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PubMed2003

Improving gene annotation of complete viral genomes.

Gene annotation in viruses often relies upon similarity search methods. These methods possess high specificity but some genes may be missed, either those unique to a particular genome or those highly divergent from known homologs. To identify potentially missing viral genes we have analyzed all complete viral genomes currently available in GenBank with a specialized and augmented version of the gene finding program GeneMarkS. In particular, by implementing genome-specific self-training protocols we have better adjusted the GeneMarkS statistical models to sequences of viral genomes. Hundreds of new genes were identified, some in well studied viral genomes. For example, a new gene predicted in the genome of the Epstein-Barr virus was shown to encode a protein similar to alpha-herpesvirus minor tegument protein UL14 with heat shock functions. Convincing evidence of this similarity was obtained after only 12 PSI-BLAST iterations. In another example, several iterations of PSI-BLAST were required to demonstrate that a gene predicted in the genome of Alcelaphine herpesvirus 1 encodes a BALF1-like protein which is thought to be involved in apoptosis regulation and, potentially, carcinogenesis. New predictions were used to refine annotations of viral genomes in the RefSeq collection curated by the National Center for Biotechnology Information. Importantly, even in those cases where no sequence similarities were detected, GeneMarkS significantly reduced the number of primary targets for experimental characterization by identifying the most probable candidate genes. The new genome annotations were stored in VIOLIN, an interactive database which provides access to similarity search tools for up-to-date analysis of predicted viral proteins.

Amino Acid Sequence↗
PubMed2004

A System for Automated Bacterial (genome) Integrated Annotation--SABIA.

UNLABELLED: A web-based software suite, SABIA (System for Automated Bacterial Integrated Annotation), is described that provides a comprehensive computational support for the assembly and annotation of whole bacterial genomes from the data derived from sequencing projects. AVAILABILITY: Both SABIA and supplementary materials are available at http://www.sabia.lncc.br

Algorithms↗
PubMed2002

SUPERFAMILY: HMMs representing all proteins of known structure. SCOP sequence searches, alignments and genome assignments.

The SUPERFAMILY database contains a library of hidden Markov models representing all proteins of known structure. The database is based on the SCOP 'superfamily' level of protein domain classification which groups together the most distantly related proteins which have a common evolutionary ancestor. There is a public server at http://supfam.org which provides three services: sequence searching, multiple alignments to sequences of known structure, and structural assignments to all complete genomes. Given an amino acid or nucleotide query sequence the server will return the domain architecture and SCOP classification. The server produces alignments of the query sequences with sequences of known structure, and includes multiple alignments of genome and PDB sequences. The structural assignments are carried out on all complete genomes (currently 59) covering approximately half of the soluble protein domains. The assignments, superfamily breakdown and statistics on them are available from the server. The database is currently used by this group and others for genome annotation, structural genomics, gene prediction and domain-based genomic studies.

Amino Acid Sequence↗
PubMed2026

'PePApipe': A complete bioinformatics analysis pipeline for African Swine Fever Virus genome.

African Swine Fever Virus (ASFV) is of high concern in porcine livestock across the world due to both the high mortality rates and the trade restrictions imposed on affected regions. The viral genome is large and complex, and genomic analysis is essential for tracing its origin and evolution. Although several bioinformatics tools exist for genome assembly and analysis, no single platform integrates all necessary steps in an accessible and systematic way. In this study the authors developed 'PePApipe', a custom-built, user-friendly pipeline that enables rapid, complete, and efficient ASFV genome analysis. It is specifically designed for laboratory professionals with limited bioinformatics experience, requiring only basic command-line knowledge. Starting from raw sequencing data, PePApipe integrates thirteen software tools into one automated workflow, covering quality control and pre-processing of raw reads, de novo genome assembly and variant calling. Programmed in Python, it can be executed locally through bash scripts, or using a Slurm protocol for batch processing of multiple samples. The main outputs are the ASFV consensus genome sequence and a file listing its putative variants compared to the selected reference genome. PePApipe classifies generated files into structured folders and produces intermediate files that can be used as inputs for further or parallel analyses; users can also enable or disable specific steps in each particular case. This pipeline is adaptable and complementary to downstream steps such as viral genome annotation or genome visualization. By consolidating all stages of viral genome analysis into a single automated workflow, PePApipe reduces the likelihood of user error, and enhances reproducibility and efficiency. This user-friendly pipeline facilitates the transition from sequencing to assembly and downstream analysis of viral genomes, ensuring a fast and reliable response to molecular analysis demands. Finally, the pipeline can be easily adapted to the study of other viral species, expanding its application in infectious diseases surveillance.

