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Newly sequenced genomes are being added to the tree of life at an unprecedented fast pace. A large proportion of such new genomes are phylogenetically close to previously sequenced and annotated genomes. In other cases, whole clades of closely related species or strains ought to be annotated simultaneously. Often, in subsequent studies, differences between the closely related species or strains are in the focus of research when the shared gene structures prevail. We here review methods for comparative structural genome annotation. The reviewed methods include classical approaches such as the alignment of protein sequences or protein profiles against the genome and comparative gene prediction methods that exploit a genome alignment to annotate either a single target genome or all input genomes simultaneously. We discuss how the methods depend on the phylogenetic placement of genomes, give advice on the choice of methods, and examine the consistency between gene structure annotations in an example. Furthermore, we provide practical advice on genome annotation in general., (© 2024. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

In the last decade, the high-throughput and relatively low cost of short-read sequencing technologies have revolutionized prokaryotic genomics. This has led to an exponential increase in the number of bacterial and archaeal genome sequences available, as well as corresponding increase of genome assembly and annotation tools developed. Together, these hardware and software technologies have given scientists unprecedented options to study their chosen microbial systems without the need for large teams of bioinformaticists or supercomputing facilities. While these analysis tools largely fall into only a few categories, each may have different requirements, caveats and file formats, and some may be rarely updated or even abandoned. And so, despite the apparent ease in sequencing and analyzing a prokaryotic genome, it is no wonder that the budding genomicist may quickly find oneself overwhelmed. Here, we aim to provide the reader with an overview of genome annotation and its most important considerations, as well as an easy-to-follow protocol to get started with annotating a prokaryotic genome., (© 2022. Springer Science+Business Media, LLC, part of Springer Nature.)

Background : Small open reading frames (sORFs) with protein-coding ability present unprecedented challenge for genome annotation because of their short sequence and low expression level. In the past decade, only several prediction methods have been proposed for discovery of protein-coding sORFs and lack of objective and uniform negative datasets has become an important obstacle to sORFs prediction. The prediction efficiency of current sORFs prediction methods needs to be further evaluated to provide better research strategies for protein-coding sORFs discovery. Methods : In this work, nine mainstream existing methods for predicting protein-coding potential of ORFs are comprehensively evaluated based on a random sequence strategy. Results : The results show that the current methods perform poorly on different sORFs datasets. For comparison, a sequence based prediction algorithm trained on prokaryotic sORFs is proposed and its better prediction performance indicates that the random sequence strategy can provide feasible ideas for protein-coding sORFs predictions. Conclusions : As a kind of important functional genomic element, discovery of protein-coding sORFs has shed light on the dark proteomes. This evaluation work indicates that there is an urgent need for developing specialized prediction tools for protein-coding sORFs in both eukaryotes and prokaryotes. It is expected that the present work may provide novel ideas for future sORFs researches., (© 2021 The Author(s). Published by BRI.)

Over the last 15 years, the costs of DNA sequencing have sharply fallen, effectively shifting the costs of DNA analysis from sequencing to bioinformatic curation and storage. A huge number of available DNA sequences (including genomes and metagenomes) resulted in the development of various tools for sequence annotation. While much effort has been invested into the development of automatic annotation pipelines, manual curation of their results is still necessary in order to obtain a reliable and strictly validated data. Unfortunately, due to its time-consuming nature, manual annotation is now rarely used.In this chapter, a protocol for efficient manual annotation of prokaryotic DNA sequences using a novel bioinformatic tool-MAISEN ( http://maisen.ddlemb.com ), is presented. MAISEN is a free, web-based tool designed to accelerate manual annotation, by providing the user with simple interface and precomputed alignments for each predicted feature. It was designed to be available for every scientist, regardless of their bioinformatic proficiency.

Gene-structure-aware multiple sequence alignment (GSA-MSA) is conventionally used as a tool for analyzing evolutionary changes in gene structure, i.e., gain and loss of introns during the course of evolution of homologous eukaryotic genes. Recently, however, it has become apparent that GSA-MSA is a powerful tool for detecting and remedying gene-prediction errors prevalent in genome annotations produced by various genome projects. Unfortunately, the construction of GSA-MSAs has so far required tedious procedures, thereby preventing researchers from enjoying the potential benefits of GSA-MSAs. In this chapter, we introduce a straightforward way for constructing GSA-MSAs when one or more genomic sequences and a set of transcript sequences (protein or full-length cDNAs/CDSs) are given. Our method requires no external tool or extra data, such as annotation files, although a supplementary script can generate a gene-structure-informed (GSI) transcript sequence file from annotation files.

Background: The release of the first reference genome of walnut (Juglans regia L.) enabled many achievements in the characterization of walnut genetic and functional variation. However, it is highly fragmented, preventing the integration of genetic, transcriptomic, and proteomic information to fully elucidate walnut biological processes., Findings: Here, we report the new chromosome-scale assembly of the walnut reference genome (Chandler v2.0) obtained by combining Oxford Nanopore long-read sequencing with chromosome conformation capture (Hi-C) technology. Relative to the previous reference genome, the new assembly features an 84.4-fold increase in N50 size, with the 16 chromosomal pseudomolecules assembled and representing 95% of its total length. Using full-length transcripts from single-molecule real-time sequencing, we predicted 37,554 gene models, with a mean gene length higher than the previous gene annotations. Most of the new protein-coding genes (90%) present both start and stop codons, which represents a significant improvement compared with Chandler v1.0 (only 48%). We then tested the potential impact of the new chromosome-level genome on different areas of walnut research. By studying the proteome changes occurring during male flower development, we observed that the virtual proteome obtained from Chandler v2.0 presents fewer artifacts than the previous reference genome, enabling the identification of a new potential pollen allergen in walnut. Also, the new chromosome-scale genome facilitates in-depth studies of intraspecies genetic diversity by revealing previously undetected autozygous regions in Chandler, likely resulting from inbreeding, and 195 genomic regions highly differentiated between Western and Eastern walnut cultivars., Conclusion: Overall, Chandler v2.0 will serve as a valuable resource to better understand and explore walnut biology., (© The Author(s) 2020. Published by Oxford University Press.)

Background: The red flour beetle Tribolium castaneum has emerged as an important model organism for the study of gene function in development and physiology, for ecological and evolutionary genomics, for pest control and a plethora of other topics. RNA interference (RNAi), transgenesis and genome editing are well established and the resources for genome-wide RNAi screening have become available in this model. All these techniques depend on a high quality genome assembly and precise gene models. However, the first version of the genome assembly was generated by Sanger sequencing, and with a small set of RNA sequence data limiting annotation quality., Results: Here, we present an improved genome assembly (Tcas5.2) and an enhanced genome annotation resulting in a new official gene set (OGS3) for Tribolium castaneum, which significantly increase the quality of the genomic resources. By adding large-distance jumping library DNA sequencing to join scaffolds and fill small gaps, the gaps in the genome assembly were reduced and the N50 increased to 4753kbp. The precision of the gene models was enhanced by the use of a large body of RNA-Seq reads of different life history stages and tissue types, leading to the discovery of 1452 novel gene sequences. We also added new features such as alternative splicing, well defined UTRs and microRNA target predictions. For quality control, 399 gene models were evaluated by manual inspection. The current gene set was submitted to Genbank and accepted as a RefSeq genome by NCBI., Conclusions: The new genome assembly (Tcas5.2) and the official gene set (OGS3) provide enhanced genomic resources for genetic work in Tribolium castaneum. The much improved information on transcription start sites supports transgenic and gene editing approaches. Further, novel types of information such as splice variants and microRNA target genes open additional possibilities for analysis.

