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Modelling is everywhere

Mutual contributions: algorithmics and genomics

Algorithmics, which are computer-generated methods of resolving a given problem, play an essential role in genome sequencing. Sequence alignment, the discovery of non-coding RNA, and the comparison of annotations are just a few examples of how optimized algorithms are used.

By Catherine Foucaud-Scheunemann - Pascale Mollier, translated by Emma Morton-Saliou
Updated on 05/30/2013
Published on 04/15/2013

Matthias Zytnicki, researcher at URGI, the genomics and bioinformatics research unit of INRA–Versailles-Grignon. © INRA, Portrait by Catherine Foucaud-Scheunemann. © INRA, Catherine Foucaud-Scheunemann
Matthias Zytnicki, researcher at URGI, the genomics and bioinformatics research unit of INRA–Versailles-Grignon. © INRA, Portrait by Catherine Foucaud-Scheunemann © INRA, Catherine Foucaud-Scheunemann

Nowadays, automatic sequencing programs generate as much data in one day as a lab technician used to in a month. The human brain is not powerful enough to process this avalanche of data on its own. To exploit this raw data, scientists need the incredible calculating capacities of computers based on software, theoretical models and structured databases developed by mathematicians and computer scientists.

Interview with Matthias Zytnicki, researcher at URGI, the genomics and bioinformatics research unit of INRA–Versailles-Grignon.

What is an algorithm?

Matthias Zytnicki: An algorithm is a finite set of unambiguous operations or instructions performed to solve a problem. The study of algorithms is known as algorithmics, which also refers to the design of effective computer-based methods to resolve specific problems. Whereas modelling is performed to express observed phenomena in computer or mathematical language, algorithmics assumes that the problem has already been computationally modelled. Algorithmics plays a central role in biology research today.

How is algorithmics used in genome analysis?

M. Z.: High-speed sequencing generates so many sequences that optimised algorithms are needed to align them (when a reference genome is known) or assemble them (when the genome is unknown). Here are three examples.

-      To find similarities between sequences:

BLAST, for Basic Local Alignment Search Tool, is an heuristic research tool used to find regions of similarity between two or more nucleotide or amino acid sequences. The program compares the nucleotide or protein sequences to sequence databases and calculates the statistical significance of matches. BLAST can be used to infer functional and evolutionary relationships between sequences as well as help identify members of gene families. Behind one simple sequence alignment using BLAST are decades of algorithmic development.

-      To find non-coding RNA (ncRNA) in a genome:

Three-dimensional structure of ncRNA. © INRA. © INRA
Three-dimensional structure of ncRNA. © INRA © INRA

Unlike RNA-coding genes, non-coding RNA (ncRNA) contain sequences that are not often conserved. They are, however, known for their three-dimensional structures which encode their function. This structure is difficult to model. We developed a program that searches a genome to locate places, or loci, where a given ncRNA structure may be found. The process is far more complex than a sequence similarity search. We used a formalism of artificial intelligence known as a Weighted Constraint Network (WCN). This formalism had never been used to resolve problems which sometimes required inspecting very long genomes. The algorithms we developed have also been used to solve planning problems involving an observation satellite designed to snap as many shots as possible in a given space of time.

-      To predict gene function

This involves identifying the significant elements of genomes, in particular transcript sequences, which indicate gene expression. High-throughput sequencing techniques such as RNA-Seq, used in specific contexts (such as to study water stress in plants), provide new data about gene expression which we compare with already accessible annotations. By doing so we can identify new transcribed regions. These comparisons take time and require a lot of memory. We designed a fast and effective algorithm called S-Mart, which fixes this problem. S-Mart has been integrated into Galaxy, a popular and intuitive workflow toolbox for designing and implementing analysis pipelines. It is available via a web browser. It offers new perspectives on geometric planning and analysis.

Conclusion

M. Z.: These examples – especially BLAST, which is truly groundbreaking – highlight how much genomics has benefitted from advances in algorithmics. Similarly, problems in genomics are an opportunity to create new applications for algorithmics, which can only go further by solving concrete examples of problems.

See Matthias Zytnicki’s presentation, given at a conference on modelling held on 16 November 2012 during the "Forums des labos" event hosted by INRA Versailles-Grignon:

Matthias Zytnicki's presentation (in French)

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Plant Biology and Breeding
Associated Centre(s):
Versailles-Grignon

REFERENCES

- Zytnicki M. et al. 2008. DARN! A Weighted Constraint Solver for RNA Motif Localization. Constraints 13: 91.
- Zytnicki M. and H. Quesneville. 2011. S-MART, A Software Toolbox to Aid RNA-seq Data Analysis. PLoS One 6: e25988.

Sudoku and Rubik’s cubes – putting the fun in algorithmics

Sudoku is a game in which the player completes a partially filled-in square grid that is divided into N sub-grids containing N number of squares. The numbers 1 to N must only appear once in each row, column and sub-grid. Researchers have shown that sudoku puzzles can be solved using weighted constraint networks and that it is possible to generate problems at will.

Click to see an example of Sudoku

The Rubik’s Cube is a three-dimensional, geometric combination puzzle composed of 26 smaller cubes. In 2010, an international team of researchers demonstrated that the six sides of the cube could be arranged as required in as little as twenty moves.

Click to see method