Gene Finding Research Articles

First-generation annotations for the fathead minnow (Pimephales promelas) genome.

Ab initio gene prediction and evidence alignment were used to produce the first annotations for the fathead minnow (Pimephales promelas) genome. We also describe a genome browser, hosted by the Society of Environmental Toxicology and Chemistry, that provides simplified access to the annotation data in context with the genomic sequence. The present study extends the utility of the fathead minnow genome and supports the continued development of this species as a model organism for predictive toxicology. Environ Toxicol Chem 2017;36:3436-3442. Published 2017 Wiley Periodicals Inc. on behalf of SETAC. This article is a US government work and, as such, is in the public domain in the United States of America.

DIGNiFI: Discovering causative genes for orphan diseases using protein-protein interaction networks

BackgroundAn orphan disease is any disease that affects a small percentage of the population. Orphan diseases are a great burden to patients and society, and most of them are genetic in origin. Unfortunately, our current understanding of the genes responsible for inherited orphan diseases is still quite limited. Developing effective computational algorithms to discover disease-causing genes would help unveil disease mechanisms and may enable better diagnosis and treatment.ResultsWe have developed a novel method, named as DIGNiFI (Disease causIng GeNe FInder), which uses Protein-Protein Interaction (PPI) network-based features to discover and rank candidate disease-causing genes. Specifically, our approach computes topologically similar genes by taking into account both local and global connected paths in PPI networks via Direct Neighbors and Local Random Walks, respectively. Furthermore, since genes with similar phenotypes tend to be functionally related, we have integrated PPI data with gene ontology (GO) annotations and protein complex data to further improve the performance of this approach. Results of 128 orphan diseases with 1184 known disease genes collected from the Orphanet show that our proposed methods outperform existing state-of-the-art methods for discovering candidate disease-causing genes. We also show that further performance improvement can be achieved when enriching the human-curated PPI network data with text-mined interactions from the biomedical literature. Finally, we demonstrate the utility of our approach by applying our method to identifying novel candidate genes for a set of four inherited retinal dystrophies. In this study, we found the top predictions for these retinal dystrophies consistent with literature reports and online databases of other retinal dystrophies.ConclusionsOur method successfully prioritizes orphan-disease-causative genes. This method has great potential to benefit the field of orphan disease research, where resources are scarce and greatly needed.

Alt-Splice Gene Predictor Using Multitrack-Clique Analysis: Verification of Statistical Support for Modelling in Genomes of Multicellular Eukaryotes

One of the main limitations of the typical hidden Markov model (HMM) implementation for gene structure identification is that a single structure is identified on a given sequence of genomic data—i.e., identification of overlapping structure is not directly possible, and certainly not possible within the confines of the optimal Viterbi path evaluation. This is a huge limitation given that we now know that significant portions of eukaryotic genomes, particularly mammalian genomes, are alternatively spliced, and, thus, have overlapping structure in the sense of the mRNA transcripts that result. Using the general meta-state HMM approach developed in prior work, however, more than one ‘track’ of annotation can be accommodated, thereby allowing a direct implementation of an alternative-splice gene-structure identifier. In this paper we examine the representation of alternative splicing annotation in the multi-track context, and show that the proliferation on states is manageable, and has sufficient statistical support on the genomes examined (human, mouse, worm, and fly) that a full alt-splice meta-state HMM gene finder can be implemented with sufficient statistical support. In the process of performing the alternative splicing analysis on alt-splice event counts we expected to see an increase in alternative splicing complexity as the organism becomes more complex, and this is seen with the percentage of genes with alt-splice variants increasing from worm to fly to the mammalian genomes (mouse and human). Of particular note is an increase in alternative splicing variants at the start and end of coding with the more complex organisms studied (mouse and human), indicating rapid new first and last exon recruitment that is possibly spliceosome mediated. This suggests that spliceosome-mediated refinements (acceleration) of gene structure variation and selection, with increasing levels of sophistication, has occurred in eukaryotes and in mammals especially.

