Improved profile HMM performance by assessment of critical algorithmic features in SAM and HMMER

BACKGROUND: Profile hidden Markov model (HMM) techniques are among the most powerful methods for protein homology detection. Yet, the critical features for successful modelling are not fully known. In the present work we approached this by using two of the most popular HMM packages: SAM and HMMER. The programs' abilities to build models and score sequences were compared on a SCOP/Pfam based test set. The comparison was done separately for local and global HMM scoring. RESULTS: Using default settings, SAM was overall more sensitive. SAM's model estimation was superior, while HMMER's model scoring was more accurate. Critical features for model building were then analysed by comparing the two packages' algorithmic choices and parameters. The weighting between prior probabilities and multiple alignment counts held the primary explanation why SAM's model building was superior. Our analysis suggests that HMMER gives too much weight to the sequence counts. SAM's emission prior probabilities were also shown to be more sensitive. The relative sequence weighting schemes are different in the two packages but performed equivalently. CONCLUSION: SAM model estimation was more sensitive, while HMMER model scoring was more accurate. By combining the best algorithmic features from both packages the accuracy was substantially improved compared to their default performance

Kalign – an accurate and fast multiple sequence alignment algorithm

BACKGROUND: The alignment of multiple protein sequences is a fundamental step in the analysis of biological data. It has traditionally been applied to analyzing protein families for conserved motifs, phylogeny, structural properties, and to improve sensitivity in homology searching. The availability of complete genome sequences has increased the demands on multiple sequence alignment (MSA) programs. Current MSA methods suffer from being either too inaccurate or too computationally expensive to be applied effectively in large-scale comparative genomics. RESULTS: We developed Kalign, a method employing the Wu-Manber string-matching algorithm, to improve both the accuracy and speed of multiple sequence alignment. We compared the speed and accuracy of Kalign to other popular methods using Balibase, Prefab, and a new large test set. Kalign was as accurate as the best other methods on small alignments, but significantly more accurate when aligning large and distantly related sets of sequences. In our comparisons, Kalign was about 10 times faster than ClustalW and, depending on the alignment size, up to 50 times faster than popular iterative methods. CONCLUSION: Kalign is a fast and robust alignment method. It is especially well suited for the increasingly important task of aligning large numbers of sequences

Scoredist: A simple and robust protein sequence distance estimator

BACKGROUND: Distance-based methods are popular for reconstructing evolutionary trees thanks to their speed and generality. A number of methods exist for estimating distances from sequence alignments, which often involves some sort of correction for multiple substitutions. The problem is to accurately estimate the number of true substitutions given an observed alignment. So far, the most accurate protein distance estimators have looked for the optimal matrix in a series of transition probability matrices, e.g. the Dayhoff series. The evolutionary distance between two aligned sequences is here estimated as the evolutionary distance of the optimal matrix. The optimal matrix can be found either by an iterative search for the Maximum Likelihood matrix, or by integration to find the Expected Distance. As a consequence, these methods are more complex to implement and computationally heavier than correction-based methods. Another problem is that the result may vary substantially depending on the evolutionary model used for the matrices. An ideal distance estimator should produce consistent and accurate distances independent of the evolutionary model used. RESULTS: We propose a correction-based protein sequence estimator called Scoredist. It uses a logarithmic correction of observed divergence based on the alignment score according to the BLOSUM62 score matrix. We evaluated Scoredist and a number of optimal matrix methods using three evolutionary models for both training and testing Dayhoff, Jones-Taylor-Thornton, and Müller-Vingron, as well as Whelan and Goldman solely for testing. Test alignments with known distances between 0.01 and 2 substitutions per position (1–200 PAM) were simulated using ROSE. Scoredist proved as accurate as the optimal matrix methods, yet substantially more robust. When trained on one model but tested on another one, Scoredist was nearly always more accurate. The Jukes-Cantor and Kimura correction methods were also tested, but were substantially less accurate. CONCLUSION: The Scoredist distance estimator is fast to implement and run, and combines robustness with accuracy. Scoredist has been incorporated into the Belvu alignment viewer, which is available at

