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1. A Method for Identification and Ranking of Requirements Sources

Author

Klotins, E., Boeva, V., Wnuk, Krzysztof, Unterkalmsteiner, M., Gorschek, T., Jansen, S., Klotins, E., Boeva, V., Wnuk, Krzysztof, Unterkalmsteiner, M., Gorschek, T., and Jansen, S.

Abstract

Context: Requirements engineering (RE) literature acknowledges the importance of early stakeholder identification. The sources of requirements are many and also constantly changing as the market and business constantly change. Identifying and consulting all stakeholders on the market is impractical; thus many companies utilize indirect data sources, e.g. documents and representatives of larger groups of stakeholders. However, companies often collect irrelevant data or develop their products based on the sub-optimal information sources that may lead to missing market opportunities. Objectives: We propose a collaborative method for identification and selection of data sources. The method consists of four steps and aims to build consensus between different perspectives in an organization. Methods: We develop the method following the design science research method. We demonstrate the use of the method with three industrial case studies. Results: Our results show that the method can support the identification and selection of data sources in three ways: (1) by providing systematic steps to identify and prioritize data sources for RE, (2) by highlighting and resolving discrepancies between different perspectives in an organization, and (3) by analyzing the underlying rationale for using certain data sources. Conclusion: We conclude that our proposed method is well suited to support systematic identification of requirements sources in industry. Further work on the method include validation and adaptation for use different contexts, and developing tool support.

Published
2022

2. A Method for Identification and Ranking of Requirements Sources

Author

Sub Organization and Information, Organization and Information, Klotins, E., Boeva, V., Wnuk, Krzysztof, Unterkalmsteiner, M., Gorschek, T., Jansen, S., Sub Organization and Information, Organization and Information, Klotins, E., Boeva, V., Wnuk, Krzysztof, Unterkalmsteiner, M., Gorschek, T., and Jansen, S.

Published
2022

3. Targeted therapy of TERT-rearranged neuroblastoma with BET bromodomain inhibitor and proteasome inhibitor combination therapy

Author

Chen, J ; https://orcid.org/0000-0002-6774-0199, Nelson, C, Wong, M ; https://orcid.org/0000-0002-0324-8664, Tee, AE, Liu, PY ; https://orcid.org/0000-0001-9237-5990, La, T, Fletcher, JI ; https://orcid.org/0000-0003-2949-9469, Kamili, A ; https://orcid.org/0000-0002-4864-1195, Mayoh, C ; https://orcid.org/0000-0002-6398-3046, Bartenhagen, C, Trahair, TN ; https://orcid.org/0000-0002-3295-228X, Xu, N, Jayatilleke, N ; https://orcid.org/0000-0003-4086-0622, Wong, M, Peng, H, Atmadibrata, B, Cheung, BB ; https://orcid.org/0000-0001-8784-860X, Lan, Q, Bryan, TM, Mestdagh, P, Vandesompele, J, Combaret, V, Boeva, V, Wang, JY ; https://orcid.org/0000-0002-1325-7943, Janoueix-Lerosey, I, Cowley, MJ ; https://orcid.org/0000-0002-9519-5714, MacKenzie, KL, Dolnikov, A, Li, J, Polly, P ; https://orcid.org/0000-0001-7250-5217, Marshall, GM, Reddel, RR, Norris, MD ; https://orcid.org/0000-0002-0632-4589, Haber, M ; https://orcid.org/0000-0003-2036-8817, Fischer, M, Zhang, XD, Pickett, HA, Liu, T ; https://orcid.org/0000-0001-6244-7316, Chen, J ; https://orcid.org/0000-0002-6774-0199, Nelson, C, Wong, M ; https://orcid.org/0000-0002-0324-8664, Tee, AE, Liu, PY ; https://orcid.org/0000-0001-9237-5990, La, T, Fletcher, JI ; https://orcid.org/0000-0003-2949-9469, Kamili, A ; https://orcid.org/0000-0002-4864-1195, Mayoh, C ; https://orcid.org/0000-0002-6398-3046, Bartenhagen, C, Trahair, TN ; https://orcid.org/0000-0002-3295-228X, Xu, N, Jayatilleke, N ; https://orcid.org/0000-0003-4086-0622, Wong, M, Peng, H, Atmadibrata, B, Cheung, BB ; https://orcid.org/0000-0001-8784-860X, Lan, Q, Bryan, TM, Mestdagh, P, Vandesompele, J, Combaret, V, Boeva, V, Wang, JY ; https://orcid.org/0000-0002-1325-7943, Janoueix-Lerosey, I, Cowley, MJ ; https://orcid.org/0000-0002-9519-5714, MacKenzie, KL, Dolnikov, A, Li, J, Polly, P ; https://orcid.org/0000-0001-7250-5217, Marshall, GM, Reddel, RR, Norris, MD ; https://orcid.org/0000-0002-0632-4589, Haber, M ; https://orcid.org/0000-0003-2036-8817, Fischer, M, Zhang, XD, Pickett, HA, and Liu, T ; https://orcid.org/0000-0001-6244-7316

