DraGnET: Software for storing,managing and analyzing annotated draft genome sequence data

Background  

New "next generation" DNA sequencing technologies offer individual researchers the ability to rapidly generate large amounts of genome sequence data at dramatically reduced costs. As a result, a need has arisen for new software tools for storage, management and analysis of genome sequence data. Although bioinformatic tools are available for the analysis and management of genome sequences, limitations still remain. For example, restrictions on the submission of data and use of these tools may be imposed, thereby making them unsuitable for sequencing projects that need to remain in-house or proprietary during their initial stages. Furthermore, the availability and use of next generation sequencing in industrial, governmental and academic environments requires biologist to have access to computational support for the curation and analysis of the data generated; however, this type of support is not always immediately available.     

Automated genome sequence analysis and annotation.

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     

New computational tools for brassica genome research

With the increasing quantities of Brassica genomic data being entered into the public domain and in preparation for the complete Brassica genome sequencing effort, there is a growing requirement for the structuring and detailed bioinformatic analysis of Brassica genomic information within a user-friendly database. At the Plant Biotechnology Centre, Melbourne, Australia, we have developed a series of tools and computational pipelines to assist in the processing and structuring of genomic data, to aid its application to agricultural biotechnology research. These tools include a sequence database, ASTRA, a sequence processing pipeline incorporating annotation against GenBank, SwissProt and Arabidopsis Gene Ontology (GO) data and tools for molecular marker discovery and comparative genome analysis. All sequences are mined for simple sequence repeat (SSR) molecular markers using 'SSR primer' and mapped onto the complete Arabidopsis thaliana genome by sequence comparison. The database may be queried using a text-based search of sequence annotation or GO terms, BLAST comparison against resident sequences, or by the position of candidate orthologues within the Arabidopsis genome. Tools have also been developed and applied to the discovery of single nucleotide polymorphism (SNP) molecular markers and the in silico mapping of Brassica BAC end sequences onto the Arabidopsis genome. Planned extensions to this resource include the integration of gene expression data and the development of an EnsEMBL-based genome viewer.     

Non-referenced genome assembly from epigenomic short-read data

Current computational methods used to analyze changes in DNA methylation and chromatin modification rely on sequenced genomes. Here we describe a pipeline for the detection of these changes from short-read sequence data that does not require a reference genome. Open source software packages were used for sequence assembly, alignment, and measurement of differential enrichment. The method was evaluated by comparing results with reference-based results showing a strong correlation between chromatin modification and gene expression. We then used our de novo sequence assembly to build the DNA methylation profile for the non-referenced Psammomys obesus genome. The pipeline described uses open source software for fast annotation and visualization of unreferenced genomic regions from short-read data.     

euGenes: a eukaryote genome information system

euGenes is a genome information system and database that provides a common summary of eukaryote genes and genomes, at http://iubio.bio.indiana.edu/eugenes/. Seven popular genomes are included: human, mouse, fruitfly, Caenorhabditis elegans worm, Saccharomyces yeast, Arabidopsis mustard weed and zebrafish, with more planned. This information, automatically extracted and updated from several source databases, offers features not readily available through other genome databases to bioscientists looking for gene relationships across organisms. The database describes 150 000 known, predicted and orphan genes, using consistent gene names along with their homologies and associations with a standard vocabulary of molecular functions, cell locations and biological processes. Usable whole-genome maps including features, chromosome locations and molecular data integration are available, as are options to retrieve sequences from these genomes. Search and retrieval methods for these data are easy to use and efficient, allowing one to ask combined questions of sequence features, protein functions and other gene attributes, and fetch results in reports, computable tabular outputs or bulk database forms. These summarized data are useful for integration in other projects, such as gene expression databases. euGenes provides an extensible, flexible genome information system for many organisms.     