Background: The allotetraploid tobacco species Nicotiana benthamiana native to Australia has become a popular host for recombinant protein production. Although its usage grows every year, little is known on this plant's genomic and transcriptomic features. Most N. benthamiana accessions currently used in research lack proper documentation of their breeding history and provenance. One of these, the glycoengineered N. benthamiana line ΔXT/FT is increasingly used for the production of biopharmaceutical proteins., Results: Based on an existing draft assembly of the N. benthamiana genome we predict 50,516 protein -encoding genes (62,216 transcripts) supported by expression data derived from 2.35 billion mRNA-seq reads. Using single-copy core genes we show high completeness of the predicted gene set. We functionally annotate more than two thirds of the gene set through sequence homology to genes from other Nicotiana species. We demonstrate that the expression profiles from leaf tissue of ΔXT/FT and its wild type progenitor only show minimal differences. We identify the transgene insertion sites in ΔXT/FT and show that one of the transgenes was inserted inside another predicted gene that most likely lost its function upon insertion. Based on publicly available mRNA-seq data, we confirm that the N. benthamiana accessions used by different research institutions most likely derive from a single source., Conclusions: This work provides gene annotation of the N. benthamiana genome, a genomic and transcriptomic characterization of a transgenic N. benthamiana line in comparison to its wild-type progenitor, and sheds light onto the relatedness of N. benthamiana accessions that are used in laboratories around the world.

Published genomes frequently contain erroneous gene models that represent issues associated with identification of open reading frames, start sites, splice sites, and related structural features. The source of these inconsistencies is often traced back to integration across text file formats designed to describe long read alignments and predicted gene structures. In addition, the majority of gene prediction frameworks do not provide robust downstream filtering to remove problematic gene annotations, nor do they represent these annotations in a format consistent with current file standards. These frameworks also lack consideration for functional attributes, such as the presence or absence of protein domains that can be used for gene model validation. To provide oversight to the increasing number of published genome annotations, we present a software package, the Gene Filtering, Analysis, and Conversion (gFACs), to filter, analyze, and convert predicted gene models and alignments. The software operates across a wide range of alignment, analysis, and gene prediction files with a flexible framework for defining gene models with reliable structural and functional attributes. gFACs supports common downstream applications, including genome browsers, and generates extensive details on the filtering process, including distributions that can be visualized to further assess the proposed gene space. gFACs is freely available and implemented in Perl with support from BioPerl libraries at https://gitlab.com/PlantGenomicsLab/gFACs., (Copyright © 2019 The Authors. Published by Elsevier B.V. All rights reserved.)

Background: More than 3,000 species of octocorals (Cnidaria, Anthozoa) inhabit an expansive range of environments, from shallow tropical seas to the deep-ocean floor. They are important foundation species that create coral "forests," which provide unique niches and 3-dimensional living space for other organisms. The octocoral genus Renilla inhabits sandy, continental shelves in the subtropical and tropical Atlantic and eastern Pacific Oceans. Renilla is especially interesting because it produces secondary metabolites for defense, exhibits bioluminescence, and produces a luciferase that is widely used in dual-reporter assays in molecular biology. Although several anthozoan genomes are currently available, the majority of these are hexacorals. Here, we present a de novo assembly of an azooxanthellate shallow-water octocoral, Renilla muelleri., Findings: We generated a hybrid de novo assembly using MaSuRCA v.3.2.6. The final assembly included 4,825 scaffolds and a haploid genome size of 172 megabases (Mb). A BUSCO assessment found 88% of metazoan orthologs present in the genome. An Augustus ab initio gene prediction found 23,660 genes, of which 66% (15,635) had detectable similarity to annotated genes from the starlet sea anemone, Nematostella vectensis, or to the Uniprot database. Although the R. muelleri genome may be smaller (172 Mb minimum size) than other publicly available coral genomes (256-448 Mb), the R. muelleri genome is similar to other coral genomes in terms of the number of complete metazoan BUSCOs and predicted gene models., Conclusions: The R. muelleri hybrid genome provides a novel resource for researchers to investigate the evolution of genes and gene families within Octocorallia and more widely across Anthozoa. It will be a key resource for future comparative genomics with other corals and for understanding the genomic basis of coral diversity., (© The Author(s) 2019. Published by Oxford University Press.)

GeMoMa is a homology-based gene prediction program that predicts gene models in target species based on gene models in evolutionary related reference species. GeMoMa utilizes amino acid sequence conservation, intron position conservation, and RNA-seq data to accurately predict protein-coding transcripts. Furthermore, GeMoMa supports the combination of predictions based on several reference species allowing to transfer high-quality annotation of different reference species to a target species. Here, we present a detailed description of GeMoMa modules and the GeMoMa pipeline and how they can be used on the command line to address particular biological problems.

Transfer RNAs are the largest, most complex non-coding RNA family, universal to all living organisms. tRNAscan-SE has been the de facto tool for predicting tRNA genes in whole genomes. The newly developed version 2.0 has incorporated advanced methodologies with improved probabilistic search software and a suite of new gene models, enabling better functional classification of predicted genes. This chapter describes the use of the UNIX command-driven and online web versions, illustrating different search modes and options.

Scipio and WebScipio are homology-based gene prediction software designed for annotating multigenic families and for transferring annotations from one species to closely related species. The strengths include the power to cope with sequencing-related problems such as sequencing errors and assemblies with short contigs but also the ability to correctly predict genes with unusually long introns and/or rather short exons. WebScipio is connected to diArk, the largest collection of eukaryotic genome assemblies, and thereby offers a very convenient way to correct existing annotations and to extend protein family datasets. WebScipio is also a key resource for researchers interested in mutually exclusive splicing, allowing to search for alternative exons not only in introns but also in up- and downstream regions in case of incompleteness of the search sequence. In this chapter, I describe how to use Scipio and WebScipio keeping a first-time user in mind.

Backround: Complete and accurate annotation of sequenced genomes is of paramount importance to their utility and analysis. Differences in gene prediction pipelines mean that genome annotations for a species can differ considerably in the quality and quantity of their predicted genes. Furthermore, genes that are present in genome sequences sometimes fail to be detected by computational gene prediction methods. Erroneously unannotated genes can lead to oversights and inaccurate assertions in biological investigations, especially for smaller-scale genome projects, which rely heavily on computational prediction., Results: Here we present OrthoFiller, a tool designed to address the problem of finding and adding such missing genes to genome annotations. OrthoFiller leverages information from multiple related species to identify those genes whose existence can be verified through comparison with known gene families, but which have not been predicted. By simulating missing gene annotations in real sequence datasets from both plants and fungi we demonstrate the accuracy and utility of OrthoFiller for finding missing genes and improving genome annotations. Furthermore, we show that applying OrthoFiller to existing "complete" genome annotations can identify and correct substantial numbers of erroneously missing genes in these two sets of species., Conclusions: We show that significant improvements in the completeness of genome annotations can be made by leveraging information from multiple species.

Background: Chinese clearhead icefish, Protosalanx hyalocranius , is a representative icefish species with economic importance and special appearance. Due to its great economic value in China, the fish was introduced into Lake Dianchi and several other lakes from the Lake Taihu half a century ago. Similar to the Sinocyclocheilus cavefish, the clearhead icefish has certain cavefish-like traits, such as transparent body and nearly scaleless skin. Here, we provide the whole genome sequence of this surface-dwelling fish and generated a draft genome assembly, aiming at exploring molecular mechanisms for the biological interests. A total of 252.1 Gb of raw reads were sequenced. Subsequently, a novel draft genome assembly was generated, with the scaffold N50 reaching 1.163 Mb. The genome completeness was estimated to be 98.39 % by using the CEGMA evaluation. Finally, we annotated 19 884 protein-coding genes and observed that repeat sequences account for 24.43 % of the genome assembly. We report the first draft genome of the Chinese clearhead icefish. The genome assembly will provide a solid foundation for further molecular breeding and germplasm resource protection in Chinese clearhead icefish, as well as other icefishes. It is also a valuable genetic resource for revealing the molecular mechanisms for the cavefish-like characters., (© The Authors 2017. Published by Oxford University Press.)

