The impact of various seed, accessibility and interaction constraints on sRNA target prediction- a systematic assessment
Abstract Background Seed and accessibility constraints are core features to enable highly accurate sRNA target screens based on RNA-RNA interaction prediction. Currently, available tools provide different (sets of) constraints and default parameter sets. Thus, it is hard to impossible for users to e...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2020-01-01
|
| Series: | BMC Bioinformatics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12859-019-3143-4 |
| _version_ | 1819056385085669376 |
|---|---|
| author | Martin Raden Teresa Müller Stefan Mautner Rick Gelhausen Rolf Backofen |
| author_facet | Martin Raden Teresa Müller Stefan Mautner Rick Gelhausen Rolf Backofen |
| author_sort | Martin Raden |
| collection | DOAJ |
| description | Abstract Background Seed and accessibility constraints are core features to enable highly accurate sRNA target screens based on RNA-RNA interaction prediction. Currently, available tools provide different (sets of) constraints and default parameter sets. Thus, it is hard to impossible for users to estimate the influence of individual restrictions on the prediction results. Results Here, we present a systematic assessment of the impact of established and new constraints on sRNA target prediction both on a qualitative as well as computational level. This is done exemplarily based on the performance of IntaRNA, one of the most exact sRNA target prediction tools. IntaRNA provides various ways to constrain considered seed interactions, e.g. based on seed length, its accessibility, minimal unpaired probabilities, or energy thresholds, beside analogous constraints for the overall interaction. Thus, our results reveal the impact of individual constraints and their combinations. Conclusions This provides both a guide for users what is important and recommendations for existing and upcoming sRNA target prediction approaches.We show on a large sRNA target screen benchmark data set that only by altering the parameter set, IntaRNA recovers 30% more verified interactions while becoming 5-times faster. This exemplifies the potential of seed, accessibility and interaction constraints for sRNA target prediction. |
| first_indexed | 2024-12-21T13:22:34Z |
| format | Article |
| id | doaj.art-91830773cdd540379923383df9160b68 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-12-21T13:22:34Z |
| publishDate | 2020-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-91830773cdd540379923383df9160b682022-12-21T19:02:33ZengBMCBMC Bioinformatics1471-21052020-01-0121111110.1186/s12859-019-3143-4The impact of various seed, accessibility and interaction constraints on sRNA target prediction- a systematic assessmentMartin Raden0Teresa Müller1Stefan Mautner2Rick Gelhausen3Rolf Backofen4Bioinformatics Group, Department of Computer Science, University of FreiburgBioinformatics Group, Department of Computer Science, University of FreiburgBioinformatics Group, Department of Computer Science, University of FreiburgBioinformatics Group, Department of Computer Science, University of FreiburgBioinformatics Group, Department of Computer Science, University of FreiburgAbstract Background Seed and accessibility constraints are core features to enable highly accurate sRNA target screens based on RNA-RNA interaction prediction. Currently, available tools provide different (sets of) constraints and default parameter sets. Thus, it is hard to impossible for users to estimate the influence of individual restrictions on the prediction results. Results Here, we present a systematic assessment of the impact of established and new constraints on sRNA target prediction both on a qualitative as well as computational level. This is done exemplarily based on the performance of IntaRNA, one of the most exact sRNA target prediction tools. IntaRNA provides various ways to constrain considered seed interactions, e.g. based on seed length, its accessibility, minimal unpaired probabilities, or energy thresholds, beside analogous constraints for the overall interaction. Thus, our results reveal the impact of individual constraints and their combinations. Conclusions This provides both a guide for users what is important and recommendations for existing and upcoming sRNA target prediction approaches.We show on a large sRNA target screen benchmark data set that only by altering the parameter set, IntaRNA recovers 30% more verified interactions while becoming 5-times faster. This exemplifies the potential of seed, accessibility and interaction constraints for sRNA target prediction.https://doi.org/10.1186/s12859-019-3143-4RNA-RNA interactionsRNATarget predictionSeedAccessibilityConstraints |
| spellingShingle | Martin Raden Teresa Müller Stefan Mautner Rick Gelhausen Rolf Backofen The impact of various seed, accessibility and interaction constraints on sRNA target prediction- a systematic assessment BMC Bioinformatics RNA-RNA interaction sRNA Target prediction Seed Accessibility Constraints |
| title | The impact of various seed, accessibility and interaction constraints on sRNA target prediction- a systematic assessment |
| title_full | The impact of various seed, accessibility and interaction constraints on sRNA target prediction- a systematic assessment |
| title_fullStr | The impact of various seed, accessibility and interaction constraints on sRNA target prediction- a systematic assessment |
| title_full_unstemmed | The impact of various seed, accessibility and interaction constraints on sRNA target prediction- a systematic assessment |
| title_short | The impact of various seed, accessibility and interaction constraints on sRNA target prediction- a systematic assessment |
| title_sort | impact of various seed accessibility and interaction constraints on srna target prediction a systematic assessment |
| topic | RNA-RNA interaction sRNA Target prediction Seed Accessibility Constraints |
| url | https://doi.org/10.1186/s12859-019-3143-4 |
| work_keys_str_mv | AT martinraden theimpactofvariousseedaccessibilityandinteractionconstraintsonsrnatargetpredictionasystematicassessment AT teresamuller theimpactofvariousseedaccessibilityandinteractionconstraintsonsrnatargetpredictionasystematicassessment AT stefanmautner theimpactofvariousseedaccessibilityandinteractionconstraintsonsrnatargetpredictionasystematicassessment AT rickgelhausen theimpactofvariousseedaccessibilityandinteractionconstraintsonsrnatargetpredictionasystematicassessment AT rolfbackofen theimpactofvariousseedaccessibilityandinteractionconstraintsonsrnatargetpredictionasystematicassessment AT martinraden impactofvariousseedaccessibilityandinteractionconstraintsonsrnatargetpredictionasystematicassessment AT teresamuller impactofvariousseedaccessibilityandinteractionconstraintsonsrnatargetpredictionasystematicassessment AT stefanmautner impactofvariousseedaccessibilityandinteractionconstraintsonsrnatargetpredictionasystematicassessment AT rickgelhausen impactofvariousseedaccessibilityandinteractionconstraintsonsrnatargetpredictionasystematicassessment AT rolfbackofen impactofvariousseedaccessibilityandinteractionconstraintsonsrnatargetpredictionasystematicassessment |