Efficient gene orthology inference via large-scale rearrangements
Background Recently we developed a gene orthology inference tool based on genome rearrangements ( Journal of Bioinformatics and Computational Biology 19:6, 2021). Given a set of genomes our method first computes all pairwise gene similarities. Then it runs pairwise ILP comparisons to compute optimal...
Uložené v:
| Vydané v: | Algorithms for molecular biology Ročník 18; číslo 1; s. 1 - 18 |
|---|---|
| Hlavní autori: | , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
London
BioMed Central
28.09.2023
BioMed Central Ltd Springer Nature B.V BMC |
| Predmet: | |
| ISSN: | 1748-7188, 1748-7188 |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | Background
Recently we developed a gene orthology inference tool based on genome rearrangements (
Journal of Bioinformatics and Computational Biology
19:6, 2021). Given a set of genomes our method first computes all pairwise gene similarities. Then it runs pairwise ILP comparisons to compute optimal gene matchings, which minimize, by taking the similarities into account, the weighted rearrangement distance between the analyzed genomes (a problem that is NP-hard). The gene matchings are then integrated into gene families in the final step. The mentioned ILP includes an optimal
capping
that connects each end of a linear segment of one genome to an end of a linear segment in the other genome, producing an exponential increase of the search space.
Results
In this work, we design and implement a heuristic capping algorithm that replaces the optimal capping by clustering (based on their gene content intersections) the linear segments into
m
≥
1
subsets, whose ends are capped independently. Furthermore, in each subset, instead of allowing all possible connections, we let only the ends of content-related segments be connected. Although there is no guarantee that
m
is much bigger than one, and with the possible side effect of resulting in sub-optimal instead of optimal gene matchings, the heuristic works very well in practice, from both the speed performance and the quality of computed solutions. Our experiments on primate and fruit fly genomes show two positive results. First, for complete assemblies of five primates the version with heuristic capping reports orthologies that are very similar to the orthologies computed by the version of our tool with optimal capping. Second, we were able to efficiently analyze fruit fly genomes with incomplete assemblies distributed in hundreds or even thousands of contigs, obtaining gene families that are very similar to
F
L
Y
B
A
S
E
families. Indeed, our tool inferred a higher number of complete cliques, with a higher intersection with
F
L
Y
B
A
S
E
, when compared to gene families computed by other inference tools. We added a post-processing for refining, with the aid of the
M
C
L
algorithm, our
ambiguous
families (those with more than one gene per genome), improving even more the accuracy of our results. Our approach is implemented into a pipeline incorporating the pre-computation of gene similarities and the post-processing refinement of ambiguous families with
M
C
L
. Both the original version with optimal capping and the new modified version with heuristic capping can be downloaded, together with their detailed documentations, at
https://gitlab.ub.uni-bielefeld.de/gi/FFGC
or as a Conda package at
https://anaconda.org/bioconda/ffgc
. |
|---|---|
| AbstractList | BackgroundRecently we developed a gene orthology inference tool based on genome rearrangements (Journal of Bioinformatics and Computational Biology 19:6, 2021). Given a set of genomes our method first computes all pairwise gene similarities. Then it runs pairwise ILP comparisons to compute optimal gene matchings, which minimize, by taking the similarities into account, the weighted rearrangement distance between the analyzed genomes (a problem that is NP-hard). The gene matchings are then integrated into gene families in the final step. The mentioned ILP includes an optimal capping that connects each end of a linear segment of one genome to an end of a linear segment in the other genome, producing an exponential increase of the search space.ResultsIn this work, we design and implement a heuristic capping algorithm that replaces the optimal capping by clustering (based on their gene content intersections) the linear segments into \(m\ge 1\) subsets, whose ends are capped independently. Furthermore, in each subset, instead of allowing all possible connections, we let only the ends of content-related segments be connected. Although there is no guarantee that m is much bigger than one, and with the possible side effect of resulting in sub-optimal instead of optimal gene matchings, the heuristic works very well in practice, from both the speed performance and the quality of computed solutions. Our experiments on primate and fruit fly genomes show two positive results. First, for complete assemblies of five primates the version with heuristic capping reports orthologies that are very similar to the orthologies computed by the version of our tool with optimal capping. Second, we were able to efficiently analyze fruit fly genomes with incomplete assemblies distributed in hundreds or even thousands of contigs, obtaining gene families that are very similar to \({\text{F}} {\textsc{ly}} {\text{B}} {\textsc{ase}}\) families. Indeed, our tool inferred a higher number of complete cliques, with a higher intersection with \({\text{F}} {\textsc{ly}} {\text{B}} {\textsc{ase}}\), when compared to gene families computed by other inference tools. We added