The Northern snakehead (Channa argus), a member of the Channidae family of the Perciformes, is an economically important freshwater fish native to East Asia. In North America, it has become notorious as an intentionally released invasive species. Its ability to breathe air with gills and migrate short distances over land makes it a good model for bimodal breath research. Therefore, recent research has focused on the identification of relevant candidate genes. Here, we performed whole genome sequencing of C. argus to construct its draft genome, aiming to offer useful information for further functional studies and identification of target genes related to its unusual facultative air breathing. Findings: We assembled the C. argus genome with a total of 140.3 Gb of raw reads, which were sequenced using the Illumina HiSeq2000 platform. The final draft genome assembly was approximately 615.3 Mb, with a contig N50 of 81.4 kb and scaffold N50 of 4.5 Mb. The identified repeat sequences account for 18.9% of the whole genome. The 19 877 protein-coding genes were predicted from the genome assembly, with an average of 10.5 exons per gene. Conclusion: We generated a high-quality draft genome of C. argus, which will provide a valuable genetic resource for further biomedical investigations of this economically important teleost fish., (© The Author 2017. Published by Oxford University Press.)

Background: Gene prediction is one of the most important steps in the genome annotation process. A large number of software tools and pipelines developed by various computing techniques are available for gene prediction. However, these systems have yet to accurately predict all or even most of the protein-coding regions. Furthermore, none of the currently available gene-finders has a universal Hidden Markov Model (HMM) that can perform gene prediction for all organisms equally well in an automatic fashion., Results: We present an automated gene prediction pipeline, Seqping that uses self-training HMM models and transcriptomic data. The pipeline processes the genome and transcriptome sequences of the target species using GlimmerHMM, SNAP, and AUGUSTUS pipelines, followed by MAKER2 program to combine predictions from the three tools in association with the transcriptomic evidence. Seqping generates species-specific HMMs that are able to offer unbiased gene predictions. The pipeline was evaluated using the Oryza sativa and Arabidopsis thaliana genomes. Benchmarking Universal Single-Copy Orthologs (BUSCO) analysis showed that the pipeline was able to identify at least 95% of BUSCO's plantae dataset. Our evaluation shows that Seqping was able to generate better gene predictions compared to three HMM-based programs (MAKER2, GlimmerHMM and AUGUSTUS) using their respective available HMMs. Seqping had the highest accuracy in rice (0.5648 for CDS, 0.4468 for exon, and 0.6695 nucleotide structure) and A. thaliana (0.5808 for CDS, 0.5955 for exon, and 0.8839 nucleotide structure)., Conclusions: Seqping provides researchers a seamless pipeline to train species-specific HMMs and predict genes in newly sequenced or less-studied genomes. We conclude that the Seqping pipeline predictions are more accurate than gene predictions using the other three approaches with the default or available HMMs.

Background: Comparative analysis of whole genome sequence data from closely related prokaryotic species or strains is becoming an increasingly important and accessible approach for addressing both fundamental and applied biological questions. While there are number of excellent tools developed for performing this task, most scale poorly when faced with hundreds of genome sequences, and many require extensive manual curation., Results: We have developed a de-novo genome analysis pipeline (DeNoGAP) for the automated, iterative and high-throughput analysis of data from comparative genomics projects involving hundreds of whole genome sequences. The pipeline is designed to perform reference-assisted and de novo gene prediction, homolog protein family assignment, ortholog prediction, functional annotation, and pan-genome analysis using a range of proven tools and databases. While most existing methods scale quadratically with the number of genomes since they rely on pairwise comparisons among predicted protein sequences, DeNoGAP scales linearly since the homology assignment is based on iteratively refined hidden Markov models. This iterative clustering strategy enables DeNoGAP to handle a very large number of genomes using minimal computational resources. Moreover, the modular structure of the pipeline permits easy updates as new analysis programs become available., Conclusion: DeNoGAP integrates bioinformatics tools and databases for comparative analysis of a large number of genomes. The pipeline offers tools and algorithms for annotation and analysis of completed and draft genome sequences. The pipeline is developed using Perl, BioPerl and SQLite on Ubuntu Linux version 12.04 LTS. Currently, the software package accompanies script for automated installation of necessary external programs on Ubuntu Linux; however, the pipeline should be also compatible with other Linux and Unix systems after necessary external programs are installed. DeNoGAP is freely available at https://sourceforge.net/projects/denogap/ .

Previously, we reported an arsenic resistant bacterium Lysinibacillus sphaericus B1-CDA, isolated from an arsenic contaminated lands. Here, we have investigated its genetic composition and evolutionary history by using massively parallel sequencing and comparative analysis with other known Lysinibacillus genomes. Assembly of the sequencing reads revealed a genome of ~4.5 Mb in size encompassing ~80% of the chromosomal DNA. We found that the set of ordered contigs contains abundant regions of similarity with other Lysinibacillus genomes and clearly identifiable genome rearrangements. Furthermore, all genes of B1-CDA that were predicted be involved in its resistance to arsenic and/or other heavy metals were annotated. The presence of arsenic responsive genes was verified by PCR in vitro conditions. The findings of this study highlight the significance of this bacterium in removing arsenics and other toxic metals from the contaminated sources. The genetic mechanisms of the isolate could be used to cope with arsenic toxicity., (Copyright © 2015 Elsevier Inc. All rights reserved.)

Plant genomics plays a pivotal role in enhancing global food security and sustainability by offering innovative solutions for improving crop yield, disease resistance, and stress tolerance. As the number of sequenced genomes grows and the accuracy and contiguity of genome assemblies improve, structural annotation of plant genomes continues to be a significant challenge due to their large size, polyploidy, and rich repeat content. In this paper, we present an overview of the current landscape in crop genomics research, highlighting the diversity of genomic characteristics across various crop species. We also assessed the accuracy of popular gene prediction tools in identifying genes within crop genomes and examined the factors that impact their performance. Our findings highlight the strengths and limitations of BRAKER2 and Helixer as leading structural genome annotation tools and underscore the impact of genome complexity, fragmentation, and repeat content on their performance. Furthermore, we evaluated the suitability of the predicted proteins as a reliable search space in proteomics studies using mass spectrometry data. Our results provide valuable insights for future efforts to refine and advance the field of structural genome annotation. [ABSTRACT FROM AUTHOR]

Gene prediction is required to obtain optimal biologically meaningful information from genomic sequences, but automated gene prediction software is imperfect. In this study, we compare the original annotation of the Coccidioides immitis RS genome (the reference strain of C. immitis) to annotations using the Funannotate and Augustus genome prediction pipelines. A total of 25% of the originally predicted genes (denoted CIMG) were not found in either the Funannotate or Augustus predictions. A comparison of Funannotate and Augustus predictions also found overlapping but not identical sets of genes. The predicted genes found only in the original annotation (referred to as CIMG-unique) were less likely to have a meaningful functional annotation and a lower number of orthologs and homologs in other fungi than all CIMG genes predicted by the original annotation. The CIMG-unique genes were also more likely to be lineage-specific and poorly expressed. In addition, the CIMG-unique genes were found in clusters and tended to be more frequently associated with transposable elements than all CIMG-predicted genes. The CIMG-unique genes were more likely to have experimentally determined transcription start sites that were further away from the originally predicted transcription start sites, and experimentally determined initial transcription was less likely to result in stable CIMG-unique transcripts. A sample of CIMG-unique genes that were relatively well expressed and differentially expressed in mycelia and spherules was inspected in a genome browser, and the structure of only about half of them was found to be supported by RNA-seq data. These data suggest that some of the CIMG-unique genes are not authentic gene predictions. Genes that were predicted only by the Funannotate pipeline were also less likely to have a meaningful functional annotation, be shorter, and express less well than all the genes predicted by Funannotate. C. immitis genes predicted by more than one annotation are more likely to have predicted functions, many orthologs and homologs, and be well expressed. Lineage-specific genes are relatively uncommon in this group. These data emphasize the importance and limitations of gene prediction software and suggest that improvements to the annotation of the C. immitis genome should be considered.

