Biology subjects

Mile Sikic

Publications and source records attributed to Mile Sikic.

5 recordsLinked to original sources

bioRxiv2016

Edlib: A C/C++ library for fast, exact sequence alignment using edit distance

We present Edlib, an open-source C/C++ library for exact pairwise sequence alignment using edit distance. We compare Edlib to other libraries and show that it is the fastest while not lacking in functionality, and can also easily handle very large sequences. Being easy to use, flexible, fast and low on memory usage, we expect it to be a cornerstone for many future bioinformatics tools.\n\nSource code, installation instructions and test data are freely available for download at https://github.com/Martinsos/edlib, implemented in C/C++ and supported on Linux, MS Windows, and Mac OS.\n\nContact: mile.sikic@fer.hr

Bioinformatics↗

bioRxiv2016

Fast and accurate de novo genome assembly from long uncorrected reads

The assembly of long reads from Pacific Biosciences and Oxford Nanopore Technologies typically requires resource intensive error correction and consensus generation steps to obtain high quality assemblies. We show that the error correction step can be omitted and high quality consensus sequences can be generated efficiently with a SIMD accelerated, partial order alignment based stand-alone consensus module called Racon. Based on tests with PacBio and Oxford Nanopore datasets we show that Racon coupled with Miniasm enables consensus genomes with similar or better quality than state-of-the-art methods while being an order of magnitude faster.\n\nRacon is available open source under the MIT license at https://github.com/isovic/racon.git.

Bioinformatics↗

bioRxiv2015

Evaluation of hybrid and non-hybrid methods for de novo assembly of nanopore reads

Recent emergence of nanopore sequencing technology set a challenge for the established assembly methods not optimized for the combination of read lengths and high error rates of nanopore reads. In this work we assessed how existing de novo assembly methods perform on these reads. We benchmarked three non-hybrid (in terms of both error correction and scaffolding) assembly pipelines as well as two hybrid assemblers which use third generation sequencing data to scaffold Illumina assemblies. Tests were performed on several publicly available MinION and Illumina datasets of E. coli K-12, using several sequencing coverages of nanopore data (20x, 30x, 40x and 50x). We attempted to assess the quality of assembly at each of these coverages, to estimate the requirements for closed bacterial genome assembly. Results show that hybrid methods are highly dependent on the quality of NGS data, but much less on the quality and coverage of nanopore data and perform relatively well on lower nanopore coverages. Furthermore, when coverage is above 40x, all non-hybrid methods correctly assemble the E. coli genome, even a non-hybrid method tailored for Pacific Bioscience reads. While it requires higher coverage compared to a method designed particularly for nanopore reads, its running time is significantly lower.

Bioinformatics↗

bioRxiv2015

Fast and sensitive mapping of error-prone nanopore sequencing reads with GraphMap

Exploiting the power of nanopore sequencing requires the development of new bioinformatics approaches to deal with its specific error characteristics. We present the first nanopore read mapper (GraphMap) that uses a read-funneling paradigm to robustly handle variable error rates and fast graph traversal to align long reads with speed and very high precision (>95%). Evaluation on MinION sequencing datasets against short and long-read mappers indicates that GraphMap increases mapping sensitivity by at least 15-80%. GraphMap alignments are the first to demonstrate consensus calling with <1 error in 100,000 bases, variant calling on the human genome with 76% improvement in sensitivity over the next best mapper (BWA-MEM), precise detection of structural variants from 100bp to 4kbp in length and species and strain-specific identification of pathogens using MinION reads. GraphMap is available open source under the MIT license at https://github.com/isovic/graphmap.

Genomics↗

bioRxiv2015

SW#db: GPU-accelerated exact sequence similarity database search

The deluge of next-generation sequencing (NGS) data and expanding database poses higher requirements for protein similarity search. State-of-the-art tools such as BLAST are not fast enough to cope with these requirements. Because of that it is necessary to create new algorithms that will be faster while keeping similar sensitivity levels. The majority of protein similarity search methods are based on a seed-and-extend approach which uses standard dynamic programming algorithms in the extend phase. In this paper we present a SW#db tool and library for exact similarity search. Although its running times, as standalone tool, are comparable to running times of BLAST it is primarily designed for the extend phase where there are reduced number of candidates in the database. It uses both GPU and CPU parallelization and when we measured multiple queries on Swiss-prot and Uniref90 databases SW#db was 4 time faster than SSEARCH, 6-10 times faster than CUDASW++ and more than 20 times faster than SSW.

Bioinformatics↗