Abstract

Purpose: TERT gene rearrangement with transcriptional superenhancers leads to TERT overexpression and neuroblastoma. No targeted therapy is available for clinical trials in patients with TERT-rearranged neuroblastoma. Experimental Design: Anticancer agents exerting the best synergistic anticancer effects with BET bromodomain inhibitors were identified by screening an FDA-approved oncology drug library. The synergistic effects of the BET bromodomain inhibitor OTX015 and the proteasome inhibitor carfilzomib were examined by immunoblot and flow cytometry analysis. The anticancer efficacy of OTX015 and carfilzomib combination therapy was investigated in mice xenografted with TERT-rearranged neuroblastoma cell lines or patient-derived xenograft (PDX) tumor cells, and the role of TERT reduction in the anticancer efficacy was examined through rescue experiments in mice. Results: The BET bromodomain protein BRD4 promoted TERT-rearranged neuroblastoma cell proliferation through upregulating TERT expression. Screening of an approved oncology drug library identified the proteasome inhibitor carfilzomib as the agent exerting the best synergistic anticancer effects with BET bromodomain inhibitors including OTX015. OTX015 and carfilzomib synergistically reduced TERT protein expression, induced endoplasmic reticulum stress, and induced TERT-rearranged neuroblastoma cell apoptosis which was blocked by TERT overexpression and endoplasmic reticulum stress antagonists. In mice xenografted with TERT-rearranged neuroblastoma cell lines or PDX tumor cells, OTX015 and carfilzomib synergistically blocked TERT expression, induced tumor cell apoptosis, suppressed tumor progression, and improved mouse survival, which was largely reversed by forced TERT overexpression. Conclusions: OTX015 and carfilzomib combination therapy is likely to be translated into the first clinical trial of a targeted therapy in patients with TERT-rearranged neuroblastoma.

Published
2021

4. Energy-aware very fast decision tree

Author

García-Martín, E., Lavesson, Niklas, Grahn, H., Casalicchio, E., Boeva, V., García-Martín, E., Lavesson, Niklas, Grahn, H., Casalicchio, E., and Boeva, V.

Abstract

Recently machine learning researchers are designing algorithms that can run in embedded and mobile devices, which introduces additional constraints compared to traditional algorithm design approaches. One of these constraints is energy consumption, which directly translates to battery capacity for these devices. Streaming algorithms, such as the Very Fast Decision Tree (VFDT), are designed to run in such devices due to their high velocity and low memory requirements. However, they have not been designed with an energy efficiency focus. This paper addresses this challenge by presenting the nmin adaptation method, which reduces the energy consumption of the VFDT algorithm with only minor effects on accuracy. nmin adaptation allows the algorithm to grow faster in those branches where there is more confidence to create a split, and delays the split on the less confident branches. This removes unnecessary computations related to checking for splits but maintains similar levels of accuracy. We have conducted extensive experiments on 29 public datasets, showing that the VFDT with nmin adaptation consumes up to 31% less energy than the original VFDT, and up to 96% less energy than the CVFDT (VFDT adapted for concept drift scenarios), trading off up to 1.7 percent of accuracy.

Published
2021
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5. Targeted Therapy of TERT-Rearranged Neuroblastoma with BET Bromodomain Inhibitor and Proteasome Inhibitor Combination Therapy.

Author

Chen, J, Nelson, C, Wong, M, Tee, AE, Liu, PY, La, T, Fletcher, JI, Kamili, A, Mayoh, C, Bartenhagen, C, Trahair, TN, Xu, N, Jayatilleke, N, Peng, H, Atmadibrata, B, Cheung, BB, Lan, Q, Bryan, TM, Mestdagh, P, Vandesompele, J, Combaret, V, Boeva, V, Wang, JY, Janoueix-Lerosey, I, Cowley, MJ, MacKenzie, KL, Dolnikov, A, Li, J, Polly, P, Marshall, GM, Reddel, RR, Norris, MD, Haber, M, Fischer, M, Zhang, XD, Pickett, HA, Liu, T, Chen, J, Nelson, C, Wong, M, Tee, AE, Liu, PY, La, T, Fletcher, JI, Kamili, A, Mayoh, C, Bartenhagen, C, Trahair, TN, Xu, N, Jayatilleke, N, Peng, H, Atmadibrata, B, Cheung, BB, Lan, Q, Bryan, TM, Mestdagh, P, Vandesompele, J, Combaret, V, Boeva, V, Wang, JY, Janoueix-Lerosey, I, Cowley, MJ, MacKenzie, KL, Dolnikov, A, Li, J, Polly, P, Marshall, GM, Reddel, RR, Norris, MD, Haber, M, Fischer, M, Zhang, XD, Pickett, HA, and Liu, T

Abstract

Purpose

TERT gene rearrangement with transcriptional superenhancers leads to TERT overexpression and neuroblastoma. No targeted therapy is available for clinical trials in patients with TERT-rearranged neuroblastoma.

