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New protein coding genes can emerge from genomic regions that previously did not contain any genes, via a process called de novo gene emergence. To synthesize a protein, DNA must be transcribed as well as translated. Both processes need certain DNA sequence features. Stable transcription requires promoters and a polyadenylation signal, while translation requires at least an open reading frame. We develop mathematical models based on mutation probabilities, and the assumption of neutral evolution, to find out how quickly genes emerge and are lost. We also investigate the effect of the order by which DNA features evolve, and if sequence composition is biased by mutation rate. We rationalize how genes are lost much more rapidly than they emerge, and how they preferentially arise in regions that are already transcribed. Our study not only answers some fundamental questions on the topic of de novo emergence but also provides a modeling framework for future studies.

Even in the era of next generation sequencing, in which bioinformatics tools abound, annotating transcriptomes and proteomes remains a challenge. This can have major implications for the reliability of studies based on these datasets. Therefore, quality assessment represents a crucial step prior to downstream analyses on novel transcriptomes and proteomes. DOGMA allows such a quality assessment to be carried out. The data of interest are evaluated based on a comparison with a core set of conserved protein domains and domain arrangements. Depending on the studied species, DOGMA offers precomputed core sets for different phylogenetic clades. We now developed a web server for the DOGMA software, offering a user-friendly, simple to use interface. Additionally, the server provides a graphical representation of the analysis results and their placement in comparison to publicly available data. The server is freely available under https://domainworld-services.uni-muenster.de/dogma/. Additionally, for large scale analyses the software can be downloaded free of charge from https://domainworld.uni-muenster.de.

Repeats are ubiquitous elements of proteins and they play important roles for cellular function and during evolution. Repeats are, however, also notoriously difficult to capture computationally and large scale studies so far had difficulties in linking genetic causes, structural properties and evolutionary trajectories of protein repeats. Here we apply recently developed methods for repeat detection and analysis to a large dataset comprising over hundred metazoan genomes. We find that repeats in larger protein families experience generally very few insertions or deletions (indels) of repeat units but there is also a significant fraction of noteworthy volatile outliers with very high indel rates. Analysis of structural data indicates that repeats with an open structure and independently folding units are more volatile and more likely to be intrinsically disordered. Such disordered repeats are also significantly enriched in sites with a high functional potential such as linear motifs. Furthermore, the most volatile repeats have a high sequence similarity between their units. Since many volatile repeats also show signs of recombination, we conclude they are often shaped by concerted evolution. Intriguingly, many of these conserved yet volatile repeats are involved in host-pathogen interactions where they might foster fast but subtle adaptation in biological arms races. KEY WORDS: protein evolution, domain rearrangements, protein repeats, concerted evolution.

Transcriptions factors (TFs) are pivotal for the regulation of virtually all cellular processes, including growth and development. Expansions of TF families are causally linked to increases in organismal complexity. Here we study the evolutionary dynamics, genetic causes and functional implications of the five largest metazoan TF families. We find that family expansions dominate across the whole metazoan tree; however, some branches experience exceptional family-specific accelerated expansions. Additionally, we find that such expansions are often predated by modular domain rearrangements, which spur the expansion of a new sub-family by separating it from the rest of the TF family in terms of protein–protein interactions. This separation allows for radical shifts in the functional spectrum of a duplicated TF. We also find functional differentiation inside TF sub-families as changes in expression specificity. Furthermore, accelerated family expansions are facilitated by repeats of sequence motifs such as C2H2 zinc fingers. We quantify whole genome duplications and single gene duplications as sources of TF family expansions, implying that some, but not all, TF duplicates are preferentially retained. We conclude that trans-regulatory changes (domain rearrangements) are instrumental for fundamental functional innovations, that cis-regulatory changes (affecting expression) accomplish wide-spread fine tuning and both jointly contribute to the functional diversification of TFs.