Chromatin structure analysis enables detection of DNA insertions into the mammalian nuclear genome

BackgroundGenetically modified organisms (GMOs) have numerous biomedical, agricultural and environmental applications. Development of accurate methods for the detection of GMOs is a prerequisite for the identification and control of authorized and unauthorized release of these engineered organisms into the environment and into the food chain. Current detection methods are unable to detect uncharacterized GMOs, since either the DNA sequence of the transgene or the amino acid sequence of the protein must be known for DNA-based or immunological-based detection, respectively.MethodsHere we describe the application of an epigenetics-based approach for the detection of mammalian GMOs via analysis of chromatin structural changes occurring in the host nucleus upon the insertion of foreign or endogenous DNA.ResultsImmunological methods combined with DNA next generation sequencing enabled direct interrogation of chromatin structure and identification of insertions of various size foreign (human or viral) DNA sequences, DNA sequences often used as genome modification tools (e.g. viral sequences, transposon elements), or endogenous DNA sequences into the nuclear genome of a model animal organism.ConclusionsThe results provide a proof-of-concept that epigenetic approaches can be used to detect the insertion of endogenous and exogenous sequences into the genome of higher organisms where the method of genetic modification, the sequence of inserted DNA, and the exact genomic insertion site(s) are unknown.General significanceMeasurement of chromatin dynamics as a sensor for detection of genomic manipulation and, more broadly, organism exposure to environmental or other factors affecting the epigenomic landscape are discussed.     

Automated bacterial genome analysis and annotation

More than 300 bacterial genome sequences are publicly available, and many more are scheduled to be completed and released in the near future. Converting this raw sequence information into a better understanding of the biology of bacteria involves the identification and annotation of genes, proteins and pathways. This processing is typically done using sequence annotation pipelines comprised of a variety of software modules and, in some cases, human experts. The reference databases, computational methods and knowledge that form the basis of these pipelines are constantly evolving, and thus there is a need to reprocess genome annotations on a regular basis. The combined challenge of revising existing annotations and extracting useful information from the flood of new genome sequences will necessitate more reliance on completely automated systems.     

In silico prediction of splice-altering single nucleotide variants in the human genome

In silico tools have been developed to predict variants that may have an impact on pre-mRNA splicing. The major limitation of the application of these tools to basic research and clinical practice is the difficulty in interpreting the output. Most tools only predict potential splice sites given a DNA sequence without measuring splicing signal changes caused by a variant. Another limitation is the lack of large-scale evaluation studies of these tools. We compared eight in silico tools on 2959 single nucleotide variants within splicing consensus regions (scSNVs) using receiver operating characteristic analysis. The Position Weight Matrix model and MaxEntScan outperformed other methods. Two ensemble learning methods, adaptive boosting and random forests, were used to construct models that take advantage of individual methods. Both models further improved prediction, with outputs of directly interpretable prediction scores. We applied our ensemble scores to scSNVs from the Catalogue of Somatic Mutations in Cancer database. Analysis showed that predicted splice-altering scSNVs are enriched in recurrent scSNVs and known cancer genes. We pre-computed our ensemble scores for all potential scSNVs across the human genome, providing a whole genome level resource for identifying splice-altering scSNVs discovered from large-scale sequencing studies.     

Inference of protein function and protein linkages in <Emphasis Type="Italic">Mycobacterium tuberculosis</Emphasis> based on prokaryotic genome organization: a combined computational approach

The genome of Mycobacterium tuberculosis was analyzed using recently developed computational approaches to infer protein function and protein linkages. We evaluated and employed a method to infer genes likely to belong to the same operon, as judged by the nucleotide distance between genes in the same genomic orientation, and combined this method with those of the Rosetta Stone, Phylogenetic Profile and conserved Gene Neighbor computational methods for the inference of protein function.     

SubtiList: the reference database for the Bacillus subtilis genome

SubtiList is the reference database dedicated to the genome of Bacillus subtilis 168, the paradigm of Gram-positive endospore-forming bacteria. Developed in the framework of the B.subtilis genome project, SubtiList provides a curated dataset of DNA and protein sequences, combined with the relevant annotations and functional assignments. Information about gene functions and products is continuously updated by linking relevant bibliographic references. Recently, sequence corrections arising from both systematic verifications and submissions by individual scientists were included in the reference genome sequence. SubtiList is based on a generic relational data schema and a World Wide Web interface developed for the handling of bacterial genomes, called GenoList. The World Wide Web interface was designed to allow users to easily browse through genome data and retrieve information according to common biological queries. SubtiList also provides more elaborate tools, such as pattern searching, which are tightly connected to the overall browsing system. SubtiList is accessible at http://genolist.pasteur.fr/SubtiList/. Similar bacterial databases are accessible at http://genolist.pasteur.fr/.     