Experimental design

Anticancer agents exerting the best synergistic anticancer effects with BET bromodomain inhibitors were identified by screening an FDA-approved oncology drug library. The synergistic effects of the BET bromodomain inhibitor OTX015 and the proteasome inhibitor carfilzomib were examined by immunoblot and flow cytometry analysis. The anticancer efficacy of OTX015 and carfilzomib combination therapy was investigated in mice xenografted with TERT-rearranged neuroblastoma cell lines or patient-derived xenograft (PDX) tumor cells, and the role of TERT reduction in the anticancer efficacy was examined through rescue experiments in mice.

Results

The BET bromodomain protein BRD4 promoted TERT-rearranged neuroblastoma cell proliferation through upregulating TERT expression. Screening of an approved oncology drug library identified the proteasome inhibitor carfilzomib as the agent exerting the best synergistic anticancer effects with BET bromodomain inhibitors including OTX015. OTX015 and carfilzomib synergistically reduced TERT protein expression, induced endoplasmic reticulum stress, and induced TERT-rearranged neuroblastoma cell apoptosis which was blocked by TERT overexpression and endoplasmic reticulum stress antagonists. In mice xenografted with TERT-rearranged neuroblastoma cell lines or PDX tumor cells, OTX015 and carfilzomib synergistically blocked TERT expression, induced tumor cell apoptosis, suppressed tumor progression, and improved mouse survival, which was largely reversed by forced TERT overexpression.

Conclusions

OTX015 and carfilzomib combination therapy is likely to be translated into the first clinical trial of a targeted therapy in patients with
Published
2021

6. Higher order mining for monitoring district heating substations

Author

Abghari, S., Boeva, V., Brage, J., Johansson, C., Grahn, H., Lavesson, Niklas, Abghari, S., Boeva, V., Brage, J., Johansson, C., Grahn, H., and Lavesson, Niklas

Abstract

We propose a higher order mining (HOM) approach for modelling, monitoring and analyzing district heating (DH) substations' operational behaviour and performance. HOM is concerned with mining over patterns rather than primary or raw data. The proposed approach uses a combination of different data analysis techniques such as sequential pattern mining, clustering analysis, consensus clustering and minimum spanning tree (MST). Initially, a substation's operational behaviour is modeled by extracting weekly patterns and performing clustering analysis. The substation's performance is monitored by assessing its modeled behaviour for every two consecutive weeks. In case some significant difference is observed, further analysis is performed by integrating the built models into a consensus clustering and applying an MST for identifying deviating behaviours. The results of the study show that our method is robust for detecting deviating and sub-optimal behaviours of DH substations. In addition, the proposed method can facilitate domain experts in the interpretation and understanding of the substations' behaviour and performance by providing different data analysis and visualization techniques., Funding details: Stiftelsen för Kunskaps- och Kompetensutveckling, KK, 201400This work is part of the research project “Scalable resource-efficient systems for big data analytics“ funded by the Knowledge Foundation (grant: 20140032) in Sweden.

Published
2019
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7. Computational pan-genomics: Status, promises and challenges

Author

Marschall, T. (Tanja), Marz, M. (Manja), Abeel, T. (Thomas), Dijkstra, L. (Louis), Dutilh, B.E. (Bas), Ghaffaari, A. (Ali), Kersey, P. (Paul), Kloosterman, W.P. (Wigard), Mäkinen, V. (Veli), Novak, A.M. (Adam M.), Paten, B. (Benedict), Porubsky, D. (David), Rivals, E. (Eric), Alkan, C. (Can), Baaijens, J.A. (Jasmijn A.), Bakker, P.I.W. (Paul) de, Boeva, V. (Valentina), Bonnal, R.J.P. (Raoul J.P.), Chiaromonte, F. (Francesca), Chikhi, R. (Rayan), Ciccarelli, F.D. (Francesca D.), Cijvat, R. (Robin), Datema, E. (Erwin), Duijn, C.M. (Cornelia) van, Eichler, E.E. (Evan), Ernst, C. (Corinna), Eskin, E. (E.), Garrison, E. (Erik), El-Kebir, M. (Mohammed), Klau, G.W. (Gunnar W.), Korbel, J.O. (Jan), Lameijer, E.-W. (Eric-Wubbo), Langmead, B. (Benjamin), Martin, M. (Marcel), Medvedev, P. (Paul), Mu, J.C. (John C.), Neerincx, P.B.T. (Pieter B T), Ouwens, K. (Klaasjan), Peterlongo, P. (Pierre), Pisanti, N. (Nadia), Rahmann, S. (S.), Raphael, B.J. (Benjamin J.), Reinert, K. (Knut), Ridder, D. (Dick) de, de Ridder, J. (Jeroen), Schlesner, M. (Matthias), Schulz-Trieglaff, O. (Ole), Sanders, A.D. (Ashley D.), Sheikhizadeh, S. (Siavash), Shneider, C. (Carl), Smit, S. (Sandra), Valenzuela, D. (Daniel), Wang, J. (Jiayin), Wessels, L. (Lodewyk), Zhang, Y. (Ying), Guryev, V. (Victor), Vandin, F. (Fabio), Ye, K. (Kai), Schönhuth, A. (Alexander), Marschall, T. (Tanja), Marz, M. (Manja), Abeel, T. (Thomas), Dijkstra, L. (Louis), Dutilh, B.E. (Bas), Ghaffaari, A. (Ali), Kersey, P. (Paul), Kloosterman, W.P. (Wigard), Mäkinen, V. (Veli), Novak, A.M. (Adam M.), Paten, B. (Benedict), Porubsky, D. (David), Rivals, E. (Eric), Alkan, C. (Can), Baaijens, J.A. (Jasmijn A.), Bakker, P.I.W. (Paul) de, Boeva, V. (Valentina), Bonnal, R.J.P. (Raoul J.P.), Chiaromonte, F. (Francesca), Chikhi, R. (Rayan), Ciccarelli, F.D. (Francesca D.), Cijvat, R. (Robin), Datema, E. (Erwin), Duijn, C.M. (Cornelia) van, Eichler, E.E. (Evan), Ernst, C. (Corinna), Eskin, E. (E.), Garrison, E. (Erik), El-Kebir, M. (Mohammed), Klau, G.W. (Gunnar W.), Korbel, J.O. (Jan), Lameijer, E.-W. (Eric-Wubbo), Langmead, B. (Benjamin), Martin, M. (Marcel), Medvedev, P. (Paul), Mu, J.C. (John C.), Neerincx, P.B.T. (Pieter B T), Ouwens, K. (Klaasjan), Peterlongo, P. (Pierre), Pisanti, N. (Nadia), Rahmann, S. (S.), Raphael, B.J. (Benjamin J.), Reinert, K. (Knut), Ridder, D. (Dick) de, de Ridder, J. (Jeroen), Schlesner, M. (Matthias), Schulz-Trieglaff, O. (Ole), Sanders, A.D. (Ashley D.), Sheikhizadeh, S. (Siavash), Shneider, C. (Carl), Smit, S. (Sandra), Valenzuela, D. (Daniel), Wang, J. (Jiayin), Wessels, L. (Lodewyk), Zhang, Y. (Ying), Guryev, V. (Victor), Vandin, F. (Fabio), Ye, K. (Kai), and Schönhuth, A. (Alexander)

