Seungmyung Lee1, Hanjoo Kim1, Siqi Tian2, Taehoon Lee1, Sungroh Yoon3, Rhiju Das4. 1. Department of ECE, Seoul National University, Seoul 151-744, Korea. 2. Department of Biochemistry, Stanford University School of Medicine, Stanford, CA 94305, USA. 3. Department of ECE, Seoul National University, Seoul 151-744, Korea, Interdisciplinary Program in Bionformatics, Seoul National University, Seoul 151-744, Korea and. 4. Department of Biochemistry, Stanford University School of Medicine, Stanford, CA 94305, USA, Department of Physics, Stanford University, Stanford, CA 94305, USA.
Abstract
MOTIVATION: Capillary electrophoresis (CE) is a powerful approach for structural analysis of nucleic acids, with recent high-throughput variants enabling three-dimensional RNA modeling and the discovery of new rules for RNA structure design. Among the steps composing CE analysis, the process of finding each band in an electrophoretic trace and mapping it to a position in the nucleic acid sequence has required significant manual inspection and remains the most time-consuming and error-prone step. The few available tools seeking to automate this band annotation have achieved limited accuracy and have not taken advantage of information across dozens of profiles routinely acquired in high-throughput measurements. RESULTS: We present a dynamic-programming-based approach to automate band annotation for high-throughput capillary electrophoresis. The approach is uniquely able to define and optimize a robust target function that takes into account multiple CE profiles (sequencing ladders, different chemical probes, different mutants) collected for the RNA. Over a large benchmark of multi-profile datasets for biological RNAs and designed RNAs from the EteRNA project, the method outperforms prior tools (QuSHAPE and FAST) significantly in terms of accuracy compared with gold-standard manual annotations. The amount of computation required is reasonable at a few seconds per dataset. We also introduce an 'E-score' metric to automatically assess the reliability of the band annotation and show it to be practically useful in flagging uncertainties in band annotation for further inspection. AVAILABILITY AND IMPLEMENTATION: The implementation of the proposed algorithm is included in the HiTRACE software, freely available as an online server and for download at http://hitrace.stanford.edu. CONTACT: sryoon@snu.ac.kr or rhiju@stanford.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: Capillary electrophoresis (CE) is a powerful approach for structural analysis of nucleic acids, with recent high-throughput variants enabling three-dimensional RNA modeling and the discovery of new rules for RNA structure design. Among the steps composing CE analysis, the process of finding each band in an electrophoretic trace and mapping it to a position in the nucleic acid sequence has required significant manual inspection and remains the most time-consuming and error-prone step. The few available tools seeking to automate this band annotation have achieved limited accuracy and have not taken advantage of information across dozens of profiles routinely acquired in high-throughput measurements. RESULTS: We present a dynamic-programming-based approach to automate band annotation for high-throughput capillary electrophoresis. The approach is uniquely able to define and optimize a robust target function that takes into account multiple CE profiles (sequencing ladders, different chemical probes, different mutants) collected for the RNA. Over a large benchmark of multi-profile datasets for biological RNAs and designed RNAs from the EteRNA project, the method outperforms prior tools (QuSHAPE and FAST) significantly in terms of accuracy compared with gold-standard manual annotations. The amount of computation required is reasonable at a few seconds per dataset. We also introduce an 'E-score' metric to automatically assess the reliability of the band annotation and show it to be practically useful in flagging uncertainties in band annotation for further inspection. AVAILABILITY AND IMPLEMENTATION: The implementation of the proposed algorithm is included in the HiTRACE software, freely available as an online server and for download at http://hitrace.stanford.edu. CONTACT: sryoon@snu.ac.kr or rhiju@stanford.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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