Algae-driven processes, such as direct CO2 fixation into glycerol, provide new routes for sustainable chemical production in synergy with greenhouse gas mitigation. The marine microalgae Dunaliella tertiolecta is reported to accumulate high amounts of intracellular glycerol upon exposure to high salt concentrations. We have conducted a comprehensive, time-resolved systems biology study to decipher the metabolic response of D. tertiolecta up to 24 h under continuous light conditions. Initially, due to a lack of reference sequences required for MS/MS-based protein identification, a high-quality draft genome of D. tertiolecta was generated. Subsequently, a database was designed by combining the genome with transcriptome data obtained before and after salt stress. This database allowed for detection of differentially expressed proteins and identification of phosphorylated proteins, which are involved in the short- and long-term adaptation to salt stress, respectively. Specifically, in the rapid salt adaptation response, proteins linked to the Ca2+ signaling pathway and ion channel proteins were significantly increased. While phosphorylation is key in maintaining ion homeostasis during the rapid adaptation to salt stress, phosphofructokinase is required for long-term adaption. Lacking β-carotene, synthesis under salt stress conditions might be substituted by the redox-sensitive protein CP12. Furthermore, salt stress induces upregulation of Calvin–Benson cycle-related proteins. [ABSTRACT FROM AUTHOR]

BackgroundGene annotation in eukaryotes is a non-trivial task that requires meticulous analysis of accumulated transcript data. Challenges include transcriptionally active regions of the genome that contain overlapping genes, genes that produce numerous transcripts, transposable elements and numerous diverse sequence repeats. Currently available gene annotation software applications depend on pre-constructed full-length gene sequence assemblies which are not guaranteed to be error-free. The origins of these sequences are often uncertain, making it difficult to identify and rectify errors in them. This hinders the creation of an accurate and holistic representation of the transcriptomic landscape across multiple tissue types and experimental conditions. Therefore, to gauge the extent of diversity in gene structures, a comprehensive analysis of genome-wide expression data is imperative.ResultsWe present FINDER, a fully automated computational tool that optimizes the entire process of annotating genes and transcript structures. Unlike current state-of-the-art pipelines, FINDER automates the RNA-Seq pre-processing step by working directly with raw sequence reads and optimizes gene prediction from BRAKER2 by supplementing these reads with associated proteins. The FINDER pipeline (1) reports transcripts and recognizes genes that are expressed under specific conditions, (2) generates all possible alternatively spliced transcripts from expressed RNA-Seq data, (3) analyzes read coverage patterns to modify existing transcript models and create new ones, and (4) scores genes as high- or low-confidence based on the available evidence across multiple datasets. We demonstrate the ability of FINDER to automatically annotate a diverse pool of genomes from eight species.ConclusionsFINDER takes a completely automated approach to annotate genes directly from raw expression data. It is capable of processing eukaryotic genomes of all sizes and requires no manual supervision-ideal for bench researchers with limited experience in handling computational tools.

Transfer RNAs are the largest, most complex non-coding RNA family, universal to all living organisms. tRNAscan-SE has been the de facto tool for predicting tRNA genes in whole genomes. The newly developed version 2.0 has incorporated advanced methodologies with improved probabilistic search software and a suite of new gene models, enabling better functional classification of predicted genes. This chapter describes the use of the UNIX command-driven and online web versions, illustrating different search modes and options.

Gene prediction is required to obtain optimal biologically meaningful information from genomic sequences, but automated gene prediction software is imperfect. In this study, we compare the original annotation of the Coccidioides immitis RS genome (the reference strain of C. immitis) to annotations using the Funannotate and Augustus genome prediction pipelines. A total of 25% of the originally predicted genes (denoted CIMG) were not found in either the Funannotate or Augustus predictions. A comparison of Funannotate and Augustus predictions also found overlapping but not identical sets of genes. The predicted genes found only in the original annotation (referred to as CIMG-unique) were less likely to have a meaningful functional annotation and a lower number of orthologs and homologs in other fungi than all CIMG genes predicted by the original annotation. The CIMG-unique genes were also more likely to be lineage-specific and poorly expressed. In addition, the CIMG-unique genes were found in clusters and tended to be more frequently associated with transposable elements than all CIMG-predicted genes. The CIMG-unique genes were more likely to have experimentally determined transcription start sites that were further away from the originally predicted transcription start sites, and experimentally determined initial transcription was less likely to result in stable CIMG-unique transcripts. A sample of CIMG-unique genes that were relatively well expressed and differentially expressed in mycelia and spherules was inspected in a genome browser, and the structure of only about half of them was found to be supported by RNA-seq data. These data suggest that some of the CIMG-unique genes are not authentic gene predictions. Genes that were predicted only by the Funannotate pipeline were also less likely to have a meaningful functional annotation, be shorter, and express less well than all the genes predicted by Funannotate. C. immitis genes predicted by more than one annotation are more likely to have predicted functions, many orthologs and homologs, and be well expressed. Lineage-specific genes are relatively uncommon in this group. These data emphasize the importance and limitations of gene prediction software and suggest that improvements to the annotation of the C. immitis genome should be considered.

Algae-driven processes, such as direct CO2 fixation into glycerol, provide new routes for sustainable chemical production in synergy with greenhouse gas mitigation. The marine microalgae Dunaliella tertiolecta is reported to accumulate high amounts of intracellular glycerol upon exposure to high salt concentrations. We have conducted a comprehensive, time-resolved systems biology study to decipher the metabolic response of D. tertiolecta up to 24 h under continuous light conditions. Initially, due to a lack of reference sequences required for MS/MS-based protein identification, a high-quality draft genome of D. tertiolecta was generated. Subsequently, a database was designed by combining the genome with transcriptome data obtained before and after salt stress. This database allowed for detection of differentially expressed proteins and identification of phosphorylated proteins, which are involved in the short- and long-term adaptation to salt stress, respectively. Specifically, in the rapid salt adaptation response, proteins linked to the Ca2+ signaling pathway and ion channel proteins were significantly increased. While phosphorylation is key in maintaining ion homeostasis during the rapid adaptation to salt stress, phosphofructokinase is required for long-term adaption. Lacking β-carotene, synthesis under salt stress conditions might be substituted by the redox-sensitive protein CP12. Furthermore, salt stress induces upregulation of Calvin–Benson cycle-related proteins.

