Relevant bibliographies by topics / Basecalling

Academic literature on the topic 'Basecalling'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Basecalling"

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Walther, D., G. Bartha, and M. Morris. "Basecalling with LifeTrace." Genome Research 11, no. 5 (May 1, 2001): 875–88. http://dx.doi.org/10.1101/gr.177901.

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Boufounos, Petros, Sameh El-Difrawy, and Dan Ehrlich. "Basecalling using hidden Markov models." Journal of the Franklin Institute 341, no. 1-2 (January 2004): 23–36. http://dx.doi.org/10.1016/j.jfranklin.2003.12.008.

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Elbialy, Ali, M. A. El-Dosuky, and Ibrahim M. El-Henawy. "Quality of Third Generation Sequencing." Journal of Computational and Theoretical Nanoscience 17, no. 12 (December 1, 2020): 5205–9. http://dx.doi.org/10.1166/jctn.2020.9630.

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Third generation sequencing (TGS) relates to long reads but with relatively high error rates. Quality of TGS is a hot topic, dealing with errors. This paper combines and investigates three quality related metrics. They are basecalling accuracy, Phred Quality Scores, and GC content. For basecalling accuracy, a deep neural network is adopted. The measured loss does not exceed 5.42.
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Napieralski, Adam, and Robert Nowak. "Basecalling Using Joint Raw and Event Nanopore Data Sequence-to-Sequence Processing." Sensors 22, no. 6 (March 15, 2022): 2275. http://dx.doi.org/10.3390/s22062275.

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Third-generation DNA sequencers provided by Oxford Nanopore Technologies (ONT) produce a series of samples of an electrical current in the nanopore. Such a time series is used to detect the sequence of nucleotides. The task of translation of current values into nucleotide symbols is called basecalling. Various solutions for basecalling have already been proposed. The earlier ones were based on Hidden Markov Models, but the best ones use neural networks or other machine learning models. Unfortunately, achieved accuracy scores are still lower than competitive sequencing techniques, like Illumina’s. Basecallers differ in the input data type—currently, most of them work on a raw data straight from the sequencer (time series of current). Still, the approach of using event data is also explored. Event data is obtained by preprocessing of raw data and dividing it into segments described by several features computed from raw data values within each segment. We propose a novel basecaller that uses joint processing of raw and event data. We define basecalling as a sequence-to-sequence translation, and we use a machine learning model based on an encoder–decoder architecture of recurrent neural networks. Our model incorporates twin encoders and an attention mechanism. We tested our solution on simulated and real datasets. We compare the full model accuracy results with its components: processing only raw or event data. We compare our solution with the existing ONT basecaller—Guppy. Results of numerical experiments show that joint raw and event data processing provides better basecalling accuracy than processing each data type separately. We implement an application called Ravvent, freely available under MIT licence.
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Liang, Kuo-ching, Xiaodong Wang, and Dimitris Anastassiou. "Bayesian Basecalling for DNA Sequence Analysis Using Hidden Markov Models." IEEE/ACM Transactions on Computational Biology and Bioinformatics 4, no. 3 (July 2007): 430–40. http://dx.doi.org/10.1109/tcbb.2007.1027.

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Bonet, Jose, Mandi Chen, Marc Dabad, Simon Heath, Abel Gonzalez-Perez, Nuria Lopez-Bigas, and Jens Lagergren. "DeepMP: a deep learning tool to detect DNA base modifications on Nanopore sequencing data." Bioinformatics 38, no. 5 (October 28, 2021): 1235–43. http://dx.doi.org/10.1093/bioinformatics/btab745.

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Abstract Motivation DNA methylation plays a key role in a variety of biological processes. Recently, Nanopore long-read sequencing has enabled direct detection of these modifications. As a consequence, a range of computational methods have been developed to exploit Nanopore data for methylation detection. However, current approaches rely on a human-defined threshold to detect the methylation status of a genomic position and are not optimized to detect sites methylated at low frequency. Furthermore, most methods use either the Nanopore signals or the basecalling errors as the model input and do not take advantage of their combination. Results Here, we present DeepMP, a convolutional neural network-based model that takes information from Nanopore signals and basecalling errors to detect whether a given motif in a read is methylated or not. Besides, DeepMP introduces a threshold-free position modification calling model sensitive to sites methylated at low frequency across cells. We comprehensively benchmarked DeepMP against state-of-the-art methods on Escherichia coli, human and pUC19 datasets. DeepMP outperforms current approaches at read-based and position-based methylation detection across sites methylated at different frequencies in the three datasets. Availability and implementation DeepMP is implemented and freely available under MIT license at https://github.com/pepebonet/DeepMP. Supplementary information Supplementary data are available at Bioinformatics online.
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Zhan, Y., and D. Kulp. "Model-P: a basecalling method for resequencing microarrays of diploid samples." Bioinformatics 21, Suppl 2 (September 1, 2005): ii182—ii189. http://dx.doi.org/10.1093/bioinformatics/bti1129.

