Public-domain availability for bioinformatics software resources is a key requirement that ensures long-term permanence and methodological reproducibility for research and development across the life sciences. These issues are particularly critical for widely used, efficient, and well-proven methods, especially those developed in research settings that often face funding discontinuities. We re-launch a range of established software components for computational genomics, as legacy version 1.0.1, suitable for sequence matching, masking, searching, clustering and visualization for protein family discovery, annotation and functional characterization on a genome scale. These applications are made available online as open source and include MagicMatch, GeneCAST, support scripts for CoGenT-like sequence collections, GeneRAGE and DifFuse, supported by centrally administered bioinformatics infrastructure funding. The toolkit may also be conceived as a flexible genome comparison software pipeline that supports research in this domain. We illustrate basic use by examples and pictorial representations of the registered tools, which are further described with appropriate documentation files in the corresponding GitHub release.

There are multiple definitions for low complexity regions (LCRs) in protein sequences, with all of them broadly considering LCRs as regions with fewer amino acid types compared to an average composition. Following this view, LCRs can also be defined as regions showing composition bias. In this critical review, we focus on the definition of sequence complexity of LCRs and their connection with structure. We present statistics and methodological approaches that measure low complexity (LC) and related sequence properties. Composition bias is often associated with LC and disorder, but repeats, while compositionally biased, might also induce ordered structures. We illustrate this dichotomy, and more generally the overlaps between different properties related to LCRs, using examples. We argue that statistical measures alone cannot capture all structural aspects of LCRs and recommend the combined usage of a variety of predictive tools and measurements. While the methodologies available to study LCRs are already very advanced, we foresee that a more comprehensive annotation of sequences in the databases will enable the improvement of predictions and a better understanding of the evolution and the connection between structure and function of LCRs. This will require the use of standards for the generation and exchange of data describing all aspects of LCRs. SHORT ABSTRACT: There are multiple definitions for low complexity regions (LCRs) in protein sequences. In this critical review, we focus on the definition of sequence complexity of LCRs and their connection with structure. We present statistics and methodological approaches that measure low complexity (LC) and related sequence properties. Composition bias is often associated with LC and disorder, but repeats, while compositionally biased, might also induce ordered structures. We illustrate this dichotomy, plus overlaps between different properties related to LCRs, using examples.