In recent decades, antimicrobial resistance has been augmented as a global concern to public health owing to the global spread of multidrug-resistant strains from different ESKAPE pathogens. This alarming trend and the lack of new antibiotics with novel modes of action in the pipeline necessitate the development of non-antibiotic ways to treat illnesses caused by these isolates. In molecular biology, computational approaches have become crucial tools, particularly in one of the most challenging areas of multidrug resistance. The rapid advancements in bioinformatics have led to a plethora of computational approaches involving genomics, systems biology, and structural biology currently gaining momentum among molecular biologists since they can be useful and provide valuable information on the complex mechanisms of AMR research in ESKAPE pathogens. These computational approaches would be helpful in elucidating the AMR mechanisms, identifying important hub genes/proteins, and their promising targets together with their interactions with important drug targets, which is a crucial step in drug discovery. Therefore, the present review aims to provide holistic information on currently employed bioinformatic tools and their application in the discovery of multifunctional novel therapeutic drugs to combat the current problem of AMR in ESKAPE pathogens. The review also summarizes the recent advancement in the AMR research in ESKAPE pathogens utilizing the in silico approaches. [ABSTRACT FROM AUTHOR]

Abstract Background Gene annotation in eukaryotes is a non-trivial task that requires meticulous analysis of accumulated transcript data. Challenges include transcriptionally active regions of the genome that contain overlapping genes, genes that produce numerous transcripts, transposable elements and numerous diverse sequence repeats. Currently available gene annotation software applications depend on pre-constructed full-length gene sequence assemblies which are not guaranteed to be error-free. The origins of these sequences are often uncertain, making it difficult to identify and rectify errors in them. This hinders the creation of an accurate and holistic representation of the transcriptomic landscape across multiple tissue types and experimental conditions. Therefore, to gauge the extent of diversity in gene structures, a comprehensive analysis of genome-wide expression data is imperative. Results We present FINDER, a fully automated computational tool that optimizes the entire process of annotating genes and transcript structures. Unlike current state-of-the-art pipelines, FINDER automates the RNA-Seq pre-processing step by working directly with raw sequence reads and optimizes gene prediction from BRAKER2 by supplementing these reads with associated proteins. The FINDER pipeline (1) reports transcripts and recognizes genes that are expressed under specific conditions, (2) generates all possible alternatively spliced transcripts from expressed RNA-Seq data, (3) analyzes read coverage patterns to modify existing transcript models and create new ones, and (4) scores genes as high- or low-confidence based on the available evidence across multiple datasets. We demonstrate the ability of FINDER to automatically annotate a diverse pool of genomes from eight species. Conclusions FINDER takes a completely automated approach to annotate genes directly from raw expression data. It is capable of processing eukaryotic genomes of all sizes and requires no manual supervision—ideal for bench researchers with limited experience in handling computational tools.

Recognition of the functional sites of genes, such as translation initiation sites, donor and acceptor splice sites and stop codons, is a relevant part of many current problems in bioinformatics. The best approaches use sophisticated classifiers, such as support vector machines. However, with the rapid accumulation of sequence data, methods for combining many sources of evidence are necessary as it is unlikely that a single classifier can solve this problem with the best possible performance. A major issue is that the number of possible models to combine is large and the use of all of these models is impractical. In this paper we present a methodology for combining many sources of information to recognize any functional site using “floating search”, a powerful heuristics applicable when the cost of evaluating each solution is high. We present experiments on four functional sites in the human genome, which is used as the target genome, and use another 20 species as sources of evidence. The proposed methodology shows significant improvement over state-of-the-art methods. The results show an advantage of the proposed method and also challenge the standard assumption of using only genomes not very close and not very far from the human to improve the recognition of functional sites. [ABSTRACT FROM AUTHOR]

BackgroundPlasmodium falciparum, the deadliest malaria-causing parasite, has an extremely AT-rich (80.7 %) genome. Because of high AT-content, sequence-based annotation of genes and functional elements remains challenging. In order to better understand the regulatory network controlling gene expression in the parasite, a more complete genome annotation as well as analysis tools adapted for AT-rich genomes are needed. Recent studies on genome-wide nucleosome positioning in eukaryotes have shown that nucleosome landscapes exhibit regular characteristic patterns at the 5'- and 3'-end of protein and non-protein coding genes. In addition, nucleosome depleted regions can be found near transcription start sites. These unique nucleosome landscape patterns may be exploited for the identification of novel genes. In this paper, we propose a computational approach to discover novel putative genes based exclusively on nucleosome positioning data in the AT-rich genome of P. falciparum.ResultsUsing binary classifiers trained on nucleosome landscapes at the gene boundaries from two independent nucleosome positioning data sets, we were able to detect a total of 231 regions containing putative genes in the genome of Plasmodium falciparum, of which 67 highly confident genes were found in both data sets. Eighty-eight of these 231 newly predicted genes exhibited transcription signal in RNA-Seq data, indicative of active transcription. In addition, 20 out of 21 selected gene candidates were further validated by RT-PCR, and 28 out of the 231 genes showed significant matches using BLASTN against an expressed sequence tag (EST) database. Furthermore, 108 (47%) out of the 231 putative novel genes overlapped with previously identified but unannotated long non-coding RNAs. Collectively, these results provide experimental validation for 163 predicted genes (70.6%). Finally, 73 out of 231 genes were found to be potentially translated based on their signal in polysome-associated RNA-Seq representing transcripts that are actively being translated.ConclusionOur results clearly indicate that nucleosome positioning data contains sufficient information for novel gene discovery. As distinct nucleosome landscapes around genes are found in many other eukaryotic organisms, this methodology could be used to characterize the transcriptome of any organism, especially when coupled with other DNA-based gene finding and experimental methods (e.g., RNA-Seq).

Animals, plants, and algae rely on symbiotic microorganisms for their development and functioning. Genome sequencing and genomic analyses of these microorganisms provide opportunities to construct metabolic networks and to analyze the metabolism of the symbiotic communities they constitute. Genome-scale metabolic network reconstructions rest on information gained from genome annotation. As there are multiple annotation pipelines available, the question arises to what extent differences in annotation pipelines impact outcomes of these analyses. Here, we compare five commonly used pipelines (Prokka, MaGe, IMG, DFAST, RAST) from predicted annotation features (coding sequences, Enzyme Commission numbers, hypothetical proteins) to the metabolic network-based analysis of symbiotic communities (biochemical reactions, producible compounds, and selection of minimal complementary bacterial communities). While Prokka and IMG produced the most extensive networks, RAST and DFAST networks produced the fewest false positives and the most connected networks with the fewest dead-end metabolites. Our results underline differences between the outputs of the tested pipelines at all examined levels, with small differences in the draft metabolic networks resulting in the selection of different microbial consortia to expand the metabolic capabilities of the algal host. However, the consortia generated yielded similar predicted producible compounds and could therefore be considered functionally interchangeable. This contrast between selected communities and community functions depending on the annotation pipeline needs to be taken into consideration when interpreting the results of metabolic complementarity analyses. In the future, experimental validation of bioinformatic predictions will likely be crucial to both evaluate and refine the pipelines and needs to be coupled with increased efforts to expand and improve annotations in reference databases. [ABSTRACT FROM AUTHOR]