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Tonazzini, Anna, and Luigi Bedini. "Statistical analysis of electrophoresis time series for improving basecalling in DNA sequencing." International Journal of Signal and Imaging Systems Engineering 1, no. 1 (2008): 36. http://dx.doi.org/10.1504/ijsise.2008.017772.

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Lou, Qian, and Lei Jiang. "BRAWL: A Spintronics-Based Portable Basecalling-in-Memory Architecture for Nanopore Genome Sequencing." IEEE Computer Architecture Letters 17, no. 2 (July 1, 2018): 241–44. http://dx.doi.org/10.1109/lca.2018.2882384.

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Dumschott, Kathryn, Maximilian H.-W. Schmidt, Harmeet Singh Chawla, Rod Snowdon, and Björn Usadel. "Oxford Nanopore sequencing: new opportunities for plant genomics?" Journal of Experimental Botany 71, no. 18 (May 27, 2020): 5313–22. http://dx.doi.org/10.1093/jxb/eraa263.

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Abstract DNA sequencing was dominated by Sanger’s chain termination method until the mid-2000s, when it was progressively supplanted by new sequencing technologies that can generate much larger quantities of data in a shorter time. At the forefront of these developments, long-read sequencing technologies (third-generation sequencing) can produce reads that are several kilobases in length. This greatly improves the accuracy of genome assemblies by spanning the highly repetitive segments that cause difficulty for second-generation short-read technologies. Third-generation sequencing is especially appealing for plant genomes, which can be extremely large with long stretches of highly repetitive DNA. Until recently, the low basecalling accuracy of third-generation technologies meant that accurate genome assembly required expensive, high-coverage sequencing followed by computational analysis to correct for errors. However, today’s long-read technologies are more accurate and less expensive, making them the method of choice for the assembly of complex genomes. Oxford Nanopore Technologies (ONT), a third-generation platform for the sequencing of native DNA strands, is particularly suitable for the generation of high-quality assemblies of highly repetitive plant genomes. Here we discuss the benefits of ONT, especially for the plant science community, and describe the issues that remain to be addressed when using ONT for plant genome sequencing.
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Book chapters on the topic "Basecalling"

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Grzesik, Piotr, and Dariusz Mrozek. "Serverless Nanopore Basecalling with AWS Lambda." In Computational Science – ICCS 2021, 578–86. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-77964-1_44.

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Conference papers on the topic "Basecalling"

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Wu, ZhongPan, Karim Hammad, Robinson Mittmann, Sebastian Magierowski, Ebrahim Ghafar-Zadeh, and Xiaoyong Zhong. "FPGA-based DNA Basecalling Hardware Acceleration." In 2018 IEEE 61st International Midwest Symposium on Circuits and Systems (MWSCAS). IEEE, 2018. http://dx.doi.org/10.1109/mwscas.2018.8623988.

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Wang, Chengjie, Junli Zheng, Sebastian Magierowski, and Ebrahim Ghafar-Zadeh. "Embedded CMOS basecalling for nanopore DNA sequencing." In 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2016. http://dx.doi.org/10.1109/embc.2016.7592032.

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Huang, Yiyun, Sebastian Magierowski, and Ebrahim Ghafar-Zadeh. "CMOS for high-speed nanopore DNA basecalling." In 2016 IEEE International Symposium on Circuits and Systems (ISCAS). IEEE, 2016. http://dx.doi.org/10.1109/iscas.2016.7527431.

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Thornley, David, and Stavros Petridis. "Machine Learning in Basecalling ¿ Decoding Trace Peak Behaviour." In 2006 IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology. IEEE, 2006. http://dx.doi.org/10.1109/cibcb.2006.330992.

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Liang, Kuo-ching, Xiaodong Wang, and Dimitris Anastassiou. "Bayesian Basecalling for DNA Sequence Analysis using Hidden Markov Models." In 2006 40th Annual Conference on Information Sciences and Systems. IEEE, 2006. http://dx.doi.org/10.1109/ciss.2006.286391.

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Mao, Haiyu, Mohammed Alser, Mohammad Sadrosadati, Can Firtina, Akanksha Baranwal, Damla Senol Cali, Aditya Manglik, Nour Almadhoun Alserr, and Onur Mutlu. "GenPIP: In-Memory Acceleration of Genome Analysis via Tight Integration of Basecalling and Read Mapping." In 2022 55th IEEE/ACM International Symposium on Microarchitecture (MICRO). IEEE, 2022. http://dx.doi.org/10.1109/micro56248.2022.00056.

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