Biomedical subjects

Gad M Landau

Publications and source records attributed to Gad M Landau.

4 recordsLinked to original sources

PubMed2006

On the complexity of sparse exon assembly.

Gene structure prediction is one of the most important problems in computational molecular biology. It involves two steps: the first is finding the evidence (e.g., predicting splice sites) and the second is interpreting the evidence, that is, trying to determine the whole gene structure by assembling its pieces. In this paper, we suggest a combinatorial solution to the second step, which is also referred to as the "Exon Assembly Problem." We use a similarity-based approach that aims to produce a single gene structure based on similarities to a known homologous sequence. We target the sparse case, where filtering has been applied to the data, resulting in a set of O(n) candidate exon blocks. Our algorithm yields an O(n(2) square root of n) solution.

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PubMed2005

Gene proximity analysis across whole genomes via PQ trees.

Permutations on strings representing gene clusters on genomes have been studied earlier by Uno and Yagiura (2000), Heber and Stoye (2001), Bergeron et al. (2002), Eres et al. (2003), and Schmidt and Stoye (2004) and the idea of a maximal permutation pattern was introduced by Eres et al. (2003). In this paper, we present a new tool for representation and detection of gene clusters in multiple genomes, using PQ trees (Booth and Leuker, 1976): this describes the inner structure and the relations between clusters succinctly, aids in filtering meaningful from apparently meaningless clusters, and also gives a natural and meaningful way of visualizing complex clusters. We identify a minimal consensus PQ tree and prove that it is equivalent to a maximal pi pattern (Eres et al., 2003) and each subgraph of the PQ tree corresponds to a nonmaximal permutation pattern. We present a general scheme to handle multiplicity in permutations and also give a linear time algorithm to construct the minimal consensus PQ tree. Further, we demonstrate the results on whole genome datasets. In our analysis of the whole genomes of human and rat, we found about 1.5 million common gene clusters but only about 500 minimal consensus PQ trees, with E. Coli K-12 and B. Subtilis genomes, we found only about 450 minimal consensus PQ trees out of about 15,000 gene clusters, and when comparing eight different Chloroplast genomes, we found only 77 minimal consensus PQ trees out of about 6,700 gene clusters. Further, we show specific instances of functionally related genes in two of the cases.

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PubMed2004

Permutation pattern discovery in biosequences.

Functionally related genes often appear in each other's neighborhood on the genome; however, the order of the genes may not be the same. These groups or clusters of genes may have an ancient evolutionary origin or may signify some other critical phenomenon and may also aid in function prediction of genes. Such gene clusters also aid toward solving the problem of local alignment of genes. Similarly, clusters of protein domains, albeit appearing in different orders in the protein sequence, suggest common functionality in spite of being nonhomologous. In the paper, we address the problem of automatically discovering clusters of entities, be they genes or domains: we formalize the abstract problem as a discovery problem called the (pi)pattern problem and give an algorithm that automatically discovers the clusters of patterns in multiple data sequences. We take a model-less approach and introduce a notation for maximal patterns that drastically reduces the number of valid cluster patterns, without any loss of information, We demonstrate the automatic pattern discovery tool on motifs on E. Coli protein sequences.

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PubMed2002

Sequence complexity profiles of prokaryotic genomic sequences: a fast algorithm for calculating linguistic complexity.

MOTIVATION: One of the major features of genomic DNA sequences, distinguishing them from texts in most spoken or artificial languages, is their high repetitiveness. Variation in the repetitiveness of genomic texts reflects the presence and density of different biologically important messages. Thus, deviation from an expected number of repeats in both directions indicates a possible presence of a biological signal. Linguistic complexity corresponds to repetitiveness of a genomic text, and potential regulatory sites may be discovered through construction of typical patterns of complexity distribution. RESULTS: We developed software for fast calculation of linguistic sequence complexity of DNA sequences. Our program utilizes suffix trees to compute the number of subwords present in genomic sequences, thereby allowing calculation of linguistic complexity in time linear in genome size. The measure of linguistic complexity was applied to the complete genome of Haemophilus influenzae. Maps of complexity along the entire genome were obtained using sliding windows of 40, 100, and 2000 nucleotides. This approach provided an efficient way to detect simple sequence repeats in this genome. In addition, local profiles of complexity distribution around the starts of translation were constructed for 21 complete prokaryotic genomes. We hypothesize that complexity profiles correspond to evolutionary relationships between organisms. We found principal differences in profiles of the GC-rich and other (non-GC-rich) genomes. We also found characteristic differences in profiles of AT genomes, which probably reflect individual species variations in translational regulation. AVAILABILITY: The program is available upon request from Alexander Bolshoy or at http://csweb.haifa.ac.il/library/#complex.

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