Perspectives on automated composition of workflows in the life sciences

Anna Lena Lamprecht; Magnus Palmblad; Jon Ison; Veit Schwämmle; Mohammad Sadnan Al Manir; Ilkay Altintas; Christopher J.O. Baker; Ammar Ben Hadj Amor; Salvador Capella-Gutierrez; Paulos Charonyktakis; Michael R. Crusoe; Yolanda Gil; Carole Goble; Timothy J. Griffin; Paul Groth; Hans Ienasescu; Pratik Jagtap; Matúš Kalaš; Vedran Kasalica; Alireza Khanteymoori; Tobias Kuhn; Hailiang Mei; Hervé Ménager; Steffen Möller; Robin A. Richardson; Vincent Robert; Stian Soiland-Reyes; Robert Stevens; Szoke Szaniszlo; Suzan Verberne; Aswin Verhoeven; Katherine Wolstencroft

doi:10.12688/f1000research.54159.1

Perspectives on automated composition of workflows in the life sciences

Anna Lena Lamprecht^*, Magnus Palmblad, Jon Ison, Veit Schwämmle, Mohammad Sadnan Al Manir, Ilkay Altintas, Christopher J.O. Baker, Ammar Ben Hadj Amor, Salvador Capella-Gutierrez, Paulos Charonyktakis, Michael R. Crusoe, Yolanda Gil, Carole Goble, Timothy J. Griffin, Paul Groth, Hans Ienasescu, Pratik Jagtap, Matúš Kalaš, Vedran Kasalica, Alireza KhanteymooriTobias Kuhn, Hailiang Mei, Hervé Ménager, Steffen Möller, Robin A. Richardson, Vincent Robert, Stian Soiland-Reyes, Robert Stevens, Szoke Szaniszlo, Suzan Verberne, Aswin Verhoeven, Katherine Wolstencroft

^*Corresponding author for this work

Research output: Contribution to Journal › Article › Academic › peer-review

Abstract

Scientific data analyses often combine several computational tools in automated pipelines, or workflows. Thousands of such workflows have been used in the life sciences, though their composition has remained a cumbersome manual process due to a lack of standards for annotation, assembly, and implementation. Recent technological advances have returned the long-standing vision of automated workflow composition into focus. This article summarizes a recent Lorentz Center workshop dedicated to automated composition of workflows in the life sciences. We survey previous initiatives to automate the composition process, and discuss the current state of the art and future perspectives. We start by drawing the 'big picture' of the scientific workflow development life cycle, before surveying and discussing current methods, technologies and practices for semantic domain modelling, automation in workflow development, and workflow assessment. Finally, we derive a roadmap of individual and community-based actions to work toward the vision of automated workflow development in the forthcoming years. A central outcome of the workshop is a general description of the workflow life cycle in six stages: 1) scientific question or hypothesis, 2) conceptual workflow, 3) abstract workflow, 4) concrete workflow, 5) production workflow, and 6) scientific results. The transitions between stages are facilitated by diverse tools and methods, usually incorporating domain knowledge in some form. Formal semantic domain modelling is hard and often a bottleneck for the application of semantic technologies. However, life science communities have made considerable progress here in recent years and are continuously improving, renewing interest in the application of semantic technologies for workflow exploration, composition and instantiation. Combined with systematic benchmarking with reference data and large-scale deployment of production-stage workflows, such technologies enable a more systematic process of workflow development than we know today. We believe that this can lead to more robust, reusable, and sustainable workflows in the future.

Original language	English
Article number	897
Pages (from-to)	1-28
Number of pages	28
Journal	F1000Research
Volume	10
DOIs	https://doi.org/10.12688/f1000research.54159.1
Publication status	Published - 28 Oct 2021

Bibliographical note

Funding Information:
We would like to thank all of the people that contributed to the construction of this data set, and in particular the XGUMS group (besides the authors: Massimo Frapiccini from OGSM, Macerata; Marco Romanelli, Giorgio Durì and Fausto Ponton from OGS, Trieste; Guido Meton and Bruno della Vedova from DINMA, Trieste; Dino Bindi, Giacomo Carenzo, Elena Eva, Valeria Lanza, Stefano Parolai, Marco Pasta and Enzo Zunino from Dister – Genova; Giuliano Milana from SSN-Roma; Luisa Filippi and Elisa Zambonelli from the ‘Università dell’Aquila’). The field operation was sponsored by the CNR-GNDT and the SSN.

Publisher Copyright:
© 2021 Lamprecht AL et al.

