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.

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.

A comparison of protein-coding genes from 134 archaeal genomes with their homologues in 1,847 bacterial genomes reveals that, during evolution, genes are transferred more often from bacteria to archaea than vice versa, and that gene influxes from bacteria can bring about the origin of major archaeal groups. Lateral (or horizontal) gene transfer between individual cells is recognized as an important factor in genome evolution and species formation in prokaryotes such as cyanobacteria and proteobacteria. This study of gene of distribution and phylogenies in 134 archaeal genomes shows that origins of the 13 traditionally recognized higher taxa in the archaea correspond to 2,264 group-specific lateral gene acquisitions from bacteria. Transfers from bacteria to archaea are more than fivefold more frequent than vice versa. Gene acquisitions for metabolic functions from bacteria represent key innovations in the origin of higher archaeal taxa. 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 homologues from 1,847 reference bacterial genomes. Archaeal-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 fivefold 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.

Life is the harnessing of chemical energy in such a way that the energy-harnessing device makes a copy of itself. This paper outlines an energetically feasible path from a particular inorganic setting for the origin of life to the first free-living cells. The sources of energy available to early organic synthesis, early evolving systems and early cells stand in the foreground, as do the possible mechanisms of their conversion into harnessable chemical energy for synthetic reactions. With regard to the possible temporal sequence of events, we focus on: (i) alkaline hydrothermal vents as the far-from-equilibrium setting, (ii) the Wood–Ljungdahl (acetyl-CoA) pathway as the route that could have underpinned carbon assimilation for these processes, (iii) biochemical divergence, within the naturally formed inorganic compartments at a hydrothermal mound, of geochemically confined replicating entities with a complexity below that of free-living prokaryotes, and (iv) acetogenesis and methanogenesis as the ancestral forms of carbon and energy metabolism in the first free-living ancestors of the eubacteria and archaebacteria, respectively. In terms of the main evolutionary transitions in early bioenergetic evolution, we focus on: (i) thioester-dependent substrate-level phosphorylations, (ii) harnessing of naturally existing proton gradients at the vent–ocean interface via the ATP synthase, (iii) harnessing of Na ⁺ gradients generated by H ⁺ /Na ⁺ antiporters, (iv) flavin-based bifurcation-dependent gradient generation, and finally (v) quinone-based (and Q-cycle-dependent) proton gradient generation. Of those five transitions, the first four are posited to have taken place at the vent. Ultimately, all of these bioenergetic processes depend, even today, upon CO ₂ reduction with low-potential ferredoxin (Fd), generated either chemosynthetically or photosynthetically, suggesting a reaction of the type ‘reduced iron → reduced carbon’ at the beginning of bioenergetic evolution.