Driver gene classification reveals a substantial overrepresentation of tumor suppressors among very large chromatin-regulating proteins.

Compiling a comprehensive list of cancer driver genes is imperative for oncology diagnostics and drug development. While driver genes are typically discovered by analysis of tumor genomes, infrequently mutated driver genes often evade detection due to limited sample sizes. Here, we address sample size limitations by integrating tumor genomics data with a wide spectrum of gene-specific properties to search for rare drivers, functionally classify them, and detect features characteristic of driver genes. We show that our approach, CAnceR geNe similarity-based Annotator and Finder (CARNAF), enables detection of potentially novel drivers that eluded over a dozen pan-cancer/multi-tumor type studies. In particular, feature analysis reveals a highly concentrated pool of known and putative tumor suppressors among the <1% of genes that encode very large, chromatin-regulating proteins. Thus, our study highlights the need for deeper characterization of very large, epigenetic regulators in the context of cancer causality.

Draft Genome Sequence of Capniomyces stellatus, the Obligate Gut Fungal Symbiont of Stonefly.

Capniomyces stellatus is a host-specific endosymbiotic fungus, living in the hindgut of stoneflies (especially in Allocapnia). Here, we present the first draft genome sequence of the fungus, as well as the ab initio gene prediction and function analyses, which will facilitate the study and comparative analyses of insect-associated fungi.

NCBI prokaryotic genome annotation pipeline.

Recent technological advances have opened unprecedented opportunities for large-scale sequencing and analysis of populations of pathogenic species in disease outbreaks, as well as for large-scale diversity studies aimed at expanding our knowledge across the whole domain of prokaryotes. To meet the challenge of timely interpretation of structure, function and meaning of this vast genetic information, a comprehensive approach to automatic genome annotation is critically needed. In collaboration with Georgia Tech, NCBI has developed a new approach to genome annotation that combines alignment based methods with methods of predicting protein-coding and RNA genes and other functional elements directly from sequence. A new gene finding tool, GeneMarkS+, uses the combined evidence of protein and RNA placement by homology as an initial map of annotation to generate and modify ab initio gene predictions across the whole genome. Thus, the new NCBI's Prokaryotic Genome Annotation Pipeline (PGAP) relies more on sequence similarity when confident comparative data are available, while it relies more on statistical predictions in the absence of external evidence. The pipeline provides a framework for generation and analysis of annotation on the full breadth of prokaryotic taxonomy. For additional information on PGAP see https://www.ncbi.nlm.nih.gov/genome/annotation_prok/ and the NCBI Handbook, https://www.ncbi.nlm.nih.gov/books/NBK174280/.

Proteogenomic Analysis of Mycobacterium smegmatis Using High Resolution Mass Spectrometry.

Biochemical evidence is vital for accurate genome annotation. The integration of experimental data collected at the proteome level using high resolution mass spectrometry allows for improvements in genome annotation by providing evidence for novel gene models, while validating or modifying others. Here, we report the results of a proteogenomic analysis of a reference strain of Mycobacterium smegmatis (mc2155), a fast growing model organism for the pathogenic Mycobacterium tuberculosis—the causative agent for Tuberculosis. By integrating high throughput LC/MS/MS proteomic data with genomic six frame translation and ab initio gene prediction databases, a total of 2887 ORFs were identified, including 2810 ORFs annotated to a Reference protein, and 63 ORFs not previously annotated to a Reference protein. Further, the translational start site (TSS) was validated for 558 Reference proteome gene models, while upstream translational evidence was identified for 81. In addition, N-terminus derived peptide identifications allowed for downstream TSS modification of a further 24 gene models. We validated the existence of six previously described interrupted coding sequences at the peptide level, and provide evidence for four novel frameshift positions. Analysis of peptide posterior error probability (PEP) scores indicates high-confidence novel peptide identifications and shows that the genome of M. smegmatis mc2155 is not yet fully annotated. Data are available via ProteomeXchange with identifier PXD003500.