SARS-CoV-2 genomic sequencing efforts have scaled dramatically to address the current global pandemic and aid public health. However, autonomous genome annotation of SARS-CoV-2 genes, proteins, and domains is not readily accomplished by existing methods and results in missing or incorrect sequences. To overcome this limitation, we developed a novel semi-supervised pipeline for automated gene, protein, and functional domain annotation of SARS-CoV-2 genomes that differentiates itself by not relying on the use of a single reference genome and by overcoming atypical genomic traits that challenge traditional bioinformatic methods. We analyzed an initial corpus of 66,000 SARS-CoV-2 genome sequences collected from labs across the world using our method and identified the comprehensive set of known proteins with 98.5% set membership accuracy and 99.1% accuracy in length prediction, compared to proteome references, including Replicase polyprotein 1ab (with its transcriptional slippage site). Compared to other published tools, such as Prokka (base) and VAPiD, we yielded a 6.4- and 1.8-fold increase in protein annotations. Our method generated 13,000,000 gene, protein, and domain sequences—some conserved across time and geography and others representing emerging variants. We observed 3362 non-redundant sequences per protein on average within this corpus and described key D614G and N501Y variants spatiotemporally in the initial genome corpus. For spike glycoprotein domains, we achieved greater than 97.9% sequence identity to references and characterized receptor binding domain variants. We further demonstrated the robustness and extensibility of our method on an additional 4000 variant diverse genomes containing all named variants of concern and interest as of August 2021. In this cohort, we successfully identified all keystone spike glycoprotein mutations in our predicted protein sequences with greater than 99% accuracy as well as demonstrating high accuracy of the protein and domain annotations. This work comprehensively presents the molecular targets to refine biomedical interventions for SARS-CoV-2 with a scalable, high-accuracy method to analyze newly sequenced infections as they arise.

Summary: Genome sequencing has fundamentally changed how plant biologists think about genes. All or nearly all genes can ultimately be associated with a gene model. However, many gene models appear to play little or no role in the traits of an organism. A range of structural, molecular, population and evolutionary features all show a separation between genes with known phenotypes and the overall set of annotated gene models. These different features could be combined to develop models to distinguish the genes that determine the traits of plants from the subset gene other annotated gene models which are unlikely to play a role in doing so. Efforts to identify the subset of annotated gene models likely involved in specifying the characteristics of plants would help aid a wide range of researchers. James Schnable is an honourably mentioned finalist of the 2018 New Phytologist Tansley Medal competition for excellence in plant science. See Lennon & Dolan, in this issue of New Phytologist (2020, 228: 5), for more details. [ABSTRACT FROM AUTHOR]

Cancer driver genes, i.e., oncogenes and tumor suppressor genes, are involved in the acquisition of important functions in tumors, providing a selective growth advantage, allowing uncontrolled proliferation and avoiding apoptosis. It is therefore important to identify these driver genes, both for the fundamental understanding of cancer and to help finding new therapeutic targets or biomarkers. Although the most frequently mutated driver genes have been identified, it is believed that many more remain to be discovered, particularly for driver genes specific to some cancer types. In this paper, we propose a new computational method called LOTUS to predict new driver genes. LOTUS is a machine-learning based approach which allows to integrate various types of data in a versatile manner, including information about gene mutations and protein-protein interactions. In addition, LOTUS can predict cancer driver genes in a pan-cancer setting as well as for specific cancer types, using a multitask learning strategy to share information across cancer types. We empirically show that LOTUS outperforms five other state-of-the-art driver gene prediction methods, both in terms of intrinsic consistency and prediction accuracy, and provide predictions of new cancer genes across many cancer types. [ABSTRACT FROM AUTHOR]

Comprehensive information on the timing and location of gene expression is fundamental to our understanding of embryonic development and tissue formation. While high-throughput in situ hybridization projects provide invaluable information about developmental gene expression patterns for model organisms like Drosophila, the output of these experiments is primarily qualitative, and a high proportion of protein coding genes and most non-coding genes lack any annotation. Accurate data-centric predictions of spatio-temporal gene expression will therefore complement current in situ hybridization efforts. Here, we applied a machine learning approach by training models on all public gene expression and chromatin data, even from whole-organism experiments, to provide genome-wide, quantitative spatio-temporal predictions for all genes. We developed structured in silico nano-dissection, a computational approach that predicts gene expression in >200 tissue-developmental stages. The algorithm integrates expression signals from a compendium of 6,378 genome-wide expression and chromatin profiling experiments in a cell lineage-aware fashion. We systematically evaluated our performance via cross-validation and experimentally confirmed 22 new predictions for four different embryonic tissues. The model also predicts complex, multi-tissue expression and developmental regulation with high accuracy. We further show the potential of applying these genome-wide predictions to extract tissue specificity signals from non-tissue-dissected experiments, and to prioritize tissues and stages for disease modeling. This resource, together with the exploratory tools are freely available at our webserver , which provides a valuable tool for a range of applications, from predicting spatio-temporal expression patterns to recognizing tissue signatures from differential gene expression profiles. [ABSTRACT FROM AUTHOR]

HIV-1 replicates via a low-fidelity polymerase with a high mutation rate; strong conservation of individual nucleotides is highly indicative of the presence of critical structural or functional properties. Identifying such conservation can reveal novel insights into viral behaviour. We analysed 3651 publicly available sequences for the presence of nucleic acid conservation beyond that required by amino acid constraints, using a novel scale-free method that identifies regions of outlying score together with a codon scoring algorithm. Sequences with outlying score were further analysed using an algorithm for producing local RNA folds whilst accounting for alignment properties. 11 different conserved regions were identified, some corresponding to well-known cis-acting functions of the HIV-1 genome but also others whose conservation has not previously been noted. We identify rational causes for many of these, including cis functions, possible additional reading frame usage, a plausible mechanism by which the central polypurine tract primes second-strand DNA synthesis and a conformational stabilising function of a region at the 5′ end of env. [ABSTRACT FROM AUTHOR]

The cellular DNA sensor cGMP-AMP synthase (cGAS) detects cytosolic viral DNA via the stimulator of interferon genes (STING) to initiate innate antiviral response. Herpesviruses are known to target key immune signaling pathways to persist in an immune-competent host. Marek’s disease virus (MDV), a highly pathogenic and oncogenic herpesvirus of chickens, can antagonize host innate immune responses to achieve persistent infection. With a functional screen, we identified five MDV proteins that blocked beta interferon (IFN-β) induction downstream of the cGAS-STING pathway. Specifically, the MDV major oncoprotein Meq impeded the recruitment of TANK-binding kinase 1 and IFN regulatory factor 7 (IRF7) to the STING complex, thereby inhibiting IRF7 activation and IFN-β induction. Meq overexpression markedly reduced antiviral responses stimulated by cytosolic DNA, whereas knockdown of Meq heightened MDV-triggered induction of IFN-β and downstream antiviral genes. Moreover, Meq-deficient MDV induced more IFN-β production than wild-type MDV. Meq-deficient MDV also triggered a more robust CD8+ T cell response than wild-type MDV. As such, the Meq-deficient MDV was highly attenuated in replication and lymphoma induction compared to wild-type MDV. Taken together, these results revealed that MDV evades the cGAS-STING DNA sensing pathway, which underpins the efficient replication and oncogenesis. These findings improve our understanding of the virus-host interaction in MDV-induced lymphoma and may contribute to the development of novel vaccines against MDV infection. [ABSTRACT FROM AUTHOR]

Ribosome profiling (ribo-seq) provides a means to analyze active translation by determining ribosome occupancy in a transcriptome-wide manner. The vast majority of ribosome protected fragments (RPFs) resides within the protein-coding sequence of mRNAs. However, commonly reads are also found within the transcript leader sequence (TLS) (aka 5’ untranslated region) preceding the main open reading frame (ORF), indicating the translation of regulatory upstream ORFs (uORFs). Here, we present a workflow for the identification of translation-regulatory uORFs. Specifically, uORF-Tools uses Ribo-TISH to identify uORFs within a given dataset and generates a uORF annotation file. In addition, a comprehensive human uORF annotation file, based on 35 ribo-seq files, is provided, which can serve as an alternative input file for the workflow. To assess the translation-regulatory activity of the uORFs, stimulus-induced changes in the ratio of the RPFs residing in the main ORFs relative to those found in the associated uORFs are determined. The resulting output file allows for the easy identification of candidate uORFs, which have translation-inhibitory effects on their associated main ORFs. uORF-Tools is available as a free and open Snakemake workflow at . It is easily installed and all necessary tools are provided in a version-controlled manner, which also ensures lasting usability. uORF-Tools is designed for intuitive use and requires only limited computing times and resources. [ABSTRACT FROM AUTHOR]