African Swine Fever Virus↗
PubMed2003

Computer software to find genes in plant genomic DNA.

Gene finding is the most important phase of genome annotation. Eukaryotic genomes contain thousands of protein coding genes, and computational gene prediction would rapidly increase the pace of experimental confirmation of expressed genes at the bench. The purpose of this chapter is to discuss the use of different computer programs that identify protein-coding genes in large genomic sequences. We describe most commonly used gene prediction programs that are available on the World Wide Web and demonstrate the use of some of these programs by an example. We provide a list of these programs along with their. Web uniform resource locators (URLs) and suggest guidelines for successful gene finding.

Arabidopsis↗
PubMed2006

Using hidden Markov models and observed evolution to annotate viral genomes.

MOTIVATION: ssRNA (single stranded) viral genomes are generally constrained in length and utilize overlapping reading frames to maximally exploit the coding potential within the genome length restrictions. This overlapping coding phenomenon leads to complex evolutionary constraints operating on the genome. In regions which code for more than one protein, silent mutations in one reading frame generally have a protein coding effect in another. To maximize coding flexibility in all reading frames, overlapping regions are often compositionally biased towards amino acids which are 6-fold degenerate with respect to the 64 codon alphabet. Previous methodologies have used this fact in an ad hoc manner to look for overlapping genes by motif matching. In this paper differentiated nucleotide compositional patterns in overlapping regions are incorporated into a probabilistic hidden Markov model (HMM) framework which is used to annotate ssRNA viral genomes. This work focuses on single sequence annotation and applies an HMM framework to ssRNA viral annotation. A description of how the HMM is parameterized, whilst annotating within a missing data framework is given. A Phylogenetic HMM (Phylo-HMM) extension, as applied to 14 aligned HIV2 sequences is also presented. This evolutionary extension serves as an illustration of the potential of the Phylo-HMM framework for ssRNA viral genomic annotation. RESULTS: The single sequence annotation procedure (SSA) is applied to 14 different strains of the HIV2 virus. Further results on alternative ssRNA viral genomes are presented to illustrate more generally the performance of the method. The results of the SSA method are encouraging however there is still room for improvement, and since there is overwhelming evidence to indicate that comparative methods can improve coding sequence (CDS) annotation, the SSA method is extended to a Phylo-HMM to incorporate evolutionary information. The Phylo-HMM extension is applied to the same set of 14 HIV2 sequences which are pre-aligned. The performance improvement that results from including the evolutionary information in the analysis is illustrated.

Algorithms↗
PubMed2003

ASAP: the Alternative Splicing Annotation Project.

Recently, genomics analyses have demonstrated that alternative splicing is widespread in mammalian genomes (30-60% of genes reported to have multiple isoforms), and may be one of their most important mechanisms of functional regulation. However, by comparison with other genomics data such as genome annotation, SNPs, or gene expression, there exists relatively little database infrastructure for the study of alternative splicing. We have constructed an online database ASAP (the Alternative Splicing Annotation Project) for biologists to access and mine the enormous wealth of alternative splicing information coming from genomics and proteomics. ASAP is based on genome-wide analyses of alternative splicing in human (30 793 alternative splice relationships found) from detailed alignment of expressed sequences onto the genomic sequence. ASAP provides precise gene exon-intron structure, alternative splicing, tissue specificity of alternative splice forms, and protein isoform sequences resulting from alternative splicing. Moreover, it can help biologists design probe sequences for distinguishing specific mRNA isoforms. ASAP is intended to be a community resource for collaborative annotation of alternative splice forms, their regulation, and biological functions. The URL for ASAP is http://www.bioinformatics.ucla.edu/ASAP.