Construction of a virtual Mycobacterium tuberculosis consensus genome and its application to data from a next generation sequencer

Background

Although Mycobacterium tuberculosis isolates are consisted of several different lineages and the epidemiology analyses are usually assessed relative to a particular reference genome, M. tuberculosis H37Rv, which might introduce some biased results. Those analyses are essentially based genome sequence information of M. tuberculosis and could be performed in sillico in theory, with whole genome sequence (WGS) data available in the databases and obtained by next generation sequencers (NGSs). As an approach to establish higher resolution methods for such analyses, whole genome sequences of the M. tuberculosis complexes (MTBCs) strains available on databases were aligned to construct virtual reference genome sequences called the consensus sequence (CS), and evaluated its feasibility in in sillico epidemiological analyses.

Results

The consensus sequence (CS) was successfully constructed and utilized to perform phylogenetic analysis, evaluation of read mapping efficacy, which is crucial for detecting single nucleotide polymorphisms (SNPs), and various MTBC typing methods virtually including spoligotyping, VNTR, Long sequence polymorphism and Beijing typing. SNPs detected based on CS, in comparison with H37Rv, were utilized in concatemer-based phylogenetic analysis to determine their reliability relative to a phylogenetic tree based on whole genome alignment as the gold standard. Statistical comparison of phylogenic trees based on CS with that of H37Rv indicated the former showed always better results that that of later. SNP detection and concatenation with CS was advantageous because the frequency of crucial SNPs distinguishing among strain lineages was higher than those of H37Rv. The number of SNPs detected was lower with the consensus than with the H37Rv sequence, resulting in a significant reduction in computational time. Performance of each virtual typing was satisfactory and accorded with those published when those are available.

Conclusions

These results indicated that virtual CS constructed from genome sequence data is an ideal approach as a reference for MTBC studies.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1368-9) contains supplementary material, which is available to authorized users.     

Immuno-informatics: Mining genomes for vaccine components

The complete genome sequences of more than 60 microbes have been completed in the past decade. Concurrently, a series of new informatics tools, designed to harness this new wealth of information, have been developed. Some of these new tools allow researchers to select regions of microbial genomes that trigger immune responses. These regions, termed epitopes, are ideal components of vaccines. When the new tools are used to search for epitopes, this search is usually coupled with in vitro screening methods; an approach that has been termed computational immunology or immuno-informatics.Researchers are now implementing these combined methods to scan genomic sequences for vaccine components. They are thereby expanding the number of different proteins that can be screened for vaccine development, while narrowing this search to those regions of the proteins that are extremely likely to induce an immune response.As the tools improve, it may soon be feasible to skip over many of the in vitro screening steps, moving directly from genome sequence to vaccine design. The present article reviews the work of several groups engaged in the development of immuno-informatics tools and illustrates the application of these tools to the process of vaccine discovery.     

Optimal choice of k-mer in composition vector method for genome sequence comparison

Several proteins and genes are members of families that share a public evolutionary. In order to outline the evolutionary relationships and to recognize conserved patterns, sequence comparison becomes an emerging process. The current work investigates critically the k-mer role in composition vector method for comparing genome sequences. Generally, composition vector methods using k-mer are applied under choice of different value of k to compare genome sequences. For some values of k, results are satisfactory, but for other values of k, results are unsatisfactory. Standard composition vector method is carried out in the proposed work using 3-mer string length. In addition, special type of information based similarity index is used as a distance measure. It establishes that use of 3-mer and information based similarity index provide satisfactory results especially for comparison of whole genome sequences in all cases. These selections provide a sort of unified approach towards comparison of genome sequences.     

Strategies for complete plastid genome sequencing

Plastid sequencing is an essential tool in the study of plant evolution. This high‐copy organelle is one of the most technically accessible regions of the genome, and its sequence conservation makes it a valuable region for comparative genome evolution, phylogenetic analysis and population studies. Here, we discuss recent innovations and approaches for de novo plastid assembly that harness genomic tools. We focus on technical developments including low‐cost sequence library preparation approaches for genome skimming, enrichment via hybrid baits and methylation‐sensitive capture, sequence platforms with higher read outputs and longer read lengths, and automated tools for assembly. These developments allow for a much more streamlined assembly than via conventional short‐range PCR. Although newer methods make complete plastid sequencing possible for any land plant or green alga, there are still challenges for producing finished plastomes particularly from herbarium material or from structurally divergent plastids such as those of parasitic plants.     