The Identification of Novel Diagnostic Marker Genes for the Detection of Beer Spoiling Pediococcus damnosus Strains Using the BlAst Diagnostic Gene findEr.

As the number of bacterial genomes increases dramatically, the demand for easy to use tools with transparent functionality and comprehensible output for applied comparative genomics grows as well. We present BlAst Diagnostic Gene findEr (BADGE), a tool for the rapid prediction of diagnostic marker genes (DMGs) for the differentiation of bacterial groups (e.g. pathogenic / nonpathogenic). DMG identification settings can be modified easily and installing and running BADGE does not require specific bioinformatics skills. During the BADGE run the user is informed step by step about the DMG finding process, thus making it easy to evaluate the impact of chosen settings and options. On the basis of an example with relevance for beer brewing, being one of the oldest biotechnological processes known, we show a straightforward procedure, from phenotyping, genome sequencing, assembly and annotation, up to a discriminant marker gene PCR assay, making comparative genomics a means to an end. The value and the functionality of BADGE were thoroughly examined, resulting in the successful identification and validation of an outstanding novel DMG (fabZ) for the discrimination of harmless and harmful contaminations of Pediococcus damnosus, which can be applied for spoilage risk determination in breweries. Concomitantly, we present and compare five complete P. damnosus genomes sequenced in this study, finding that the ability to produce the unwanted, spoilage associated off-flavor diacetyl is a plasmid encoded trait in this important beer spoiling species.

First draft genome sequencing of indole acetic acid producing and plant growth promoting fungus Preussia sp. BSL10

Preussia sp. BSL10, family Sporormiaceae, was actively producing phytohormone (indole-3-acetic acid) and extra-cellular enzymes (phosphatases and glucosidases). The fungus was also promoting the growth of arid-land tree—Boswellia sacra. Looking at such prospects of this fungus, we sequenced its draft genome for the first time. The Illumina based sequence analysis reveals an approximate genome size of 31.4Mbp for Preussia sp. BSL10. Based on ab initio gene prediction, total 32,312 coding sequences were annotated consisting of 11,967 coding genes, pseudogenes, and 221 tRNA genes. Furthermore, 321 carbohydrate-active enzymes were predicted and classified into many functional families.

Well-characterized sequence features of eukaryote genomes and implications for ab initio gene prediction

In silico analysis of DNA sequences is an important area of computational biology in the post-genomic era. Over the past two decades, computational approaches for ab initio prediction of gene structure from genome sequence alone have largely facilitated our understanding on a variety of biological questions. Although the computational prediction of protein-coding genes has already been well-established, we are also facing challenges to robustly find the non-coding RNA genes, such as miRNA and lncRNA. Two main aspects of ab initio gene prediction include the computed values for describing sequence features and used algorithm for training the discriminant function, and by which different combinations are employed into various bioinformatic tools. Herein, we briefly review these well-characterized sequence features in eukaryote genomes and applications to ab initio gene prediction. The main purpose of this article is to provide an overview to beginners who aim to develop the related bioinformatic tools.

BRAKER1: Unsupervised RNA-Seq-Based Genome Annotation with GeneMark-ET and AUGUSTUS.

