The extraction of k-mers from sequencing reads is an important task in many bioinformatics applications, such as all DNA sequence analysis methods based on de Bruijn graphs. These tend to be more accurate when the used are unique analyzed DNA, and thus use longer preferred. When read lengths short technologies increase, error rate will become determining factor for largest possible value k. Her...

MOTIVATION Alignment of biological sequences is one of the most frequently performed computer tasks. The current state of the art involves the use of (multiple) spaced seeds for producing high quality alignments. A particular important class is that of neighbor seeds which combine high sensitivity with reduced space requirements. Current algorithms for computing good neighbor seeds are very slo...

Spaced seeds are important tools for similarity search in bioinformatics, and using several seeds together often significantly improves their performance. With existing approaches, however, for each seed we keep a separate linear-size data structure, either a hash table or a spaced suffix array (SSA). In this paper we show how to compress SSAs relative to normal suffix arrays (SAs) and still su...

Heuristic sequence alignment and database search algorithms, such as PatternHunter and BLAST, are based on the initial discovery of so-called alignment seeds of well-conserved alignment patterns, which are subsequently extended to full local alignments. In recent years, the theory of classical seeds (matching contiguous q-grams) has been extended to spaced seeds, which allow mismatches within a...

Despite much research [1] [2] [3] [4] [5] [6] [7] has been devoted to the optimization of multiple spaced seeds for different sensitivity criteria, we proposed the following three methods to generate full sensitive periodic multiple seeds. For large genome re-sequencing application, multiple index tables can be queried with the MapReduce framework as proposed in [8] to increase the mapping effi...

Several algorithms for similarity search employ seeding techniques to quickly discard very dissimilar regions. In this paper, we study theoretical properties of lossless seeds, i.e., spaced seeds having full sensitivity. We prove that lossless seeds coincide with languages of certain sofic subshifts, hence they can be recognized by finite automata. Moreover, we show that these subshifts are ful...

We propose a general approach to compute the seed sensitivity, that can be applied to different definitions of seeds. It treats separately three components of the seed sensitivity problem – a set of target alignments, an associated probability distribution, and a seed model – that are specified by distinct finite automata. The approach is then applied to a new concept of subset seeds for which ...

We propose a general approach to compute the seed sensitivity, that can be applied to different definitions of seeds. It treats separately three components of the seed sensitivity problem – a set of target alignments, an associated probability distribution, and a seed model – that are specified by distinct finite automata. The approach is then applied to a new concept of subset seeds for which ...

We propose a general approach to compute the seed sensitivity, that can be applied to di erent de nitions of seeds. It treats separately three components of the seed sensitivity problem { a set of target alignments, an associated probability distribution, and a seed model { that are speci ed by distinct nite automata. The approach is then applied to a new concept of subset seeds for which we pr...

We propose a general approach to compute the seed sensitivity, that can be applied to different definitions of seeds. It treats separately three components of the seed sensitivity problem--a set of target alignments, an associated probability distribution, and a seed model--that are specified by distinct finite automata. The approach is then applied to a new concept of subset seeds for which we...