Recent advances in developing molecular tools for targeted genome engineering of mammalian cells

Various biological molecules naturally existing in diversified species including fungi, bacteria, and bacteriophage have functionalities for DNA binding and processing. The biological molecules have been recently actively engineered for use in customized genome editing of mammalian cells as the molecule-encoding DNA sequence information and the underlying mechanisms how the molecules work are unveiled. Excitingly, multiple novel methods based on the newly constructed artificial molecular tools have enabled modifications of specific endogenous genetic elements in the genome context at efficiencies that are much higher than that of the conventional homologous recombination based methods. This minireview introduces the most recently spotlighted molecular genome engineering tools with their key features and ongoing modifications for better performance. Such ongoing efforts have mainly focused on the removal of the inherent DNA sequence recognition rigidity from the original molecular platforms, the addition of newly tailored targeting functions into the engineered molecules, and the enhancement of their targeting specificity. Effective targeted genome engineering of mammalian cells will enable not only sophisticated genetic studies in the context of the genome, but also widely-applicable universal therapeutics based on the pinpointing and correction of the disease-causing genetic elements within the genome in the near future. [BMB Reports 2015; 48(1): 6-12]     

DNA-binding protein prediction using plant specific support vector machines: validation and application of a new genome annotation tool

There are currently 151 plants with draft genomes available but levels of functional annotation for putative protein products are low. Therefore, accurate computational predictions are essential to annotate genomes in the first instance, and to provide focus for the more costly and time consuming functional assays that follow. DNA-binding proteins are an important class of proteins that require annotation, but current computational methods are not applicable for genome wide predictions in plant species. Here, we explore the use of species and lineage specific models for the prediction of DNA-binding proteins in plants. We show that a species specific support vector machine model based on Arabidopsis sequence data is more accurate (accuracy 81%) than a generic model (74%), and based on this we develop a plant specific model for predicting DNA-binding proteins. We apply this model to the tomato proteome and demonstrate its ability to perform accurate high-throughput prediction of DNA-binding proteins. In doing so, we have annotated 36 currently uncharacterised proteins by assigning a putative DNA-binding function. Our model is publically available and we propose it be used in combination with existing tools to help increase annotation levels of DNA-binding proteins encoded in plant genomes.     

Aligning the proteome and genome of the silkworm, Bombyx mori

A technology of mass spectrometry (MS) was used in this study for the large-scale proteomic identification and verification of protein-encoding genes present in the silkworm (Bombyx mori) genome. Peptide sequences identified by MS were compared with those from an open reading frame (ORF) library of the B. mori genome and a cDNA library, to validate the coding attributes of ORFs. Two databases were created. The first was based on a 9× draft sequence of the silkworm genome and contained 14,632 putative proteins. The second was based on a B. mori pupal cDNA library containing 3,187 putative proteins of at least 30 amino acid residues in length. A total of 81,000 peptide sequences with a threshold score of 60% were generated by the MS/MS analysis, and 55,400 of these were chosen for a sequence alignment. By searching these two databases, 6,649 and 250 proteins were matched, which accounted for approximately 45.4% and 7.8% of the peptide sequences and putative proteins, respectively. Further analyses carried out by several bioinformatic tools suggested that the matches included proteins with predicted transmembrane domains (1,393) and preproteins with a signal peptide (976). These results provide a fundamental understanding of the expression and function of silkworm proteins.     

Computational techniques for human genome resequencing using mated gapped reads

Unchained base reads on self-assembling DNA nanoarrays have recently emerged as a promising approach to low-cost, high-quality resequencing of human genomes. Because of unique characteristics of these mated pair reads, existing computational methods for resequencing assembly, such as those based on map-consensus calling, are not adequate for accurate variant calling. We describe novel computational methods developed for accurate calling of SNPs and short substitutions and indels (<100 bp); the same methods apply to evaluation of hypothesized larger, structural variations. We use an optimization process that iteratively adjusts the genome sequence to maximize its a posteriori probability given the observed reads. For each candidate sequence, this probability is computed using Bayesian statistics with a simple read generation model and simplifying assumptions that make the problem computationally tractable. The optimization process iteratively applies one-base substitutions, insertions, and deletions until convergence is achieved to an optimum diploid sequence. A local de novo assembly procedure that generalizes approaches based on De Bruijn graphs is used to seed the optimization process in order to reduce the chance of converging to local optima. Finally, a correlation-based filter is applied to reduce the false positive rate caused by the presence of repetitive regions in the reference genome.