Name
Title | ||
|---|---|---|
Strand-seq enables reliable separation of long reads by chromosome via expectation maximization. |
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 x coverage. To our knowledge, SaaRclust is the first approach for the in silico separation of long reads by chromosome prior to assembly. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1093/bioinformatics/bty290 | BIOINFORMATICS |
Field | DocType | Volume |
Genome,Data mining,Chromosome,Coding strand,Computer science,Expectation–maximization algorithm,Latent variable model,Posterior probability,Computational biology,Sequence assembly | Journal | 34 |
Issue | ISSN | Citations |
13 | 1367-4803 | 0 |
PageRank | References | Authors |
0.34 | 3 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Maryam Ghareghani | 1 | 0 | 0.34 |
| David Porubsky | 2 | 0 | 0.34 |
| Ashley D. Sanders | 3 | 0 | 1.35 |
| Sascha Meiers | 4 | 0 | 0.34 |
| Evan E. Eichler | 5 | 156 | 16.89 |
| Jan O. Korbel | 6 | 111 | 10.36 |
| Tobias Marschall | 7 | 121 | 13.65 |