In bibliometrics, scientific output is typically measured in terms of quantity, e.g. number of publications, or quality, e.g. number of citations (Almeida et al., 2009). For individual researchers, absolute counts are regarded as sufficient, although it is well-known that these numbers may vary per research field (Yang et al., 2012). The h-index, the number of N publications that have been cited at least N times (Hirsch, 2005), has also been shown to vary across scientific disciplines (Lillquist and Green, 2010). Other, more complex metrics have been devised, yet the h-index is indeed a widely used measure of academic ‘success’ or impact (Alonso et al., 2009)—despite the fact that the primary metrics on which it depends are the number of publications and citations (Yong, 2014). To assess the standing of entire countries, similar measures are in use (Kahn, 2018). Numbers of publications, citations and the h-index have all been compared across nations, to investigate trends of scientific performance (Thelwall and Fairclough, 2017), identify the focus of research in countries, country groups or world regions (Lin et al., 2018), and monitor growth or decline patterns in research intensity (Jenab, 2016). For countries, normalization with econometric indices such as population size or gross domestic product (GDP) is usually necessary, if one needs to take into account relative, not absolute, performance (May, 1997). For large numbers such as publications or citations, this step is critical (Chasapi et al., 2019); it is less important for the h-index, which is a good measure of performance that reflects the impact of an entire country in science (Harzing and Giroud, 2014). The h-index can be compared against other measures, or rank-order countries in a comparative manner (Jacsó, 2009). Criticisms related to h-index such as its variation across fields, a certain lack of discriminatory power and dependence on self-citation patterns do not really apply to country-level statistics for a specific field, where the above factors are mitigated, rendering it ideal for this type of comparisons (Jacsó, 2009). To quantify the output of bioinformatics publications across countries, we have obtained numbers of publications and citations and obtained the h-index using the Web of Science (WoS) by Clarivate Analytics (formerly ISI Web of Knowledge) and a simple query, ‘bioinformatics’ for ‘all fields’ and ‘country name’ (slightly edited for accuracy) in the ‘address’ field (date: December 31, 2019; WoS Core Collection, across all years 1900-present—full list in Supplementary Table S1). This straightforward (and reproducible) query returns multiple counts for bilateral or multi-lateral collaborations, not affecting the overall picture—as counts are kept high for the top performers and in fact collaborations are taken into account as a real component of total output (King, 2004). We have used a list of 288 countries and territories and queried WoS for publications containing the search terms and requested publications, citations, the citations/publication ratio and the h-index for the returned results (four primary indices). The frequency distribution of the h-index rank follows an exponential decay curve with the following formula y = 152.94e−0.0312x, where x is the rank of the entry and y is the h-index, and R2 = 0.9812 (Supplementary Table S1). Of the 288 instances, 119 have h = 0 and 28 instances have h = 1 or 2—these are not further discussed (tiny countries or territories, or scientifically less active). The remaining 141 countries have an h-index > 2, 78 of those have an h-index > 11, just 53 of them have an h-index > 22 and 36 have an h-index > 44 (Fig. 1). The least active countries include those in the American, African and Asian tropics, as well as former Soviet republics and parts of the Middle East—unsurprisingly, and consistent with previous findings (Radosevic and Yoruk, 2014). More needs to be done to establish and develop additional activity in these areas, where possible, through international collaboration (Hennemann et al., 2012). Examples of proposed activities and recommendations from our own experience for Greece and Cyprus have been provided elsewhere (Chasapi et al., 2019). Ultimately, the ‘top’ 78 countries generate 137 072/138 015 = 99% of the world’s output in the field of bioinformatics (‘all fields’ in WoS query, as mentioned above). Distribution of number of countries and corresponding bioinformatics publications, for various h-index classes. X-axis: h-index intervals, from high (89-max) to low (minimum=0); Y-axis: left, number of countries (blue bars, values shown. clipped at 100) for corresponding h-index intervals; right, number of publications (orange bars, values not shown) for corresponding countries (and intervals), cumulative sum of all publications returned by the reported query (orange-shaded surface), amounting to a total of 138 015 publications; the red rectangle signifies the 96% output of the top 40 countries against total (refers to the left Y-axis) Distribution of number of countries and corresponding bioinformatics publications, for various h-index classes. X-axis: h-index intervals, from high (89-max) to low (minimum=0); Y-axis: left, number of countries (blue bars, values shown. clipped at 100) for corresponding h-index intervals; right, number of publications (orange bars, values not shown) for corresponding countries (and intervals), cumulative sum of all publications returned by the reported query (orange-shaded surface), amounting to a total of 138 015 publications; the red rectangle signifies the 96% output of the top 40 countries against total (refers to the left Y-axis) To examine whether the use of the h-index generates a certain bias as a single metric, we have further examined the top 78 countries for numbers of publications in the field and retained only those with >450 publications: this list includes 37 countries, all with an h-index ≥ 44 (>10% of the maximum: USA, h-index 427), with the exception of Iran (926 publications, h-index 37). We have also included three other entries in this list, namely Argentina (420 publications, h-index 44), Estonia (133 publications, h-index 44) and Hungary (350 publications, h-index 52) on the basis of their h-index performance (Fig. 2a). Interestingly, when a relative metric such as publications/million inhabitants is used, the resulting picture is slightly different promoting smaller countries with high performance in terms of the number of publications per capita, such as Switzerland or Denmark (Fig. 2b, for details please refer to Supplementary Table S2). The h-index ranks of those can be examined in comparison to a group of 30 countries that produce >98% of the world’s highly cited (top 1%) papers (EU15, before 2004 accession and the G8 group, 31 in total, EU excluded here) (King, 2004) and two derived, population-normalized indices (publications and h-index per million inhabitants) (Supplementary Table S2). These 40 ‘top’-producer countries generate 132 244/138 015 = 96% of all publications in bioinformatics, according to the WoS query (cf. 99% for the 78 countries, above; the remaining 38 have generated just 4828 publications, i.e. 3% of total, Fig. 1). The h-index ranking for bioinformatics against the ranking for the 1997–2001 contributions of the top 1% highly cited publications—arguably two independently produced sets—exhibits an astonishing similarity (Fig. 3). The rank (Spearman’s rho) correlation coefficient for the two indices is 0.914 (P-value = 0), climbing to 0.964 if Greece, Iran, Italy and Russia are excluded (h-index minus top 1% rank difference > 5, Fig. 3). Only Luxembourg (h = 27, in the top 1% list: rank 31) is missing (Table 1). Disparities between the two types of rankings may indeed arise from the significant impact of bioinformatics (Wren, 2016). It is worth noting that the ‘elite’ top 1% group has not changed significantly in the past 20 years, as reported recently (Bornmann et al., 2018). The correlation between the ranking of countries with the top 1% cited publications and the country h-index for bioinformatics suggests that the leading nations in science with the highest influence and impact in general are, by and large, also those most active in a highly specialized field such as bioinformatics, an expected yet hitherto unknown fact. Our findings also imply that much of the production in the field is generated by the most wealthy nations (GDP or GDP per capita, not shown), raising questions about barriers to entry, and despite a wealth of opportunities for international collaboration, that will need to be addressed in the future. A world map depiction highlighting the top 40 countries in bioinformatics, based on publications output. (a) Absolute numbers of bioinformatics publications (scale provided, right), (b) relative number of bioinformatics publications per capita (million inhabitants). See Supplementary Table S2 for a full list of 40 countries. Figure generated by Displayr (www.displayr.com) A world map depiction highlighting the top 40 countries in bioinformatics, based on publications output. (a) Absolute numbers of bioinformatics publications (scale provided, right), (b) relative number of bioinformatics publications per capita (million inhabitants). See Supplementary Table S2 for a full list of 40 countries. Figure generated by Displayr (www.displayr.com) Rank listing of leading countries in bioinformatics and top 1% of highly cited papers. Bioinformatics h-index rank (blue), top 1% highly cited papers rank (orange)—lower is better. Thirty countries are listed (see Table 1, and Supplementary Table S2 for a full list of 40 countries) Rank listing of leading countries in bioinformatics and top 1% of highly cited papers. Bioinformatics h-index rank (blue), top 1% highly cited papers rank (orange)—lower is better. Thirty countries are listed (see Table 1, and Supplementary Table S2 for a full list of 40 countries) List of 30 countries with significant impact in bioinformatics and highly cited publications Note: Country: country name; h-index: h-index for bioinformatics (as obtained herein), rank h-index: the rank of the h-index and top 1% rank: the rank of the country for the world’s highly cited (top 1%) papers. Ranks for the latter are available only for 30 countries, thus the selection of those out of the top 40 countries (Supplementary Table S2). List of 30 countries with significant impact in bioinformatics and highly cited publications Note: Country: country name; h-index: h-index for bioinformatics (as obtained herein), rank h-index: the rank of the h-index and top 1% rank: the rank of the country for the world’s highly cited (top 1%) papers. Ranks for the latter are available only for 30 countries, thus the selection of those out of the top 40 countries (Supplementary Table S2). As the field of bioinformatics has expanded across all of the life sciences (Ouzounis, 2012), the present analysis can form a basis upon which targeted policies for global research and training programs can be implemented, enhancing the productivity of lagging countries to align with the global activity elsewhere, where possible. Such policies might be formulated in alignment with sustainable development goals to match national priorities and perceived public views (Bain et al., 2019), while at the same time maintaining an appropriate balance between global trends and local needs (El-Chichakli et al., 2016). We thank the editor and one anonymous reviewer for valuable comments and constructive suggestions. We also thank colleagues from Hellenic Bioinformatics for additional input (http://hbio.info). This work was supported by the Elixir-GR, implemented under the Action ‘Reinforcement of the Research and Innovation Infrastructure’, Operational Programme ‘Competitiveness, Entrepreneurship and Innovation’ (NSRF 2014-2020) and co-financed by Greece and the European Union (European Regional Development Fund). Conflict of Interest: none declared.

