Search

The most common evolutionary events at the molecular level are single-base substitutions, as well as insertions and deletions (indels) of short DNA segments. A large body of research has been devoted to develop probabilistic substitution models and to infer their parameters using likelihood and Bayesian approaches. In contrast, relatively little has been done to model indel dynamics, probably due to the difficulty in writing explicit likelihood functions. Here, we contribute to the effort of modeling indel dynamics by presenting SpartaABC, an approximate Bayesian computation (ABC) approach to infer indel parameters from sequence data (either aligned or unaligned). SpartaABC circumvents the need to use an explicit likelihood function by extracting summary statistics from simulated sequences. First, summary statistics are extracted from the input sequence data. Second, SpartaABC samples indel parameters from a prior distribution and uses them to simulate sequences. Third, it computes summary statistics from the simulated sets of sequences. By computing a distance between the summary statistics extracted from the input and each simulation, SpartaABC can provide an approximation to the posterior distribution of indel parameters as well as point estimates. We study the performance of our methodology and show that it provides accurate estimates of indel parameters in simulations. We next demonstrate the utility of SpartaABC by studying the impact of alignment errors on the inference of positive selection. A C ++ program implementing SpartaABC is freely available in http://spartaabc.tau.ac.il.

Many analyses for the detection of biological phenomena rely on a multiple sequence alignment as input. The results of such analyses are often further studied through parametric bootstrap procedures, using sequence simulators. One of the problems with conducting such simulation studies is that users currently have no means to decide which insertion and deletion (indel) parameters to choose, so that the resulting sequences mimic biological data. Here, we present SpartaABC, a web server that aims to solve this issue. SpartaABC implements an approximate-Bayesian-computation rejection algorithm to infer indel parameters from sequence data. It does so by extracting summary statistics from the input. It then performs numerous sequence simulations under randomly sampled indel parameters. By computing a distance between the summary statistics extracted from the input and each simulation, SpartaABC retains only parameters behind simulations close to the real data. As output, SpartaABC provides point estimates and approximate posterior distributions of the indel parameters. In addition, SpartaABC allows simulating sequences with the inferred indel parameters. To this end, the sequence simulators, Dawg 2.0 and INDELible were integrated. Using SpartaABC we demonstrate the differences in indel dynamics among three protein-coding genes across mammalian orthologs. SpartaABC is freely available for use at http://spartaabc.tau.ac.il/webserver.

Evolutionary analysis of phyletic patterns (phylogenetic profiles) is widely used in biology, representing presence or absence of characters such as genes, restriction sites, introns, indels and methylation sites. The phyletic pattern observed in extant genomes is the result of ancestral gain and loss events along the phylogenetic tree. Here we present CoPAP (coevolution of presence–absence patterns), a user-friendly web server, which performs accurate inference of coevolving characters as manifested by co-occurring gains and losses. CoPAP uses state-of-the-art probabilistic methodologies to infer coevolution and allows for advanced network analysis and visualization. We developed a platform for comparing different algorithms that detect coevolution, which includes simulated data with pairs of coevolving sites and independent sites. Using these simulated data we demonstrate that CoPAP performance is higher than alternative methods. We exemplify CoPAP utility by analyzing coevolution among thousands of bacterial genes across 681 genomes. Clusters of coevolving genes that were detected using our method largely coincide with known biosynthesis pathways and cellular modules, thus exhibiting the capability of CoPAP to infer biologically meaningful interactions. CoPAP is freely available for use at http://copap.tau.ac.il/.

Evaluating the accuracy of multiple sequence alignment (MSA) is critical for virtually every comparative sequence analysis that uses an MSA as input. Here we present the GUIDANCE web-server, a user-friendly, open access tool for the identification of unreliable alignment regions. The web-server accepts as input a set of unaligned sequences. The server aligns the sequences and provides a simple graphic visualization of the confidence score of each column, residue and sequence of an alignment, using a color-coding scheme. The method is generic and the user is allowed to choose the alignment algorithm (ClustalW, MAFFT and PRANK are supported) as well as any type of molecular sequences (nucleotide, protein or codon sequences). The server implements two different algorithms for evaluating confidence scores: (i) the heads-or-tails (HoT) method, which measures alignment uncertainty due to co-optimal solutions; (ii) the GUIDANCE method, which measures the robustness of the alignment to guide-tree uncertainty. The server projects the confidence scores onto the MSA and points to columns and sequences that are unreliably aligned. These can be automatically removed in preparation for downstream analyses. GUIDANCE is freely available for use at http://guidance.tau.ac.il.

Peptide-expressing phage display libraries are widely used for the interrogation of antibodies. Affinity selected peptides are then analyzed to discover epitope mimetics, or are subjected to computational algorithms for epitope prediction. A critical assumption for these applications is the random representation of amino acids in the initial naïve peptide library. In a previous study, we implemented next generation sequencing to evaluate a naïve library and discovered severe deviations from randomness in UAG codon over-representation as well as in high G phosphoramidite abundance causing amino acid distribution biases. In this study, we demonstrate that the UAG over-representation can be attributed to the burden imposed on the phage upon the assembly of the recombinant Protein 8 subunits. This was corrected by constructing the libraries using supE44-containing bacteria which suppress the UAG driven abortive termination. We also demonstrate that the overabundance of G stems from variant synthesis-efficiency and can be corrected using compensating oligonucleotide-mixtures calibrated by mass spectroscopy. Construction of libraries implementing these correctives results in markedly improved libraries that display random distribution of amino acids, thus ensuring that enriched peptides obtained in biopanning represent a genuine selection event, a fundamental assumption for phage display applications.

The discrimination between functionally neutral amino acid substitutions and non-neutral mutations, affecting protein function, is very important for our understanding of diseases. The rapidly growing amounts of experimental data enable the development of computational tools to facilitate the annotation of these substitutions. Here, we describe a Random Forests-based classifier, named Mutation Detector (MuD) that utilizes structural and sequence-derived features to assess the impact of a given substitution on the protein function. In its automatic mode, MuD is comparable to alternative tools in performance. However, the uniqueness of MuD is that user-reported protein-specific structural and functional information can be added at run-time, thereby enhancing the prediction accuracy further. The MuD server, available at http://mud.tau.ac.il, assigns a reliability score to every prediction, thus offering a useful tool for the prioritization of substitutions in proteins with an available 3D structure.