Gene finding in eukaryotic genomes is notoriously difficult to automate. The task is to design a work flow with a minimal set of tools that would reach state-of-the-art performance across a wide range of species. GeneMark-ET is a gene prediction tool that incorporates RNA-Seq data into unsupervised training and subsequently generates ab initio gene predictions. AUGUSTUS is a gene finder that usually requires supervised training and uses information from RNA-Seq reads in the prediction step. Complementary strengths of GeneMark-ET and AUGUSTUS provided motivation for designing a new combined tool for automatic gene prediction. We present BRAKER1, a pipeline for unsupervised RNA-Seq-based genome annotation that combines the advantages of GeneMark-ET and AUGUSTUS. As input, BRAKER1 requires a genome assembly file and a file in bam-format with spliced alignments of RNA-Seq reads to the genome. First, GeneMark-ET performs iterative training and generates initial gene structures. Second, AUGUSTUS uses predicted genes for training and then integrates RNA-Seq read information into final gene predictions. In our experiments, we observed that BRAKER1 was more accurate than MAKER2 when it is using RNA-Seq as sole source for training and prediction. BRAKER1 does not require pre-trained parameters or a separate expert-prepared training step. BRAKER1 is available for download at http://bioinf.uni-greifswald.de/bioinf/braker/ and http://exon.gatech.edu/GeneMark/ katharina.hoff@uni-greifswald.de or borodovsky@gatech.edu Supplementary data are available at Bioinformatics online.

MGFM: a novel tool for detection of tissue and cell specific marker genes from microarray gene expression data.

BackgroundIdentification of marker genes associated with a specific tissue/cell type is a fundamental challenge in genetic and cell research. Marker genes are of great importance for determining cell identity, and for understanding tissue specific gene function and the molecular mechanisms underlying complex diseases.ResultsWe have developed a new bioinformatics tool called MGFM (Marker Gene Finder in Microarray data) to predict marker genes from microarray gene expression data. Marker genes are identified through the grouping of samples of the same type with similar marker gene expression levels. We verified our approach using two microarray data sets from the NCBI’s Gene Expression Omnibus public repository encompassing samples for similar sets of five human tissues (brain, heart, kidney, liver, and lung). Comparison with another tool for tissue-specific gene identification and validation with literature-derived established tissue markers established functionality, accuracy and simplicity of our tool. Furthermore, top ranked marker genes were experimentally validated by reverse transcriptase-polymerase chain reaction (RT-PCR). The sets of predicted marker genes associated with the five selected tissues comprised well-known genes of particular importance in these tissues. The tool is freely available from the Bioconductor web site, and it is also provided as an online application integrated into the CellFinder platform (http://cellfinder.org/analysis/marker).ConclusionsMGFM is a useful tool to predict tissue/cell type marker genes using microarray gene expression data. The implementation of the tool as an R-package as well as an application within CellFinder facilitates its use.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-015-1785-9) contains supplementary material, which is available to authorized users.

Genome sequence of a white rot fungus Schizopora paradoxa KUC8140 for wood decay and mycoremediation

Schizopora paradoxa KUC8140 is a white rot wood degrader commonly found in Korea. Tolerance to heavy metals and polycyclic aromatic hydrocarbons and dye decolorization activity make this strain a potential candidate for mycoremediation. We report the genome sequence of S. paradoxa KUC8140 containing 44.4Mbp. Based on ab initio gene prediction, homology search and RNA-seq, total 17,098 gene models were annotated. We identified 17 lignin-modifying peroxidases and other 377 carbohydrate-active enzymes for modeling lignocellulose deconstruction and mycoremediation.

IPred - integrating ab initio and evidence based gene predictions to improve prediction accuracy.

BackgroundGene prediction is a challenging but crucial part in most genome analysis pipelines. Various methods have evolved that predict genes ab initio on reference sequences or evidence based with the help of additional information, such as RNA-Seq reads or EST libraries. However, none of these strategies is bias-free and one method alone does not necessarily provide a complete set of accurate predictions.ResultsWe present IPred (Integrative gene Prediction), a method to integrate ab initio and evidence based gene identifications to complement the advantages of different prediction strategies. IPred builds on the output of gene finders and generates a new combined set of gene identifications, representing the integrated evidence of the single method predictions.ConclusionWe evaluate IPred in simulations and real data experiments on Escherichia Coli and human data. We show that IPred improves the prediction accuracy in comparison to single method predictions and to existing methods for prediction combination.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-015-1315-9) contains supplementary material, which is available to authorized users.

Overview of gene structure in C. elegans.

