DIANA-microT web server: elucidating microRNA functions through target prediction.

Computational microRNA (miRNA) target prediction is one of the key means for deciphering the role of miRNAs in development and disease. Here, we present the DIANA-microT web server as the user interface to the DIANA-microT 3.0 miRNA target prediction algorithm. The web server provides extensive information for predicted miRNA:target gene interactions with a user-friendly interface, providing extensive connectivity to online biological resources. Target gene and miRNA functions may be elucidated through automated bibliographic searches and functional information is accessible through Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. The web server offers links to nomenclature, sequence and protein databases, and users are facilitated by being able to search for targeted genes using different nomenclatures or functional features, such as the genes possible involvement in biological pathways. The target prediction algorithm supports parameters calculated individually for each miRNA:target gene interaction and provides a signal-to-noise ratio and a precision score that helps in the evaluation of the significance of the predicted results. Using a set of miRNA targets recently identified through the pSILAC method, the performance of several computational target prediction programs was assessed. DIANA-microT 3.0 achieved there with 66% the highest ratio of correctly predicted targets over all predicted targets. The DIANA-microT web server is freely available at www.microrna.gr/microT.

PRALINE: a multiple sequence alignment toolbox that integrates homology-extended and secondary structure information.

PRofile ALIgNEment (PRALINE) is a fully customizable multiple sequence alignment application. In addition to a number of available alignment strategies, PRALINE can integrate information from database homology searches to generate a homology-extended multiple alignment. PRALINE also provides a choice of seven different secondary structure prediction programs that can be used individually or in combination as a consensus for integrating structural information into the alignment process. The program can be used through two separate interfaces: one has been designed to cater to more advanced needs of researchers in the field, and the other for standard construction of high confidence alignments. The web-based output is designed to facilitate the comprehensive visualization of the generated alignments by means of five default colour schemes based on: residue type, position conservation, position reliability, residue hydrophobicity and secondary structure, depending on the options set. A user can also define a custom colour scheme by selecting which colour will represent one or more amino acids in the alignment. All generated alignments are also made available in the PDF format for easy figure generation for publications. The grouping of sequences, on which the alignment is based, can also be visualized as a dendrogram. PRALINE is available at http://ibivu.cs.vu.nl/programs/pralinewww/.

Homology-extended sequence alignment.

We present a profile-profile multiple alignment strategy that uses database searching to collect homologues for each sequence in a given set, in order to enrich their available evolutionary information for the alignment. For each of the alignment sequences, the putative homologous sequences that score above a pre-defined threshold are incorporated into a position-specific pre-alignment profile. The enriched position-specific profile is used for standard progressive alignment, thereby more accurately describing the characteristic features of the given sequence set. We show that owing to the incorporation of the pre-alignment information into a standard progressive multiple alignment routine, the alignment quality between distant sequences increases significantly and outperforms state-of-the-art methods, such as T-COFFEE and MUSCLE. We also show that although entirely sequence-based, our novel strategy is better at aligning distant sequences when compared with a recent contact-based alignment method. Therefore, our pre-alignment profile strategy should be advantageous for applications that rely on high alignment accuracy such as local structure prediction, comparative modelling and threading.

The influence of gapped positions in multiple sequence alignments on secondary structure prediction methods.

All currently leading protein secondary structure prediction methods use a multiple protein sequence alignment to predict the secondary structure of the top sequence. In most of these methods, prior to prediction, alignment positions showing a gap in the top sequence are deleted, consequently leading to shrinking of the alignment and loss of position-specific information. In this paper we investigate the effect of this removal of information on secondary structure prediction accuracy. To this end, we have designed SymSSP, an algorithm that post-processes the predicted secondary structure of all sequences in a multiple sequence alignment by (i) making use of the alignment's evolutionary information and (ii) re-introducing most of the information that would otherwise be lost. The post-processed information is then given to a new dynamic programming routine that produces an optimally segmented consensus secondary structure for each of the multiple alignment sequences. We have tested our method on the state-of-the-art secondary structure prediction methods PHD, PROFsec, SSPro2 and JNET using the HOMSTRAD database of reference alignments. Our consensus-deriving dynamic programming strategy is consistently better at improving the segmentation quality of the predictions compared to the commonly used majority voting technique. In addition, we have applied several weighting schemes from the literature to our novel consensus-deriving dynamic programming routine. Finally, we have investigated the level of noise introduced by prediction errors into the consensus and show that predictions of edges of helices and strands are half the time wrong for all the four tested prediction methods.

Integrating protein secondary structure prediction and multiple sequence alignment.

Modern protein secondary structure prediction methods are based on exploiting evolutionary information contained in multiple sequence alignments. Critical steps in the secondary structure prediction process are (i) the selection of a set of sequences that are homologous to a given query sequence, (ii) the choice of the multiple sequence alignment method, and (iii) the choice of the secondary structure prediction method. Because of the close relationship between these three steps and their critical influence on the prediction results, secondary structure prediction has received increased attention from the bioinformatics community over the last few years. In this treatise, we discuss recent developments in computational methods for protein secondary structure prediction and multiple sequence alignment, focus on the integration of these methods, and provide some recommendations for state-of-the-art secondary structure prediction in practice.

The PRALINE online server: optimising progressive multiple alignment on the web.

We introduce the online server for PRALINE (http://ibium.cs.vu.nl/programs/pralinewww/), an iterative versatile progressive multiple sequence alignment (MSA) tool. PRALINE provides various MSA optimisation strategies including weighted global and local profile pre-processing, secondary structure-guided alignment and a reliability measure for aligned individual residue positions. The latter can also be used to optimise the alignment when the profile pre-processing strategies are iterated. In addition, we have modelled the server output to enable comprehensive visualisation of the generated alignment and easy figure generation for publications. The alignment is represented in five default colour schemes based on: residue type, position conservation, position reliability, residue hydrophobicity and secondary structure; depending on the options set. We have also implemented a custom colour scheme that allows the user to select which colour will represent one or more amino acids in the alignment. The grouping of sequences, on which the alignment is based, can also be visualised as a dendrogram. The PRALINE algorithm is designed to work more as a toolkit for MSA rather than a one step process.