MOTIVATION: Short tandem repeats (STRs) are abundant in protein sequences and play important role in determining their structures and functions. Strikingly, the unusual compositional characteristics of tandem repeats break classical sequence analysis tools. RESULTS: Here, we establish the first algorithm to effectively identify and cluster STRs: Graph-Based Sequence Clustering (GBSC) features linear time complexity, and clusters protein sequence fragments based on their STRs, while allowing for insertions and mutations and supporting the analysis of imperfect or cryptic repeats. Due to its computational efficacy, our algorithm can be used to systematically scan for patterns in large datasets. We compare our method both to state-of-the-art methods for identifying STRs in proteins and alternative clustering approaches. Unlike existing STR analysis methods, GBSC clusters repeat patterns rather than raw sequences, operating at the level of structural repeat identity, while tolerating biological variations and preventing erroneous merging of structurally and functionally distinct motifs. Whereas functional annotation is typically only available at the protein level, the functions of individual STRs and sequences of adjacent STRs remain largely unknown. On a challenging use case we here demonstrate and discuss how our method can be used to associate previously unannotated repetitive protein fragments with similar ones, allowing the transfer of annotation by similarity. For the first time, GBSC offers a tool that systematically extends this fundamental bioinformatics principle to low-complexity regions across large datasets. AVAILABILITY AND IMPLEMENTATION: GBSC is available at GitHub https://github.com/patryk-jarnot/GBSC and https://doi.org/10.5281/zenodo.18965247. The data and scripts to reproduce the analysis are available at https://doi.org/10.5281/zenodo.16906653.

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.

MOTIVATION: Local compositionally biased and low complexity regions (LCRs) in amino acid sequences have initially attracted the interest of researchers due to their implication in generating artifacts in sequence database searches. There is accumulating evidence of the biological significance of LCRs both in physiological and in pathological situations. Nonetheless, LCR-related algorithms and tools have not gained wide appreciation across the research community, partly due to the fact that only a handful of user-friendly software is currently freely available. RESULTS: We developed LCR-eXXXplorer, an extensible online platform attempting to fill this gap. LCR-eXXXplorer offers tools for displaying LCRs from the UniProt/SwissProt knowledgebase, in combination with other relevant protein features, predicted or experimentally verified. Moreover, users may perform powerful queries against a custom designed sequence/LCR-centric database. We anticipate that LCR-eXXXplorer will be a useful starting point in research efforts for the elucidation of the structure, function and evolution of proteins with LCRs. AVAILABILITY AND IMPLEMENTATION: LCR-eXXXplorer is freely available at the URL http://repeat.biol.ucy.ac.cy/lcr-exxxplorer. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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.

Applying engineering precepts to biological systems has spawn the field of systems biology to investigate a network of interacting components, including the coordination of internal systems of living organisms such as endocrine, nervous, and respiratory with gene and gene product expression, and behavior and environmental factors, and understand how these components together contribute to the disease initiation and progression, biological development, and health. Proceeding from systems biology, systems medicine incorporates complex and dynamic biochemical, physiological, and environmental interactions between all components of disease and health that sustain living organisms. The current special issue includes a selected number of papers presented at the 12th IEEE International Conference on BioInformatics and BioEngineering (BIBE 2012), Nov. 11-13, 2012 under a special session with the same theme, in addition to papers submitted following an open call for papers. The Special Issue presents experiences as well as technological and scientific developments stemming from some flagship projects funded by the EU under the FP7 framework programme aiming to bring together researchers working in the fields of infrastructures and technologies for integrative biomedical research, ICT for predictive and translational medicine and the VPH community at large. A total of 15 papers are included under the following scientific subdomains: 1) mHealth,Wearable Systems and Telemonitoring Services (five papers), 2) Medical Imaging (four papers), and 3) Computational Biology (six papers).

A simple yet powerful programming tool enabling in silico experimentation, end-to-end data management through web services as well as use of grid and cloud processing power is scientific workflows. This technology is receiving considerable interest in recent years primarily due to its ability to promote and support scientific collaboration among large distributed research teams. The paper reviews the Scientific Workflows Management Systems (SWMS) field and investigates in detail popular open source workflow systems used commonly in life sciences informatics. Emphasis is placed on features which make these systems attractive for scientific use, e.g. user friendliness, use of distributed resources, reusability, provenance, collaboration, data integration, etc. Our conclusions indicate that although SWMS, including open source ones, have several open issues, their unique features and strong momentum clearly suggest that it is only a matter of time before they are adopted in even more scientific fields.

This paper presents an in-depth look of how FPGA computing can offer substantial speedups in the execution of bioinformatics algorithms, with specific results achieved to date for a broad range of algorithms. Examples and case studies are presented for sequence comparison (BLAST, CAST), multiple sequence alignment (MAFFT, T-Coffee), RNA and protein secondary structure prediction (Zuker, Predator), gene prediction (Glimmer/GlimmerHMM) and phylogenetic tree computation (RAxML), running on mainstream FPGA technologies as well as high-end FPGA-based systems (Convey HC1, BeeCube). This work also presents technological and other obstacles that need to be overcome in order for FPGA computing to become a mainstream technology in Bioinformatics.