Motivation: Homology search methods are dominated by the central paradigm that sequence similarity is a proxy for common ancestry and, by extension, functional similarity. For determining sequence similarity in proteins, most widely used methods use models of sequence evolution and compare amino-acid strings in search for conserved linear stretches. Probabilistic models or sequence profiles capture the position-specific variation in an alignment of homologous sequences and can identify conserved motifs or domains. While profile-based search methods are generally more accurate than simple sequence comparison methods, they tend to be computationally more demanding. In recent years, several methods have emerged that perform protein similarity searches based on domain composition. However, few methods have considered the linear arrangements of domains when conducting similarity searches, despite strong evidence that domain order can harbour considerable functional and evolutionary signal. Results: Here, we introduce an alignment scheme that uses a classical dynamic programming approach to the global alignment of domains. We illustrate that representing proteins as strings of domains (domain arrangements) and comparing these strings globally allows for a both fast and sensitive homology search. Further, we demonstrate that the presented methods complement existing methods by finding similar proteins missed by popular amino-acid–based comparison methods. Availability: An implementation of the presented algorithms, a web-based interface as well as a command-line program for batch searching against the UniProt database can be found at http://rads.uni-muenster.de. Furthermore, we provide a JAVA API for programmatic access to domain-string–based search methods. Contact: terrapon.nicolas@gmail.com or ebb@uni-muenster.de Supplementary information: Supplementary data are available at Bioinformatics online.

Modular rearrangements play an important role in protein evolution. Functional modules, often tantamount to structural domains or smaller fragments, are in many cases well conserved but reoccur in a different order and across many protein families. The underlying genetic mechanisms are gene duplication, fusion, and loss of sequence fragments. As a consequence, the sequential order of domains can be inverted, leading to what is known as circularly permutated proteins. Using a recently developed algorithm, we have identified a large number of such rearrangements and analyzed their evolutionary history. We searched for examples which have arisen by one of the three postulated mechanisms: independent fusion/ fission, "duplication/deletion," and plasmid-mediated "cut and paste." We conclude that all three mechanisms can be observed, with the independent fusion/fission being the most frequent. This can be partly attributed to highly mobile domains. Duplication/deletion has been found in modular proteins such as peptide synthases.

Motivation: Rearrangements of protein domains and motifs such as swaps and circular permutations (CPs) can produce erroneous results in searching sequence databases when using traditional methods based on linear sequence alignments. Circular permutations are also of biological relevance because they can help to better understand both protein evolution and functionality. Results: We have developed an algorithm, RASPODOM, which is based on the classical recursive alignment scheme. Sequences are represented as strings of domains taken from precompiled resources of domain (motif) databases such as ProDom. The algorithm works several orders of magnitude faster than a reimplementation of the existing CP detection algorithm working on strings of amino acids, produces virtually no false positives and allows the discrimination of true CPs from ‘intermediate’ CPs (iCPs). Several true CPs which have not been reported in literature so far could be identified from Swiss-Prot/TrEMBL within minutes. Availability: Source codes, additional scripts, data and a web-based interface can be found on: http://www.uni-muenster.de/Biologie.Botanik/ebb/projects/raspodom/ Contact: ebb@uni-muenster.de

Current research in the biosciences depends heavily on the effective exploitation of huge amounts of data. These are in disparate formats, remotely dispersed, and based on the different vocabularies of various disciplines. Furthermore, data are often stored or distributed using formats that leave implicit many important features relating to the structure and semantics of the data. Conceptual data modelling involves the development of implementation-independent models that capture and make explicit the principal structural properties of data. Entities such as a biopolymer or a reaction, and their relations, eg catalyses, can be formalised using a conceptual data model. Conceptual models are implementation-independent and can be transformed in systematic ways for implementation using different platforms, eg traditional database management systems. This paper describes the basics of the most widely used conceptual modelling notations, the ER (entity-relationship) model and the class diagrams of the UML (unified modelling language), and illustrates their use through several examples from bioinformatics. In particular, models are presented for protein structures and motifs, and for genomic sequences.

