LongISLND: <i>in silico</i> sequencing of lengthy and noisy datatypes

Lau, Bayo; Mohiyuddin, Marghoob; Mu, John C.; Fang, Li Tai; Asadi, Narges Bani; Dallett, Carolina; Lam, Hugo Y. K.

doi:10.1093/bioinformatics/btw602

Cited by 18 publications

(13 citation statements)

References 17 publications

(27 reference statements)

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“…To validate our simulation results we generated read profiles for Nanosim [6], PBSim [7], LONGIslnd [8], and the two simulators we have proposed. Nanopore sequencing experiments typically yield between 10,000 and 20,000 reads [6], with measured statistics being approximately identical at even 20% of this size as shown in Figure 1(C).…”

Section: Simulation Resultsmentioning

confidence: 99%

“…• We propose an algorithm for simulating reads, a variant of the Hidden Markov Model employed by Nanosim [6], with modifications to apply observed k-mer bias. We demonstrate that it models the k-mer error distribution better than other popular read simulators [6]- [8] (section IV-C).…”

Section: Introductionmentioning

confidence: 94%

“…LongISLND [8] is a read simulator developed for PacBio data. It models k-mer bias explicitly and directly by storing observed mutations for each k-mer it sees, stored as a keyvalue pair.…”

Section: Related Workmentioning

confidence: 99%

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SNaReSim: Synthetic Nanopore Read Simulator

Faucon

Balachandran

Crook

2017

Preprint

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Abstract-Nanopores represent the first commercial technology in decades to present a significantly different technique for DNA sequencing, and one of the first technologies to propose direct RNA sequencing. Despite significant differences with previous sequencing technologies, read simulators to date make similar assumptions with respect to error profiles and their analysis. This is a great disservice to both nanopore sequencing and to computer scientists who seek to optimize their tools for the platform. Previous works have discussed the occurrence of some k-mer bias, but this discussion has been focused on homopolymers, leaving unanswered the question of whether k-mer bias exists over general k-mers, how it occurs, and what can be done to reduce the effects. In this work, we demonstrate that current read simulators fail to accurately represent k-mer error distributions, We explore the sources of k-mer bias in nanopore basecalls, and we present a model for predicting k-mers that are difficult to identify. We also propose a new SNaReSim, a new state-of-theart simulator, and demonstrate that it provides higher accuracy with respect to 6-mer accuracy biases.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 94%

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SNaReSim: Synthetic Nanopore Read Simulator

Faucon

Balachandran

Crook

2017

Preprint

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“…We built the long-reads error profile using the CHM1 dataset [29] (SRA accession SRX533609). We then simulated a 100× pure normal sample using the VarSim simulation framework [30] in combination with the LongISLND in silico long-reads sequencer [31]. Using a set of random somatic mutations, we also simulated a 100× pure tumor sample with the same error profile.…”

Section: Pacbio Datamentioning

confidence: 99%

Deep convolutional neural networks for accurate somatic mutation detection

Sahraeian

Liu

Lau

et al. 2018

Preprint

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We present NeuSomatic, the first convolutional neural network approach for somatic mutation detection, which significantly outperforms previous methods on different sequencing platforms, sequencing strategies, and tumor purities. NeuSomatic summarizes sequence alignments into small matrices and incorporates more than a hundred features to capture mutation signals effectively. It can be used universally as a stand-alone somatic mutation detection method or with an ensemble of existing methods to achieve the highest accuracy.

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“…More recent technologies can produce very long reads, but at the expense of having higher costs and much higher error rates [11]. However, longer reads have been found to be more appropriate or better compared to short reads in certain studies.…”

Section: Introductionmentioning

confidence: 99%

Assembling reads improves taxonomic classification of species

Tran

Phan

2020

Preprint

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Background: Most current metagenomic classifiers and profilers employ short reads to classify, bin and profile microbial genomes that are present in metagenomic samples. Many of these methods adopt techniques that aim to identify unique genomic regions of genomes so as to differentiate them. Because of this, short-read lengths might be suboptimal. Longer read lengths might improve the performance of classification and profiling. However, longer reads produced by current technology tend to have a higher rate of sequencing errors, compared to short reads. It is not clear if the trade-off between longer length versus higher sequencing errors will increase or decrease classification and profiling performance.Results: We compared performance of popular metagenomic classifiers on short reads and longer reads, which are assembled from the same short reads. When using a number of popular assemblers to assemble long reads from the short reads, we discovered that most classifiers made fewer predictions with longer reads and that they achieved higher classification performance on synthetic metagenomic data. Specifically, across most classifiers, we observed a significant increase in precision, while recall remained the same, resulting in higher overall classification performance. On real metagenomic data, we observed a similar trend that classifiers made fewer predictions. This suggested that they might have the same performance characteristics of having higher precision while maintaining the same recall with longer reads.Conclusions: This finding has two main implications. First, it suggests that classifying species in metagenomic environments can be achieved with higher overall performance simply by assembling short reads. This suggested that they might have the same performance characteristics of having higher precision while maintaining the same recall as shorter reads. Second, this finding suggests that it might be a good idea to consider utilizing long-read technologies in species classification for metagenomic applications. Current long-read technologies tend to have higher sequencing errors and are more expensive compared to short-read technologies. The trade-offs between the pros and cons should be investigated.

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LongISLND: in silico sequencing of lengthy and noisy datatypes

Cited by 18 publications

References 17 publications

SNaReSim: Synthetic Nanopore Read Simulator

SNaReSim: Synthetic Nanopore Read Simulator

Deep convolutional neural networks for accurate somatic mutation detection

Assembling reads improves taxonomic classification of species

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