We review the establishment of computational biology in Greece and Cyprus from its inception to date and issue recommendations for future development.We compare output to other countries of similar geography, economy, and size-based on publication counts recorded in the literature-and predict future growth based on those counts as well as national priority areas.Our analysis may be pertinent to wider national or regional communities with challenges and opportunities emerging from the rapid expansion of the field and related industries.Our recommendations suggest a 2-fold growth margin for the 2 countries, as a realistic expectation for further expansion of the field and the development of a credible roadmap of national priorities, both in terms of research and infrastructure funding.

Summary: BioPAXViz is a Cytoscape (version 3) application, providing a comprehensive framework for metabolic pathway visualization. Beyond the basic parsing, viewing and browsing roles, the main novel function that BioPAXViz provides is a visual comparative analysis of metabolic pathway topologies across pre-computed pathway phylogenomic profiles given a species phylogeny. Furthermore, BioPAXViz supports the display of hierarchical trees that allow efficient navigation through sets of variants of a single reference pathway. Thus, BioPAXViz can significantly facilitate, and contribute to, the study of metabolic pathway evolution and engineering. Availability and Implementation: BioPAXViz has been developed as a Cytoscape app and is available at: https://github.com/CGU-CERTH/BioPAX.Viz. The software is distributed under the MIT License and is accompanied by example files and data. Additional documentation is available at the aforementioned GitHub repository. Contact: [email protected].

BACKGROUND: The underlying molecular processes representing stress responses to low-dose ionising radiation (LDIR) in mammals are just beginning to be understood. In particular, LDIR effects on the brain and their possible association with neurodegenerative disease are currently being explored using omics technologies. RESULTS: We describe a light-weight approach for the storage, analysis and distribution of relevant LDIR omics datasets. The data integration platform, called BRIDE, contains information from the literature as well as experimental information from transcriptomics and proteomics studies. It deploys a hybrid, distributed solution using both local storage and cloud technology. CONCLUSIONS: BRIDE can act as a knowledge broker for LDIR researchers, to facilitate molecular research on the systems biology of LDIR response in mammals. Its flexible design can capture a range of experimental information for genomics, epigenomics, transcriptomics, and proteomics. The data collection is available at: .

Bioinformatics (2014); 30(22), 3249–3256 doi: 10.1093/bioinformatics/btu524 The above article contained an incorrect email address for the corresponding author, Ioannis Iliopoulos, the correct email address is [email protected]

More than a decade ago, a number of methods were proposed for the inference of protein interactions, using whole-genome information from gene clusters, gene fusions and phylogenetic profiles. This structural and evolutionary view of entire genomes has provided a valuable approach for the functional characterization of proteins, especially those without sequence similarity to proteins of known function. Furthermore, this view has raised the real possibility to detect functional associations of genes and their corresponding proteins for any entire genome sequence. Yet, despite these exciting developments, there have been relatively few cases of real use of these methods outside the computational biology field, as reflected from citation analysis. These methods have the potential to be used in high-throughput experimental settings in functional genomics and proteomics to validate results with very high accuracy and good coverage. In this critical survey, we provide a comprehensive overview of 30 most prominent examples of single pairwise protein interaction cases in small-scale studies, where protein interactions have either been detected by gene fusion or yielded additional, corroborating evidence from biochemical observations. Our conclusion is that with the derivation of a validated gold-standard corpus and better data integration with big experiments, gene fusion detection can truly become a valuable tool for large-scale experimental biology.

