198,416 research outputs found
Multiple sequence alignment with the Divide-and-Conquer method.
No full textStoye J. Multiple sequence alignment with the Divide-and-Conquer method. Gene. 1998;211(2):GC45-GC56.An improved algorithm for the simultaneous alignment of multiple protein and nucleic acid sequences, the Divide-and-Conquer Alignment procedure (DCA), is presented. The basic method described in Tonges,et al. (1996) (Tonges, U., Perrey, S.W., Stoye, J., Dress, A.W.M., 1996. A general method for fast multiple sequence alignment. Gene, 172, GC33-GC41) is generalized to align any number of sequences to work arbitrary (e.g. affine linear) gap penalty functions. Also, the practical efficiency of the method is improved so that families of more than 10 sequences can now be aligned simultaneously within a few seconds or minutes. After a brief description of the general method, we assess the time and memory requirements of our implementation of DCA. We present several examples showing that the program is able to deal with real-world alignment problems
Rose: Generating Sequence Families
No full textStoye J, Evers D, Meyer F. Rose: Generating Sequence Families. Forschungsberichte. Technische Fakultät der Universität Bielefeld; 1997
Phylogenetic Comparative Assembly
No full textHusemann P, Stoye J. Phylogenetic Comparative Assembly. Algorithms for Molecular Biology. 2010;5(1): 3.BACKGROUND:Recent high throughput sequencing technologies are capable of generating a huge amount of data for bacterial genome sequencing projects. Although current sequence assemblers successfully merge the overlapping reads, often several contigs remain which cannot be assembled any further. It is still costly and time consuming to close all the gaps in order to acquire the whole genomic sequence. RESULTS:Here we propose an algorithm that takes several related genomes and their phylogenetic relationships into account to create a graph that contains the likelihood for each pair of contigs to be adjacent. Subsequently, this graph can be used to compute a layout graph that shows the most promising contig adjacencies in order to aid biologists in finishing the complete genomic sequence. The layout graph shows unique contig orderings where possible, and the best alternatives where necessary. CONCLUSIONS:Our new algorithm for contig ordering uses sequence similarity as well as phylogenetic information to estimate adjacencies of contigs. An evaluation of our implementation shows that it performs better than recent approaches while being much faster at the same tim
Online Abelian Pattern Matching
No full textEjaz T, Rahmann S, Stoye J. Online Abelian Pattern Matching. Forschungsberichte der Technischen Fakultät, Abteilung Informationstechnik / Universität Bielefeld. Bielefeld: Technische Fakultät der Universität Bielefeld; 2008.An abelian pattern describes the set of strings that comprise of the same combination of characters. Given an abelian pattern P and a text T [Epsilon] [Sigma]^n, the task is to find all occurrences of P in T, i.e. all substrings S = T_i...T_j such that the frequency of each character in S matches the specified frequency of that character in P.
In this report we present simple online algorithms for abelian pattern matching, and give a lower bound for online algorithms which is [Omega](n)
An iterative method for faster sum-of-pairs multiple sequence alignment
No full textReinert K, Stoye J, Will T. An iterative method for faster sum-of-pairs multiple sequence alignment. Bioinformatics. 2000;16(9):808-814
Gene family assignment-free comparative genomics
No full textDörr D, Thévenin A, Stoye J. Gene family assignment-free comparative genomics. BMC Bioinformatics. 2012;13(Suppl 19: Proc. of RECOMB-CG 2012): S3
Algorithmic Complexity of Protein Identification: Searching in Weighted Strings
No full textCieliebak M, Lipták Z, Welzl E, Erlebach T, Stoye J. Algorithmic Complexity of Protein Identification: Searching in Weighted Strings. In: Proc. of IFIP TCS 2002. 2002: 143-156
Algorithmic complexity of protein identification: combinatorics of weighted strings
No full textCieliebak M, Erlebach T, Lipták Z, Stoye J, Welzl E. Algorithmic complexity of protein identification: combinatorics of weighted strings. Discrete Applied Mathematics. 2004;137(1):27-46.We investigate a problem which arises in computational biology: Given a constant-size alphabet [Mathematical script A] with a weight function µ : [Mathematical script A] --> [Double-struck N], find an efficient data structure and query algorithm solving the following problem: For a string [sigma] over [Mathematical script A] and a weight [Mathematical italic M] [element of] [Double-struck N], decide whether [sigma] contains a substring with weight [Mathematical italic M], where the weight of a string is the sum of the weights of its letters (One-String Mass Finding Problem). If the answer is yes, then we may in addition require a witness, i.e., indices i [less-than or equal to] j such that the substring beginning at position i and ending at position j has weight [Mathematical italic M]. We allow preprocessing of the string and measure efficiency in two parameters: storage space required for the preprocessed data and running time of the query algorithm for given [Mathematical italic M]. We are interested in data structures and algorithms requiring subquadratic storage space and sublinear query time, where we measure the input size as the length n of the input string [sigma]. Among others, we present two non-trivial efficient algorithms: Lookup solves the problem with O(n) storage space and O(n/log n) time; Interval solves the problem for binary alphabets with O(n) storage space in O(log n) query time. We introduce other variants of the problem and sketch how our algorithms may be extended for these variants. Finally, we discuss combinatorial properties of weighted strings
Simple and flexible detection of contiguous repeats using a suffix tree
No full textStoye J, Gusfield D. Simple and flexible detection of contiguous repeats using a suffix tree. Theoretical Computer Science. 2002;270(1-2):843-856.We study the problem of detecting all occurrences of (primitive) tandem repeats and tandem arrays in a string. We first give a simple time- and space-optimal algorithm to find all tandem repeats, and then modify it to become a time and space-optimal algorithm for finding only the primitive tandem repeats. Both of these algorithms are then extended to handle tandem arrays. The contribution of this paper is both pedagogical and practical, giving simple algorithms and implementations based on a suffix tree, using only standard tree traversal techniques
QAlign: quality-based multiple alignments with dynamic phylogenetic analysis
No full textSammeth M, Rothgänger J, Esser W, Albert J, Stoye J, Harmsen D. QAlign: quality-based multiple alignments with dynamic phylogenetic analysis. BIOINFORMATICS. 2003;19(12):1592-1593.Integrating different alignment strategies, a layout editor and tools deriving phylogenetic trees in a 'multiple alignment environment' helps to investigate and enhance results of multiple sequence alignment by hand. QAlign combines algorithms for fast progressive and accurate simultaneous multiple alignment with a versatile editor and a dynamic phylogenetic analysis in a convenient graphical user interface
- …
