Oxford Ionics

Miten Jain, Hugh E. Olsen, B. Paten, M. Akeson

Nanopore DNA strand sequencing has emerged as a competitive, portable technology. Reads exceeding 150 kilobases have been achieved, as have in-field detection and analysis of clinical pathogens. We summarize key technical features of the Oxford Nanopore MinION, the dominant platform currently available. We then discuss pioneering applications executed by the genomics community.

Mian Umair Ahsan, Anagha Gouru, Joe Chan, Wanding Zhou, Kai Wang

Oxford Nanopore sequencing can detect DNA methylations from ionic current signal of single molecules, offering a unique advantage over conventional methods. Additionally, adaptive sampling, a software-controlled enrichment method for targeted sequencing, allows reduced representation methylation sequencing that can be applied to CpG islands or imprinted regions. Here we present DeepMod2, a comprehensive deep-learning framework for methylation detection using ionic current signal from Nanopore sequencing. DeepMod2 implements both a bidirectional long short-term memory (BiLSTM) model and a Transformer model and can analyze POD5 and FAST5 signal files generated on R9 and R10 flowcells. Additionally, DeepMod2 can run efficiently on central processing unit (CPU) through model pruning and can infer epihaplotypes or haplotype-specific methylation calls from phased reads. We use multiple publicly available and newly generated datasets to evaluate the performance of DeepMod2 under varying scenarios. DeepMod2 has comparable performance to Guppy and Dorado, which are the current state-of-the-art methods from Oxford Nanopore Technologies that remain closed-source. Moreover, we show a high correlation (r = 0.96) between reduced representation and whole-genome Nanopore sequencing. In summary, DeepMod2 is an open-source tool that enables fast and accurate DNA methylation detection from whole-genome or adaptive sequencing data on a diverse range of flowcell types.

Yong Zhi Khow, M. Liow, G. Goh, J. Chen, N. Lo, S. Yeo

BACKGROUND Minimal clinically important difference (MCID) is crucial for interpreting meaningful improvements in patient-reported outcome measures (PROMs). No previous study has evaluated the MCID for the Oxford Knee Score (OKS) in revision total knee arthroplasty (TKA). This study aimed to propose the OKS MCID for revision TKA. METHODS Prospectively collected data from 191 patients who underwent revision TKA at a single institution was analysed. Clinical assessment was performed preoperatively and at 2 years using OKS and Short-Form 36 Physical Component Score (SF-36 PCS). MCID was evaluated with a three-pronged methodology, using (1) anchor-based method with linear regression, (2) anchor-based method with receiver operating characteristic (ROC) and area under curve (AUC), (3) distribution-based method with standard deviation (SD). The anchors used were improvement in SF-36 PCS ≥ 12, patient satisfaction, and implant survivorship following revision TKA. RESULTS The MCID determined by anchor-based linear regression method using improvements in SF-36 PCS was 4.9 points. The MCID determined by anchor-based ROC was 10.5 points for satisfaction (AUC = 74.8%) and 13.5 points for implant survivorship (AUC = 73.7%). The MCID determined by distribution-based method of 0.5 SD was 4.7. CONCLUSION The proposed MCID for OKS following revision TKA is 4.9 points. Patients who achieve an improvement in OKS of at least 10.5-13.5 points by 2 years are likely to be satisfied with their surgery and not require a subsequent re-revision TKA. Patients undergoing revision TKA should aim for an improvement in OKS of at least 10.5-13.5 points as a target score.

Yoshihito Suda, T. Hiranaka, Tomoyuki Kamenaga, T. Fujishiro, K. Okamoto, Tomoyuki Matsumoto

PURPOSE To evaluate the bearing orbit of the tibial component during extension-flexion motion in Oxford unicompartmental knee arthroplasty. MATERIALS AND METHODS A total of 32 knees in 25 patients with medial osteoarthritis who underwent Oxford unicompartmental knee arthroplasty were evaluated. The distance between the vertical wall of the tibial component and the bearing (wall-bearing distance) and that between the anterior edge of the tibial component and the bearing (sagittal bearing position) were measured at 0°, 30°, 60°, 90° and 120° knee flexion with neutral tibial rotation (extension-flexion motion), and internal and external tibial rotation with 90° knee flexion (tibial rotation motion). A custom-made rounded trial bearing and caliper were used for this measurement. We calculated the wall-bearing distance, change in extension-flexion motion and tibial rotation motion. Wall-bearing distances and change in wall-bearing distance were compared using ANOVA or t-test. RESULTS The wall-bearing distance was smallest at 60° and increased 1.0 ± 1.1 mm in knee extension and 1.1 ± 1.5 mm in knee flexion. The bearing moved posteriorly with knee flexion, and the sagittal bearing position increased by 8.1 ± 3.4 mm during extension-flexion motion. Consequently, the bearing moved in a rough C-shaped orbit of the tibial component. CONCLUSIONS The mobile bearing moves in a rough C-shape and is mostly close to the vertical wall of the tibial component at 60°. The wall-bearing distance can change during extension-flexion motion and might be influenced by tibial component rotation. To avoid bearing separation from or contact with the vertical wall that may cause bearing dislocation, the wall-bearing distance should be evaluated before keel slot preparation.

Xiuquan Wang, Mian Umair Ahsan, Yunyun Zhou, Kai Wang

Transformer is an algorithm that adopts self‐attention architecture in the neural networks and has been widely used in natural language processing. In the current study, we apply Transformer architecture to detect DNA methylation on ionic signals from Oxford Nanopore sequencing data. We evaluated this idea using real data sets (Escherichia coli data and the human genome NA12878 sequenced by Simpson et al.) and demonstrated the ability of Transformers to detect methylation on ionic signal data.