Insertions and deletions (indels) play a significant role in genome evolution across species. Realistic modelling of indel evolution is challenging and is still an open research question. Several attempts have been made to explicitly model multi-character (long) indels, such as TKF92, by relaxing the site independence assumption and introducing fragments. However, these methods are computationally expensive. On the other hand, the Poisson Indel Process (PIP) assumes site independence but allows one to infer single-character indels on the phylogenetic tree, distinguishing insertions from deletions. PIP's marginal likelihood computation has linear time complexity, enabling ancestral sequence reconstruction (ASR) with indels in linear time. Recently, we developed ARPIP, an ASR method using PIP, capable of inferring indel events with explicit evolutionary interpretations. Here, we investigate the effect of the single-character indel assumption on reconstructed ancestral sequences on mammalian protein orthologs and on simulated data. We show that ARPIP's ancestral estimates preserve the gap length distribution observed in the input alignment. In mammalian proteins the lengths of inserted segments appear to be substantially longer compared to deleted segments. Further, we confirm the well-established deletion bias observed in real data. To date, ARPIP is the only ancestral reconstruction method that explicitly models insertion and deletion events over time. Given a good quality input alignment, it can capture ancestral long indel events on the phylogeny.

BACKGROUND: Current alignment tools typically lack an explicit model of indel evolution, leading to artificially short inferred alignments (i.e., over-alignment) due to inconsistencies between the indel history and the phylogeny relating the input sequences. RESULTS: We present a new progressive multiple sequence alignment tool ProPIP. The process of insertions and deletions is described using an explicit evolutionary model-the Poisson Indel Process or PIP. The method is based on dynamic programming and is implemented in a frequentist framework. The source code can be compiled on Linux, macOS and Microsoft Windows platforms. The algorithm is implemented in C++ as standalone program. The source code is freely available on GitHub at https://github.com/acg-team/ProPIP and is distributed under the terms of the GNU GPL v3 license. CONCLUSIONS: The use of an explicit indel evolution model allows to avoid over-alignment, to infer gaps in a phylogenetically consistent way and to make inferences about the rates of insertions and deletions. Instead of the arbitrary gap penalties, the parameters used by ProPIP are the insertion and deletion rates, which have biological interpretation and are contextualized in a probabilistic environment. As a result, indel rate settings may be optimised in order to infer phylogenetically meaningful gap patterns.

BACKGROUND: Sequence alignment is crucial in genomics studies. However, optimal multiple sequence alignment (MSA) is NP-hard. Thus, modern MSA methods employ progressive heuristics, breaking the problem into a series of pairwise alignments guided by a phylogeny. Changes between homologous characters are typically modelled by a Markov substitution model. In contrast, the dynamics of indels are not modelled explicitly, because the computation of the marginal likelihood under such models has exponential time complexity in the number of taxa. But the failure to model indel evolution may lead to artificially short alignments due to biased indel placement, inconsistent with phylogenetic relationship. RESULTS: Recently, the classical indel model TKF91 was modified to describe indel evolution on a phylogeny via a Poisson process, termed PIP. PIP allows to compute the joint marginal probability of an MSA and a tree in linear time. We present a new dynamic programming algorithm to align two MSAs -represented by the underlying homology paths- by full maximum likelihood under PIP in polynomial time, and apply it progressively along a guide tree. We have corroborated the correctness of our method by simulation, and compared it with competitive methods on an illustrative real dataset. CONCLUSIONS: Our MSA method is the first polynomial time progressive aligner with a rigorous mathematical formulation of indel evolution. The new method infers phylogenetically meaningful gap patterns alternative to the popular PRANK, while producing alignments of similar length. Moreover, the inferred gap patterns agree with what was predicted qualitatively by previous studies. The algorithm is implemented in a standalone C++ program: https://github.com/acg-team/ProPIP . Supplementary data are available at BMC Bioinformatics online.

A group of eight Spanish lidars was formed in order to extend the European Aerosol Research Lidar Network-Advanced Sustainable Observation System (EARLINET-ASOS) project. This study presents intercomparisons at the hardware and software levels. Results of the system intercomparisons are based on range-square-corrected signals in cases where the lidars viewed the same atmospheres. Comparisons were also made for aerosol backscatter coefficients at 1064 nm (2 systems) and 532 nm (all systems), and for extinction coefficients at 532 nm (2 systems). In total, three field campaigns were carried out between 2006 and 2007. Comparisons were limited to the highest layer found before the free troposphere, i.e., either the atmospheric boundary layer or the aerosol layer just above it. Some groups did not pass the quality assurance criterion on the first attempt. Following modification and improvement to these systems, all systems met the quality criterion. The backscatter algorithm intercomparison consisted of processing lidar signal profiles simulated for two types of atmospheric conditions. Three stages with increasing knowledge of the input parameters were considered. The results showed that all algorithms work well when all inputs are known. They also showed the necessity to perform, when possible, additional measurements to attain better estimation of the lidar ratio, which is the most critical unknown in the elastic lidar inversion.

BACKGROUND: The estimation of the difference between two evolutionary distances within a triplet of homologs is a common operation that is used for example to determine which of two sequences is closer to a third one. The most accurate method is currently maximum likelihood over the entire triplet. However, this approach is relatively time consuming. RESULTS: We show that an alternative estimator, based on pairwise estimates and therefore much faster to compute, has almost the same statistical power as the maximum likelihood estimator. We also provide a numerical approximation for its variance, which could otherwise only be estimated through an expensive re-sampling approach such as bootstrapping. An extensive simulation demonstrates that the approximation delivers precise confidence intervals. To illustrate the possible applications of these results, we show how they improve the detection of asymmetric evolution, and the identification of the closest relative to a given sequence in a group of homologs. CONCLUSION: The results presented in this paper constitute a basis for large-scale protein cross-comparisons of pairwise evolutionary distances.

We present a biophysically constrained cerebellar model of classical conditioning, implemented using a neuromorphic analog VLSI (aVLSI) chip. Like its biological counterpart, our cerebellar model is able to control adaptive behavior by predicting the precise timing of events. Here we describe the functionality of the chip and present its learning performance, as evaluated in simulated conditioning experiments at the circuit level and in behavioral experiments using a mobile robot. We show that this aVLSI model supports the acquisition and extinction of adaptively timed conditioned responses under real-world conditions with ultra-low power consumption.