Keywords

Automated workflow composition
Bioinformatics
Computational pipelines
Life sciences
Scientific workflows
Semantic domain modelling
Workflow benchmarking

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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Lamprecht, A. L., Palmblad, M., Ison, J., Schwämmle, V., Al Manir, M. S., Altintas, I., Baker, C. J. O., Ben Hadj Amor, A., Capella-Gutierrez, S., Charonyktakis, P., Crusoe, M. R., Gil, Y., Goble, C., Griffin, T. J., Groth, P., Ienasescu, H., Jagtap, P., Kalaš, M., Kasalica, V., ... Wolstencroft, K. (2021). Perspectives on automated composition of workflows in the life sciences. F1000Research, 10, 1-28. Article 897. https://doi.org/10.12688/f1000research.54159.1

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title = "Perspectives on automated composition of workflows in the life sciences",

abstract = "Scientific data analyses often combine several computational tools in automated pipelines, or workflows. Thousands of such workflows have been used in the life sciences, though their composition has remained a cumbersome manual process due to a lack of standards for annotation, assembly, and implementation. Recent technological advances have returned the long-standing vision of automated workflow composition into focus. This article summarizes a recent Lorentz Center workshop dedicated to automated composition of workflows in the life sciences. We survey previous initiatives to automate the composition process, and discuss the current state of the art and future perspectives. We start by drawing the 'big picture' of the scientific workflow development life cycle, before surveying and discussing current methods, technologies and practices for semantic domain modelling, automation in workflow development, and workflow assessment. Finally, we derive a roadmap of individual and community-based actions to work toward the vision of automated workflow development in the forthcoming years. A central outcome of the workshop is a general description of the workflow life cycle in six stages: 1) scientific question or hypothesis, 2) conceptual workflow, 3) abstract workflow, 4) concrete workflow, 5) production workflow, and 6) scientific results. The transitions between stages are facilitated by diverse tools and methods, usually incorporating domain knowledge in some form. Formal semantic domain modelling is hard and often a bottleneck for the application of semantic technologies. However, life science communities have made considerable progress here in recent years and are continuously improving, renewing interest in the application of semantic technologies for workflow exploration, composition and instantiation. Combined with systematic benchmarking with reference data and large-scale deployment of production-stage workflows, such technologies enable a more systematic process of workflow development than we know today. We believe that this can lead to more robust, reusable, and sustainable workflows in the future.",

keywords = "Automated workflow composition, Bioinformatics, Computational pipelines, Life sciences, Scientific workflows, Semantic domain modelling, Workflow benchmarking",

author = "Lamprecht, {Anna Lena} and Magnus Palmblad and Jon Ison and Veit Schw{\"a}mmle and {Al Manir}, {Mohammad Sadnan} and Ilkay Altintas and Baker, {Christopher J.O.} and {Ben Hadj Amor}, Ammar and Salvador Capella-Gutierrez and Paulos Charonyktakis and Crusoe, {Michael R.} and Yolanda Gil and Carole Goble and Griffin, {Timothy J.} and Paul Groth and Hans Ienasescu and Pratik Jagtap and Mat{\'u}{\v s} Kala{\v s} and Vedran Kasalica and Alireza Khanteymoori and Tobias Kuhn and Hailiang Mei and Herv{\'e} M{\'e}nager and Steffen M{\"o}ller and Richardson, {Robin A.} and Vincent Robert and Stian Soiland-Reyes and Robert Stevens and Szoke Szaniszlo and Suzan Verberne and Aswin Verhoeven and Katherine Wolstencroft",

note = "Funding Information: We would like to thank all of the people that contributed to the construction of this data set, and in particular the XGUMS group (besides the authors: Massimo Frapiccini from OGSM, Macerata; Marco Romanelli, Giorgio Dur{\`i} and Fausto Ponton from OGS, Trieste; Guido Meton and Bruno della Vedova from DINMA, Trieste; Dino Bindi, Giacomo Carenzo, Elena Eva, Valeria Lanza, Stefano Parolai, Marco Pasta and Enzo Zunino from Dister – Genova; Giuliano Milana from SSN-Roma; Luisa Filippi and Elisa Zambonelli from the {\textquoteleft}Universit{\`a} dell{\textquoteright}Aquila{\textquoteright}). The field operation was sponsored by the CNR-GNDT and the SSN. Publisher Copyright: {\textcopyright} 2021 Lamprecht AL et al.",

year = "2021",

month = oct,

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doi = "10.12688/f1000research.54159.1",

language = "English",

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Lamprecht, AL, Palmblad, M, Ison, J, Schwämmle, V, Al Manir, MS, Altintas, I, Baker, CJO, Ben Hadj Amor, A, Capella-Gutierrez, S, Charonyktakis, P, Crusoe, MR, Gil, Y, Goble, C, Griffin, TJ, Groth, P, Ienasescu, H, Jagtap, P, Kalaš, M, Kasalica, V, Khanteymoori, A, Kuhn, T, Mei, H, Ménager, H, Möller, S, Richardson, RA, Robert, V, Soiland-Reyes, S, Stevens, R, Szaniszlo, S, Verberne, S, Verhoeven, A & Wolstencroft, K 2021, 'Perspectives on automated composition of workflows in the life sciences', F1000Research, vol. 10, 897, pp. 1-28. https://doi.org/10.12688/f1000research.54159.1

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T1 - Perspectives on automated composition of workflows in the life sciences

AU - Lamprecht, Anna Lena

AU - Palmblad, Magnus

AU - Ison, Jon

AU - Schwämmle, Veit

AU - Al Manir, Mohammad Sadnan

AU - Altintas, Ilkay

AU - Baker, Christopher J.O.

