published Version","month":"11","has_accepted_license":"1","publication":"Bioinformatics","volume":34,"ddc":["570"],"day":"01","doi":"10.1093/bioinformatics/bty340","abstract":[{"text":"Motivation\r\nComputational prediction of the effect of mutations on protein stability is used by researchers in many fields. The utility of the prediction methods is affected by their accuracy and bias. Bias, a systematic shift of the predicted change of stability, has been noted as an issue for several methods, but has not been investigated systematically. Presence of the bias may lead to misleading results especially when exploring the effects of combination of different mutations.\r\n\r\nResults\r\nHere we use a protocol to measure the bias as a function of the number of introduced mutations. It is based on a self-consistency test of the reciprocity the effect of a mutation. An advantage of the used approach is that it relies solely on crystal structures without experimentally measured stability values. We applied the protocol to four popular algorithms predicting change of protein stability upon mutation, FoldX, Eris, Rosetta and I-Mutant, and found an inherent bias. For one program, FoldX, we manage to substantially reduce the bias using additional relaxation by Modeller. Authors using algorithms for predicting effects of mutations should be aware of the bias described here.","lang":"eng"}],"citation":{"ieee":"D. R. Usmanova <i>et al.</i>, “Self-consistency test reveals systematic bias in programs for prediction change of stability upon mutation,” <i>Bioinformatics</i>, vol. 34, no. 21. Oxford University Press , pp. 3653–3658, 2018.","chicago":"Usmanova, Dinara R, Natalya S Bogatyreva, Joan Ariño Bernad, Aleksandra A Eremina, Anastasiya A Gorshkova, German M Kanevskiy, Lyubov R Lonishin, et al. “Self-Consistency Test Reveals Systematic Bias in Programs for Prediction Change of Stability upon Mutation.” <i>Bioinformatics</i>. Oxford University Press , 2018. <a href=\"https://doi.org/10.1093/bioinformatics/bty340\">https://doi.org/10.1093/bioinformatics/bty340</a>.","ama":"Usmanova DR, Bogatyreva NS, Ariño Bernad J, et al. Self-consistency test reveals systematic bias in programs for prediction change of stability upon mutation. <i>Bioinformatics</i>. 2018;34(21):3653-3658. doi:<a href=\"https://doi.org/10.1093/bioinformatics/bty340\">10.1093/bioinformatics/bty340</a>","apa":"Usmanova, D. R., Bogatyreva, N. S., Ariño Bernad, J., Eremina, A. A., Gorshkova, A. A., Kanevskiy, G. M., … Ivankov, D. (2018). Self-consistency test reveals systematic bias in programs for prediction change of stability upon mutation. <i>Bioinformatics</i>. Oxford University Press . <a href=\"https://doi.org/10.1093/bioinformatics/bty340\">https://doi.org/10.1093/bioinformatics/bty340</a>","ista":"Usmanova DR, Bogatyreva NS, Ariño Bernad J, Eremina AA, Gorshkova AA, Kanevskiy GM, Lonishin LR, Meister AV, Yakupova AG, Kondrashov F, Ivankov D. 2018. Self-consistency test reveals systematic bias in programs for prediction change of stability upon mutation. Bioinformatics. 34(21), 3653–3658.","mla":"Usmanova, Dinara R., et al. “Self-Consistency Test Reveals Systematic Bias in Programs for Prediction Change of Stability upon Mutation.” <i>Bioinformatics</i>, vol. 34, no. 21, Oxford University Press , 2018, pp. 3653–58, doi:<a href=\"https://doi.org/10.1093/bioinformatics/bty340\">10.1093/bioinformatics/bty340</a>.","short":"D.R. Usmanova, N.S. Bogatyreva, J. Ariño Bernad, A.A. Eremina, A.A. Gorshkova, G.M. Kanevskiy, L.R. Lonishin, A.V. Meister, A.G. Yakupova, F. Kondrashov, D. Ivankov, Bioinformatics 34 (2018) 3653–3658."