Biology subjects

Han, R.

Publications and source records attributed to Han, R..

57 records · Page 4Linked to original sources

bioRxiv2018

Mate-pair Library Construction with Controlled Polymerization Enables Comprehensive Structural Rearrangement Detection

The diversity of disease presentations warrants one single assay for detection and delineation of various genomic disorders. Herein, we describe a gel-free and biotin-capture-free mate-pair method through coupling Controlled Polymerizations by Adapter-Ligation (CP-AL). We first demonstrated the feasibility and ease-of-use in monitoring DNA nick-translation and primer extension by limiting the nucleotide input. By coupling these two controlled polymerizations by a reported non-conventional adapter ligation reaction 3 branch ligation, we evidenced that CP-AL significantly increased DNA-circularization efficiency (by 4-fold) and was applicable for different sequencing methods but at a faction of current cost. Its advantages were further demonstrated by fully elimination of small-insert-contaminated (by 39.3-fold) with a ~50% increment of physical coverage, and producing uniform genome/exome coverage and the lowest chimeric rate. It achieved single-nucleotide variants detection with sensitivity and specificity up to 97.3 and 99.7%, respectively, compared with data from small-insert libraries. In addition, this method can provide a comprehensive delineation of structural rearrangements, evidenced by a potential diagnosis in a patient with oligo-atheno-terato-spermia. Moreover, it enables accurate mutation identification by integration of genomic variants from different aberration types. Overall, it provides a potential single-integrated solution for detecting various genomic variants, facilitating a genetic diagnosis in human diseases.

genomics↗

bioRxiv2017

An accurate and rapid continuous wavelet dynamic time warping algorithm for unbalanced global mapping in nanopore sequencing

Long-reads, point-of-care, and PCR-free are the promises brought by nanopore sequencing. Among various steps in nanopore data analysis, the global mapping between the raw electrical current signal sequence and the expected signal sequence from the pore model serves as the key building block to base calling, reads mapping, variant identification, and methylation detection. However, the ultra-long reads of nanopore sequencing and an order of magnitude difference in the sampling speeds of the two sequences make the classical dynamic time warping (DTW) and its variants infeasible to solve the problem. Here, we propose a novel multi-level DTW algorithm, cwDTW, based on continuous wavelet transforms with different scales of the two signal sequences. Our algorithm starts from low-resolution wavelet transforms of the two sequences, such that the transformed sequences are short and have similar sampling rates. Then the peaks and nadirs of the transformed sequences are extracted to form feature sequences with similar lengths, which can be easily mapped by the original DTW. Our algorithm then recursively projects the warping path from a lower-resolution level to a higher-resolution one by building a context-dependent boundary and enabling a constrained search for the warping path in the latter. Comprehensive experiments on two real nanopore datasets on human and on Pandoraea pnomenusa, as well as two benchmark datasets from previous studies, demonstrate the efficiency and effectiveness of the proposed algorithm. In particular, cwDTW can almost always generate warping paths that are very close to the original DTW, which are remarkably more accurate than the state-of-the-art methods including Fast-DTW and PrunedDTW. Meanwhile, on the real nanopore datasets, cwDTW is about 440 times faster than FastDTW and 3000 times faster than the original DTW. Our program is available at https://github.com/realbigws/cwDTW.

bioinformatics↗

bioRxiv2017

DeepSimulator: a deep simulator for Nanopore sequencing

MotivationOxford Nanopore sequencing is a rapidly developed sequencing technology in recent years. To keep pace with the explosion of the downstream data analytical tools, a versatile Nanopore sequencing simulator is needed to complement the experimental data as well as to benchmark those newly developed tools. However, all the currently available simulators are based on simple statistics of the produced reads, which have difficulty in capturing the complex nature of the Nanopore sequencing procedure, the main task of which is the generation of raw electrical current signals.\n\nResultsHere we propose a deep learning based simulator, DeepSimulator, to mimic the entire pipeline of Nanopore sequencing. Starting from a given reference genome or assembled contigs, we simulate the electrical current signals by a context-dependent deep learning model, followed by a base-calling procedure to yield simulated reads. This workflow mimics the sequencing procedure more naturally. The thorough experiments performed across four species show that the signals generated by our context-dependent model are more similar to the experimentally obtained signals than the ones generated by the official context-independent pore model. In terms of the simulated reads, we provide a parameter interface to users so that they can obtain the reads with different accuracies ranging from 83% to 97%. The reads generated by the default parameter have almost the same properties as the real data. Two case studies demonstrate the application of DeepSimulator to benefit the development of tools in de novo assembly and in low coverage SNP detection.\n\nAvailabilityThe software can be accessed freely at: https://github.com/lykaust15/deep_simulator.

bioinformatics↗