a post-processing for refining, with the aid of the \({\textsc{mcl}}\) algorithm, our ambiguous families (those with more than one gene per genome), improving even more the accuracy of our results. Our approach is implemented into a pipeline incorporating the pre-computation of gene similarities and the post-processing refinement of ambiguous families with \(\textsc {mcl}\). Both the original version with optimal capping and the new modified version with heuristic capping can be downloaded, together with their detailed documentations, at https://gitlab.ub.uni-bielefeld.de/gi/FFGC or as a Conda package at https://anaconda.org/bioconda/ffgc. Recently we developed a gene orthology inference tool based on genome rearrangements (Journal of Bioinformatics and Computational Biology 19:6, 2021). Given a set of genomes our method first computes all pairwise gene similarities. Then it runs pairwise ILP comparisons to compute optimal gene matchings, which minimize, by taking the similarities into account, the weighted rearrangement distance between the analyzed genomes (a problem that is NP-hard). The gene matchings are then integrated into gene families in the final step. The mentioned ILP includes an optimal capping that connects each end of a linear segment of one genome to an end of a linear segment in the other genome, producing an exponential increase of the search space. In this work, we design and implement a heuristic capping algorithm that replaces the optimal capping by clustering (based on their gene content intersections) the linear segments into [formula omitted] subsets, whose ends are capped independently. Furthermore, in each subset, instead of allowing all possible connections, we let only the ends of content-related segments be connected. Although there is no guarantee that m is much bigger than one, and with the possible side effect of resulting in sub-optimal instead of optimal gene matchings, the heuristic works very well in practice, from both the speed performance and the quality of computed solutions. Our experiments on primate and fruit fly genomes show two positive results. First, for complete assemblies of five primates the version with heuristic capping reports orthologies that are very similar to the orthologies computed by the version of our tool with optimal capping. Second, we were able to efficiently analyze fruit fly genomes with incomplete assemblies distributed in hundreds or even thousands of contigs, obtaining gene families that are very similar to [formula omitted] families. Indeed, our tool inferred a higher number of complete cliques, with a higher intersection with [formula omitted], when compared to gene families computed by other inference tools. We added a post-processing for refining, with the aid of the [formula omitted] algorithm, our ambiguous families (those with more than one gene per genome), improving even more the accuracy of our results. Our approach is implemented into a pipeline incorporating the pre-computation of gene similarities and the post-processing refinement of ambiguous families with [formula omitted]. Both the original version with optimal capping and the new modified version with heuristic capping can be downloaded, together with their detailed documentations, at https://gitlab.ub.uni-bielefeld.de/gi/FFGC or as a Conda package at https://anaconda.org/bioconda/ffgc. Background Recently we developed a gene orthology inference tool based on genome rearrangements (Journal of Bioinformatics and Computational Biology 19:6, 2021). Given a set of genomes our method first computes all pairwise gene similarities. Then it runs pairwise ILP comparisons to compute optimal gene matchings, which minimize, by taking the similarities into account, the weighted rearrangement distance between the analyzed genomes (a problem that is NP-hard). The gene matchings are then integrated into gene families in the final step. The mentioned ILP includes an optimal capping that connects each end of a linear segment of one genome to an end of a linear segment in the other genome, producing an exponential increase of the search space. Results In this work, we design and implement a heuristic capping algorithm that replaces the optimal capping by clustering (based on their gene content intersections) the linear segments into [formula omitted] subsets, whose ends are capped independently. Furthermore, in each subset, instead of allowing all possible connections, we let only the ends of content-related segments be connected. Although there is no guarantee that m is much bigger than one, and with the possible side effect of resulting in sub-optimal instead of optimal gene matchings, the heuristic works very well in practice, from both the speed performance and the quality of computed solutions. Our experiments on primate and fruit fly genomes show two positive results. First, for complete assemblies of five primates the version with heuristic capping reports orthologies that are very similar to the orthologies computed by the version of our tool with optimal capping. Second, we were able to efficiently analyze fruit fly genomes with incomplete assemblies distributed in hundreds or even thousands of contigs, obtaining gene families that are very similar to [formula omitted] families. Indeed, our tool inferred a higher number of complete cliques, with a higher intersection with [formula omitted], when compared to gene families computed by other inference tools. We added a post-processing for refining, with the aid of the [formula omitted] algorithm, our ambiguous families (those with more than one gene per