Computer-aided drug discovery techniques have been widely used in recent years to support the development of new pharmaceuticals. Virtual screening, the computational counterpart of experimental screening, attempts to replicate the results from in vitro and in vivo methods through the use of in silico models and tools. This paper presents the LISIs platform; a web based scientific workflow system for virtual screening that has been implemented primarily for the discovery of chemoprevention agents. We describe the overall design of the system as well as the implementation of its various components. Indicative results from early applications of the system are also presented to illustrate its potential uses and functionalities.

A considerable research effort has already been put on the identification (and consequently filtering) of local segments of “unusual” composition (Compositionally Biased or Low Complexity Regions; CBRs or LCRs) in protein sequences. This interest was mainly initiated due to the fact that CBR existence is known to create artifacts (i.e. biologically irrelevant hits) in sequence database search methods. Even though no general biological significance has been demonstrated for CBRs so far, they are often associated with the lack of regular structure. However, application of commonly used methods for CBR detection illustrates that instances of CBRs can be found in proteins with experimentally determined three dimensional structures. In this work, we highlight sequential properties of CBRs detected by two of the most widely used CBR detection algorithms in carefully compiled datasets of proteins with experimentally determined structures. Our goal is to shed light on the properties of CBR sequences, with the future prospect of elucidating their relation to protein three dimensional structure.

Modern biological research has greatly benefited from genomics. Such research however requires extensive computational power, traditionally employed on large-scale cluster machines as well as multi-core systems. Recent research in reconfigurable architectures however suggests that FPGA-based acceleration of genomic algorithms greatly improves the performance and energy efficiency when compared to multi-core systems and clusters. In this work, we present an initial attempt for massive systolic acceleration of the popular CAST algorithm employed by biologists for complexity analysis of genomic data. CAST is used for detecting (and subsequently masking) low-complexity regions (LCRs) in protein sequences. We designed and implemented a high-performance hardware-accelerated version of CAST for which we built an FPGA prototype, and benchmarked its performance against serial and multithreaded versions of the CAST algorithm in software. The proposed architecture achieves remarkable speedup compared to both serial and multithreaded CAST implementations ranging from approx. 100x-9500x, depending on the dataset features, such as low-complexity content and sequence length distribution. Such performance may enable complex analyses of voluminous sequence datasets, and has the potential to interoperate with other hardware architectures for protein sequence analysis.

UNLABELLED: We present LaTcOm, a new web tool, which offers several alternative methods for 'rare codon cluster' (RCC) identification from a single and simple graphical user interface. In the current version, three RCC detection schemes are implemented: the recently described %MinMax algorithm and a simplified sliding window approach, along with a novel modification of a linear-time algorithm for the detection of maximally scoring subsequences tailored to the RCC detection problem. Among a number of user tunable parameters, several codon-based scales relevant for RCC detection are available, including tRNA abundance values from Escherichia coli and several codon usage tables from a selection of genomes. Furthermore, useful scale transformations may be performed upon user request (e.g. linear, sigmoid). Users may choose to visualize RCC positions within the submitted sequences either with graphical representations or in textual form for further processing. AVAILABILITY: LaTcOm is freely available online at the URL http://troodos.biol.ucy.ac.cy/latcom.html.

Filtering of Protein Secondary Structure Prediction (PSSP) aims to provide physicochemically realistic results, while it usually improves the predictive performance. We performed a comparative study on this challenging problem, utilizing both machine learning techniques and empirical rules and we found that combinations of the two lead to the highest improvement.

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.

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

BACKGROUND: The availability of increasing amounts of sequence data from completely sequenced genomes boosts the development of new computational methods for automated genome annotation and comparative genomics. Therefore, there is a need for tools that facilitate the visualization of raw data and results produced by bioinformatics analysis, providing new means for interactive genome exploration. Visual inspection can be used as a basis to assess the quality of various analysis algorithms and to aid in-depth genomic studies. RESULTS: GeneViTo is a JAVA-based computer application that serves as a workbench for genome-wide analysis through visual interaction. The application deals with various experimental information concerning both DNA and protein sequences (derived from public sequence databases or proprietary data sources) and meta-data obtained by various prediction algorithms, classification schemes or user-defined features. Interaction with a Graphical User Interface (GUI) allows easy extraction of genomic and proteomic data referring to the sequence itself, sequence features, or general structural and functional features. Emphasis is laid on the potential comparison between annotation and prediction data in order to offer a supplement to the provided information, especially in cases of "poor" annotation, or an evaluation of available predictions. Moreover, desired information can be output in high quality JPEG image files for further elaboration and scientific use. A compilation of properly formatted GeneViTo input data for demonstration is available to interested readers for two completely sequenced prokaryotes, Chlamydia trachomatis and Methanococcus jannaschii. CONCLUSIONS: GeneViTo offers an inspectional view of genomic functional elements, concerning data stemming both from database annotation and analysis tools for an overall analysis of existing genomes. The application is compatible with Linux or Windows ME-2000-XP operating systems, provided that the appropriate Java Runtime Environment is already installed in the system.

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/