In the early stage of the C. elegans sequencing project, the ab initio gene prediction program Genefinder was used to find protein-coding genes. Subsequently, protein-coding genes structures have been actively curated by WormBase using evidence from all available data sources. Most coding loci were identified by the Genefinder program, but the process of gene curation results in a continual refinement of the details of gene structure, involving the correction and confirmation of intron splice sites, the addition of alternate splicing forms, the merging and splitting of incorrect predictions, and the creation and extension of 5' and 3' ends. The development of new technologies results in the availability of further data sources, and these are incorporated into the evidence used to support the curated structures. Non-coding genes are more difficult to curate using this methodology, and so the structures for most of these have been imported from the literature or from specialist databases of ncRNA data. This article describes the structure and curation of transcribed regions of genes.

Gene finding in metatranscriptomic sequences.

BackgroundMetatranscriptomic sequencing is a highly sensitive bioassay of functional activity in a microbial community, providing complementary information to the metagenomic sequencing of the community. The acquisition of the metatranscriptomic sequences will enable us to refine the annotations of the metagenomes, and to study the gene activities and their regulation in complex microbial communities and their dynamics.ResultsIn this paper, we present TransGeneScan, a software tool for finding genes in assembled transcripts from metatranscriptomic sequences. By incorporating several features of metatranscriptomic sequencing, including strand-specificity, short intergenic regions, and putative antisense transcripts into a Hidden Markov Model, TranGeneScan can predict a sense transcript containing one or multiple genes (in an operon) or an antisense transcript.ConclusionWe tested TransGeneScan on a mock metatranscriptomic data set containing three known bacterial genomes. The results showed that TranGeneScan performs better than metagenomic gene finders (MetaGeneMark and FragGeneScan) on predicting protein coding genes in assembled transcripts, and achieves comparable or even higher accuracy than gene finders for microbial genomes (Glimmer and GeneMark). These results imply, with the assistance of metatranscriptomic sequencing, we can obtain a broad and precise picture about the genes (and their functions) in a microbial community.AvailabilityTransGeneScan is available as open-source software on SourceForge at https://sourceforge.net/projects/transgenescan/.

The Draft Genome Sequence of European Pear (Pyrus communis L. ‘Bartlett’)

We present a draft assembly of the genome of European pear (Pyrus communis) ‘Bartlett’. Our assembly was developed employing second generation sequencing technology (Roche 454), from single-end, 2 kb, and 7 kb insert paired-end reads using Newbler (version 2.7). It contains 142,083 scaffolds greater than 499 bases (maximum scaffold length of 1.2 Mb) and covers a total of 577.3 Mb, representing most of the expected 600 Mb Pyrus genome. A total of 829,823 putative single nucleotide polymorphisms (SNPs) were detected using re-sequencing of ‘Louise Bonne de Jersey’ and ‘Old Home’. A total of 2,279 genetically mapped SNP markers anchor 171 Mb of the assembled genome. Ab initio gene prediction combined with prediction based on homology searching detected 43,419 putative gene models. Of these, 1219 proteins (556 clusters) are unique to European pear compared to 12 other sequenced plant genomes. Analysis of the expansin gene family provided an example of the quality of the gene prediction and an insight into the relationships among one class of cell wall related genes that control fruit softening in both European pear and apple (Malus×domestica). The ‘Bartlett’ genome assembly v1.0 (http://www.rosaceae.org/species/pyrus/pyrus_communis/genome_v1.0) is an invaluable tool for identifying the genetic control of key horticultural traits in pear and will enable the wide application of marker-assisted and genomic selection that will enhance the speed and efficiency of pear cultivar development.