Alternative Splicing↗
PubMed1998

Sources of systematic error in functional annotation of genomes: domain rearrangement, non-orthologous gene displacement and operon disruption.

Functional annotation of proteins encoded in newly sequenced genomes can be expected to meet two conflicting objectives: (i) provide as much information as possible, and (ii) avoid erroneous functional assignments and over-predictions. The continuing exponential growth of the number of sequenced genomes makes the quality of sequence annotation a critical factor in the efforts to utilize this new information. When dubious functional assignments are used as a basis for subsequent predictions, they tend to proliferate, leading to "database explosion". It is therefore important to identify the common factors that hamper functional annotation. As a first step towards that goal, we have compared the annotations of the Mycoplasma genitalium and Methanococcus jannaschii genomes produced in several independent studies. The most common causes of questionable predictions appear to be: i) non-critical use of annotations from existing database entries; ii) taking into account only the annotation of the best database hit; iii) insufficient masking of low complexity regions (e.g. non-globular domains) in protein sequences, resulting in spurious database hits obscuring relevant ones; iv) ignoring multi-domain organization of the query proteins and/or the database hits; v) non-critical functional inferences on the basis of the functions of neighboring genes in an operon; vi) non-orthologous gene displacement, i.e. involvement of structurally unrelated proteins in the same function. These observations suggest that case by case validation of functional annotation by expert biologists remains crucial for productive genome analysis.

Databases, Factual↗
PubMed2026

VDJ-Insights: simplifying the annotation of genomic immunoglobulin and T cell receptor regions.

MOTIVATION: Accurate annotation of germline immunoglobulin (IG) and T cell receptor (TCR) loci is critical for understanding adaptive immunity. RESULTS: VDJ-Insights provides a user-friendly software package for characterizing these complex immune regions. In addition, it assesses gene segment functionality, identifies recombination signal sequences, and annotates complementarity-determining regions 1 and 2. VDJ-Insights achieved over 99% concordance with curated annotations from multiple species, outperforming existing annotation tools. When applied to 95 haplotypes from the Human Pangenome Reference Consortium, VDJ-Insights identified 652 and 275 novel IG and TCR alleles, respectively, highlighting its scalability for large immunogenetic studies. AVAILABILITY AND IMPLEMENTATION: Datasets and software package are available in the VDJ-insights repository, https://github.com/BPRC-Bioinfo and https://doi.org/10.5281/zenodo.17588835. Additional intermediate datasets used and analyzed during the current study are available from the corresponding authors upon reasonable request.

Software↗
PubMed2002

Applications of InterPro in protein annotation and genome analysis.

The applications of InterPro span a range of biologically important areas that includes automatic annotation of protein sequences and genome analysis. In automatic annotation of protein sequences InterPro has been utilised to provide reliable characterisation of sequences, identifying them as candidates for functional annotation. Rules based on the InterPro characterisation are stored and operated through a database called RuleBase. RuleBase is used as the main tool in the sequence database group at the EBI to apply automatic annotation to unknown sequences. The annotated sequences are stored and distributed in the TrEMBL protein sequence database. InterPro also provides a means to carry out statistical and comparative analyses of whole genomes. In the Proteome Analysis Database, InterPro analyses have been combined with other analyses based on CluSTr, the Gene Ontology (GO) and structural information on the proteins.

Amino Acid Sequence↗
PubMed2001

In silico predictions of Escherichia coli metabolic capabilities are consistent with experimental data.