genome), improving even more the accuracy of our results. Our approach is implemented into a pipeline incorporating the pre-computation of gene similarities and the post-processing refinement of ambiguous families with [formula omitted]. Both the original version with optimal capping and the new modified version with heuristic capping can be downloaded, together with their detailed documentations, at Keywords: Comparative genomics, Double-cut-and-join, Indels, Gene orthology Background Recently we developed a gene orthology inference tool based on genome rearrangements ( Journal of Bioinformatics and Computational Biology 19:6, 2021). Given a set of genomes our method first computes all pairwise gene similarities. Then it runs pairwise ILP comparisons to compute optimal gene matchings, which minimize, by taking the similarities into account, the weighted rearrangement distance between the analyzed genomes (a problem that is NP-hard). The gene matchings are then integrated into gene families in the final step. The mentioned ILP includes an optimal capping that connects each end of a linear segment of one genome to an end of a linear segment in the other genome, producing an exponential increase of the search space. Results In this work, we design and implement a heuristic capping algorithm that replaces the optimal capping by clustering (based on their gene content intersections) the linear segments into m ≥ 1 subsets, whose ends are capped independently. Furthermore, in each subset, instead of allowing all possible connections, we let only the ends of content-related segments be connected. Although there is no guarantee that m is much bigger than one, and with the possible side effect of resulting in sub-optimal instead of optimal gene matchings, the heuristic works very well in practice, from both the speed performance and the quality of computed solutions. Our experiments on primate and fruit fly genomes show two positive results. First, for complete assemblies of five primates the version with heuristic capping reports orthologies that are very similar to the orthologies computed by the version of our tool with optimal capping. Second, we were able to efficiently analyze fruit fly genomes with incomplete assemblies distributed in hundreds or even thousands of contigs, obtaining gene families that are very similar to F L Y B A S E families. Indeed, our tool inferred a higher number of complete cliques, with a higher intersection with F L Y B A S E , when compared to gene families computed by other inference tools. We added a post-processing for refining, with the aid of the M C L algorithm, our ambiguous families (those with more than one gene per genome), improving even more the accuracy of our results. Our approach is implemented into a pipeline incorporating the pre-computation of gene similarities and the post-processing refinement of ambiguous families with M C L . Both the original version with optimal capping and the new modified version with heuristic capping can be downloaded, together with their detailed documentations, at https://gitlab.ub.uni-bielefeld.de/gi/FFGC or as a Conda package at https://anaconda.org/bioconda/ffgc . Abstract Background Recently we developed a gene orthology inference tool based on genome rearrangements (Journal of Bioinformatics and Computational Biology 19:6, 2021). Given a set of genomes our method first computes all pairwise gene similarities. Then it runs pairwise ILP comparisons to compute optimal gene matchings, which minimize, by taking the similarities into account, the weighted rearrangement distance between the analyzed genomes (a problem that is NP-hard). The gene matchings are then integrated into gene families in the final step. The mentioned ILP includes an optimal capping that connects each end of a linear segment of one genome to an end of a linear segment in the other genome, producing an exponential increase of the search space. Results In this work, we design and implement a heuristic capping algorithm that replaces the optimal capping by clustering (based on their gene content intersections) the linear segments into $$m\ge 1$$ m ≥ 1 subsets, whose ends are capped independently. Furthermore, in each subset, instead of allowing all possible connections, we let only the ends of content-related segments be connected. Although there is no guarantee that m is much bigger than one, and with the possible side effect of resulting in sub-optimal instead of optimal gene matchings, the heuristic works very well in practice, from both the speed performance and the quality of computed solutions. Our experiments on primate and fruit fly genomes show two positive results. First, for complete assemblies of five primates the version with heuristic capping reports orthologies that are very similar to the orthologies computed by the version of our tool with optimal capping. Second, we were able to efficiently analyze fruit fly genomes with incomplete assemblies distributed in hundreds or even thousands of contigs, obtaining gene families that are very similar to $${\text{F}} {\textsc{ly}} {\text{B}} {\textsc{ase}}$$ F L Y B A S E families. Indeed, our tool inferred a higher number of complete cliques, with a higher intersection with $${\text{F}} {\textsc{ly}} {\text{B}} {\textsc{ase}}$$ F L Y B A S E , when compared to gene families computed by other inference tools. We added a post-processing for refining, with the aid of the $${\textsc{mcl}}$$ M C L algorithm, our ambiguous families (those with more than one gene per genome), improving even more the accuracy of our results. Our approach is implemented