Obtaining a comprehensive understanding of the gene regulatory networks, or gene cascades, involved in cell fate determination and cell lineage segregation in Caenorhabditis elegans is a long-standing challenge. Although RNA-sequencing (RNA-Seq) is a promising technique to resolve these questions, the bioinformatics tools to identify associated gene cascades from RNA-Seq data remain inadequate. To overcome these limitations, we developed Gene Cascade Finder (GCF) as a novel tool for building gene cascades by comparison of mutant and wild-type RNA-Seq data along with integrated information of protein-protein interactions, expression timing, and domains. Application of GCF to RNA-Seq data confirmed that SPN-4 and MEX-3 regulate the canonical Wnt pathway during embryonic development. Moreover, lin-35, hsp-3, and gpa-12 were found to be involved in MEX-1-dependent neurogenesis, and MEX-3 was found to control the gene cascade promoting neurogenesis through lin-35 and apl-1. Thus, GCF could be a useful tool for building gene cascades from RNA-Seq data. [ABSTRACT FROM AUTHOR]

In-silico identification of potential target genes for disease is an essential aspect of drug target discovery. Recent studies suggest that successful targets can be found through by leveraging genetic, genomic and protein interaction information. Here, we systematically tested the ability of 12 varied algorithms, based on network propagation, to identify genes that have been targeted by any drug, on gene-disease data from 22 common non-cancerous diseases in OpenTargets. We considered two biological networks, six performance metrics and compared two types of input gene-disease association scores. The impact of the design factors in performance was quantified through additive explanatory models. Standard cross-validation led to over-optimistic performance estimates due to the presence of protein complexes. In order to obtain realistic estimates, we introduced two novel protein complex-aware cross-validation schemes. When seeding biological networks with known drug targets, machine learning and diffusion-based methods found around 2-4 true targets within the top 20 suggestions. Seeding the networks with genes associated to disease by genetics decreased performance below 1 true hit on average. The use of a larger network, although noisier, improved overall performance. We conclude that diffusion-based prioritisers and machine learning applied to diffusion-based features are suited for drug discovery in practice and improve over simpler neighbour-voting methods. We also demonstrate the large impact of choosing an adequate validation strategy and the definition of seed disease genes. [ABSTRACT FROM AUTHOR]

In recent years it became clear that in eukaryotic genome evolution gene loss is prevalent over gene gain. However, the absence of genes in an annotated genome is not always equivalent to the loss of genes. Due to sequencing issues, or incorrect gene prediction, genes can be falsely inferred as absent. This implies that loss estimates are overestimated and, more generally, that falsely inferred absences impact genomic comparative studies. However, reliable estimates of how prevalent this issue is are lacking. Here we quantified the impact of gene prediction on gene loss estimates in eukaryotes by analysing 209 phylogenetically diverse eukaryotic organisms and comparing their predicted proteomes to that of their respective six-frame translated genomes. We observe that 4.61% of domains per species were falsely inferred to be absent for Pfam domains predicted to have been present in the last eukaryotic common ancestor. Between phylogenetically different categories this estimate varies substantially: for clade-specific loss (ancestral loss) we found 1.30% and for species-specific loss 16.88% to be falsely inferred as absent. For BUSCO 1-to-1 orthologous families, 18.30% were falsely inferred to be absent. Finally, we showed that falsely inferred absences indeed impact loss estimates, with the number of losses decreasing by 11.78%. Our work strengthens the increasing number of studies showing that gene loss is an important factor in eukaryotic genome evolution. However, while we demonstrate that on average inferring gene absences from predicted proteomes is reliable, caution is warranted when inferring species-specific absences. [ABSTRACT FROM AUTHOR]

Introns are a prevalent feature of eukaryotic genomes, yet their origins and contributions to genome function and evolution remain mysterious. In budding yeast, repression of the highly transcribed intron-containing ribosomal protein genes (RPGs) globally increases splicing of non-RPG transcripts through reduced competition for the spliceosome. We show that under these “hungry spliceosome” conditions, splicing occurs at more than 150 previously unannotated locations we call protointrons that do not overlap known introns. Protointrons use a less constrained set of splice sites and branchpoints than standard introns, including in one case AT-AC in place of GT-AG. Protointrons are not conserved in all closely related species, suggesting that most are not under positive selection and are fated to disappear. Some are found in non-coding RNAs (e. g. CUTs and SUTs), where they may contribute to the creation of new genes. Others are found across boundaries between noncoding and coding sequences, or within coding sequences, where they offer pathways to the creation of new protein variants, or new regulatory controls for existing genes. We define protointrons as (1) nonconserved intron-like sequences that are (2) infrequently spliced, and importantly (3) are not currently understood to contribute to gene expression or regulation in the way that standard introns function. A very few protointrons in S. cerevisiae challenge this classification by their increased splicing frequency and potential function, consistent with the proposed evolutionary process of “intronization”, whereby new standard introns are created. This snapshot of intron evolution highlights the important role of the spliceosome in the expansion of transcribed genomic sequence space, providing a pathway for the rare events that may lead to the birth of new eukaryotic genes and the refinement of existing gene function. [ABSTRACT FROM AUTHOR]

Introduction: The sponging of microRNAs by a long non-coding RNA (lncRNA) away from their coding gene targets is a conceptually-simple, yet biologically-complex method of lncRNA-mediated gene regulation. Currently, predictions of genes that participate in sponge-based regulation are largely based on sequence homology alone, which may not adequately reflect the cellular environment in which lncRNA:miRNA pairs interact. The vast number of potential interactions generated by these predictions impedes the identification of functional gene regulatory relationships, which necessitates an approach that considers biological context. XIST, the female-specific lncRNA canonically involved in silencing the X chromosome, has been suggested by many studies to act as a miRNA sponge. The sex-specificity of XIST provides the opportunity to study the biological feasibility of proposed XIST-miRNA interactions. Here we take a comprehensive approach by considering factors that affect possible regulation through XIST-miRNA sponging. Results: To identify the most feasible candidates in a particular tissue (lung adenocarcinomas), we considered protein-coding genes that (1) were positively correlated with XIST expression within sexes, (2) were targeted by miRNAs shared with XIST, and (3) expressed in lung adenocarcinoma. This revealed a robust set of 124 genes potentially positively regulated by XIST through the sequestration of 804 shared miRNAs. We then used the basic sex-specific nature of XIST to compare the changes in miRNA-target gene relationships in endogenously high-XIST and low-XIST systems to discover a high-confidence set of only 13 miRNA-gene pairs. As XIST is expressed exclusively in the nucleus, we validated the nuclear presence of several of these high-confidence miRNAs using RT-qPCR, confirming the co-localization required for XIST to interact with these species. Conclusions: We use a biology-driven approach to identify genes defended from miRNA-based inhibition by the lncRNA XIST. Importantly, we identify that only a small subset of miRNAs predicted by sequence homology alone have the capacity to mediate the XIST-target gene axis, as they are enriched in the nucleus and able to co-localize with XIST for sponging. Our results reinforce the necessary consideration of biological features in future studies of lncRNA:miRNA interactions. [ABSTRACT FROM AUTHOR]