Abstract

Many disciplines, from human genetics and oncology to plant breeding, microbiology and virology, commonly face the challenge of analyzing rapidly increasing numbers of genomes. In case of Homo sapiens, the number of sequenced genomes will approach hundreds of thousands in the next few years. Simply scaling up established bioinformatics pipelines will not be sufficient for leveraging the full potential of such rich genomic data sets. Instead, novel, qualitatively different Computational methods and paradigms are needed.We will witness the rapid extension of Computational pan-genomics, a new sub-area of research in Computational biology. In this article, we generalize existing definitions and understand a pangenome as any collection of genomic sequences to be analyzed jointly or to be used as a reference. We examine already available approaches to construct and use pan-genomes, discuss the potential benefits of future technologies and methodologies and review open challenges from the vantage point of the above-mentioned biological disciplines. As a prominent example for a Computational paradigm shift, we particularly highlight the transition from the representation of reference genomes as strings to representations a

Published
2018
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8. Computational pan-genomics: status, promises and challenges.

Author

Marschall, T., Marz, M., Abeel, T., Dijkstra, L., Dutilh, B.E., Ghaffaari, A., Kersey, P., Kloosterman, W.P., Mäkinen, V., Novak, A.M., Paten, B., Porubsky, D., Rivals, E., Alkan, C., Baaijens, J., Bakker, P.I. de, Boeva, V., Bonnal, R.J., Chiaromonte, F., Chikhi, R., Ciccarelli, F.D., Cijvat, R., Datema, E., Duijn, C.M. van, Eichler, E.E., Ernst, C., Eskin, E., Garrison, E., El-Kebir, M., Klau, G.W., Korbel, J.O., Lameijer, E.W., Langmead, B., Martin, M., Medvedev, P., Mu, J.C., Neerincx, P., Ouwens, K., Peterlongo, P., Pisanti, N., Rahmann, S., Raphael, B., Reinert, K., Ridder, D. de, Ridder, J. de, Schlesner, M., Schulz-Trieglaff, O., Sanders, A.D., Sheikhizadeh, S., Shneider, C., Smit, S., Valenzuela, D., Wang, J, Wessels, L., Zhang, Y, Guryev, V., Vandin, F., Ye, K., Schönhuth, A., Marschall, T., Marz, M., Abeel, T., Dijkstra, L., Dutilh, B.E., Ghaffaari, A., Kersey, P., Kloosterman, W.P., Mäkinen, V., Novak, A.M., Paten, B., Porubsky, D., Rivals, E., Alkan, C., Baaijens, J., Bakker, P.I. de, Boeva, V., Bonnal, R.J., Chiaromonte, F., Chikhi, R., Ciccarelli, F.D., Cijvat, R., Datema, E., Duijn, C.M. van, Eichler, E.E., Ernst, C., Eskin, E., Garrison, E., El-Kebir, M., Klau, G.W., Korbel, J.O., Lameijer, E.W., Langmead, B., Martin, M., Medvedev, P., Mu, J.C., Neerincx, P., Ouwens, K., Peterlongo, P., Pisanti, N., Rahmann, S., Raphael, B., Reinert, K., Ridder, D. de, Ridder, J. de, Schlesner, M., Schulz-Trieglaff, O., Sanders, A.D., Sheikhizadeh, S., Shneider, C., Smit, S., Valenzuela, D., Wang, J, Wessels, L., Zhang, Y, Guryev, V., Vandin, F., Ye, K., and Schönhuth, A.

