YASS: enhancing the sensitivity of DNA similarity search

YASS is a DNA local alignment tool based on an efficient and sensitive filtering algorithm. It applies transition-constrained seeds to specify the most probable conserved motifs between homologous sequences, combined with a flexible hit criterion used to identify groups of seeds that are likely to exhibit significant alignments. A web interface () is available to upload input sequences in fasta format, query the program and visualize the results obtained in several forms (dot-plot, tabular output and others). A standalone version is available for download from the web page

A unifying framework for seed sensitivity and its application to subset seeds

We propose a general approach to compute the seed sensitivity, that can be applied to different definitions of seeds. It treats separately three components of the seed sensitivity problem -- a set of target alignments, an associated probability distribution, and a seed model -- that are specified by distinct finite automata. The approach is then applied to a new concept of subset seeds for which we propose an efficient automaton construction. Experimental results confirm that sensitive subset seeds can be efficiently designed using our approach, and can then be used in similarity search producing better results than ordinary spaced seeds

Improved hit criteria for DNA local alignment

BACKGROUND: The hit criterion is a key component of heuristic local alignment algorithms. It specifies a class of patterns assumed to witness a potential similarity, and this choice is decisive for the selectivity and sensitivity of the whole method. RESULTS: In this paper, we propose two ways to improve the hit criterion. First, we define the group criterion combining the advantages of the single-seed and double-seed approaches used in existing algorithms. Second, we introduce transition-constrained seeds that extend spaced seeds by the possibility of distinguishing transition and transversion mismatches. We provide analytical data as well as experimental results, obtained with the YASS software, supporting both improvements. CONCLUSIONS: Proposed algorithmic ideas allow to obtain a significant gain in sensitivity of similarity search without increase in execution time. The method has been implemented in YASS software available at

Universal seeds for cDNA-to-genome comparison

<p>Abstract</p> <p>Background</p> <p>To meet the needs of gene annotation for newly sequenced organisms, optimized spaced seeds can be implemented into cross-species sequence alignment programs to accurately align gene sequences to the genome of a related species. So far, seed performance has been tested for comparisons between closely related species, such as human and mouse, or on simulated data. As the number and variety of genomes increases, it becomes desirable to identify a small set of <it>universal </it>seeds that perform optimally or near-optimally on a large range of comparisons.</p> <p>Results</p> <p>Using statistical regression methods, we investigate the sensitivity of seeds, in particular good seeds, between four cDNA-to-genome comparisons at different evolutionary distances (human-dog, human-mouse, human-chicken and human-zebrafish), and identify classes of comparisons that show similar seed behavior and therefore can employ the same seed. In addition, we find that with high confidence good seeds for more distant comparisons perform well on closer comparisons, within 98–99% of the optimal seeds, and thus represent universal good seeds.</p> <p>Conclusion</p> <p>We show for the first time that optimal and near-optimal seeds for distant species-to-species comparisons are more generally applicable to a wide range of comparisons. This finding will be instrumental in developing practical and user-friendly cDNA-to-genome alignment applications, to aid in the annotation of new model organisms.</p

Pash 3.0: A versatile software package for read mapping and integrative analysis of genomic and epigenomic variation using massively parallel DNA sequencing

<p>Abstract</p> <p>Background</p> <p>Massively parallel sequencing readouts of epigenomic assays are enabling integrative genome-wide analyses of genomic and epigenomic variation. Pash 3.0 performs sequence comparison and read mapping and can be employed as a module within diverse configurable analysis pipelines, including ChIP-Seq and methylome mapping by whole-genome bisulfite sequencing.</p> <p>Results</p> <p>Pash 3.0 generally matches the accuracy and speed of niche programs for fast mapping of short reads, and exceeds their performance on longer reads generated by a new generation of massively parallel sequencing technologies. By exploiting longer read lengths, Pash 3.0 maps reads onto the large fraction of genomic DNA that contains repetitive elements and polymorphic sites, including indel polymorphisms.</p> <p>Conclusions</p> <p>We demonstrate the versatility of Pash 3.0 by analyzing the interaction between CpG methylation, CpG SNPs, and imprinting based on publicly available whole-genome shotgun bisulfite sequencing data. Pash 3.0 makes use of gapped k-mer alignment, a non-seed based comparison method, which is implemented using multi-positional hash tables. This allows Pash 3.0 to run on diverse hardware platforms, including individual computers with standard RAM capacity, multi-core hardware architectures and large clusters.</p

A unifying framework for seed sensitivity and its application to subset seeds (Extended abstract)

We propose a general approach to compute the seed sensitivity, that can be applied to different definitions of seeds. It treats separately three components of the seed sensitivity problem - a set of target alignments, an associated probability distribution, and a seed model - that are specified by distinct finite automata. The approach is then applied to a new concept of subset seeds for which we propose an efficient automaton construction. Experimental results confirm that sensitive subset seeds can be efficiently designed using our approach, and can then be used in similarity search producing better results than ordinary spaced seeds

Improvements on Seeding Based Protein Sequence Similarity Search

The primary goal of bioinformatics is to increase an understanding in the biology of organisms. Computational, statistical, and mathematical theories and techniques have been developed on formal and practical problems that assist to achieve this primary goal. For the past three decades, the primary application of bioinformatics has been biological data analysis. The DNA or protein sequence similarity search is perhaps the most common, yet vitally important task for analyzing biological data. The sequence similarity search is a process of finding optimal sequence alignments. On the theoretical level, the problem of sequence similarity search is complex. On the applicational level, the sequences similarity search onto a biological database has been one of the most basic tasks today. Using traditional quadratic time complexity solutions becomes a challenge due to the size of the database. Seeding (or filtration) based approaches, which trade sensitivity for speed, are a popular choice among those available. Two main phases usually exist in a seeding based approach. The first phase is referred to as the hit generation, and the second phase is referred to as the hit extension. In this thesis, two improvements on the seeding based protein sequence similarity search are presented. First, for the hit generation, a new seeding idea, namely spaced k-mer neighbors, is presented. We present our effective algorithms to find a good set of spaced k-mer neighbors. Secondly, for the hit generation, a new method, namely HexFilter, is proposed to reduce the number of hit extensions while achieving better selectivity. We show our HexFilters with optimized configurations