into a pipeline incorporating the pre-computation of gene similarities and the post-processing refinement of ambiguous families with $$\textsc {mcl}$$ M C L . Both the original version with optimal capping and the new modified version with heuristic capping can be downloaded, together with their detailed documentations, at https://gitlab.ub.uni-bielefeld.de/gi/FFGC or as a Conda package at https://anaconda.org/bioconda/ffgc . Recently we developed a gene orthology inference tool based on genome rearrangements (Journal of Bioinformatics and Computational Biology 19:6, 2021). Given a set of genomes our method first computes all pairwise gene similarities. Then it runs pairwise ILP comparisons to compute optimal gene matchings, which minimize, by taking the similarities into account, the weighted rearrangement distance between the analyzed genomes (a problem that is NP-hard). The gene matchings are then integrated into gene families in the final step. The mentioned ILP includes an optimal capping that connects each end of a linear segment of one genome to an end of a linear segment in the other genome, producing an exponential increase of the search space.BACKGROUNDRecently we developed a gene orthology inference tool based on genome rearrangements (Journal of Bioinformatics and Computational Biology 19:6, 2021). Given a set of genomes our method first computes all pairwise gene similarities. Then it runs pairwise ILP comparisons to compute optimal gene matchings, which minimize, by taking the similarities into account, the weighted rearrangement distance between the analyzed genomes (a problem that is NP-hard). The gene matchings are then integrated into gene families in the final step. The mentioned ILP includes an optimal capping that connects each end of a linear segment of one genome to an end of a linear segment in the other genome, producing an exponential increase of the search space.In this work, we design and implement a heuristic capping algorithm that replaces the optimal capping by clustering (based on their gene content intersections) the linear segments into [Formula: see text] subsets, whose ends are capped independently. Furthermore, in each subset, instead of allowing all possible connections, we let only the ends of content-related segments be connected. Although there is no guarantee that m is much bigger than one, and with the possible side effect of resulting in sub-optimal instead of optimal gene matchings, the heuristic works very well in practice, from both the speed performance and the quality of computed solutions. Our experiments on primate and fruit fly genomes show two positive results. First, for complete assemblies of five primates the version with heuristic capping reports orthologies that are very similar to the orthologies computed by the version of our tool with optimal capping. Second, we were able to efficiently analyze fruit fly genomes with incomplete assemblies distributed in hundreds or even thousands of contigs, obtaining gene families that are very similar to [Formula: see text] families. Indeed, our tool inferred a higher number of complete cliques, with a higher intersection with [Formula: see text], when compared to gene families computed by other inference tools. We added a post-processing for refining, with the aid of the [Formula: see text] algorithm, our ambiguous families (those with more than one gene per genome), improving even more the accuracy of our results. Our approach is implemented into a pipeline incorporating the pre-computation of gene similarities and the post-processing refinement of ambiguous families with [Formula: see text]. Both the original version with optimal capping and the new modified version with heuristic capping can be downloaded, together with their detailed documentations, at https://gitlab.ub.uni-bielefeld.de/gi/FFGC or as a Conda package at https://anaconda.org/bioconda/ffgc .RESULTSIn this work, we design and implement a heuristic capping algorithm that replaces the optimal capping by clustering (based on their gene content intersections) the linear segments into [Formula: see text] subsets, whose ends are capped independently. Furthermore, in each subset, instead of allowing all possible connections, we let only the ends of content-related segments be connected. Although there is no guarantee that m is much bigger than one, and with the possible side effect of resulting in sub-optimal instead of optimal gene matchings, the heuristic works very well in practice, from both the speed performance and the quality of computed solutions. Our experiments on primate and fruit fly genomes show two positive results. First, for complete assemblies of five primates the version with heuristic capping reports orthologies that are very similar to the orthologies computed by the version of our tool with optimal capping. Second, we were able to efficiently analyze fruit fly genomes with incomplete assemblies distributed in hundreds or even thousands of contigs, obtaining gene families that are very similar to [Formula: see text] families. Indeed, our tool inferred a higher number of complete cliques, with a higher intersection with [Formula: see text], when compared to gene families computed by other inference tools. We added a post-processing for refining, with the aid of the [Formula: see text] algorithm, our ambiguous families (those with more than one gene per genome), improving even more the accuracy of our results. Our approach is implemented into a pipeline incorporating the pre-computation of gene similarities and the post-processing refinement of ambiguous families with [Formula: see text]. Both the original version with optimal capping and the new modified version with heuristic capping can be downloaded, together with their detailed documentations, at https://gitlab.ub.uni-bielefeld.de/gi/FFGC or as a Conda package at https://anaconda.org/bioconda/ffgc . |
