Algorithms to analyse nucleic acid sequences Bioinformatics exploits software tools to analyse complex biological data. Improved methods for processing of sequence data related to nucleic acid sequences and proteins overcome current limitations and are supporting sequencing efforts. Digital Economy © Thinkstock Computers have changed the way that fields from industrial automation to quantum mechanics to biomedicine address complex problems. Bioinformatics seeks to establish a pathway linking genetic information (sequences of nucleic acids or proteins) with phenotype (observable characteristics, symptoms or dysfunctions). The EU-funded research project 'Algorithms and tools for mining biological sequence data' (ALMOND) was launched to develop new techniques for several important problems in computational molecular biology. Methodology emphasised dynamic programming methods that identify simpler 'sub-problems' of a difficult problem, recurrent patterns that relate them and subsequent solution of the base cases. ALMOND researchers developed novel efficient algorithms for comparison of protein sequences. They focused on a new variant of special alignment path-constrained sequence alignments (called sequence alignment with regular expression path constraint (SA-REPC)). Researchers delivered two new solutions to this sequence analysis problem and both are available for download from the host group's website. Investigators also developed new algorithms for comparison of RNA sequences and structures for the case when the RNA sequences are in a coding region, as commonly occurs in viruses and bacteria. The methods enable prediction of the most likely common ancestor of two RNAs, overcoming the limitations of usual comparison algorithms. A number of algorithms addressed issues related to next-generation sequencing (NGS). Mapping short reads against an existing reference genome is the first step of many NGS data analyses. New mapping methods outperform existing algorithms, providing substantial improvements. A novel data structure for a graph used by most practical genome assembly methods for NGS data overcomes a major barrier in computational processing of the data. Its 30-40 % increase in memory space is now being exploited in third-party software (Minia). ALMOND delivered important new algorithms overcoming limitations to current tools addressing bioinformatics and sequence analysis. The above results and others have been published widely and the project has spawned new collaborations between France and Israel. The project is thus expected to have long-lasting impact on the socioeconomically important field of bioinformatics. Keywords Nucleic acid sequences, bioinformatics, biological data, molecular biology, next-generation sequencing