Abstract

Contains fulltext : 190288.pdf (publisher's version ) (Open Access)

Published
2018

9. Computational pan-genomics: status, promises and challenges

Author

The Computational Pan-Genomics Consortium, Marschall, T. (Tobias), Marz, M. (Manja), Abeel, T. (Thomas), Dijkstra, L.J. (Louis), Dutilh, B.E. (Bas), Ghaffaari, A. (Ali), Kersey, P. (Paul), Kloosterman, W.P. (Wigard), Mäkinen, V. (Veli), Novak, A.M. (Adam), Paten, B. (Benedict), Porubsky, D. (David), Rivals, E. (Eric), Alkan, C. (Can), Baaijens, J.A. (Jasmijn), Bakker, P.I.W. (Paul) de, Boeva, V. (Valentina), Bonnal, R.J.P. (Raoul), Chiaromonte, F. (Francesca), Chikhi, R. (Rayan), Ciccarelli, F.D. (Francesca), Cijvat, C.P. (Robin), Datema, E. (Erwin), Duijn, C.M. (Cornelia) van, Eichler, E.E. (Evan), Ernst, C. (Corinna), Eskin, E. (Eleazar), Garrison, E. (Erik), El-Kebir, M. (Mohammed), Klau, G.W. (Gunnar), Korbel, J.O. (Jan), Lameijer, E.-W. (Eric-Wubbo), Langmead, B. (Benjamin), Martin, M. (Marcel), Medvedev, P. (Paul), Mu, J.C. (John), Neerincx, P.B.T. (Pieter), Ouwens, K. (Klaasjan), Peterlongo, P. (Pierre), Pisanti, N. (Nadia), Rahmann, S. (Sven), Raphael, B.J. (Benjamin), Reinert, K. (Knut), Ridder, D. (Dick) de, Ridder, J. (Jeroen) de, Schlesner, M. (Matthias), Schulz-Trieglaff, O. (Ole), Sanders, A.D. (Ashley), Sheikhizadeh, S. (Siavash), Shneider, C. (Carl), Smit, S. (Sandra), Valenzuela, D. (Daniel), Wang, J. (Jiayin), Wessels, L.F.A. (Lodewyk), Zhang, Y. (Ying), Guryev, V. (Victor), Vandin, F. (Fabio), Ye, K. (Kai), Schönhuth, A. (Alexander), The Computational Pan-Genomics Consortium, Marschall, T. (Tobias), Marz, M. (Manja), Abeel, T. (Thomas), Dijkstra, L.J. (Louis), Dutilh, B.E. (Bas), Ghaffaari, A. (Ali), Kersey, P. (Paul), Kloosterman, W.P. (Wigard), Mäkinen, V. (Veli), Novak, A.M. (Adam), Paten, B. (Benedict), Porubsky, D. (David), Rivals, E. (Eric), Alkan, C. (Can), Baaijens, J.A. (Jasmijn), Bakker, P.I.W. (Paul) de, Boeva, V. (Valentina), Bonnal, R.J.P. (Raoul), Chiaromonte, F. (Francesca), Chikhi, R. (Rayan), Ciccarelli, F.D. (Francesca), Cijvat, C.P. (Robin), Datema, E. (Erwin), Duijn, C.M. (Cornelia) van, Eichler, E.E. (Evan), Ernst, C. (Corinna), Eskin, E. (Eleazar), Garrison, E. (Erik), El-Kebir, M. (Mohammed), Klau, G.W. (Gunnar), Korbel, J.O. (Jan), Lameijer, E.-W. (Eric-Wubbo), Langmead, B. (Benjamin), Martin, M. (Marcel), Medvedev, P. (Paul), Mu, J.C. (John), Neerincx, P.B.T. (Pieter), Ouwens, K. (Klaasjan), Peterlongo, P. (Pierre), Pisanti, N. (Nadia), Rahmann, S. (Sven), Raphael, B.J. (Benjamin), Reinert, K. (Knut), Ridder, D. (Dick) de, Ridder, J. (Jeroen) de, Schlesner, M. (Matthias), Schulz-Trieglaff, O. (Ole), Sanders, A.D. (Ashley), Sheikhizadeh, S. (Siavash), Shneider, C. (Carl), Smit, S. (Sandra), Valenzuela, D. (Daniel), Wang, J. (Jiayin), Wessels, L.F.A. (Lodewyk), Zhang, Y. (Ying), Guryev, V. (Victor), Vandin, F. (Fabio), Ye, K. (Kai), and Schönhuth, A. (Alexander)