SUMMARY: The iterative process of finding relevant information in biomedical literature and performing bioinformatics analyses might result in an endless loop for an inexperienced user, considering the exponential growth of scientific corpora and the plethora of tools designed to mine PubMed(®) and related biological databases. Herein, we describe BioTextQuest(+), a web-based interactive knowledge exploration platform with significant advances to its predecessor (BioTextQuest), aiming to bridge processes such as bioentity recognition, functional annotation, document clustering and data integration towards literature mining and concept discovery. BioTextQuest(+) enables PubMed and OMIM querying, retrieval of abstracts related to a targeted request and optimal detection of genes, proteins, molecular functions, pathways and biological processes within the retrieved documents. The front-end interface facilitates the browsing of document clustering per subject, the analysis of term co-occurrence, the generation of tag clouds containing highly represented terms per cluster and at-a-glance popup windows with information about relevant genes and proteins. Moreover, to support experimental research, BioTextQuest(+) addresses integration of its primary functionality with biological repositories and software tools able to deliver further bioinformatics services. The Google-like interface extends beyond simple use by offering a range of advanced parameterization for expert users. We demonstrate the functionality of BioTextQuest(+) through several exemplary research scenarios including author disambiguation, functional term enrichment, knowledge acquisition and concept discovery linking major human diseases, such as obesity and ageing. AVAILABILITY: The service is accessible at http://bioinformatics.med.uoc.gr/biotextquest. CONTACT: [email protected] or [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

These tutorials/keynote speeches discuss the following: the effects of nicotine exposure on the complexity and the genetic patterns of dopamine neurons in VTA; from 6-Ps medicine to cardiovascular health informatics; computer-aided interpretation of vascular images towards valid diagnosis and risk stratification of atherosclerosis; turning data into predictions of gene and protein function; from reading to writing (and rewriting) the code of life: the future of biology - scientific, ethical, legal, civil and social issues.

The field of bioinformatics and computational biology has gone through a number of transformations during the past 15 years, establishing itself as a key component of new biology. This spectacular growth has been challenged by a number of disruptive changes in science and technology. Despite the apparent fatigue of the linguistic use of the term itself, bioinformatics has grown perhaps to a point beyond recognition. We explore both historical aspects and future trends and argue that as the field expands, key questions remain unanswered and acquire new meaning while at the same time the range of applications is widening to cover an ever increasing number of biological disciplines. These trends appear to be pointing to a redefinition of certain objectives, milestones, and possibly the field itself.

SUMMARY: BioTextQuest combines automated discovery of significant terms in article clusters with structured knowledge annotation, via Named Entity Recognition services, offering interactive user-friendly visualization. A tag-cloud-based illustration of terms labeling each document cluster are semantically annotated according to the biological entity, and a list of document titles enable users to simultaneously compare terms and documents of each cluster, facilitating concept association and hypothesis generation. BioTextQuest allows customization of analysis parameters, e.g. clustering/stemming algorithms, exclusion of documents/significant terms, to better match the biological question addressed. AVAILABILITY: http://biotextquest.biol.ucy.ac.cy CONTACT: [email protected]; [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The mechanisms of stress tolerance in sessile animals, such as molluscs, can offer fundamental insights into the adaptation of organisms for a wide range of environmental challenges. One of the best studied processes at the molecular level relevant to stress tolerance is the heat shock response in the genus Mytilus. We focus on the upstream region of Mytilus galloprovincialis Hsp90 genes and their structural and functional associations, using comparative genomics and network inference. Sequence comparison of this region provides novel evidence that the transcription of Hsp90 is regulated via a dense region of transcription factor binding sites, also containing a region with similarity to the Gamera family of LINE-like repetitive sequences and a genus-specific element of unknown function. Furthermore, we infer a set of gene networks from tissue-specific expression data, and specifically extract an Hsp class-associated network, with 174 genes and 2,226 associations, exhibiting a complex pattern of expression across multiple tissue types. Our results (i) suggest that the heat shock response in the genus Mytilus is regulated by an unexpectedly complex upstream region, and (ii) provide new directions for the use of the heat shock process as a biosensor system for environmental monitoring.

Inflammation is characterized by altered cytokine levels produced by cell populations in a highly interdependent manner. To elucidate the mechanism of an inflammatory reaction, we have developed a mathematical model for immune cell interactions via the specific, dose-dependent cytokine production rates of cell populations. The model describes the criteria required for normal and pathological immune system responses and suggests that alterations in the cytokine production rates can lead to various stable levels which manifest themselves in different disease phenotypes. The model predicts that pairs of interacting immune cell populations can maintain homeostatic and elevated extracellular cytokine concentration levels, enabling them to operate as an immune system switch. The concept described here is developed in the context of psoriasis, an immune-mediated disease, but it can also offer mechanistic insights into other inflammatory pathologies as it explains how interactions between immune cell populations can lead to disease phenotypes.