Prediction of Translation Initiation Site in Bacterial and Archaeal Genomes

Abstract: Driven by the rapid growth of the complete genome sequences, it is accepted that genome annotation has been resorted mostly to automatic methodology. For computational annotation to bacterial and archaeal genomes, accurate prediction of translation initiation sites (TISs) is essential to locate protein coding regions of genes. Therefore, TIS prediction has been a challenge to a number of gene finders and TIS processors, leading to recent studies of TIS prediction or correction in prokaryotic genome annotation as well as of the mechanism of translation initiation. It is time for the research community to review the available mathematical models of TIS of prokaryotic gene, and the resultant algorithms for a series of current TIS processors and TIS prediction modules in gene finders. In fact, the TIS models have been improved along with the knowledge of the mechanism of translation initiation. Several studies of the mechanism of translation initiation in prokaryotic genomes have been summarized. With a few of published data sets widely-used in evaluation of TIS identification, the performances of the existing methods are assessed and discussed in this article. It is also interesting to discuss the relation between the algorithms and the understanding of prokaryotic translation initiation mechanism, which can enlighten us on the state-of-the-art studies of TIS prediction in bacterial and archaeal genomes.

Annotating Viral Genomes - A Cannon is Needed to Kill Mosquitoes

The majority of viruses have a small genome. However, these small genomes often have complex gene features with transcriptional and translational exceptions, for instance, gene overlapping, alternative splicing, RNA editing, ribosomal slippage and stop codon read-through. These complex features and exceptions increase gene density and improve the gene coding efficiency of viral genomes. They also pose immense challenges to gene prediction algorithms. Most gene prediction programs for eukaryotic and prokaryotic genomes cannot detect or predict these exceptions correctly. It is critical to predict these complex features and exceptions with high precision and accuracy in order to interpret viral genomic data correctly. This paper describes the most commonly used programs for viral gene predictions, focusing on the ab initio and similarity-based gene prediction programs, including GeneMarkS, ZCURVE_V, FgenesV, Phylo-HMM, MLOGD, GATU, VirGen, FLAN, VIGOR and others. Viral genome complex features and the basic algorithms of the gene prediction programs are introduced briefly, with identification of advantages and disadvantages, followed by a list of application scopes and specific features. Gene prediction programs for bacteriophages and viral meta-genomic sequences are reviewed separately. The last section of this review presents the future directions and challenges for viral gene prediction program development. Keywords: Mature peptide prediction, viral gene complex feature, viral gene prediction, viral genome annotation, VIGOR.

Characterization of novel wheat NBS domain-containing sequences and their utilization, in silico, for genome-scale R-gene mining

In crop improvement, the isolation, cloning and transfer of disease resistance genes (R-genes) is an ultimate goal usually starting from tentative R-gene analogs (RGAs) that are identified on the basis of their structure. For bread wheat, recent advances in genome sequencing are supporting the efforts of wheat geneticists worldwide. Among wheat R-genes, nucleotide-binding site (NBS)-encoding ones represent a major class. In this study, we have used a polymerase chain reaction-based approach to amplify and clone NBS-type RGAs from a bread wheat cultivar, 'Salambo 80.' Four novel complete ORF sequences showing similarities to previously reported R-genes/RGAs were used for in silico analyses. In a first step, where analyses were focused on the NBS domain, these sequences were phylogenetically assigned to two distinct groups: a first group close to leaf rust Lr21 resistance proteins; and a second one similar to cyst nematode resistance proteins. In a second step, sequences were used as initial seeds to walk up and downstream the NBS domain. This procedure enabled identifying 8 loci ranging in size between 2,115 and 7,653 bp. Ab initio gene prediction identified 8 gene models, among which two had complete ORFs. While GenBank survey confirmed the belonging of sequences to two groups, subsequent characterization using IWGSC genomic and proteomic data showed that the 8 gene models, reported in this study, were unique and their loci matched scaffolds on chromosome arms 1AS, 1BS, 4BS and 1DS. The gene model located on 1DS is a pseudo-Lr21 that was shown to have an NBS-LRR domain structure, while the potential association of the RGAs, here reported, is discussed. This study has produced novel R-gene-like loci and models in the wheat genome and provides the first steps toward further elucidation of their role in wheat disease resistance.