Essential tremor (ET) is one of the most common movement disorders. The etiology of ET remains largely unexplained. Whole genome sequencing (WGS) is likely to be of value in understanding a large proportion of ET with Mendelian and complex disease inheritance patterns. In ET families with Mendelian inheritance patterns, WGS may lead to gene identification where WES analysis failed to identify the causative single nucleotide variant (SNV) or indel due to incomplete coverage of the entire coding region of the genome, in addition to accurate detection of larger structural variants (SVs) and copy number variants (CNVs). Alternatively, in ET families with complex disease inheritance patterns with gene x gene and gene x environment interactions enrichment of functional rare coding and non-coding variants may explain the heritability of ET. We performed WGS in eight ET families (n = 40 individuals) enrolled in the Family Study of Essential Tremor. The analysis included filtering WGS data based on allele frequency in population databases, rare SNV and indel classification and association testing using the Mixed-Model Kernel Based Adaptive Cluster (MM-KBAC) test. A separate analysis of rare SV and CNVs segregating within ET families was also performed. Prioritization of candidate genes identified within families was performed using phenolyzer. WGS analysis identified candidate genes for ET in 5/8 (62.5%) of the families analyzed. WES analysis in a subset of these families in our previously published study failed to identify candidate genes. In one family, we identified a deleterious and damaging variant (c.1367G>A, p.(Arg456Gln)) in the candidate gene, CACNA1G, which encodes the pore forming subunit of T-type Ca(2+) channels, CaV3.1, and is expressed in various motor pathways and has been previously implicated in neuronal autorhythmicity and ET. Other candidate genes identified include SLIT3 which encodes an axon guidance molecule and in three families, phenolyzer prioritized genes that are associated with hereditary neuropathies (family A, KARS, family B, KIF5A and family F, NTRK1). Functional studies of CACNA1G and SLIT3 suggest a role for these genes in ET disease pathogenesis. [ABSTRACT FROM AUTHOR]

Scleractinia of the Maputaland reef complex (MRC) in South Africa exist at the margins of the Western Indian Ocean (WIO) coral distribution and are the only substantial hermatypic coral communities in South Africa. Pocillopora species occupy a conspicuous component of the MRC, and previous investigations identified three species of Pocillopora utilizing conventional taxonomy. Thus, our aims were four-fold: to elucidate Pocillopora species diversity using genetic techniques, primarily using species delimitation methods based on the ORF gene; to test for the presence of hybridisation within the Pocillopora community on the South-West margin of distribution in the Indian Ocean using two nuclear and two mitochondrial markers; to test the presence of cryptic species, using 13 microsatellite markers, finally, to elucidate the degree of genetic diversity within each Pocillopora species found and compare this to communities in lower latitudes. We illustrate taxonomic inconsistencies between these inventories and our phylogenetic data. The MRC harbours unique populations of Pocillopora, consisting of three species hypothetically co-occurring throughout the south WIO, namely: P. meandrina/P. eydouxi, commonly misidentified as P. verrucosa, P. verrucosa, sometimes correctly identified, but also commonly misidentified as P. damicornis sensu lato, and P. villosa, almost always misidentified as P. eydouxi. The hypothesis that hybrid swarms of Pocillopora occur in marginal environments such as the MRC was not supported, with low levels of introgressive hybridization reported instead. Analyses illustrate low genetic diversity at the species and population resolutions, suggesting a small founder population for each species. Nevertheless, these populations are demographically unique, exhibiting high levels of ITS2 haplotype endemism compared to higher latitude populations and the rest of the WIO. Pocillopora diversity on the MRC represents a unique assemblage and warrants further protection. [ABSTRACT FROM AUTHOR]

The distribution and diversity of RNA viruses in fungi is incompletely understood due to the often cryptic nature of mycoviral infections and the focused study of primarily pathogenic and/or economically important fungi. As most viruses that are known to infect fungi possess either single-stranded or double-stranded RNA genomes, transcriptomic data provides the opportunity to query for viruses in diverse fungal samples without any a priori knowledge of virus infection. Here we describe a systematic survey of all transcriptomic datasets from fungi belonging to the subphylum Pezizomycotina. Using a simple but effective computational pipeline that uses reads discarded during normal RNA-seq analyses, followed by identification of a viral RNA-dependent RNA polymerase (RdRP) motif in de novo assembled contigs, 59 viruses from 44 different fungi were identified. Among the viruses identified, 88% were determined to be new species and 68% are, to our knowledge, the first virus described from the fungal species. Comprehensive analyses of both nucleotide and inferred protein sequences characterize the phylogenetic relationships between these viruses and the known set of mycoviral sequences and support the classification of up to four new families and two new genera. Thus the results provide a deeper understanding of the scope of mycoviral diversity while also increasing the distribution of fungal hosts. Further, this study demonstrates the suitability of analyzing RNA-seq data to facilitate rapid discovery of new viruses. [ABSTRACT FROM AUTHOR]

Plant-pathogenic Xanthomonas bacteria secrete transcription activator-like effectors (TALEs) into host cells, where they act as transcriptional activators on plant target genes to support bacterial virulence. TALEs have a unique modular DNA-binding domain composed of tandem repeats. Two amino acids within each tandem repeat, termed repeat-variable diresidues, bind to contiguous nucleotides on the DNA sequence and determine target specificity. In this paper, we propose a novel approach for TALE target prediction to identify potential virulence targets. Our approach accounts for recent findings concerning TALE targeting, including frame-shift binding by repeats of aberrant lengths, and the flexible strand orientation of target boxes relative to the transcription start of the downstream target gene. The computational model can account for dependencies between adjacent RVD positions. Model parameters are learned from the wealth of quantitative data that have been generated over the last years. We benchmark the novel approach, termed PrediTALE, using RNA-seq data after Xanthomonas infection in rice, and find an overall improvement of prediction performance compared with previous approaches. Using PrediTALE, we are able to predict several novel putative virulence targets. However, we also observe that no target genes are predicted by any prediction tool for several TALEs, which we term orphan TALEs for this reason. We postulate that one explanation for orphan TALEs are incomplete gene annotations and, hence, propose to replace promoterome-wide by genome-wide scans for target boxes. We demonstrate that known targets from promoterome-wide scans may be recovered by genome-wide scans, whereas the latter, combined with RNA-seq data, are able to detect putative targets independent of existing gene annotations. [ABSTRACT FROM AUTHOR]

Background: Primate T-lymphotropic viruses type 1 (PTLV-1) are complex retroviruses infecting both human (HTLV-1) and simian (STLV-1) hosts. They share common epidemiological, clinical and molecular features. In addition to the canonical gag, pol, env retroviral genes, PTLV-1 purportedly encodes regulatory (i.e. Tax, Rex, and HBZ) and accessory proteins (i.e. P12/8, P13, P30). The latter have been found essential for viral persistence in vivo. Methodology/Principal findings: We have isolated a STLV-1 virus from a bonnet macaque (Macaca radiata–Mra18C9), a monkey from India. The complete sequence was obtained and phylogenetic analyses were performed. The Mra18C9 strain is highly divergent from the known PTLV-1 strains. Intriguingly, the Mra18C9 lacks the 3 accessory open reading frames. In order to determine if the absence of accessory proteins is specific to this particular strain, a comprehensive analysis of the complete PTLV-1 genomes available in Genbank was performed and found that the lack of one or many accessory ORF is common among PTLV-1. Conclusion: This study raises many questions regarding the actual nature, role and importance of accessory proteins in the PTLV-1 biology. [ABSTRACT FROM AUTHOR]