BACKGROUND: Previous methods of detecting the taxonomic origins of arbitrary sequence collections, with a significant impact to genome analysis and in particular metagenomics, have primarily focused on compositional features of genomes. The evolutionary patterns of phylogenetic distribution of genes or proteins, represented by phylogenetic profiles, provide an alternative approach for the detection of taxonomic origins, but typically suffer from low accuracy. Herein, we present rank-BLAST, a novel approach for the assignment of protein sequences into genomic groups of the same taxonomic origin, based on the ranking order of phylogenetic profiles of target genes or proteins across the reference database. RESULTS: The rank-BLAST approach is validated by computing the phylogenetic profiles of all sequences for five distinct microbial species of varying degrees of phylogenetic proximity, against a reference database of 243 fully sequenced genomes. The approach - a combination of sequence searches, statistical estimation and clustering - analyses the degree of sequence divergence between sets of protein sequences and allows the classification of protein sequences according to the species of origin with high accuracy, allowing taxonomic classification of 64% of the proteins studied. In most cases, a main cluster is detected, representing the corresponding species. Secondary, functionally distinct and species-specific clusters exhibit different patterns of phylogenetic distribution, thus flagging gene groups of interest. Detailed analyses of such cases are provided as examples. CONCLUSION: Our results indicate that the rank-BLAST approach can capture the taxonomic origins of sequence collections in an accurate and efficient manner. The approach can be useful both for the analysis of genome evolution and the detection of species groups in metagenomics samples.

MOTIVATION: Biomedical literature is the principal repository of biomedical knowledge, with PubMed being the most complete database collecting, organizing and analyzing such textual knowledge. There are numerous efforts that attempt to exploit this information by using text mining and machine learning techniques. We developed a novel approach, called PuReD-MCL (Pubmed Related Documents-MCL), which is based on the graph clustering algorithm MCL and relevant resources from PubMed. METHODS: PuReD-MCL avoids using natural language processing (NLP) techniques directly; instead, it takes advantage of existing resources, available from PubMed. PuReD-MCL then clusters documents efficiently using the MCL graph clustering algorithm, which is based on graph flow simulation. This process allows users to analyse the results by highlighting important clues, and finally to visualize the clusters and all relevant information using an interactive graph layout algorithm, for instance BioLayout Express 3D. RESULTS: The methodology was applied to two different datasets, previously used for the validation of the document clustering tool TextQuest. The first dataset involves the organisms Escherichia coli and yeast, whereas the second is related to Drosophila development. PuReD-MCL successfully reproduces the annotated results obtained from TextQuest, while at the same time provides additional insights into the clusters and the corresponding documents. AVAILABILITY: Source code in perl and R are available from http://tartara.csd.auth.gr/~theodos/

Using a previously developed automated method for enzyme annotation, we report the re-annotation of the ENZYME database and the analysis of local error rates per class. In control experiments, we demonstrate that the method is able to correctly re-annotate 91% of all Enzyme Classification (EC) classes with high coverage (755 out of 827). Only 44 enzyme classes are found to contain false positives, while the remaining 28 enzyme classes are not represented. We also show cases where the re-annotation procedure results in partial overlaps for those few enzyme classes where a certain inconsistency might appear between homologous proteins, mostly due to function specificity. Our results allow the interactive exploration of the EC hierarchy for known enzyme families as well as putative enzyme sequences that may need to be classified within the EC hierarchy. These aspects of our framework have been incorporated into a web-server, called CORRIE, which stands for Correspondence Indicator Estimation and allows the interactive prediction of a functional class for putative enzymes from sequence alone, supported by probabilistic measures in the context of the pre-calculated Correspondence Indicators of known enzymes with the functional classes of the EC hierarchy. The CORRIE server is available at: http://www.genomes.org/services/corrie/.

Over 1600 mammalian genes are known to cause an inherited disorder, when subjected to one or more mutations. These disease genes represent a unique resource for the identification and quantification of relationships between phenotypic attributes of a disease and the molecular features of the associated disease genes, including their ascribed annotated functional classes and expression patterns. Such analyses can provide a more global perspective and a deeper understanding of the probable causes underlying human hereditary diseases. In this perspective and critical view of disease genomics, we present a comparative analysis of genes reported to cause inherited diseases in humans in terms of their causative effects on physiology, their genetics and inheritance modes, the functional processes they are involved in and their expression profiles across a wide spectrum of tissues. Our analysis reveals that there are more extensive correlations between these attributes of genetic disease genes than previously appreciated. For instance, the functional pattern of genes causing dominant and recessive diseases is markedly different. Also, the function of the genes and their expression correlate with the type of disease they cause when mutated. The results further indicate that a comparative genomics approach for the analysis of genes linked to human genetic diseases will facilitate the elucidation of the underlying molecular and cellular mechanisms.

MOTIVATION: CoGenT++ is a data environment for computational research in comparative and functional genomics, designed to address issues of consistency, reproducibility, scalability and accessibility. DESCRIPTION: CoGenT++ facilitates the re-distribution of all fully sequenced and published genomes, storing information about species, gene names and protein sequences. We describe our scalable implementation of ProXSim, a continually updated all-against-all similarity database, which stores pairwise relationships between all genome sequences. Based on these similarities, derived databases are generated for gene fusions--AllFuse, putative orthologs--OFAM, protein families--TRIBES, phylogenetic profiles--ProfUse and phylogenetic trees. Extensions based on the CoGenT++ environment include disease gene prediction, pattern discovery, automated domain detection, genome annotation and ancestral reconstruction. CONCLUSION: CoGenT++ provides a comprehensive environment for computational genomics, accessible primarily for large-scale analyses as well as manual browsing.