},"year":"2018","date_updated":"2023-09-19T14:31:13Z","external_id":{"isi":["000450038900008"],"pmid":["29722803"]},"isi":1,"publisher":"Oxford University Press ","ec_funded":1,"quality_controlled":"1","page":"3653-3658","file_date_updated":"2020-07-14T12:47:15Z","date_created":"2019-02-14T12:48:00Z","department":[{"_id":"FyKo"}],"article_processing_charge":"No","publication_status":"published","intvolume":"        34","title":"Self-consistency test reveals systematic bias in programs for prediction change of stability upon mutation","scopus_import":"1","_id":"5995","pmid":1,"issue":"21","author":[{"first_name":"Dinara R","last_name":"Usmanova","full_name":"Usmanova, Dinara R"},{"full_name":"Bogatyreva, Natalya S","first_name":"Natalya S","last_name":"Bogatyreva"},{"full_name":"Ariño Bernad, Joan","first_name":"Joan","last_name":"Ariño Bernad"},{"full_name":"Eremina, Aleksandra A","first_name":"Aleksandra A","last_name":"Eremina"},{"last_name":"Gorshkova","first_name":"Anastasiya A","full_name":"Gorshkova, Anastasiya A"},{"full_name":"Kanevskiy, German M","last_name":"Kanevskiy","first_name":"German M"},{"last_name":"Lonishin","first_name":"Lyubov R","full_name":"Lonishin, Lyubov R"},{"full_name":"Meister, Alexander V","first_name":"Alexander V","last_name":"Meister"},{"full_name":"Yakupova, Alisa G","last_name":"Yakupova","first_name":"Alisa G"},{"full_name":"Kondrashov, Fyodor","orcid":"0000-0001-8243-4694","last_name":"Kondrashov","first_name":"Fyodor","id":"44FDEF62-F248-11E8-B48F-1D18A9856A87"},{"id":"49FF1036-F248-11E8-B48F-1D18A9856A87","first_name":"Dmitry","last_name":"Ivankov","full_name":"Ivankov, Dmitry"}]},{"date_published":"2014-08-01T00:00:00Z","type":"journal_article","publication_identifier":{"issn":["1367-4803","1460-2059"]},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","related_material":{"link":[{"relation":"erratum","url":"https://doi.org/10.1093/bioinformatics/btz397"}]},"status":"public","publication":"Bioinformatics","oa_version":"None","month":"08","language":[{"iso":"eng"}],"keyword":["Statistics and Probability","Computational Theory and Mathematics","Biochemistry","Molecular Biology","Computational Mathematics","Computer Science Applications"],"date_updated":"2021-01-12T08:19:25Z","year":"2014","citation":{"ista":"Morin S, Linnet TE, Lescanne M, Schanda P, Thompson GS, Tollinger M, Teilum K, Gagné S, Marion D, Griesinger C, Blackledge M, d’Auvergne EJ. 2014. Relax: The analysis of biomolecular kinetics and thermodynamics using NMR relaxation dispersion data. Bioinformatics. 30(15), 2219–2220.","short":"S. Morin, T.E. Linnet, M. Lescanne, P. Schanda, G.S. Thompson, M. Tollinger, K. Teilum, S. Gagné, D. Marion, C. Griesinger, M. Blackledge, E.J. d’Auvergne, Bioinformatics 30 (2014) 2219–2220.","mla":"Morin, Sébastien, et al. “Relax: The Analysis of Biomolecular Kinetics and Thermodynamics Using NMR Relaxation Dispersion Data.” <i>Bioinformatics</i>, vol. 30, no. 15, Oxford University Press, 2014, pp. 2219–20, doi:<a href=\"https://doi.org/10.1093/bioinformatics/btu166\">10.1093/bioinformatics/btu166</a>.","ieee":"S. Morin <i>et al.</i>, “Relax: The analysis of biomolecular kinetics and thermodynamics using NMR relaxation dispersion data,” <i>Bioinformatics</i>, vol. 30, no. 15. Oxford University Press, pp. 2219–2220, 2014.","chicago":"Morin, Sébastien, Troels E Linnet, Mathilde Lescanne, Paul Schanda, Gary S Thompson, Martin Tollinger, Kaare Teilum, et al. “Relax: The Analysis of Biomolecular Kinetics and Thermodynamics Using NMR Relaxation Dispersion Data.” <i>Bioinformatics</i>. Oxford University Press, 2014. <a href=\"https://doi.org/10.1093/bioinformatics/btu166\">https://doi.org/10.1093/bioinformatics/btu166</a>.","ama":"Morin S, Linnet TE, Lescanne M, et al. Relax: The analysis of biomolecular kinetics and thermodynamics using NMR relaxation dispersion data. <i>Bioinformatics</i>. 