Abstract

Many disciplines, from human genetics and oncology to plant breeding, microbiology and virology, commonly face the challenge of analyzing rapidly increasing numbers of genomes. In case of Homo sapiens, the number of sequenced genomes will approach hundreds of thousands in the next few years. Simply scaling up established bioinformatics pipelines will not be sufficient for leveraging the full potential of such rich genomic data sets. Instead, novel, qualitatively different computational methods and paradigms are needed. We will witness the rapid extension of computational pan-genomics, a new sub-area of research in computational biology. In this article, we generalize existing definitions and understand a pan-genome as any collection of genomic sequences to be analyzed jointly or to be used as a reference. We examine already available approaches to construct and use pan-genomes, discuss the potential benefits of future technologies and methodologies and review open challenges from the vantage point of the above-mentioned biological disciplines. As a prominent example for a computational paradigm shift, we particularly highlight the transition from the representation of reference genomes as strings to representations as graphs. We outline how this and other challenges from different application domains translate into common computational problems, point out relevant bioinformatics techniques and identify open problems in computer science. With this review, we aim to increase awareness that a joint approach to computational pan-genomics can help address many of the problems currently faced in various domains.

Published
2018
Full Text
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10. Computational pan-genomics: status, promises and challenges.

Author

Marschall, T., Marz, M., Abeel, T., Dijkstra, L., Dutilh, B.E., Ghaffaari, A., Kersey, P., Kloosterman, W.P., Mäkinen, V., Novak, A.M., Paten, B., Porubsky, D., Rivals, E., Alkan, C., Baaijens, J., Bakker, P.I. de, Boeva, V., Bonnal, R.J., Chiaromonte, F., Chikhi, R., Ciccarelli, F.D., Cijvat, R., Datema, E., Duijn, C.M. van, Eichler, E.E., Ernst, C., Eskin, E., Garrison, E., El-Kebir, M., Klau, G.W., Korbel, J.O., Lameijer, E.W., Langmead, B., Martin, M., Medvedev, P., Mu, J.C., Neerincx, P., Ouwens, K., Peterlongo, P., Pisanti, N., Rahmann, S., Raphael, B., Reinert, K., Ridder, D. de, Ridder, J. de, Schlesner, M., Schulz-Trieglaff, O., Sanders, A.D., Sheikhizadeh, S., Shneider, C., Smit, S., Valenzuela, D., Wang, J, Wessels, L., Zhang, Y, Guryev, V., Vandin, F., Ye, K., Schönhuth, A., Marschall, T., Marz, M., Abeel, T., Dijkstra, L., Dutilh, B.E., Ghaffaari, A., Kersey, P., Kloosterman, W.P., Mäkinen, V., Novak, A.M., Paten, B., Porubsky, D., Rivals, E., Alkan, C., Baaijens, J., Bakker, P.I. de, Boeva, V., Bonnal, R.J., Chiaromonte, F., Chikhi, R., Ciccarelli, F.D., Cijvat, R., Datema, E., Duijn, C.M. van, Eichler, E.E., Ernst, C., Eskin, E., Garrison, E., El-Kebir, M., Klau, G.W., Korbel, J.O., Lameijer, E.W., Langmead, B., Martin, M., Medvedev, P., Mu, J.C., Neerincx, P., Ouwens, K., Peterlongo, P., Pisanti, N., Rahmann, S., Raphael, B., Reinert, K., Ridder, D. de, Ridder, J. de, Schlesner, M., Schulz-Trieglaff, O., Sanders, A.D., Sheikhizadeh, S., Shneider, C., Smit, S., Valenzuela, D., Wang, J, Wessels, L., Zhang, Y, Guryev, V., Vandin, F., Ye, K., and Schönhuth, A.

Abstract

Contains fulltext : 190288.pdf (publisher's version ) (Open Access)

Published
2018

11. Computational pan-genomics: status, promises and challenges.

Author

Marschall, T., Marz, M., Abeel, T., Dijkstra, L., Dutilh, B.E., Ghaffaari, A., Kersey, P., Kloosterman, W.P., Mäkinen, V., Novak, A.M., Paten, B., Porubsky, D., Rivals, E., Alkan, C., Baaijens, J., Bakker, P.I. de, Boeva, V., Bonnal, R.J., Chiaromonte, F., Chikhi, R., Ciccarelli, F.D., Cijvat, R., Datema, E., Duijn, C.M. van, Eichler, E.E., Ernst, C., Eskin, E., Garrison, E., El-Kebir, M., Klau, G.W., Korbel, J.O., Lameijer, E.W., Langmead, B., Martin, M., Medvedev, P., Mu, J.C., Neerincx, P., Ouwens, K., Peterlongo, P., Pisanti, N., Rahmann, S., Raphael, B., Reinert, K., Ridder, D. de, Ridder, J. de, Schlesner, M., Schulz-Trieglaff, O., Sanders, A.D., Sheikhizadeh, S., Shneider, C., Smit, S., Valenzuela, D., Wang, J, Wessels, L., Zhang, Y, Guryev, V., Vandin, F., Ye, K., Schönhuth, A., Marschall, T., Marz, M., Abeel, T., Dijkstra, L., Dutilh, B.E., Ghaffaari, A., Kersey, P., Kloosterman, W.P., Mäkinen, V., Novak, A.M., Paten, B., Porubsky, D., Rivals, E., Alkan, C., Baaijens, J., Bakker, P.I. de, Boeva, V., Bonnal, R.J., Chiaromonte, F., Chikhi, R., Ciccarelli, F.D., Cijvat, R., Datema, E., Duijn, C.M. van, Eichler, E.E., Ernst, C., Eskin, E., Garrison, E., El-Kebir, M., Klau, G.W., Korbel, J.O., Lameijer, E.W., Langmead, B., Martin, M., Medvedev, P., Mu, J.C., Neerincx, P., Ouwens, K., Peterlongo, P., Pisanti, N., Rahmann, S., Raphael, B., Reinert, K., Ridder, D. de, Ridder, J. de, Schlesner, M., Schulz-Trieglaff, O., Sanders, A.D., Sheikhizadeh, S., Shneider, C., Smit, S., Valenzuela, D., Wang, J, Wessels, L., Zhang, Y, Guryev, V., Vandin, F., Ye, K., and Schönhuth, A.