| ArticleNumber | 14 |
| Audience | Academic |
| Author | Rubert, Diego P. Braga, Marília D. V. |
| Author_xml | – sequence: 1 givenname: Diego P. surname: Rubert fullname: Rubert, Diego P. organization: Faculdade de Computação, Universidade Federal de Mato Grosso do Sul, Faculty of Technology and Center for Biotechnology (CeBiTec), Bielefeld University – sequence: 2 givenname: Marília D. V. surname: Braga fullname: Braga, Marília D. V. email: mbraga@cebitec.uni-bielefeld.de organization: Faculty of Technology and Center for Biotechnology (CeBiTec), Bielefeld University |
| BookMark | eNp9Uktr3DAYNCWlebR_oCdDL7041cuSfCpLSJtAoJf2LGT5k6PFtlLJG9h_n89xQrqhBIEkpJkRM5rT4miKExTFZ0rOKdXyW6ac0LoijFcEJ13t3xUnVAldKar10T_74-I05y0houaEfyiOuVKKNKI-KTaX3gcXYJrLHiYoY5pv4xD7fRkmDwkmB-V9sOVgUw9VdnaAMoFNyU49jEjLH4v33g4ZPj2tZ8WfH5e_L66qm18_ry82N5WrdT1XXipqleiIoJ0kDbCmo5S2QjTUU-W9F6yV1nreiroB2QGT1jWsY0LyTuianxXXq24X7dbcpTDatDfRBvN4EFNvbJqDG8AIKRqiWMtrSoSjSlNiGUdd0nnt1KL1fdW627UjdA59JDsciB7eTOHW9PHeUFILIiRFha9PCin-3UGezRiyg2GwE8RdNkxjwA2TSiL0yyvoNu7ShFktKPwiJWnzguoxYoPhR3zYLaJmo6QipJGMIOr8PygcHYzBYT18wPMDgl4JLsWcE3jjwmznEBdfYUBDZumSWbtksEbmsUtmj1T2ivqcz5skvpIygrEi6cXsG6wH9sDaMQ |
| CitedBy_id | crossref_primary_10_1007_s11227_025_07408_2 crossref_primary_10_1186_s13015_025_00279_5 |
| Cites_doi | 10.1016/S0022-2836(05)80360-2 10.21203/rs.3.rs-2396629/v1 10.1007/978-1-4471-5298-9_13 10.1112/jlms/s1-10.37.26 10.1142/S021972002140014X 10.1007/11554714_6 10.1101/gr.243212.118 10.1089/cmb.2014.0096 10.1137/040608635 10.1007/11851561_16 10.1089/cmb.2020.0434 10.1371/journal.pone.0105015 10.1089/cmb.2011.0118 10.1016/j.tcs.2011.12.071 10.1007/978-94-011-4309-7_19 10.37236/834 10.1186/1471-2105-9-S5-S4 10.1109/SFCS.1995.492588 10.1093/bioinformatics/15.11.909 10.1093/bioinformatics/bti535 10.1038/nmeth.3176 10.1007/978-1-4939-7463-4_12 |
| ContentType | Journal Article |
| Copyright | The Author(s) 2023 COPYRIGHT 2023 BioMed Central Ltd. 2023. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2023. BioMed Central Ltd., part of Springer Nature. BioMed Central Ltd., part of Springer Nature 2023 |
| Copyright_xml | – notice: The Author(s) 2023 – notice: COPYRIGHT 2023 BioMed Central Ltd. – notice: 2023. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. – notice: 2023. BioMed Central Ltd., part of Springer Nature. – notice: BioMed Central Ltd., part of Springer Nature 2023 |
| DBID | C6C AAYXX CITATION 3V. 7QO 7QP 7QR 7TK 7X7 7XB 8FD 8FE 8FG 8FH 8FI 8FJ 8FK ABJCF ABUWG AEUYN AFKRA ARAPS AZQEC BBNVY BENPR BGLVJ BHPHI CCPQU DWQXO FR3 FYUFA GHDGH GNUQQ HCIFZ K9. L6V LK8 M0S M7P M7S P5Z P62 P64 PHGZM PHGZT PIMPY PJZUB PKEHL PPXIY PQEST PQGLB PQQKQ PQUKI PRINS PTHSS 7X8 5PM DOA |
| DOI | 10.1186/s13015-023-00238-y |
| DatabaseName | Springer Nature OA Free Journals CrossRef ProQuest Central (Corporate) Biotechnology Research Abstracts Calcium & Calcified Tissue Abstracts Chemoreception Abstracts Neurosciences Abstracts Health & Medical Collection ProQuest Central (purchase pre-March 2016) Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection ProQuest Natural Science Collection ProQuest Hospital Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) Materials Science & Engineering Collection ProQuest Central (Alumni) ProQuest One Sustainability ProQuest Central UK/Ireland Advanced Technologies & Computer Science Collection ProQuest Central Essentials Biological Science Collection ProQuest Central ProQuest Technology Collection Natural Science Collection ProQuest One ProQuest Central Korea Engineering Research Database Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Central Student SciTech Premium Collection ProQuest Health & Medical Complete (Alumni) ProQuest Engineering Collection ProQuest Biological Science Collection ProQuest Health & Medical Collection Biological Science Database Engineering Database Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection Biotechnology and BioEngineering Abstracts ProQuest Central Premium ProQuest One Academic (New) Publicly Available Content Database ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) ProQuest One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China Engineering Collection MEDLINE - Academic PubMed Central (Full Participant titles) DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef Publicly Available Content Database ProQuest Central Student ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials SciTech Premium Collection ProQuest Central China ProQuest One Applied & Life Sciences ProQuest One Sustainability Health Research Premium Collection Natural Science Collection Biological Science Collection Chemoreception Abstracts ProQuest Central (New) Engineering Collection Advanced Technologies & Aerospace Collection Engineering Database ProQuest Biological Science Collection ProQuest One Academic Eastern Edition ProQuest Hospital Collection ProQuest Technology Collection Health Research Premium Collection (Alumni) Biological Science Database Neurosciences Abstracts ProQuest Hospital Collection (Alumni) Biotechnology and BioEngineering Abstracts ProQuest Health & Medical Complete ProQuest One Academic UKI Edition Engineering Research Database ProQuest One Academic Calcium & Calcified Tissue Abstracts ProQuest One Academic (New) Technology Collection Technology Research Database ProQuest One Academic Middle East (New) ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) ProQuest One Community College ProQuest One Health & Nursing ProQuest Natural Science Collection ProQuest Central ProQuest Health & Medical Research Collection ProQuest Engineering Collection Biotechnology Research Abstracts Health and Medicine Complete (Alumni Edition) ProQuest Central Korea ProQuest SciTech Collection Advanced Technologies & Aerospace Database Materials Science & Engineering Collection ProQuest Central (Alumni) MEDLINE - Academic |