2014;30(15):2219-2220. doi:<a href=\"https://doi.org/10.1093/bioinformatics/btu166\">10.1093/bioinformatics/btu166</a>","apa":"Morin, S., Linnet, T. E., Lescanne, M., Schanda, P., Thompson, G. S., Tollinger, M., … d’Auvergne, E. J. (2014). Relax: The analysis of biomolecular kinetics and thermodynamics using NMR relaxation dispersion data. <i>Bioinformatics</i>. Oxford University Press. <a href=\"https://doi.org/10.1093/bioinformatics/btu166\">https://doi.org/10.1093/bioinformatics/btu166</a>"},"doi":"10.1093/bioinformatics/btu166","day":"01","abstract":[{"text":"Nuclear magnetic resonance (NMR) is a powerful tool for observing the motion of biomolecules at the atomic level. One technique, the analysis of relaxation dispersion phenomenon, is highly suited for studying the kinetics and thermodynamics of biological processes. Built on top of the relax computational environment for NMR dynamics is a new dispersion analysis designed to be comprehensive, accurate and easy-to-use. The software supports more models, both numeric and analytic, than current solutions. An automated protocol, available for scripting and driving the graphical user interface (GUI), is designed to simplify the analysis of dispersion data for NMR spectroscopists. Decreases in optimization time are granted by parallelization for running on computer clusters and by skipping an initial grid search by using parameters from one solution as the starting point for another —using analytic model results for the numeric models, taking advantage of model nesting, and using averaged non-clustered results for the clustered analysis.","lang":"eng"}],"volume":30,"extern":"1","_id":"8459","author":[{"full_name":"Morin, Sébastien","last_name":"Morin","first_name":"Sébastien"},{"full_name":"Linnet, Troels E","last_name":"Linnet","first_name":"Troels E"},{"full_name":"Lescanne, Mathilde","last_name":"Lescanne","first_name":"Mathilde"},{"orcid":"0000-0002-9350-7606","full_name":"Schanda, Paul","first_name":"Paul","last_name":"Schanda","id":"7B541462-FAF6-11E9-A490-E8DFE5697425"},{"last_name":"Thompson","first_name":"Gary S","full_name":"Thompson, Gary S"},{"full_name":"Tollinger, Martin","last_name":"Tollinger","first_name":"Martin"},{"first_name":"Kaare","last_name":"Teilum","full_name":"Teilum, Kaare"},{"full_name":"Gagné, Stéphane","first_name":"Stéphane","last_name":"Gagné"},{"first_name":"Dominique","last_name":"Marion","full_name":"Marion, Dominique"},{"last_name":"Griesinger","first_name":"Christian","full_name":"Griesinger, Christian"},{"first_name":"Martin","last_name":"Blackledge","full_name":"Blackledge, Martin"},{"full_name":"d’Auvergne, Edward J","first_name":"Edward J","last_name":"d’Auvergne"}],"issue":"15","publication_status":"published","article_processing_charge":"No","date_created":"2020-09-18T10:08:07Z","title":"Relax: The analysis of biomolecular kinetics and thermodynamics using NMR relaxation dispersion data","intvolume":"        30","page":"2219-2220","quality_controlled":"1","publisher":"Oxford University Press","article_type":"original"},{"publisher":"Oxford University Press","article_type":"original","page":"890 - 900","quality_controlled":"1","publication_status":"published","article_processing_charge":"No","date_created":"2018-12-11T11:48:52Z","title":"Presence of ATG triplets in 5′ untranslated regions of eukaryotic cDNAs correlates with a 'weak'context of the start codon","intvolume":"        17","_id":"855","pmid":1,"scopus_import":"1","author":[{"first_name":"Igor","last_name":"Rogozin","full_name":"Rogozin, Igor"},{"first_name":"Alex","last_name":"Kochetov","full_name":"Kochetov, Alex"},{"id":"44FDEF62-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0001-8243-4694","full_name":"Kondrashov, Fyodor","first_name":"Fyodor","last_name":"Kondrashov"},{"full_name":"Koonin, Eugene","first_name":"Eugene","last_name":"Koonin"},{"full_name":"Milanesi, Luciano","last_name":"Milanesi","first_name":"Luciano"}],"issue":"10","volume":17,"acknowledgement":"This work has been partially supported by EU 'TRADAT' project and by CNR Genetic Engineering (Italy), the RFBR grant for support of scientific schools (00-15-97968) and SD RAS grant for young scientists (AVK). The authors wish to thank J.Lyons-Weiler for helpful comments and A. Sorokin for help with the ATG_EVALUATOR program.","extern":"1","doi":"10.1093/bioinformatics/17.10.890","day":"01","abstract":[{"lang":"eng","text":"Motivation: The context of the start codon (typically, AUG) and the features of the 5′ Untranslated Regions (5′ UTRs) are important for understanding