Abstract

Contains fulltext : 190288.pdf (publisher's version ) (Open Access)

Published
2018

12. Computational pan-genomics: status, promises and challenges

Author

The Computational Pan-Genomics Consortium, Marschall, T. (Tobias), Marz, M. (Manja), Abeel, T. (Thomas), Dijkstra, L.J. (Louis), Dutilh, B.E. (Bas), Ghaffaari, A. (Ali), Kersey, P. (Paul), Kloosterman, W.P. (Wigard), Mäkinen, V. (Veli), Novak, A.M. (Adam), Paten, B. (Benedict), Porubsky, D. (David), Rivals, E. (Eric), Alkan, C. (Can), Baaijens, J.A. (Jasmijn), Bakker, P.I.W. (Paul) de, Boeva, V. (Valentina), Bonnal, R.J.P. (Raoul), Chiaromonte, F. (Francesca), Chikhi, R. (Rayan), Ciccarelli, F.D. (Francesca), Cijvat, C.P. (Robin), Datema, E. (Erwin), Duijn, C.M. (Cornelia) van, Eichler, E.E. (Evan), Ernst, C. (Corinna), Eskin, E. (Eleazar), Garrison, E. (Erik), El-Kebir, M. (Mohammed), Klau, G.W. (Gunnar), Korbel, J.O. (Jan), Lameijer, E.-W. (Eric-Wubbo), Langmead, B. (Benjamin), Martin, M. (Marcel), Medvedev, P. (Paul), Mu, J.C. (John), Neerincx, P.B.T. (Pieter), Ouwens, K. (Klaasjan), Peterlongo, P. (Pierre), Pisanti, N. (Nadia), Rahmann, S. (Sven), Raphael, B.J. (Benjamin), Reinert, K. (Knut), Ridder, D. (Dick) de, Ridder, J. (Jeroen) de, Schlesner, M. (Matthias), Schulz-Trieglaff, O. (Ole), Sanders, A.D. (Ashley), Sheikhizadeh, S. (Siavash), Shneider, C. (Carl), Smit, S. (Sandra), Valenzuela, D. (Daniel), Wang, J. (Jiayin), Wessels, L.F.A. (Lodewyk), Zhang, Y. (Ying), Guryev, V. (Victor), Vandin, F. (Fabio), Ye, K. (Kai), Schönhuth, A. (Alexander), The Computational Pan-Genomics Consortium, Marschall, T. (Tobias), Marz, M. (Manja), Abeel, T. (Thomas), Dijkstra, L.J. (Louis), Dutilh, B.E. (Bas), Ghaffaari, A. (Ali), Kersey, P. (Paul), Kloosterman, W.P. (Wigard), Mäkinen, V. (Veli), Novak, A.M. (Adam), Paten, B. (Benedict), Porubsky, D. (David), Rivals, E. (Eric), Alkan, C. (Can), Baaijens, J.A. (Jasmijn), Bakker, P.I.W. (Paul) de, Boeva, V. (Valentina), Bonnal, R.J.P. (Raoul), Chiaromonte, F. (Francesca), Chikhi, R. (Rayan), Ciccarelli, F.D. (Francesca), Cijvat, C.P. (Robin), Datema, E. (Erwin), Duijn, C.M. (Cornelia) van, Eichler, E.E. (Evan), Ernst, C. (Corinna), Eskin, E. (Eleazar), Garrison, E. (Erik), El-Kebir, M. (Mohammed), Klau, G.W. (Gunnar), Korbel, J.O. (Jan), Lameijer, E.-W. (Eric-Wubbo), Langmead, B. (Benjamin), Martin, M. (Marcel), Medvedev, P. (Paul), Mu, J.C. (John), Neerincx, P.B.T. (Pieter), Ouwens, K. (Klaasjan), Peterlongo, P. (Pierre), Pisanti, N. (Nadia), Rahmann, S. (Sven), Raphael, B.J. (Benjamin), Reinert, K. (Knut), Ridder, D. (Dick) de, Ridder, J. (Jeroen) de, Schlesner, M. (Matthias), Schulz-Trieglaff, O. (Ole), Sanders, A.D. (Ashley), Sheikhizadeh, S. (Siavash), Shneider, C. (Carl), Smit, S. (Sandra), Valenzuela, D. (Daniel), Wang, J. (Jiayin), Wessels, L.F.A. (Lodewyk), Zhang, Y. (Ying), Guryev, V. (Victor), Vandin, F. (Fabio), Ye, K. (Kai), and Schönhuth, A. (Alexander)

