Biology subjects

Salmela, L.

Publications and source records attributed to Salmela, L..

3 recordsLinked to original sources

bioRxiv2018

Error Correcting Optical Mapping Data

Optical mapping is a unique system that is capable of producing high-resolution, high-throughput genomic map data that gives information about the structure of a genome [21]. Recently it has been used for scaffolding contigs and assembly validation for large-scale sequencing projects, including the maize [32], goat [6], and amborella [4] genomes. However, a major impediment in the use of this data is the variety and quantity of errors in the raw optical mapping data, which are called Rmaps. The challenges associated with using Rmap data are analogous to dealing with insertions and deletions in the alignment of long reads. Moreover, they are arguably harder to tackle since the data is numerical and susceptible to inaccuracy. We develop cOMet to error correct Rmap data, which to the best of our knowledge is the only optical mapping error correction method. Our experimental results demonstrate that cOMet has high prevision and corrects 82.49% of insertion errors and 77.38% of deletion errors in Rmap data generated from the E. coli K-12 reference genome. Out of the deletion errors corrected, 98.26% are true errors. Similarly, out of the insertion errors corrected, 82.19% are true errors. It also successfully scales to large genomes, improving the quality of 78% and 99% of the Rmaps in the plum and goat genomes, respectively. Lastly, we show the utility of error correction by demonstrating how it improves the assembly of Rmap data. Error corrected Rmap data results in an assembly that is more contiguous, and covers a larger fraction of the genome.

bioinformatics↗

bioRxiv2017

An Experiment in Learning the Language of Sequence Motifs: Sequence Logos vs. Finite-State Machines

Position weight matrices (PWMs) are the standard way to model binding site affinities in bioinformatics. However, they assume that symbol occurrences are position independent and, hence, they do not take into account symbols co-occurrence at different sequence positions. To address this problem, we propose to construct finite-state machines (FSMs) instead. A modified version of the Evidence-Driven State Merging (EDSM) heuristic is used to reduce the number of states as FSMs grow too quickly as a function of the number of sequences to reveal any useful structure. We tested our approach on sequence data for the transcription factor HNF4 and found out that the constructed FSMs provide small representations and an intuitive visualization. Furthermore, the FSM was better than PWMs at discriminating the positive and negative sequences in our data set.

bioinformatics↗

bioRxiv2016

Disentangled Long-Read De Bruijn Graphs via Optical Maps

Pacific Biosciences (PacBio), the main third generation sequencing technology can produce scalable, high-throughput, unprecedented sequencing results through long reads with uniform coverage. Although these long reads have been shown to increase the quality of draft genomes in repetitive regions, fundamental computational challenges remain in overcoming their high error rate and assembling them efficiently. In this paper we show that the de Bruijn graph built on the long reads can be efficiently and substantially disentangled using optical mapping data as auxiliary information. Fundamental to our approach is the use of the positional de Bruijn graph and a succinct data structure for constructing and traversing this graph. Our experimental results show that over 97.7% of directed cycles have been removed from the resulting positional de Bruijn graph as compared to its non-positional counterpart. Our results thus indicate that disentangling the de Bruijn graph using positional information is a promising direction for developing a simple and efficient assembly algorithm for long reads.

bioinformatics↗