translation regulation in eukaryotic mRNAs and for accurate prediction of the coding region in genomic and cDNA sequences. The presence of AUG triplets in 5′ UTRs (upstream AUGs) might effect the initiation rate and, in the context of gene prediction, could reduce the accuracy of the identification of the authentic start. To reveal potential connections between the presence of upstream AUGs and other features of 5′ UTRs, such as their length and the start codon context, we undertook a systematic analysis of the available eukaryotic 5′ UTR sequences. Results: We show that a large fraction of 5′ UTRs in the available cDNA sequences, 15-53% depending on the organism, contain upstream ATGs. A negative correlation was observed between the information content of the translation start signal and the length of the 5′ UTR. Similarly, a negative correlation exists between the 'strength' of the start context and the number of upstream ATGs. Typically, cDNAs containing long 5′ UTRs with multiple upstream ATGs have a 'weak' start context, and in contrast, cDNAs containing short 5′ UTRs without ATGs have 'strong' starts. These counter-intuitive results may be interpreted in terms of upstream AUGs having an important role in the regulation of translation efficiency by ensuring low basal translation level via double negative control and creating the potential for additional regulatory mechanisms. One of such mechanisms, supported by experimental studies of some mRNAs, includes removal of the AUG-containing portion of the 5′ UTR by alternative splicing."}],"date_updated":"2023-06-02T09:08:25Z","year":"2001","citation":{"mla":"Rogozin, Igor, et al. “Presence of ATG Triplets in 5′ Untranslated Regions of Eukaryotic CDNAs Correlates with a ’weak’context of the Start Codon.” <i>Bioinformatics</i>, vol. 17, no. 10, Oxford University Press, 2001, pp. 890–900, doi:<a href=\"https://doi.org/10.1093/bioinformatics/17.10.890\">10.1093/bioinformatics/17.10.890</a>.","short":"I. Rogozin, A. Kochetov, F. Kondrashov, E. Koonin, L. Milanesi, Bioinformatics 17 (2001) 890–900.","ista":"Rogozin I, Kochetov A, Kondrashov F, Koonin E, Milanesi L. 2001. Presence of ATG triplets in 5′ untranslated regions of eukaryotic cDNAs correlates with a ’weak’context of the start codon. Bioinformatics. 17(10), 890–900.","ama":"Rogozin I, Kochetov A, Kondrashov F, Koonin E, Milanesi L. Presence of ATG triplets in 5′ untranslated regions of eukaryotic cDNAs correlates with a ’weak’context of the start codon. <i>Bioinformatics</i>. 2001;17(10):890-900. doi:<a href=\"https://doi.org/10.1093/bioinformatics/17.10.890\">10.1093/bioinformatics/17.10.890</a>","apa":"Rogozin, I., Kochetov, A., Kondrashov, F., Koonin, E., &#38; Milanesi, L. (2001). Presence of ATG triplets in 5′ untranslated regions of eukaryotic cDNAs correlates with a ’weak’context of the start codon. <i>Bioinformatics</i>. Oxford University Press. <a href=\"https://doi.org/10.1093/bioinformatics/17.10.890\">https://doi.org/10.1093/bioinformatics/17.10.890</a>","ieee":"I. Rogozin, A. Kochetov, F. Kondrashov, E. Koonin, and L. Milanesi, “Presence of ATG triplets in 5′ untranslated regions of eukaryotic cDNAs correlates with a ’weak’context of the start codon,” <i>Bioinformatics</i>, vol. 17, no. 10. Oxford University Press, pp. 890–900, 2001.","chicago":"Rogozin, Igor, Alex Kochetov, Fyodor Kondrashov, Eugene Koonin, and Luciano Milanesi. “Presence of ATG Triplets in 5′ Untranslated Regions of Eukaryotic CDNAs Correlates with a ’weak’context of the Start Codon.” <i>Bioinformatics</i>. Oxford University Press, 2001. <a href=\"https://doi.org/10.1093/bioinformatics/17.10.890\">https://doi.org/10.1093/bioinformatics/17.10.890</a>."},"external_id":{"pmid":["11673233"]},"language":[{"iso":"eng"}],"oa_version":"None","month":"10","publication":"Bioinformatics","status":"public","user_id":"ea97e931-d5af-11eb-85d4-e6957dddbf17","publication_identifier":{"issn":["1367-4803"]},"publist_id":"6795","date_published":"2001-10-01T00:00:00Z","type":"journal_article"}]