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.

Chloroplasts arose from cyanobacteria, mitochondria arose from proteobacteria. Both organelles have conserved their prokaryotic biochemistry, but their genomes are reduced, and most organelle proteins are encoded in the nucleus. Endosymbiotic theory posits that bacterial genes in eukaryotic genomes entered the eukaryotic lineage via organelle ancestors. It predicts episodic influx of prokaryotic genes into the eukaryotic lineage, with acquisition corresponding to endosymbiotic events. Eukaryotic genome sequences, however, increasingly implicate lateral gene transfer, both from prokaryotes to eukaryotes and among eukaryotes, as a source of gene content variation in eukaryotic genomes, which predicts continuous, lineage-specific acquisition of prokaryotic genes in divergent eukaryotic groups. Here we discriminate between these two alternatives by clustering and phylogenetic analysis of eukaryotic gene families having prokaryotic homologues. Our results indicate (1) that gene transfer from bacteria to eukaryotes is episodic, as revealed by gene distributions, and coincides with major evolutionary transitions at the origin of chloroplasts and mitochondria; (2) that gene inheritance in eukaryotes is vertical, as revealed by extensive topological comparison, sparse gene distributions stemming from differential loss; and (3) that continuous, lineage-specific lateral gene transfer, although it sometimes occurs, does not contribute to long-term gene content evolution in eukaryotic genomes.

The mechanisms that underlie the origin of major prokaryotic groups are poorly understood. In principle, the origin of both species and higher taxa among prokaryotes should entail similar mechanisms — ecological interactions with the environment paired with natural genetic variation involving lineage-specific gene innovations and lineage-specific gene acquisitions1,2,3,4. To investigate the origin of higher taxa in archaea, we have determined gene distributions and gene phylogenies for the 267,568 protein coding genes of 134 sequenced archaeal genomes in the context of their homologs from 1,847 reference bacterial genomes. Archaea-specific gene families define 13 traditionally recognized archaeal higher taxa in our sample. Here we report that the origins of these 13 groups unexpectedly correspond to 2,264 group-specific gene acquisitions from bacteria. Interdomain gene transfer is highly asymmetric, transfers from bacteria to archaea are more than 5-fold more frequent than vice versa. Gene transfers identified at major evolutionary transitions among prokaryotes specifically implicate gene acquisitions for metabolic functions from bacteria as key innovations in the origin of higher archaeal taxa.

Multiple sequence alignment (MSA) is the basis for a wide range of comparative sequence analyses from molecular phylogenetics to 3D structure prediction. Sophisticated algorithms have been developed for sequence alignment, but in practice, many errors can be expected and extensive portions of the MSA are unreliable. Hence, it is imperative to understand and characterize the various sources of errors in MSAs and to quantify site-specific alignment confidence. In this paper, we show that uncertainties in the guide tree used by progressive alignment methods are a major source of alignment uncertainty. We use this insight to develop a novel method for quantifying the robustness of each alignment column to guide tree uncertainty. We build on the widely used bootstrap method for perturbing the phylogenetic tree. Specifically, we generate a collection of trees and use each as a guide tree in the alignment algorithm, thus producing a set of MSAs. We next test the consistency of every column of the MSA obtained from the unperturbed guide tree with respect to the set of MSAs. We name this measure the "GUIDe tree based AligNment ConfidencE" (GUIDANCE) score. Using the Benchmark Alignment data BASE benchmark as well as simulation studies, we show that GUIDANCE scores accurately identify errors in MSAs. Additionally, we compare our results with the previously published Heads-or-Tails score and show that the GUIDANCE score is a better predictor of unreliably aligned regions.