Motivation: The rapidly increasing amount and disparity of biological data requires interpretation at many levels of description. Human judgement and intuition are important because not all data can be automatically and comprehensively analyzed. Visualization of trees and substructures corresponding to certain features are often used to analyze phylogenies or taxonomies. Unfortunately, most existing tools do not cope with the size of current datasets, the required functionality, or both. Results: We introduce a program for visualization of huge trees and also for the interactive exploration of their content. We have developed a range of new schemes which are tailored for biological problems. Users can get an overview, zoom in, filter out data and retrieve details from standard databases such as SWISS-PROT. Furthermore, it is possible to analyze the relationship between chosen leaf sets that are specified by common features on a second level of representation. On a PC (with ≈ 512 MB RAM), trees of up to several tens of thousands of leaves can be loaded and both rapidly and interactively explored. We demonstrate the use of this program for the analysis of the SYSTERS data set (which contains hierarchically clustered protein sequences) to which PFAM domains were added as features. Availability: TreeWiz is written in JAVA and is free for academic use. The full application as well as a restricted applet version can be found on the web-page http://www.eml.org/english/staff/homes/ulla/rost.html. Contact: Ursula.Rost@eml.villa-bosch.de * To whom correspondence should be addressed.

MOTIVATION: Predicting the ground state of biopolymers is a notoriously hard problem in biocomputing. Model systems, such as lattice proteins, are simple tools and valuable to test and improve new methods. Best known are models with sequences composed from a binary (hydrophobic and polar) alphabet. The major drawback is the degeneracy, i.e. the number of different ground state conformations. RESULTS: We show how recently developed constraint programming techniques can be used to solve the structure prediction problem efficiently for a higher order alphabet. To our knowledge it is the first report of an exact and computationally feasible solution to model proteins of length up to 36 and without resorting to maximally compact states. We further show that degeneracy is reduced by more than one order of magnitude and that ground state conformations are not necessarily compact. Therefore, more realistic protein simulations become feasible with our model.

Motivation: Since protein domains are the units of evolution, databases of domain signatures such as ProDom or Pfam enable both a sensitive and selective sequence analysis. However, manually curated databases have a low coverage and automatically generated ones often miss relationships which have not yet been discovered between domains or cannot display similarities between domains which have drifted apart. Methods: We present a tool which makes use of the fact that overall domain arrangements are often conserved. AIDAN (Automated Improvement of Domain ANnotations) identifies potential annotation artifacts and domains which have drifted apart. The underlying database supplements ProDom and is interfaced by a graphical tool allowing the localization of single domain deletions or annotations which have been falsely made by the automated procedure. Availability: http://www.uni-muenster.de/Evolution/ebb/Services/AIDAN Contact: ebb@uni-muenster.de Supplementary information: Supplementary data are available at Bioinformatics online.

New genes arise through a variety of mechanisms, including the duplication of existing genes and the de novo birth of genes from noncoding DNA sequences. While there are numerous examples of duplicated genes with important functional roles, the functions of de novo genes remain largely unexplored. Many newly evolved genes are expressed in the male reproductive tract, suggesting that these evolutionary innovations may provide advantages to males experiencing sexual selection. Using testis-specific RNA interference, we screened 11 putative de novo genes in Drosophila melanogaster for effects on male fertility and identified two, goddard and saturn, that are essential for spermatogenesis and sperm function. Goddard knockdown (KD) males fail to produce mature sperm, while saturn KD males produce few sperm, and these function inefficiently once transferred to females. Consistent with a de novo origin, both genes are identifiable only in Drosophila and are predicted to encode proteins with no sequence similarity to any annotated protein. However, since high levels of divergence prevented the unambiguous identification of the noncoding sequences from which each gene arose, we consider goddard and saturn to be putative de novo genes. Within Drosophila, both genes have been lost in certain lineages, but show conserved, male-specific patterns of expression in the species in which they are found. Goddard is consistently found in single-copy and evolves under purifying selection. In contrast, saturn has diversified through gene duplication and positive selection. These data suggest that de novo genes can acquire essential roles in male reproduction.