Current Alignment Algorithms Research Articles

Pitfalls of genotyping microbial communities with rapidly growing genome collections.

Detecting genetic variants in metagenomic data is a priority for understanding the evolution, ecology, and functional characteristics of microbial communities. Many tools that perform this metagenotyping rely on aligning reads of unknown origin to a database of sequences from many species before calling variants. In this synthesis, we investigate how databases of increasingly diverse and closely related species have pushed the limits of current alignment algorithms, thereby degrading the performance of metagenotyping tools. We identify multi-mapping reads as a prevalent source of errors and illustrate a trade-off between retaining correct alignments versus limiting incorrect alignments, many of which map reads to the wrong species. Then we evaluate several actionable mitigation strategies and review emerging methods showing promise to further improve metagenotyping in response to the rapid growth in genome collections. Our results have implications beyond metagenotyping to the many tools in microbial genomics that depend upon accurate read mapping.

SIMILE enables alignment of tandem mass spectra with statistical significance.

Interrelating small molecules according to their aligned fragmentation spectra is central to tandem mass spectrometry-based untargeted metabolomics. Current alignment algorithms do not provide statistical significance and compounds that have multiple delocalized structural differences and therefore often fail to have their fragment ions aligned. Here we align fragmentation spectra with both statistical significance and allowance for multiple chemical differences using Significant Interrelation of MS/MS Ions via Laplacian Embedding (SIMILE). SIMILE yields spectral alignment inferred structural connections in molecular networks that are not found with cosine-based scoring algorithms. In addition, it is now possible to rank spectral alignments based on p-values in the exploration of structural relationships between compounds and enhance the chemical connectivity that can be obtained with molecular networking.

Alignment of Short Reads: A Crucial Step for Application of Next-Generation Sequencing Data in Precision Medicine.

Precision medicine or personalized medicine has been proposed as a modernized and promising medical strategy. Genetic variants of patients are the key information for implementation of precision medicine. Next-generation sequencing (NGS) is an emerging technology for deciphering genetic variants. Alignment of raw reads to a reference genome is one of the key steps in NGS data analysis. Many algorithms have been developed for alignment of short read sequences since 2008. Users have to make a decision on which alignment algorithm to use in their studies. Selection of the right alignment algorithm determines not only the alignment algorithm but also the set of suitable parameters to be used by the algorithm. Understanding these algorithms helps in selecting the appropriate alignment algorithm for different applications in precision medicine. Here, we review current available algorithms and their major strategies such as seed-and-extend and q-gram filter. We also discuss the challenges in current alignment algorithms, including alignment in multiple repeated regions, long reads alignment and alignment facilitated with known genetic variants.

Efficient protein alignment algorithm for protein search.

BackgroundProteins show a great variety of 3D conformations, which can be used to infer their evolutionary relationship and to classify them into more general groups; therefore protein structure alignment algorithms are very helpful for protein biologists. However, an accurate alignment algorithm itself may be insufficient for effective discovering of structural relationships among tens of thousands of proteins. Due to the exponentially increasing amount of protein structural data, a fast and accurate structure alignment tool is necessary to access protein classification and protein similarity search; however, the complexity of current alignment algorithms are usually too high to make a fully alignment-based classification and search practical.ResultsWe have developed an efficient protein pairwise alignment algorithm and applied it to our protein search tool, which aligns a query protein structure in the pairwise manner with all protein structures in the Protein Data Bank (PDB) to output similar protein structures. The algorithm can align hundreds of pairs of protein structures in one second. Given a protein structure, the tool efficiently discovers similar structures from tens of thousands of structures stored in the PDB always in 2 minutes in a single machine and 20 seconds in our cluster of 6 machines. The algorithm has been fully implemented and is accessible online at our webserver, which is supported by a cluster of computers.ConclusionOur algorithm can work out hundreds of pairs of protein alignments in one second. Therefore, it is very suitable for protein search. Our experimental results show that it is more accurate than other well known protein search systems in finding proteins which are structurally similar at SCOP family and superfamily levels, and its speed is also competitive with those systems. In terms of the pairwise alignment performance, it is as good as some well known alignment algorithms.

Prediction of Protein Function Improving Sequence Remote Alignment Search by a Fuzzy Logic Algorithm

The functional annotation of the new protein sequences represents a major drawback for genomic science. The best way to suggest the function of a protein from its sequence is by finding a related one for which biological information is available. Current alignment algorithms display a list of protein sequence stretches presenting significant similarity to different protein targets, ordered by their respective mathematical scores. However, statistical and biological significance do not always coincide, therefore, the rearrangement of the program output according to more biological characteristics than the mathematical scoring would help functional annotation. A new method that predicts the putative function for the protein integrating the results from the PSI-BLAST program and a fuzzy logic algorithm is described. Several protein sequence characteristics have been checked in their ability to rearrange a PSI-BLAST profile according more to their biological functions. Four of them: amino acid content, matched segment length and hydropathic and flexibility profiles positively contributed, upon being integrated by a fuzzy logic algorithm into a program, BYPASS, to the accurate prediction of the function of a protein from its sequence.

Towards a classification of glycosyltransferases based on amino acid sequence similarities: prokaryotic alpha-mannosyltransferases.

A number of genes encoding bacterial glycosyltransferases have been sequenced during the last few years, but their low sequence similarity has prevented a straightforward grouping of these enzymes into families. The sequences of several bacterial alpha-mannosyltransferases have been compared using current alignment algorithms as well as hydrophobic cluster analysis (HCA). These sequences show a similarity which is significant but too low to be reliably aligned using automatic alignment methods. However, a region spanning approx. 270 residues in these proteins could be aligned by HCA, and several invariant amino acid residues were identified. These features were also found in several other glycosyltransferases, as well as in proteins of unknown function present in sequence databases. This similarity most probably reflects the existence of a family of proteins with conserved structural and mechanistic features. It is argued that the present IUBMB classification of glycosyltransferases could be complemented by a classification of these enzymes based on sequence similarities analogous to that which we proposed for glycosyl hydrolases [Henrissat, B. (1991) Biochem. J. 280, 309-316].