A significant goal in the post-genome era is to relate the annotated genome sequence to the physiological functions of a cell. Working from the annotated genome sequence, as well as biochemical and physiological information, it is possible to reconstruct complete metabolic networks. Furthermore, computational methods have been developed to interpret and predict the optimal performance of a metabolic network under a range of growth conditions. We have tested the hypothesis that Escherichia coli uses its metabolism to grow at a maximal rate using the E. coli MG1655 metabolic reconstruction. Based on this hypothesis, we formulated experiments that describe the quantitative relationship between a primary carbon source (acetate or succinate) uptake rate, oxygen uptake rate, and maximal cellular growth rate. We found that the experimental data were consistent with the stated hypothesis, namely that the E. coli metabolic network is optimized to maximize growth under the experimental conditions considered. This study thus demonstrates how the combination of in silico and experimental biology can be used to obtain a quantitative genotype-phenotype relationship for metabolism in bacterial cells.

Acetates↗
PubMed2007

RNAs everywhere: genome-wide annotation of structured RNAs.

Starting with the discovery of microRNAs and the advent of genome-wide transcriptomics, non-protein-coding transcripts have moved from a fringe topic to a central field research in molecular biology. In this contribution we review the state of the art of "computational RNomics", i.e., the bioinformatics approaches to genome-wide RNA annotation. Instead of rehashing results from recently published surveys in detail, we focus here on the open problem in the field, namely (functional) annotation of the plethora of putative RNAs. A series of exploratory studies are used to provide non-trivial examples for the discussion of some of the difficulties.

Biological Evolution↗
PubMed2006

Annotating the genome of Medicago truncatula.

Medicago truncatula will be among the first plant species to benefit from the completion of a whole-genome sequencing project. For each of these species, Arabidopsis, rice and now poplar and Medicago, annotation, the process of identifying gene structures and defining their functions, is essential for the research community to benefit from the sequence data generated. Annotation of the Arabidopsis genome involved gene-by-gene curation of the entire genome, but the larger genomes of rice, Medicago and other species necessitate the automation of the annotation process. Profiting from the experience gained from previous whole-genome efforts, a uniform set of Medicago gene annotations has been generated by coordinated international effort and, along with other views of the genome data, has been provided to the research community at several websites.

Automation↗
PubMed2005

Systematic genome-wide annotation of spliceosomal proteins reveals differential gene family expansion.

Although more than 200 human spliceosomal and splicing-associated proteins are known, the evolution of the splicing machinery has not been studied extensively. The recent near-complete sequencing and annotation of distant vertebrate and chordate genomes provides the opportunity for an exhaustive comparative analysis of splicing factors across eukaryotes. We describe here our semiautomated computational pipeline to identify and annotate splicing factors in representative species of eukaryotes. We focused on protein families whose role in splicing is confirmed by experimental evidence. We visually inspected 1894 proteins and manually curated 224 of them. Our analysis shows a general conservation of the core spliceosomal proteins across the eukaryotic lineage, contrasting with selective expansions of protein families known to play a role in the regulation of splicing, most notably of SR proteins in metazoans and of heterogeneous nuclear ribonucleoproteins (hnRNP) in vertebrates. We also observed vertebrate-specific expansion of the CLK and SRPK kinases (which phosphorylate SR proteins), and the CUG-BP/CELF family of splicing regulators. Furthermore, we report several intronless genes amongst splicing proteins in mammals, suggesting that retrotransposition contributed to the complexity of the mammalian splicing apparatus.

Animals↗
PubMed2002

Enzyme function less conserved than anticipated.

