Genomic Signal Processing

Our role is to develop new methods in digital processing of large volume genomic data with the focus on de novo genome assembly, new bacteria strain genotyping or metataxonomic and metagenomic approaches. In contrast to the DNA processing by a common text entry approach, our methods allow for a structured system approach and computationally more efficient analysis of often periodic and redundant genomic data.

Detail

The research assistants and Ph.D. students that are part of this research team have been very successful in the development of novel methods for genomic signal processing. The research results were published in several renowned scientific journals. The results of their work have also found an essential purpose in the sphere of commercial systems for genomic data analysis. Examples of our projects follow:

Methods for multiple alignment of genomic signals

The team managed to successfully develop novel methods for multiple alignment of genomic signals by multiple dynamic time warping. The designed algorithm is an alternative to a standard multiple sequence alignment. The main advantage of the proposed method is that it is applicable to large volumes of data. The method and its detailed description was published in a couple of impacted journals (BMC Bioinformatics, Journal of Theoretical Biology) and presented at an international conference WCSB Tampere Finland 2013. The success of the proposed method is also represented by its application on a real-time sequencing technology Oxford Nanopore by a team of developers. The sequencing devices perform DNA/RNA sequencing directly and in real time. This allows for a signal alignment to be applied without any loss in conversion to a standard character-based representation.

Image 1: Basic principle of an algorithm for multiple alignment of genomic sequences represented as a signal.

An algorithm and rules for genomic signal decimation

The team was also successful in an application of rules for genomic signal decimation. Genetic information is often described as redundant. We have discovered that it is possible to perform a substantial data reduction without influencing particular types of consecutive analyses, as for example, phylogenomic analysis, delineation and genotyping.

Table 1: Overall results of the algorithm for genomic signal decimation.
DNA sequence ACTA1 mtDNA Viruses Plasmids Bacterias
Decomposition degree 1 5 5 8 12
Average length [bp] 2 859 16 335 28 962 383 646 3 830 130
Average length after decimation [-] 715 256 453 750 935
Root-mean-square difference [%] ? ? ? ? ?

Methods for processing and analysing electrophoretic data

In addition to biological sequence processing our researchers also deal with an application of digital processing methods in various signal representation forms of genetic information. As an example stand 1D signals obtained by digitalisation of DNA molecule gel electrophoresis image. We focus on gel image processing, removing image distortion, conversion to signals and methods of electrophoretic sample comparative analysis. Beside standard gel electrophoresis we also work with a more advanced automated electrophoresis and its utilization in bacteria genotyping.

Image 2: The result of an algorithm for gel electrophoresis image processing; A – original image; B – processed image
 

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