Benchmarking the next generation of homology inference tools

Motivation: Over the last decades, vast numbers of sequences were deposited in public databases. Bioinformatics tools allow homology and consequently functional inference for these sequences. New profile-based homology search tools have been introduced, allowing reliable detection of remote homologs, but have not been systematically benchmarked. To provide such a comparison, which can guide bioinformatics workflows, we extend and apply our previously developed benchmark approach to evaluate the 'next generation' of profile-based approaches, including CS-BLAST, HHSEARCH and PHMMER, in comparison with the non-profile based search tools NCBI-BLAST, USEARCH, UBLAST and FASTA. Method: We generated challenging benchmark datasets based on protein domain architectures within either the PFAM + Clan, SCOP/Superfamily or CATH/Gene3D domain definition schemes. From each dataset, homologous and non-homologous protein pairs were aligned using each tool, and standard performance metrics calculated. We further measured congruence of domain architecture assignments in the three domain databases. Results: CSBLAST and PHMMER had overall highest accuracy. FASTA, UBLAST and USEARCH showed large trade-offs of accuracy for speed optimization. Conclusion: Profile methods are superior at inferring remote homologs but the difference in accuracy between methods is relatively small. PHMMER and CSBLAST stand out with the highest accuracy, yet still at a reasonable computational cost. Additionally, we show that less than 0.1% of Swiss-Prot protein pairs considered homologous by one database are considered non-homologous by another, implying that these classifications represent equivalent underlying biological phenomena, differing mostly in coverage and granularity. Availability and Implementation: Benchmark datasets and all scripts are placed at (http://sonnhammer.org/download/Homology_benchmark). Contact: [email protected] SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online

Journal Staff

Background: The polytene nuclei of the dipteran Chironomus tentans (Ch. tentans) with their Balbiani ring (BR) genes constitute an exceptional model system for studies of the expression of endogenous eukaryotic genes. Here, we report the first draft genome of Ch. tentans and characterize its gene expression machineries and genomic architecture of the BR genes. Results: The genome of Ch. tentans is approximately 200 Mb in size, and has a low GC content (31%) and a low repeat fraction (15%) compared to other Dipteran species. Phylogenetic inference revealed that Ch. tentans is a sister clade to mosquitoes, with a split 150-250 million years ago. To characterize the Ch. tentans gene expression machineries, we identified potential orthologus sequences to more than 600 Drosophila melanogaster (D. melanogaster) proteins involved in the expression of protein-coding genes. We report novel data on the organization of the BR gene loci, including a novel putative BR gene, and we present a model for the organization of chromatin bundles in the BR2 puff based on genic and intergenic in situ hybridizations. Conclusions: We show that the molecular machineries operating in gene expression are largely conserved between Ch. tentans and D. melanogaster, and we provide enhanced insight into the organization and expression of the BR genes. Our data strengthen the generality of the BR genes as a unique model system and provide essential background for in-depth studies of the biogenesis of messenger ribonucleoprotein complexes

PathwAX: a web server for network crosstalk based pathway annotation

Pathway annotation of gene lists is often used to functionally analyse biomolecular data such as gene expression in order to establish which processes are activated in a given experiment. Databases such as KEGG or GO represent collections of how genes are known to be organized in pathways, and the challenge is to compare a given gene list with the known pathways such that all true relations are identified. Most tools apply statistical measures to the gene overlap between the gene list and pathway. It is however problematic to avoid false negatives and false positives when only using the gene overlap. The pathwAX web server (http://pathwAX.sbc.su.se/) applies a different approach which is based on network crosstalk. It uses the comprehensive network FunCoup to analyse network crosstalk between a query gene list and KEGG pathways. PathwAX runs the BinoX algorithm, which employs Monte-Carlo sampling of randomized networks and estimates a binomial distribution, for estimating the statistical significance of the crosstalk. This results in substantially higher accuracy than gene overlap methods. The system was optimized for speed and allows interactive web usage. We illustrate the usage and output of pathwAX

Advantages of combined transmembrane topology and signal peptide prediction—the Phobius web server

When using conventional transmembrane topology and signal peptide predictors, such as TMHMM and SignalP, there is a substantial overlap between these two types of predictions. Applying these methods to five complete proteomes, we found that 30–65% of all predicted signal peptides and 25–35% of all predicted transmembrane topologies overlap. This impairs predictions of 5–10% of the proteome, hence this is an important issue in protein annotation