A genome alignment algorithm based on compression

Abstract

BackgroundTraditional genome alignment methods consider sequence alignment as a variation of the string edit distance problem, and perform alignment by matching characters of the two sequences. They are often computationally expensive and unable to deal with low information regions. Furthermore, they lack a well-principled objective function to measure the performance of sets of parameters. Since genomic sequences carry genetic information, this article proposes that the information content of each nucleotide in a position should be considered in sequence alignment. An information-theoretic approach for pairwise genome local alignment, namely XMAligner, is presented. Instead of comparing sequences at the character level, XMAligner considers a pair of nucleotides from two sequences to be related if their mutual information in context is significant. The information content of nucleotides in sequences is measured by a lossless compression technique.ResultsExperiments on both simulated data and real data show that XMAligner is superior to conventional methods especially on distantly related sequences and statistically biased data. XMAligner can align sequences of eukaryote genome size with only a modest hardware requirement. Importantly, the method has an objective function which can obviate the need to choose parameter values for high quality alignment. The alignment results from XMAligner can be integrated into a visualisation tool for viewing purpose.ConclusionsThe information-theoretic approach for sequence alignment is shown to overcome the mentioned problems of conventional character matching alignment methods. The article shows that, as genomic sequences are meant to carry information, considering the information content of nucleotides is helpful for genomic sequence alignment.AvailabilityDownloadable binaries, documentation and data can be found at ftp://ftp.infotech.monash.edu.au/software/DNAcompress-XM/XMAligner/.

Streaming algorithms for identification of pathogens and antibiotic resistance potential from real-time MinIONTM sequencing

“…Such an alignment generally assumes a model and a set of parameters of the differences between the sequences. It is widely recognised that the accuracy of the alignment is sensitive to these parameters [ 37 – 39 ]. However, in the context of real-time analysis of MinION sequencing, it is not possible to select in advance a sensible set of parameters.…”

Streaming algorithms for identification of pathogens and antibiotic resistance potential from real-time MinIONTMsequencing

“…Such an alignment generally assumes a model and a set of parameters of the differences between the sequences. It is widely recognised that the accuracy of the alignment is sensitive to these parameters [43][44][45]. However, in the context of real-time analysis of MinION sequencing, it is not possible to select in advance a sensible set of parameters.…”

A Survey on Data Compression Methods for Biological Sequences

“…Also, retrieving Single Nucleotide Polymorphisms (SNP) data-detecting and localizing one base mutations on genomic data [160]. Other application is aligning DNA sequences using compression [161]; Using data compression to detect large transformations between the DNA of different individuals or species, also known as rearrangement detection, has also been shown to work efficiently [162]; It has also been used for efficient storage of data structures in pan-genome analysis, namely using de Bruijn graphs [163,164]. Here, the problem is to deal with large amounts of information and its fast retrieval.…”

“…First, the performance of de novo assembly, for example employing de Bruijn graphs [5] to create (parts of) a genome from raw sequence reads without the aid of a reference genome, can be improved by decreasing the memory usage by one order of magnitude [6,7]; Second, through compression, expert models can be trained on a specific sequence and later be exploited in a similar sequence, to produce higher quality aligners [3,8].…”