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| Item Type: | Article |
|---|---|
| Title: | Strand-seq enables reliable separation of long reads by chromosome via expectation maximization |
| Creators Name: | Ghareghani, M., Porubskỳ, D., Sanders, A.D., Meiers, S., Eichler, E.E., Korbel, J.O. and Marschall, T. |
| Abstract: | MOTIVATION: Current sequencing technologies are able to produce reads orders of magnitude longer than ever possible before. Such long reads have sparked a new interest in de novo genome assembly, which removes reference biases inherent to re-sequencing approaches and allows for a direct characterization of complex genomic variants. However, even with latest algorithmic advances, assembling a mammalian genome from long error-prone reads incurs a significant computational burden and does not preclude occasional misassemblies. Both problems could potentially be mitigated if assembly could commence for each chromosome separately. RESULTS: To address this, we show how single-cell template strand sequencing (Strand-seq) data can be leveraged for this purpose. We introduce a novel latent variable model and a corresponding Expectation Maximization algorithm, termed SaaRclust, and demonstrates its ability to reliably cluster long reads by chromosome. For each long read, this approach produces a posterior probability distribution over all chromosomes of origin and read directionalities. In this way, it allows to assess the amount of uncertainty inherent to sparse Strand-seq data on the level of individual reads. Among the reads that our algorithm confidently assigns to a chromosome, we observed more than 99% correct assignments on a subset of Pacific Bioscience reads with 30.1× coverage. To our knowledge, SaaRclust is the first approach for the in silico separation of long reads by chromosome prior to assembly. AVAILABILITY AND IMPLEMENTATION: https://github.com/daewoooo/SaaRclust. |
| Keywords: | Algorithms, Computer Simulation, DNA Sequence Analysis, Genomics, High-Throughput Nucleotide Sequencing, Human Chromosomes, Human Genome, Software |
| Source: | Bioinformatics |
| ISSN: | 1367-4803 |
| Publisher: | Oxford University Press |
| Volume: | 34 |
| Number: | 13 |
| Page Range: | i115-i123 |
| Date: | 1 July 2018 |
| Official Publication: | https://doi.org/10.1093/bioinformatics/bty290 |
| PubMed: | View item in PubMed |
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