MOTIVATION: Databases of protein families often exhibit drastically different properties of the protein family space. RESULTS: We compared the properties of protein family space as reflected by exhaustive protein family databases and databases with predefined families. We used TRIBES, Protomap, ProDom and COGs as representatives of the exhaustive databases, and Pfam-A and Superfamily as databases that predefine families. We observe a power-law distribution of family sizes in all these databases, albeit in predefined databases the power-law line collapses before reaching smaller sized families. We discuss the future trends of this power-law distribution and suggest that saturation in the sampling of protein family space will result in a distortion of the power law in small family sizes. For larger genome sizes, predefined databases show logarithmic growth of the number of families per genome, whereas exhaustive databases exhibit a virtually linear relationship. All databases consistently differ in the proportion of protein families shared between taxa. Predefined databases have a larger number of protein families shared between the three domains of life, while exhaustive databases show a much more fragmented distribution. We argue that these discrepancies reflect alternative approaches to the trade-off issue of sensitivity versus specificity in the detection of homologous proteins. We conclude that these properties are complementary rather than contradictory, while describing the protein universe from different perspectives.

Motivation: At present, mapping of sequence identifiers across databases is a daunting, time-consuming and computationally expensive process, usually achieved by sequence similarity searches with strict threshold values. Summary: We present a rapid and efficient method to map sequence identifiers across databases. The method uses the MD5 checksum algorithm for message integrity to generate sequence fingerprints and uses these fingerprints as hash strings to map sequences across databases. The program, called MagicMatch, is able to cross-link any of the major sequence databases within a few seconds on a modest desktop computer. Availability: MagicMatch is available at the following URL (http://cgg.ebi.ac.uk/services/magicmatch/), including an interactive service for major databases and binary downloads for widely used platforms. Contact: [email protected]

BACKGROUND: Current protein clustering methods rely on either sequence or functional similarities between proteins, thereby limiting inferences to one of these areas. RESULTS: Here we report a new approach, named CLAN, which clusters proteins according to both annotation and sequence similarity. This approach is extremely fast, clustering the complete SwissProt database within minutes. It is also accurate, recovering consistent protein families agreeing on average in more than 97% with sequence-based protein families from Pfam. Discrepancies between sequence- and annotation-based clusters were scrutinized and the reasons reported. We demonstrate examples for each of these cases, and thoroughly discuss an example of a propagated error in SwissProt: a vacuolar ATPase subunit M9.2 erroneously annotated as vacuolar ATP synthase subunit H. CLAN algorithm is available from the authors and the CLAN database is accessible at http://maine.ebi.ac.uk:8000/cgi-bin/clan/ClanSearch.pl CONCLUSIONS: CLAN creates refined function-and-sequence specific protein families that can be used for identification and annotation of unknown family members. It also allows easy identification of erroneous annotations by spotting inconsistencies between similarities on annotation and sequence levels.

BACKGROUND: One of the most evident achievements of bioinformatics is the development of methods that transfer biological knowledge from characterised proteins to uncharacterised sequences. This mode of protein function assignment is mostly based on the detection of sequence similarity and the premise that functional properties are conserved during evolution. Most automatic approaches developed to date rely on the identification of clusters of homologous proteins and the mapping of new proteins onto these clusters, which are expected to share functional characteristics. RESULTS: Here, we inverse the logic of this process, by considering the mapping of sequences directly to a functional classification instead of mapping functions to a sequence clustering. In this mode, the starting point is a database of labelled proteins according to a functional classification scheme, and the subsequent use of sequence similarity allows defining the membership of new proteins to these functional classes. In this framework, we define the Correspondence Indicators as measures of relationship between sequence and function and further formulate two Bayesian approaches to estimate the probability for a sequence of unknown function to belong to a functional class. This approach allows the parametrisation of different sequence search strategies and provides a direct measure of annotation error rates. We validate this approach with a database of enzymes labelled by their corresponding four-digit EC numbers and analyse specific cases. CONCLUSION: The performance of this method is significantly higher than the simple strategy consisting in transferring the annotation from the highest scoring BLAST match and is expected to find applications in automated functional annotation pipelines.

Abstract Motivation: Genome-wide functional annotation either by manual or automatic means has raised considerable concerns regarding the accuracy of assignments and the reproducibility of methodologies. In addition, a performance evaluation of automated systems that attempt to tackle sequence analyses rapidly and reproducibly is generally missing. In order to quantify the accuracy and reproducibility of function assignments on a genome-wide scale, we have re-annotated the entire genome sequence of Chlamydia trachomatis (serovar D), in a collaborative manner. Results: We have encoded all annotations in a structured format to allow further comparison and data exchange and have used a scale that records the different levels of potential annotation errors according to their propensity to propagate in the database due to transitive function assignments. We conclude that genome annotation may entail a considerable amount of errors, ranging from simple typographical errors to complex sequence analysis problems. The most surprising result of this comparative study is that automatic systems might perform as well as the teams of experts annotating genome sequences. Availability and supplementary information: http://www.ebi.ac.uk/research/cgg/annotation/cteval/ Contact: [email protected] * To whom correspondence should be addressed. † INA-EKETA, GR-57001 Thessaloniki, Greece ‡ Computational Biology Center, Memorial Sloan-Kettering Cancer Center, New York, NY 10021, USA § Aetion Technologies LLC, Worthington, OH 43085, USA ¶ Institut Curie, F-75248 Paris, France ∥ CNRS, UMR6543, F-06108 Nice, France ** Alma Bioinformatics, E-28760 Madrid, Spain †† Cap Gemini Ernst & Young, London SW1X 7LX, UK ‡‡ Univ. of Rome ‘La Sapienza’, I-00185 Rome, Italy §§ MWG-Biotech AG, Ebersberg, D-85560 Berlin, Germany ¶¶ Wellcome Trust Biocentre, Univ. of Dundee, Dundee DD1 5HN, UK

