Name
Abstract | ||
|---|---|---|
In computational biology, an important problem is to identify a word of length k present in each of a given set of sequences. Here, we investigate the problem of calculating the probability that such a word exists in a set of r random strings. Existing methods to approximate this probability are either inaccurate when r 2 or are restricted to Bernoulli models. We introduce two new methods for computing this probability under Bernoulli and Markov models. We present generalizations of the methods to compute the probability of finding a word of length k shared among q of r sequences, and to allow mismatches. We show through simulations that our approximations are significantly more accurate than methods previously published. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/11780441_13 | CPM |
Keywords | Field | DocType |
common substrings,markov model,length k present,important problem,present generalization,r random string,new method,computational biology,bernoulli model,r sequence,length k | Discrete mathematics,Combinatorics,Substring,Markov model,Generalization,Bernoulli process,String (computer science),Mathematics,Bernoulli's principle | Conference |
Volume | ISSN | ISBN |
4009 | 0302-9743 | 3-540-35455-7 |
Citations | PageRank | References |
0 | 0.34 | 7 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Eric Blais | 1 | 286 | 22.49 |
| Mathieu Blanchette | 2 | 631 | 62.65 |