| DatabaseTitleList | Publicly Available Content Database MEDLINE - Academic |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: PIMPY name: ProQuest Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Biology |
| EISSN | 1748-7188 |
| EndPage | 18 |
| ExternalDocumentID | oai_doaj_org_article_4649072b35104c17810a234590df8c75 PMC10540461 A767009620 10_1186_s13015_023_00238_y |
| GrantInformation_xml | – fundername: Universität Bielefeld (3146) – fundername: ; |
| GroupedDBID | 0R~ 23M 2WC 53G 5GY 5VS 6J9 7X7 8FE 8FG 8FH 8FI 8FJ AAFWJ AAJSJ AASML ABDBF ABJCF ABUWG ACGFO ACGFS ACIHN ACIWK ACPRK ACUHS ADBBV ADMLS ADRAZ ADUKV AEAQA AENEX AEUYN AFKRA AFPKN AFRAH AHBYD AHMBA AHYZX ALMA_UNASSIGNED_HOLDINGS AMKLP AMTXH AOIJS ARAPS BAPOH BAWUL BBNVY BCNDV BENPR BFQNJ BGLVJ BHPHI BMC BPHCQ BVXVI C6C CCPQU CS3 DIK E3Z EBD EBLON EBS ESX F5P FYUFA GROUPED_DOAJ GX1 HCIFZ HMCUK HYE IAO IGS IHR ITC KQ8 L6V LK8 M48 M7P M7S M~E O5R O5S OK1 OVT P2P P62 PGMZT PHGZM PHGZT PIMPY PQGLB PQQKQ PROAC PTHSS PUEGO RBZ RNS ROL RPM RSV SBL SOJ TR2 TUS UKHRP WOQ WOW ~8M AAYXX AFFHD CITATION 3V. 7QO 7QP 7QR 7TK 7XB 8FD 8FK AZQEC DWQXO FR3 GNUQQ K9. P64 PJZUB PKEHL PPXIY PQEST PQUKI PRINS 7X8 5PM |
| ID | FETCH-LOGICAL-c585t-f671a74d041d609e29d111b4491f17fff42b6aaf3b459e6de26ac92d2463d4853 |
| IEDL.DBID | RSV |
| ISICitedReferencesCount | 3 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001074776500001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1748-7188 |
| IngestDate | Fri Oct 03 12:42:51 EDT 2025 Tue Nov 04 02:06:22 EST 2025 Sun Nov 09 09:44:55 EST 2025 Tue Oct 07 05:48:40 EDT 2025 Tue Nov 11 11:16:03 EST 2025 Tue Nov 04 18:38:23 EST 2025 Tue Nov 18 22:29:25 EST 2025 Sat Nov 29 05:10:44 EST 2025 Sat Sep 06 07:29:42 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Keywords | Comparative genomics Double-cut-and-join Indels Gene orthology |
| Language | English |
| License | Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c585t-f671a74d041d609e29d111b4491f17fff42b6aaf3b459e6de26ac92d2463d4853 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| OpenAccessLink | https://link.springer.com/10.1186/s13015-023-00238-y |
| PMID | 37770945 |
| PQID | 2877487619 |
| PQPubID | 55040 |
| PageCount | 18 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_4649072b35104c17810a234590df8c75 pubmedcentral_primary_oai_pubmedcentral_nih_gov_10540461 proquest_miscellaneous_2870992676 proquest_journals_2877487619 gale_infotracmisc_A767009620 gale_infotracacademiconefile_A767009620 crossref_citationtrail_10_1186_s13015_023_00238_y crossref_primary_10_1186_s13015_023_00238_y springer_journals_10_1186_s13015_023_00238_y |
| PublicationCentury | 2000 |
| PublicationDate | 2023-09-28 |
| PublicationDateYYYYMMDD | 2023-09-28 |
| PublicationDate_xml | – month: 09 year: 2023 text: 2023-09-28 day: 28 |
| PublicationDecade | 2020 |
| PublicationPlace | London |
| PublicationPlace_xml | – name: London |
| PublicationTitle | Algorithms for molecular biology |
| PublicationTitleAbbrev | Algorithms Mol Biol |
| PublicationYear | 2023 |
| Publisher | BioMed Central BioMed Central Ltd Springer Nature B.V BMC |
| Publisher_xml | – name: BioMed Central – name: BioMed Central Ltd – name: Springer Nature B.V – name: BMC |
| References | S van Dongen (238_CR19) 2008; 30 D Sankoff (238_CR4) 1999; 15 P Hall (238_CR20) 1935; s1–10 SF Altschul (238_CR24) 1990; 215 D Doerr (238_CR22) 2018 DP Rubert (238_CR12) 2021; 16 FV Martinez (238_CR11) 2015; 13 D Bryant (238_CR5) 2000 ACJ Roth (238_CR15) 2008; 9 C Pesquita (238_CR26) 2008; 9 S Angibaud (238_CR6) 2009; 13 238_CR27 T Tassa (238_CR21) 2012; 423 AM Altenhoff (238_CR25) 2019; 29 S Yancopoulos (238_CR9) 2005; 21 B Buchfink (238_CR23) 2015; 12 MDV Braga (238_CR10) 2013 DP Rubert (238_CR13) 2021; 19 M Lechner (238_CR17) 2014; 9 M Shao (238_CR7) 2015; 22 MDV Braga (238_CR3) 2011; 18 M Lechner (238_CR16) 2011; 12 L Bohnenkämper (238_CR8) 2021; 28 238_CR14 238_CR1 238_CR2 238_CR18 |
| References_xml | – volume: 215 start-page: 403 issue: 3 year: 1990 ident: 238_CR24 publication-title: J Mol Biol doi: 10.1016/S0022-2836(05)80360-2 – ident: 238_CR18 doi: 10.21203/rs.3.rs-2396629/v1 – volume: 16 start-page: 1 issue: 4 year: 2021 ident: 238_CR12 publication-title: Algorithms Mol Biol – start-page: 287 volume-title: Models and Algorithms for Genome Evolution year: 2013 ident: 238_CR10 doi: 10.1007/978-1-4471-5298-9_13 – volume: s1–10 start-page: 26 issue: 1 year: 1935 ident: 238_CR20 publication-title: J London Mat Soc doi: 10.1112/jlms/s1-10.37.26 – volume: 19 start-page: 2140014 issue: 6 year: 2021 ident: 238_CR13 publication-title: J Bioinform Comput Biol doi: 10.1142/S021972002140014X – ident: 238_CR14 doi: 10.1007/11554714_6 – volume: 13 start-page: 19 issue: 1 year: 2009 ident: 238_CR6 publication-title: J Graph Algo App – volume: 29 start-page: 1152 issue: 7 year: 2019 ident: 238_CR25 publication-title: Genome Res doi: 10.1101/gr.243212.118 – volume: 12 start-page: 1 issue: 124 year: 2011 ident: 238_CR16 publication-title: BMC Bioinform – volume: 22 start-page: 425 issue: 5 year: 2015 ident: 238_CR7 publication-title: J Comput Biol doi: 10.1089/cmb.2014.0096 – volume: 30 start-page: 121 issue: 1 year: 2008 ident: 238_CR19 publication-title: SIAM Journal on Matrix Analysis and Applications doi: 10.1137/040608635 – ident: 238_CR1 doi: 10.1007/11851561_16 – volume: 28 start-page: 410 issue: 4 year: 2021 ident: 238_CR8 publication-title: J Comput Biol doi: 10.1089/cmb.2020.0434 – volume: 9 issue: 8 year: 2014 ident: 238_CR17 publication-title: PLoS ONE doi: 10.1371/journal.pone.0105015 – volume: 18 start-page: 1167 issue: 9 year: 2011 ident: 238_CR3 publication-title: J Comput Biol doi: 10.1089/cmb.2011.0118 – volume: 13 start-page: 777 issue: 10 year: 2015 ident: 238_CR11 publication-title: Algorithms Mol Biol – volume: 423 start-page: 50 year: 2012 ident: 238_CR21 publication-title: Theoret Comput Sci doi: 10.1016/j.tcs.2011.12.071 – start-page: 207 volume-title: Comparative Genomics. Computational Biology Series year: 2000 ident: 238_CR5 doi: 10.1007/978-94-011-4309-7_19 – ident: 238_CR27 doi: 10.37236/834 – volume: 9 start-page: 4 issue: Suppl 5 year: 2008 ident: 238_CR26 publication-title: BMC Bioinformat doi: 10.1186/1471-2105-9-S5-S4 – ident: 238_CR2 doi: 10.1109/SFCS.1995.492588 – volume: 15 start-page: 909 issue: 11 year: 1999 ident: 238_CR4 publication-title: Bioinformatics doi: 10.1093/bioinformatics/15.11.909 – volume: 21 start-page: 3340 issue: 16 year: 2005 ident: 238_CR9 publication-title: Bioinformatics doi: 10.1093/bioinformatics/bti535 – volume: 12 start-page: 59 year: 2015 ident: 238_CR23 publication-title: Nat Methods doi: 10.1038/nmeth.3176 – start-page: 331 volume-title: Comparative Genomics: Methods and Protocols. Methods in Molecular Biology year: 2018 ident: 238_CR22 doi: 10.1007/978-1-4939-7463-4_12 – volume: 9 start-page: 1 issue: 518 year: 2008 ident: 238_CR15 publication-title: BMC Bioinform |
| SSID | ssj0045303 |
| Score | 2.3218377 |
| Snippet | Background
Recently we developed a gene orthology inference tool based on genome rearrangements (
Journal of Bioinformatics and Computational Biology
19:6,... Background Recently we developed a gene orthology inference tool based on genome rearrangements (Journal of Bioinformatics and Computational Biology 19:6,... Recently we developed a gene orthology inference tool based on genome rearrangements (Journal of Bioinformatics and Computational Biology 19:6, 2021). Given a... BackgroundRecently we developed a gene orthology inference tool based on genome rearrangements (Journal of Bioinformatics and Computational Biology 19:6,... Abstract Background Recently we developed a gene orthology inference tool based on genome rearrangements (Journal of Bioinformatics and Computational Biology... |
| SourceID | doaj pubmedcentral proquest gale crossref springer |
| SourceType | Open Website Open Access Repository Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 1 |
| SubjectTerms | Algorithms Analysis Animal genetics Assemblies Bioinformatics Biomedical and Life Sciences Capping Cellular and Medical Topics Chromosomes Clustering Comparative genomics Computation Computational Biology/Bioinformatics Double-cut-and-join Fruit flies Fruits Gene families Gene orthology Genes Genetic research Genomes Genomics Heuristic Indels Inference Life Sciences Orthology Physiological Problem solving Segments Selected papers from WABI 2022 Similarity |
| SummonAdditionalLinks | – databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3Na9VAEB-kKHgRrYqpVVIQPOjS7Ed3N8entPRUPCj0tmS_8EFJ5b3XwvvvndkkT2NRL71mN5vN7HyyM78BeBdVQLfARPTcGskUGmzmo02sSSpG7jXhr5RmE-biwl5etl9-a_VFOWEDPPBAuGOlFcZvwktkHlzXWN50QqqTtonZBlPQSxvTTsHUoIPVCWrmqUTG6uM1ampOlciSFSPFtjMzVND67-rku3mSf1yWFht09hSejM5jvRg2_QwepH4fHg3tJLfPYXFa8CBwmRrZItV0JVOG6uVU1lffLrv6irK_2RpPJ9UrytWlAoNS6vYCvp2dfv18zsYWCSygn79hWRveGRUbxaNu2iTaiMrLK9XyzE3OWQmvuy5LjxRLOiahu9CKKJSWUaGpfgl7_XWfXkEtZfTCpqy875TNyaP68TrITNihXtkK-EQxF0b8cGpjceVKHGG1G6jskMCuUNltK_iwe-fHgJ7xz9mf6CB2Mwn5ujxAfnAjP7j_8UMF7-kYHcknbi90Y5kB_iQhXbmFocKkVoumgsPZTJSrMB-eGMGNcr12GF8aDPEw6qzgaDdMb1KuWp-ub8ocdLuFNroCO2Og2Z_NR_rl94LtzcmFVppX8HHitV9f_zvpDu6DdK_hsSgi0jJhD2Fvs7pJb-BhuN0s16u3RcB-AqXcJHE priority: 102 providerName: Directory of Open Access Journals – databaseName: Health & Medical Collection dbid: 7X7 link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lb9QwEB5BAYkLb0SgoCAhcQCrseO1nRNaUCtOFQeQ9mbFL1ipypbNttL-e2a8yVahoheua0drZ2a-Gccz3wC8C9JjWKADRm5VzSQ6bOaCiayKMgTuFPGv5GYT-vTULBbNt-GDWz-kVY6YmIE6rDx9Iz_CyF5jcI3x_qfz34y6RtHt6tBC4zbcobbZpOd6sT9wyRni81goY9RRj3jNqR65ZtlVse3EGWXO_uvIfD1b8q8r0-yJTh7-7x4ewYMhBi3nO6V5DLdi9wTu7bpSbp_C_DjTSuA6StSuWNLNTh4ql2N1YHm5bMszSiJnPQo5lmtK-aU6hVwx9wx-nBx___KVDZ0WmMfjwoYlpXmrZagkD6pqomgCYqCTsuGJ65SSFE61baqdnDVRhShU6xsRhFR1kOjxn8NBt-riCyjrOjhhYpLOtdKk6BDFnPJ1IgpSJ00BfHzl1g805NQN48zm44hRdicmixKyWUx2W8CH_TPnOxKOG2d_JknuZxKBdv5htf5pB3u0Usmm0sLViEmortrwqhU17q4KyXg9K-A96YElM8fl-XaoVsBNEmGWnWuqb2qUqAo4nMxE8_TT4VEX7AAPvb1ShALe7ofpSUp56-LqIs_B6F0orQowEw2c7Gw60i1_ZYpwTpG4VLyAj6OyXv37v1_dy5sX-wrui2w9DRPmEA4264v4Gu76y82yX7_JtvcHN5g01Q priority: 102 providerName: ProQuest |
| Title | Efficient gene orthology inference via large-scale rearrangements |
| URI | https://link.springer.com/article/10.1186/s13015-023-00238-y https://www.proquest.com/docview/2877487619 https://www.proquest.com/docview/2870992676 https://pubmed.ncbi.nlm.nih.gov/PMC10540461 https://doaj.org/article/4649072b35104c17810a234590df8c75 |
| Volume | 18 |