Abstract Summary: We present a database of fully sequenced and published genomes to facilitate the re-distribution of data and ensure reproducibility of results in the field of computational genomics. For its design we have implemented an extremely simple yet powerful schema to allow linking of genome sequence data to other resources. Availability: http://maine.ebi.ac.uk:8000/services/cogent/ Contact: [email protected] * To whom correspondence should be addressed. † The authors wish it to be known that, in their opinion, the first two authors should be regarded as joint First Authors. ‡ Present Address: External Services Group, EMBL-EBI.

MOTIVATION: Separation of protein sequence regions according to their local information complexity and subsequent masking of low complexity regions has greatly enhanced the reliability of function prediction by sequence similarity. Comparisons with alternative methods that focus on compositional sequence bias rather than information complexity measures have shown that removal of compositional bias yields at least as sensitive and much more specific results. Besides the application of sequence masking algorithms to sequence similarity searches, the study of the masked regions themselves is of great interest. Traditionally, however, these have been neglected despite evidence of their functional relevance. RESULTS: Here we demonstrate that compositional bias seems to be a more effective measure for the detection of biologically meaningful signals. Typical results on proteins are compared to results for sequences that have been randomized in various ways, conserving composition and local correlations for individual proteins or the entire set. It is remarkable that low-complexity regions have the same form of distribution in proteins as in randomized sequences, and that the signal from randomized sequences with conserved local correlations and amino acid composition almost matches the signal from proteins. This is not the case for sequence bias, which hence seems to be a genuinely biological phenomenon in contrast to patches of low complexity.

While current computational methods allow the reconstruction of individual ancestral protein sequences, reconstruction of complete gene content of ancestral species is not yet an established task. In this paper, we describe GENETRACE, an efficient linear-time algorithm that allows the reconstruction of evolutionary history of individual protein families as well as the complete gene content of ancestral species. The performance of the method was validated with a simulated evolution program called SimulEv. Our results indicate that given a set of correct phylogenetic profiles and a correct species tree, ancestral gene content can be reconstructed with sensitivity and selectivity of more than 90%. SimulEv simulations were also used to evaluate performance of the reconstruction of gene content-based phylogenetic trees, suggesting that these trees may be accurate at the terminal branches but suffer from long branch attraction near the root of the tree.

MOTIVATION: The field of bioinformatics has experienced an explosive growth in the last decade, yet this 'new' field has a long history. Some historical perspectives have been previously provided by the founders of this field. Here, we take the opportunity to review the early stages and follow developments of this discipline from a personal perspective. RESULTS: We review the early days of algorithmic questions and answers in biology, the theoretical foundations of bioinformatics, the development of algorithms and database resources and finally provide a realistic picture of what the field looked like from a resources and finally provide a realistic picture of what the field looked like from a practitioner's viewpoint 10 years ago, with a perspective for future developments.

Abstract Availability:The PseuRECA database is accesible at http://maine.ebi.ac.uk:8000/services/pseureca/ Contact: [email protected] * To whom correspondence should be addressed.

Public sequence databases contain information on the sequence, structure and function of proteins. Genome sequencing projects have led to a rapid increase in protein sequence information, but reliable, experimentally verified, information on protein function lags a long way behind. To address this deficit, functional annotation in protein databases is often inferred by sequence similarity to homologous, annotated proteins, with the attendant possibility of error. Now, the functional annotation in these homologous proteins may itself have been acquired through sequence similarity to yet other proteins, and it is generally not possible to determine how the functional annotation of any given protein has been acquired. Thus the possibility of chains of misannotation arises, a process we term 'error percolation'. With some simple assumptions, we develop a dynamical probabilistic model for these misannotation chains. By exploring the consequences of the model for annotation quality it is evident that this iterative approach leads to a systematic deterioration of database quality.

Abstract 1 Email: [email protected]; URL: http://www.genomes.org

The mechanisms controlling gene regulation appear to be fundamentally different in eukaryotes and prokaryotes (Struhl (1999) CELL, 98, 1-4). To investigate this diversity further, we have analysed the distribution of all known transcription-associated proteins (TAPs), as reflected by sequence database annotations. Our results for the primary phylogenetic domains (Archaea, Bacteria and Eukaryota) show that TAP families are mostly taxon-specific and very few transcriptional regulators are common across these domains.

