Manal Mohamed

Finding patterns with variable length gaps or don’t cares

In this paper we have presented new algorithms to handle the pattern matching problem where the pattern can contain variable length gaps. Given a pattern P with variable length gaps and a text T our algorithm works in O(n + m + α log(max

Popping Superbubbles and Discovering Clumps: Recent Developments in Biological Sequence Analysis

_{\rm 1<={\it i}<={\it l}}

On avoided words, absent words, and their application to biological sequence analysis

(bi–ai))) time where n is the length of the text, m is the summation of the lengths of the component subpatterns, α is the total number of occurrences of the component subpatterns in the text and ai and bi are, respectively, the minimum and maximum number of don’t cares allowed between the ith and (i+1)st component of the pattern. We also present another algorithm which, given a suffix array of the text, can report whether P occurs in T in O(m + α loglogn) time. Both the algorithms record information to report all the occurrences of P in T. Furthermore, the techniques used in our algorithms are shown to be useful in many other contexts.

Linear-time superbubble identification algorithm for genome assembly

The information that can be inferred or predicted from knowing the genomic sequence of an organism is astonishing. String algorithms are critical to this process. This paper provides an overview of two particular problems that arise during computational molecular biology research, and recent algorithmic developments in solving them.

Longest Repeats with a Block of Don’t Cares

Background The deviation of the observed frequency of a word w from its expected frequency in a given sequence x is used to determine whether or not the word is avoided. This concept is particularly useful in DNA linguistic analysis. The value of the deviation of w, denoted by

Linear algorithm for conservative degenerate pattern matching

\textit{dev}(w)

Efficient Computation of Clustered-Clumps in Degenerate Strings

dev(w), effectively characterises the extent of a word by its edge contrast in the context in which it occurs. A word w of length