Abstract

Many disciplines, from human genetics and oncology to plant breeding, microbiology and virology, commonly face the challenge of analyzing rapidly increasing numbers of genomes. In case of Homo sapiens, the number of sequenced genomes will approach hundreds of thousands in the next few years. Simply scaling up established bioinformatics pipelines will not be sufficient for leveraging the full potential of such rich genomic data sets. Instead, novel, qualitatively different computational methods and paradigms are needed. We will witness the rapid extension of computational pan-genomics, a new sub-area of research in computational biology. In this article, we generalize existing definitions and understand a pan-genome as any collection of genomic sequences to be analyzed jointly or to be used as a reference. We examine already available approaches to construct and use pan-genomes, discuss the potential benefits of future technologies and methodologies and review open challenges from the vantage point of the above-mentioned biological disciplines. As a prominent example for a computational paradigm shift, we particularly highlight the transition from the representation of reference genomes as strings to representations as graphs. We outline how this and other challenges from different application domains translate into common computational problems, point out relevant bioinformatics techniques and identify open problems in computer science. With this review, we aim to increase awareness that a joint approach to computational pan-genomics can help address many of the problems currently faced in various domains.

Published
2018
Full Text
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13. Computational pan-genomics: status, promises and challenges

Author

Marschall, T, Marz, M, Abeel, T, Dijkstra, L, Dutilh, BE, Ghaffaari, A, Kersey, P, Kloosterman, WP, Makinen, V, Novak, AM, Paten, B, Porubsky, D, Rivals, E, Alkan, C, Baaijens, J A, de Bakker, PIW, Boeva, V, Bonnal, RJP, Chiaromonte, F, Chikhi, R, Ciccarelli, FD, Cijvat, R, Datema, E, Duijn, Cornelia, Eichler, EE, Ernst, C, Eskin, E, Garrison, E, El-Kebir, M, Klau, GW, Korbel, JO, Lameijer, EW, Langmead, B, Martin, M, Medvedev, P, Mu, JC, Neerincx, P, Ouwens, K, Peterlongo, P, Pisanti, N, Rahmann, S, Raphael, B, Reinert, K, Ridder, D, Ridder, J (Jannemarie), Schlesner, M, Schulz-Trieglaff, O, Sanders, AD, Sheikhizadeh, S, Shneider, C, Smit, S, Valenzuela, D, Wang, JY, Wessels, L, Zhang, Y, Guryev, V, Vandin, F, Ye, K, Schonhuth, A, Marschall, T, Marz, M, Abeel, T, Dijkstra, L, Dutilh, BE, Ghaffaari, A, Kersey, P, Kloosterman, WP, Makinen, V, Novak, AM, Paten, B, Porubsky, D, Rivals, E, Alkan, C, Baaijens, J A, de Bakker, PIW, Boeva, V, Bonnal, RJP, Chiaromonte, F, Chikhi, R, Ciccarelli, FD, Cijvat, R, Datema, E, Duijn, Cornelia, Eichler, EE, Ernst, C, Eskin, E, Garrison, E, El-Kebir, M, Klau, GW, Korbel, JO, Lameijer, EW, Langmead, B, Martin, M, Medvedev, P, Mu, JC, Neerincx, P, Ouwens, K, Peterlongo, P, Pisanti, N, Rahmann, S, Raphael, B, Reinert, K, Ridder, D, Ridder, J (Jannemarie), Schlesner, M, Schulz-Trieglaff, O, Sanders, AD, Sheikhizadeh, S, Shneider, C, Smit, S, Valenzuela, D, Wang, JY, Wessels, L, Zhang, Y, Guryev, V, Vandin, F, Ye, K, and Schonhuth, A

Published
2018

14. A transition logic for schemata conflicts

Author

Boeva, V, Ekenberg, Love, Boeva, V, and Ekenberg, Love

Abstract

Conflict detection and analysis are of high importance, e.g., when integrating conceptual schemata, such as UML-Specifications, or analysing goal-fulfilment of sets of autonomous agents. In general, models for this introduce unnecessarily complicated frameworks with several disadvantages regarding semantics as well as complexity. This paper demonstrates that an important set of static and dynamic conflicts between specifications can be diagnosed using ordinary first-order modal logic. Furthermore, we show how the framework can be extended for handling situations when there are convex sets of probability measures over a state-space. Thus, representing specifications as conceptual schemata and using standard Kripke models of modal logic, augmented with an interval-valued probability measure, we propose instrumental definitions and procedures for conflict detection., QC 20110921

Published
2004
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