The level of sequence similarity that implies similarity in protein structure is well established. Recently, many groups proposed thresholds for similarity in sequence implying similarity in enzymatic function. All previous results suggest the strong conservation of enzymatic function above levels of 50% pairwise sequence identity. Here, I argue that all groups substantially overestimated the conservation of enzyme function because their data sets were either too biased, or too small. An unbiased analysis suggested that less than 30% of the pair fragments above 50% sequence identity have entirely identical EC numbers. Another surprising finding was that even BLAST E-values below 10(-50) did not suffice to automatically transfer enzyme function without errors. As expected, most misclassifications originated from similarities in relatively short regions and/or from transferring annotations for different domains. Both problems cannot be corrected easily by adjusting the thresholds for automatic transfer of genome annotations. A score relating sequence identity to alignment length (distance from HSSP-threshold) outperformed statistical BLAST scores for high sequence similarity. In particular, the distance score allowed error-free transfer of enzyme function for the 10% most similar enzyme pairs. The results illustrated how difficult it is to assess the conservation of protein function and to guarantee error-free genome annotations, in general: sets with millions of pair comparisons might not suffice to arrive at statistically significant conclusions. In practice, the revised detailed estimates for the sequence conservation of enzyme function may provide important benchmarks for everyday sequence analysis and for more cautious automatic genome annotations.

Amino Acid Sequence↗
PubMed2002

Using the transcriptome to annotate the genome.

A remaining challenge for the human genome project involves the identification and annotation of expressed genes. The public and private sequencing efforts have identified approximately 15,000 sequences that meet stringent criteria for genes, such as correspondence with known genes from humans or other species, and have made another approximately 10,000-20,000 gene predictions of lower confidence, supported by various types of in silico evidence, including homology studies, domain searches, and ab initio gene predictions. These computational methods have limitations, both because they are unable to identify a significant fraction of genes and exons and because they are unable to provide definitive evidence about whether a hypothetical gene is actually expressed. As the in silico approaches identified a smaller number of genes than anticipated, we wondered whether high-throughput experimental analyses could be used to provide evidence for the expression of hypothetical genes and to reveal previously undiscovered genes. We describe here the development of such a method--called long serial analysis of gene expression (LongSAGE), an adaption of the original SAGE approach--that can be used to rapidly identify novel genes and exons.

DNA, Complementary↗
PubMed2001

Annotation transfer for genomics: measuring functional divergence in multi-domain proteins.

Annotation transfer is a principal process in genome annotation. It involves "transferring" structural and functional annotation to uncharacterized open reading frames (ORFs) in a newly completed genome from experimentally characterized proteins similar in sequence. To prevent errors in genome annotation, it is important that this process be robust and statistically well-characterized, especially with regard to how it depends on the degree of sequence similarity. Previously, we and others have analyzed annotation transfer in single-domain proteins. Multi-domain proteins, which make up the bulk of the ORFs in eukaryotic genomes, present more complex issues in functional conservation. Here we present a large-scale survey of annotation transfer in these proteins, using scop superfamilies to define domain folds and a thesaurus based on SWISS-PROT keywords to define functional categories. Our survey reveals that multi-domain proteins have significantly less functional conservation than single-domain ones, except when they share the exact same combination of domain folds. In particular, we find that for multi-domain proteins, approximate function can be accurately transferred with only 35% certainty for pairs of proteins sharing one structural superfamily. In contrast, this value is 67% for pairs of single-domain proteins sharing the same structural superfamily. On the other hand, if two multi-domain proteins contain the same combination of two structural superfamilies the probability of their sharing the same function increases to 80% in the case of complete coverage along the full length of both proteins, this value increases further to > 90%. Moreover, we found that only 70 of the current total of 455 structural superfamilies are found in both single and multi-domain proteins and only 14 of these were associated with the same function in both categories of proteins. We also investigated the degree to which function could be transferred between pairs of multi-domain proteins with respect to the degree of sequence similarity between them, finding that functional divergence at a given amount of sequence similarity is always about two-fold greater for pairs of multi-domain proteins (sharing similarity over a single domain) in comparison to pairs of single-domain ones, though the overall shape of the relationship is quite similar. Further information is available at http://partslist.org/func or http://bioinfo.mbb.yale.edu/partslist/func.

Computational Biology↗