UNLABELLED: Graph layout is extensively used in the field of mathematics and computer science, however these ideas and methods have not been extended in a general fashion to the construction of graphs for biological data. To this end, we have implemented a version of the Fruchterman Rheingold graph layout algorithm, extensively modified for the purpose of similarity analysis in biology. This algorithm rapidly and effectively generates clear two (2D) or three-dimensional (3D) graphs representing similarity relationships such as protein sequence similarity. The implementation of the algorithm is general and applicable to most types of similarity information for biological data. AVAILABILITY: BioLayout is available for most UNIX platforms at the following web-site: http://www.ebi.ac.uk/research/cgg/services/layout.

MOTIVATION: Efficient, accurate and automatic clustering of large protein sequence datasets, such as complete proteomes, into families, according to sequence similarity. Detection and correction of false positive and negative relationships with subsequent detection and resolution of multi-domain proteins. RESULTS: A new algorithm for the automatic clustering of protein sequence datasets has been developed. This algorithm represents all similarity relationships within the dataset in a binary matrix. Removal of false positives is achieved through subsequent symmetrification of the matrix using a Smith-Waterman dynamic programming alignment algorithm. Detection of multi-domain protein families and further false positive relationships within the symmetrical matrix is achieved through iterative processing of matrix elements with successive rounds of Smith-Waterman dynamic programming alignments. Recursive single-linkage clustering of the corrected matrix allows efficient and accurate family representation for each protein in the dataset. Initial clusters containing multi-domain families, are split into their constituent clusters using the information obtained by the multi-domain detection step. This algorithm can hence quickly and accurately cluster large protein datasets into families. Problems due to the presence of multi-domain proteins are minimized, allowing more precise clustering information to be obtained automatically. AVAILABILITY: GeneRAGE (version 1.0) executable binaries for most platforms may be obtained from the authors on request. The system is available to academic users free of charge under license.

MOTIVATION: Sensitive detection and masking of low-complexity regions in protein sequences. Filtered sequences can be used in sequence comparison without the risk of matching compositionally biased regions. The main advantage of the method over similar approaches is the selective masking of single residue types without affecting other, possibly important, regions. RESULTS: A novel algorithm for low-complexity region detection and selective masking. The algorithm is based on multiple-pass Smith-Waterman comparison of the query sequence against twenty homopolymers with infinite gap penalties. The output of the algorithm is both the masked query sequence for further analysis, e.g. database searches, as well as the regions of low complexity. The detection of low-complexity regions is highly specific for single residue types. It is shown that this approach is sufficient for masking database query sequences without generating false positives. The algorithm is benchmarked against widely available algorithms using the 210 genes of Plasmodium falciparum chromosome 2, a dataset known to contain a large number of low-complexity regions. AVAILABILITY: CAST (version 1.0) executable binaries are available to academic users free of charge under license. Web site entry point, server and additional material: http://www.ebi.ac.uk/research/cgg/services/cast/

MOTIVATION: Large-scale genome projects generate a rapidly increasing number of sequences, most of them biochemically uncharacterized. Research in bioinformatics contributes to the development of methods for the computational characterization of these sequences. However, the installation and application of these methods require experience and are time consuming. RESULTS: We present here an automatic system for preliminary functional annotation of protein sequences that has been applied to the analysis of sets of sequences from complete genomes, both to refine overall performance and to make new discoveries comparable to those made by human experts. The GeneQuiz system includes a Web-based browser that allows examination of the evidence leading to an automatic annotation and offers additional information, views of the results, and links to biological databases that complement the automatic analysis. System structure and operating principles concerning the use of multiple sequence databases, underlying sequence analysis tools, lexical analyses of database annotations and decision criteria for functional assignments are detailed. The system makes automatic quality assessments of results based on prior experience with the underlying sequence analysis tools; overall error rates in functional assignment are estimated at 2.5-5% for cases annotated with highest reliability ('clear' cases). Sources of over-interpretation of results are discussed with proposals for improvement. A conservative definition for reporting 'new findings' that takes account of database maturity is presented along with examples of possible kinds of discoveries (new function, family and superfamily) made by the system. System performance in relation to sequence database coverage, database dynamics and database search methods is analysed, demonstrating the inherent advantages of an integrated automatic approach using multiple databases and search methods applied in an objective and repeatable manner. AVAILABILITY: The GeneQuiz system is publicly available for analysis of protein sequences through a Web server at http://www.sander.ebi.ac. uk/gqsrv/submit

UNLABELLED: A tool is described for the automatic classification of sequences in functional classes using their database annotations. The Euclid system is based on a simple learning procedure from examples provided by human experts. AVAILABILITY: Euclid is freely available for academics at http://www.gredos.cnb.uam.es/EUCLID, with the corresponding dictionaries for the generation of three, eight and 14 functional classes. CONTACT: E-mail: [email protected] SUPPLEMENTARY INFORMATION: The results of the EUCLID classification of different genomes are available at http://www.sander.ebi.ac. uk/genequiz/. A detailed description of the different applications mentioned in the text is available at http://www.gredos.cnb.uam. es/EUCLID/Full_Paper

Miguel Andrade, Georg Casari, Antoine de Daruvar, Chris Sander, Reinhard Schneider, Javier Tamames, Alfonso Valencia, Christos Ouzounis; Sequence analysis