AU - Ben Hadj Amor, Ammar

AU - Capella-Gutierrez, Salvador

AU - Charonyktakis, Paulos

AU - Crusoe, Michael R.

AU - Gil, Yolanda

AU - Goble, Carole

AU - Griffin, Timothy J.

AU - Groth, Paul

AU - Ienasescu, Hans

AU - Jagtap, Pratik

AU - Kalaš, Matúš

AU - Kasalica, Vedran

AU - Khanteymoori, Alireza

AU - Kuhn, Tobias

AU - Mei, Hailiang

AU - Ménager, Hervé

AU - Möller, Steffen

AU - Richardson, Robin A.

AU - Robert, Vincent

AU - Soiland-Reyes, Stian

AU - Stevens, Robert

AU - Szaniszlo, Szoke

AU - Verberne, Suzan

AU - Verhoeven, Aswin

AU - Wolstencroft, Katherine

N1 - Funding Information: We would like to thank all of the people that contributed to the construction of this data set, and in particular the XGUMS group (besides the authors: Massimo Frapiccini from OGSM, Macerata; Marco Romanelli, Giorgio Durì and Fausto Ponton from OGS, Trieste; Guido Meton and Bruno della Vedova from DINMA, Trieste; Dino Bindi, Giacomo Carenzo, Elena Eva, Valeria Lanza, Stefano Parolai, Marco Pasta and Enzo Zunino from Dister – Genova; Giuliano Milana from SSN-Roma; Luisa Filippi and Elisa Zambonelli from the ‘Università dell’Aquila’). The field operation was sponsored by the CNR-GNDT and the SSN. Publisher Copyright: © 2021 Lamprecht AL et al.

PY - 2021/10/28

Y1 - 2021/10/28

N2 - Scientific data analyses often combine several computational tools in automated pipelines, or workflows. Thousands of such workflows have been used in the life sciences, though their composition has remained a cumbersome manual process due to a lack of standards for annotation, assembly, and implementation. Recent technological advances have returned the long-standing vision of automated workflow composition into focus. This article summarizes a recent Lorentz Center workshop dedicated to automated composition of workflows in the life sciences. We survey previous initiatives to automate the composition process, and discuss the current state of the art and future perspectives. We start by drawing the 'big picture' of the scientific workflow development life cycle, before surveying and discussing current methods, technologies and practices for semantic domain modelling, automation in workflow development, and workflow assessment. Finally, we derive a roadmap of individual and community-based actions to work toward the vision of automated workflow development in the forthcoming years. A central outcome of the workshop is a general description of the workflow life cycle in six stages: 1) scientific question or hypothesis, 2) conceptual workflow, 3) abstract workflow, 4) concrete workflow, 5) production workflow, and 6) scientific results. The transitions between stages are facilitated by diverse tools and methods, usually incorporating domain knowledge in some form. Formal semantic domain modelling is hard and often a bottleneck for the application of semantic technologies. However, life science communities have made considerable progress here in recent years and are continuously improving, renewing interest in the application of semantic technologies for workflow exploration, composition and instantiation. Combined with systematic benchmarking with reference data and large-scale deployment of production-stage workflows, such technologies enable a more systematic process of workflow development than we know today. We believe that this can lead to more robust, reusable, and sustainable workflows in the future.

AB - Scientific data analyses often combine several computational tools in automated pipelines, or workflows. Thousands of such workflows have been used in the life sciences, though their composition has remained a cumbersome manual process due to a lack of standards for annotation, assembly, and implementation. Recent technological advances have returned the long-standing vision of automated workflow composition into focus. This article summarizes a recent Lorentz Center workshop dedicated to automated composition of workflows in the life sciences. We survey previous initiatives to automate the composition process, and discuss the current state of the art and future perspectives. We start by drawing the 'big picture' of the scientific workflow development life cycle, before surveying and discussing current methods, technologies and practices for semantic domain modelling, automation in workflow development, and workflow assessment. Finally, we derive a roadmap of individual and community-based actions to work toward the vision of automated workflow development in the forthcoming years. A central outcome of the workshop is a general description of the workflow life cycle in six stages: 1) scientific question or hypothesis, 2) conceptual workflow, 3) abstract workflow, 4) concrete workflow, 5) production workflow, and 6) scientific results. The transitions between stages are facilitated by diverse tools and methods, usually incorporating domain knowledge in some form. Formal semantic domain modelling is hard and often a bottleneck for the application of semantic technologies. However, life science communities have made considerable progress here in recent years and are continuously improving, renewing interest in the application of semantic technologies for workflow exploration, composition and instantiation. Combined with systematic benchmarking with reference data and large-scale deployment of production-stage workflows, such technologies enable a more systematic process of workflow development than we know today. We believe that this can lead to more robust, reusable, and sustainable workflows in the future.

KW - Automated workflow composition

KW - Bioinformatics

KW - Computational pipelines

KW - Life sciences

KW - Scientific workflows

KW - Semantic domain modelling

KW - Workflow benchmarking

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Perspectives on automated composition of workflows in the life sciences

Abstract

Bibliographical note

Keywords

UN SDGs

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