Genome-wide association study of serious blistering skin rash caused by drugs.

Stevens-Johnson syndrome (SJS) and toxic epidermal necrolysis (TEN) are rare but severe, potentially life threatening adverse drug reactions characterized by skin blistering. Previous studies have identified drug-specific and population-specific genetic risk factors with large effects. In this study, we report the first genome-wide association study (GWAS) of SJS/TEN induced by a variety of drugs. Our aim was to identify common genetic risk factors with large effects on SJS/TEN risk. We conducted a genome-wide analysis of 96 retrospective cases and 198 controls with a panel of over one million single-nucleotide polymorphisms (SNPs). We further improved power with about 4000 additional controls from publicly available datasets. No genome-wide significant associations with SNPs or copy number variants were observed, although several genomic regions were suggested that may have a role in predisposing to drug-induced SJS/TEN. Our GWAS did not find common, highly penetrant genetic risk factors responsible for SJS/TEN events in the cases selected.

The emergence of pattern discovery techniques in computational biology.

In the past few years, pattern discovery has been emerging as a generic tool of choice for tackling problems from the computational biology domain. In this presentation, and after defining the problem in its generality, we review some of the algorithms that have appeared in the literature and describe several applications of pattern discovery to problems from computational biology.

Dictionary building via unsupervised hierarchical motif discovery in the sequence space of natural proteins.

Using Teiresias, a pattern discovery method that identifies all motifs present in any given set of protein sequences without requiring alignment or explicit enumeration of the solution space, we have explored the GenPept sequence database and built a dictionary of all sequence patterns with two or more instances. The entries of this dictionary, henceforth named seqlets, cover 98.12% of all amino acid positions in the input database and in essence provide a comprehensive finite set of descriptors for protein sequence space. As such, seqlets can be effectively used to describe almost every naturally occurring protein. In fact, seqlets can be thought of as building blocks of protein molecules that are a necessary (but not sufficient) condition for function or family equivalence memberships. Thus, seqlets can either define conserved family signatures or cut across molecular families and previously undetected sequence signals deriving from functional convergence. Moreover, we show that seqlets also can capture structurally conserved motifs. The availability of a dictionary of seqlets that has been derived in such an unsupervised, hierarchical manner is generating new opportunities for addressing problems that range from reliable classification and the correlation of sequence fragments with functional categories to faster and sensitive engines for homology searches, evolutionary studies, and protein structure prediction.

Building dictionaries of 1D and 3D motifs by mining the Unaligned 1D sequences of 17 archaeal and bacterial genomes.

We have used the Teiresias algorithm to carry out unsupervised pattern discovery in a database containing the unaligned ORFs from the 17 publicly available complete archaeal and bacterial genomes and build a 1D dictionary of motifs. These motifs which we refer to as seqlets account for and cover 97.88% of this genomic input at the level of amino acid positions. Each of the seqlets in this 1D dictionary was located among the sequences in Release 38.0 of the Protein Data Bank and the structural fragments corresponding to each seqlet's instances were identified and aligned in three dimensions: those of the seqlets that resulted in RMSD errors below a pre-selected threshold of 2.5 Angstroms were entered in a 3D dictionary of structurally conserved seqlets. These two dictionaries can be thought of as cross-indices that facilitate the tackling of tasks such as automated functional annotation of genomic sequences, local homology identification, local structure characterization, comparative genomics, etc.

Combinatorial pattern discovery in biological sequences: The TEIRESIAS algorithm.

MOTIVATION: The discovery of motifs in biological sequences is an important problem. RESULTS: This paper presents a new algorithm for the discovery of rigid patterns (motifs) in biological sequences. Our method is combinatorial in nature and able to produce all patterns that appear in at least a (user-defined) minimum number of sequences, yet it manages to be very efficient by avoiding the enumeration of the entire pattern space. Furthermore, the reported patterns are maximal: any reported pattern cannot be made more specific and still keep on appearing at the exact same positions within the input sequences. The effectiveness of the proposed approach is showcased on a number of test cases which aim to: (i) validate the approach through the discovery of previously reported patterns; (ii) demonstrate the capability to identify automatically highly selective patterns particular to the sequences under consideration. Finally, experimental analysis indicates that the algorithm is output sensitive, i.e. its running time is quasi-linear to the size of the generated output.

MUSCA: An Algorithm for Constrained Alignment of Multiple Data Sequences.

Given a set of N sequences, the Multiple Sequence Alignment problem is to align these N sequences, possibly with gaps, that brings out the best commonality of the N sequences. MUSCA is a two-stage approach to the alignment problem by identifying two relatively simpler sub-problems whose solutions are used to obtain the alignment of the sequences. We first discover motifs in the N sequences and then extract an appropriate subset of compatible motifs to obtain a good alignment. The motifs of interest to us are the irredundant motifs which are only polynomial in the input size. In practice, however, the number is much smaller (sub-linear). Notice that this step aids in a direct N-wise alignment, as opposed to composing the alignments from lower order (say pairwise) alignments and the solution is also independent of the order of the input sequences; hence the algorithm works very well while dealing with a large number of sequences. The second part of the problem that deals with obtaining a good alignment is solved using a graph-theoretic approach that computes an induced subgraph satisfying certain simple constraints. We reduce a version of this problem to that of solving an instance of a set covering problem, thus offer the best possible approximate solution to the problem (provided P not equalNP). Our experimental results, while being preliminary, indicate that this approach is efficient, particularly on large numbers of long sequences, and, gives good alignments when tested on biological data such as DNA and protein sequences. We introduce the the notion of an alignment number K (2