| WOSCitedRecordID | wos001074776500001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVADU databaseName: BioMed Central customDbUrl: eissn: 1748-7188 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0045303 issn: 1748-7188 databaseCode: RBZ dateStart: 20060101 isFulltext: true titleUrlDefault: https://www.biomedcentral.com/search/ providerName: BioMedCentral – providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 1748-7188 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0045303 issn: 1748-7188 databaseCode: DOA dateStart: 20060101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 1748-7188 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0045303 issn: 1748-7188 databaseCode: M~E dateStart: 20060101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Advanced Technologies & Aerospace Database customDbUrl: eissn: 1748-7188 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0045303 issn: 1748-7188 databaseCode: P5Z dateStart: 20090101 isFulltext: true titleUrlDefault: https://search.proquest.com/hightechjournals providerName: ProQuest – providerCode: PRVPQU databaseName: Biological Science Database customDbUrl: eissn: 1748-7188 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0045303 issn: 1748-7188 databaseCode: M7P dateStart: 20090101 isFulltext: true titleUrlDefault: http://search.proquest.com/biologicalscijournals providerName: ProQuest – providerCode: PRVPQU databaseName: Engineering Database customDbUrl: eissn: 1748-7188 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0045303 issn: 1748-7188 databaseCode: M7S dateStart: 20090101 isFulltext: true titleUrlDefault: http://search.proquest.com providerName: ProQuest – providerCode: PRVPQU databaseName: Health & Medical Collection customDbUrl: eissn: 1748-7188 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0045303 issn: 1748-7188 databaseCode: 7X7 dateStart: 20090101 isFulltext: true titleUrlDefault: https://search.proquest.com/healthcomplete providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 1748-7188 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0045303 issn: 1748-7188 databaseCode: BENPR dateStart: 20090101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Publicly Available Content Database customDbUrl: eissn: 1748-7188 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0045303 issn: 1748-7188 databaseCode: PIMPY dateStart: 20090101 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest – providerCode: PRVAVX databaseName: Springer Nature - Connect here FIRST to enable access customDbUrl: eissn: 1748-7188 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0045303 issn: 1748-7188 databaseCode: RSV dateStart: 20060101 isFulltext: true titleUrlDefault: https://link.springer.com/search?facet-content-type=%22Journal%22 providerName: Springer Nature |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnR1di9QwcPDuFHzxW-x5lgqCDxps0jRJH_dkD31wKZ7K6ktomvRcOLrHdu9g_72TbLtSTwV9yUNnQpvJfDYzE4AXltfoFkiLnluaEY4GmxirHEkdt5Ya4fuvhMsm5Gym5vOi7IvCuiHbfTiSDJo6iLUSbzrUttRXE2ckGBqy2YOD3Heb8TH66ZdB__IctfJQHvPbeSMTFDr1X9fH13MkfzkoDfbn5O7_ffk9uNP7m8lkyyD34YZrH8Ct7Q2Um4cwmYYWEvj2BDnJJf4UJ4CSxVAJmFwtquTcJ4yTDjfUJSuf3utrEkJ13CP4fDL99PYd6W9VIDWGBmvSCEkryW3KqRVp4VhhUd8ZzgvaUNk0DWdGVFWTGZ4XTljHRFUXzDIuMsvRuj-G_XbZuieQZJk1TLmGG1Nx1TiDGsuIOmt8u1HDVQR0ILSu-5bj_uaLcx1CDyX0ljQaqaIDafQmgle7ORfbhht_xT72-7fD9M2yw4Pl6kz3sqe54EUqmclQ_yBrSkXTimW4utQ2qpZ5BC_97msv0vh5ddVXJuAifXMsPZG-lqkQLI3gaISJoliPwQP_6F4VdBpDUolRIQaqETzfgf1Mn97WuuVlwEFPnQkpIlAjvhutbAxpF99DO3DqvW4uaASvB_b7-fY_k-7w39Cfwm0WOLggTB3B_np16Z7BzfpqvehWMezJuQyjiuHgeDorP8bh90bsk2nLMJ7iWObfEF6-_1B-jYPU_gDevzRy |
| linkProvider | Springer Nature |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V3Nb9MwFH-aOhBc-EYEBgQJxAGsJY5rOweECmxata3qYUjj5MWxwypN7dZ2Q_2n-Bt5z006hYndduAaO4pf8nu_9xy_D4C3TpToFiiHnluSMYEGm1mnPUu8cC61kuqvhGYTajDQh4f5cA1-N7kwFFbZcGIgajcp6R_5Jnr2Cp1r9Pc_n54x6hpFp6tNC40lLHb94hdu2Waf-t_w-77jfHvr4OsOq7sKsBJd4zmrpEoLJVwiUieT3PPcob5bIfK0SlVVVYJbWRRVZkU399J5Losy544LmTmhqUsEUv46itVNOrA-7O8PfzTcL7poEZrUHC03Z2ghUsqAzlgwjmzRMn-hS8BVW3A1PvOvQ9pg-7bv_29v7QHcq73suLdUi4ew5seP4Pay7-biMfS2QuEMlDtG_fExnV2FoXjU5D_GF6MiPqEweTZDGPt4SkHNlIkRcgKfwPcbWf9T6IwnY_8M4ixzlmtfCWsLoStvkaetLLOKiqxaoSNIm09syrrQOvX7ODFhw6WlWcLCICJMgIVZRPBhdc_psszItbO_EHJWM6lEeLgwmf40NeMYIUWeKG4zZF1USKXTpOAZSpe4SpeqG8F7wp0hIsPllUWdj4FCUkkw01OUwZVLnkSw0ZqJBFS2hxvsmZoAZ-YSeBG8WQ3TnRTUN_aT8zAH9ydcKhmBbiG-JVl7ZDw6DkXQU9prCJlG8LFRjsun__vVPb9-sa_hzs7B_p7Z6w92X8BdHjQ3Z1xvQGc-Pfcv4VZ5MR_Npq9qzY_h6KbV5g9Cy5KH |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Zb9QwEB5BOcRLuYoILRAkJB7Aaux4bedxabsCgVYVl_pmxResVGWr3W2l_feMnWQhFJAQr_E4icdzyjOfAZ47bjEskA4jt6IkHB02MU55UnjuHDUi4q-kyybkdKpOTqrjn7r4U7V7fyTZ9jRElKZmtX_mQqviSuwv0fLS2FlckuR0yPoqXOOYycSirg8fv_S2mI_QQvetMr-dN3BHCbX_sm2-XC_5y6Fp8kWT2_-_ijuw3cWh-bgVnLtwxTf34EZ7M-X6PoyPErQEvitHCfN5PN1JQ_ms7xDML2Z1fhoLyckSN9rni1j2G3sVUtfcDnyeHH06eEO62xaIxZRhRYKQtJbcFZw6UVSeVQ7toOG8ooHKEAJnRtR1KA0fVV44z0RtK-YYF6Xj6PUfwFYzb_xDyMvSGaZ84MbUXAVv0JIZYcsQYUgNVxnQnunadlDk8UaMU51SEiV0yxqNXNGJNXqdwcvNnLMWiOOv1K_jXm4oI4h2ejBffNWdTmoueFVIZkq0SyiyUtGiZiWurnBBWTnK4EWUBB1VHX_P1l3HAi4ygmbpsYw9TpVgRQZ7A0pUUTsc7mVJdyZiqTFVlZgtYgKbwbPNcJwZy94aPz9PNBjBMyFFBmogg4OVDUea2bcEE05jNM4FzeBVL4o_vv5n1j36N_KncPP4cKLfv52-24VbLAlzRZjag63V4tw_huv2YjVbLp4kxfwOsMc3PQ |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Efficient+gene+orthology+inference+via+large-scale+rearrangements&rft.jtitle=Algorithms+for+molecular+biology&rft.au=Rubert%2C+Diego+P.&rft.au=Braga%2C+Mar%C3%ADlia+D.+V.&rft.date=2023-09-28&rft.issn=1748-7188&rft.eissn=1748-7188&rft.volume=18&rft.issue=1&rft_id=info:doi/10.1186%2Fs13015-023-00238-y&rft.externalDBID=n%2Fa&rft.externalDocID=10_1186_s13015_023_00238_y |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1748-7188&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1748-7188&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1748-7188&client=summon |