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Genes in coronaviruses are preceded by transcription regulatory sequences (TRSs), which play a critical role in gene expression mediated by the viral RNA-dependent RNA-polymerase via the process of discontinuous transcription. In addition to being crucial for our understanding of the regulation and expression of coronavirus genes, we demonstrate for the first time how TRSs can be leveraged to identify gene locations in the coronavirus genome. To that end, we formulate the TRS AND Gene Identification (TRS-Gene-ID) problem of simultaneously identifying TRS sites and gene locations in unannotated coronavirus genomes. We introduce CORSID (CORe Sequence IDentifier), a computational tool to solve this problem. We also present CORSID-A, which solves a constrained version of the TRS-Gene-ID problem, the TRS Identification (TRS-ID) problem, identifying TRS sites in a coronavirus genome with specified gene annotations. We show that CORSID-A outperforms existing motif-based methods in identifying TRS sites in coronaviruses and that CORSID outperforms state-of-the-art gene finding methods in finding genes in coronavirus genomes. We demonstrate that CORSID enables de novo identification of TRS sites and genes in previously unannotated coronaviruses. CORSID is the first method to perform accurate and simultaneous identification of TRS sites and genes in coronavirus genomes without the use of any prior information.

BackgroundmiRNA functional enrichment is a type of analysis that is used to predict which biological functions may be affected by a group of miRNAs or validate whether a list of dysregulated miRNAs are linked to a diseased state. The standard method for functional enrichment analysis uses the hypergeometric distribution to produce p-values, depicting the strength of the association between a group of miRNAs and a biological function. However, in 2015, it was shown that this approach suffers from a bias related to miRNA targets produced by target prediction algorithms and a new randomization test was proposed to alleviate this issue.ResultsWe demonstrate the existence of another previously unreported underlying bias which affects gene annotation data sets; additionally, we show that the statistical measure used for the established randomization test is not sensitive enough to account for it. In this context, we show that the use of Jaccard coefficient (an alternative statistical measure) is able to alleviate the aforementioned issue.ConclusionsIn this paper, we illustrate the existence of a new bias affecting the miRNA functional enrichment analysis. This bias makes Fisher’s exact test unsuitable for miRNA functional enrichment analyses and there is also a need to adjust the established unbiased test accordingly. We propose the use of a modified version of the established test and in order to facilitate its use, we introduce a novel unbiased miRNA enrichment analysis tool that implements the proposed method. At the same time, by leveraging bit vectors, our tool guarantees fast and scalable execution.AvailabilityAll datasets used in the experiments throughout this paper are openly accessible on Zenodo (https://doi.org/10.5281/zenodo.5175819).

In less than 25 years, the field of animal genome science has transformed from a discipline seeking its first glimpses into genome sequences across the Tree of Life to a global enterprise with ambitions to sequence genomes for all of Earth’s eukaryotic diversity (1). As the field rapidly moves forward, it is important to take stock of the progress that has been made to best inform the discipline’s future. In this Perspective, we provide a contemporary, quantitative overview of animal genome sequencing. We identified the best available genome assemblies on GenBank, the world’s most extensive genetic database, for 3,278 unique animal species across 24 phyla. We assessed taxonomic representation, assembly quality, and annotation status for major clades. We show that while tremendous taxonomic progress has occurred, stark disparities in genomic representation exist, highlighted by a systemic overrepresentation of vertebrates and underrepresentation of arthropods. In terms of assembly quality, long-read sequencing has dramatically improved contiguity, whereas gene annotations are available for just 34.3% of taxa. Furthermore, we show that animal genome science has diversified in recent years with an ever-expanding pool of researchers participating. However, the field still appears to be dominated by institutions in the Global North, which have been listed as the submitting institution for 77% of all assemblies. We conclude by offering recommendations for how we can collectively improve genomic resource availability and value while also broadening global representation.Significance statementThe field of animal genome science is rapidly developing, and efforts are underway to sequence genomes for all of Earth’s eukaryotic biodiversity. Here, we provide an overview of animal genome sequencing, with emphases on taxonomic representation, assembly quality, and geographic representation. We show that while a staggering 3,278 unique animal species have had their genomes sequenced, massive disparities exist in terms of the taxonomic groups receiving attention, the quality of the resources being produced, and the institutions driving the field. We highlight areas where improvements can be made, notably by continuing to increase the quality of genome assemblies, including by improving metadata and voucher specimen associations, and actively developing meaningful collaborations between researchers form the Global North and South.

We report high-quality draft assemblies and gene annotation for 13 species and/or strains of the protozoan parasite genera Leishmania, Endotrypanum and Crithidia, which span the phylogenetic diversity of the Leishmaniinae sub-family within the kinetoplastid order of the phylum Euglenazoa. These resources will support studies on the origins of parasitism.

Molecular studies of the freshwater snail Lymnaea stagnalis, a unique model organism for neurobiology research, has been severely hindered by the lack of sufficient genomic information. As part of our ongoing effort studying L. stagnalis neuronal growth and connectivity at various developmental stages, we provide the first age-specific transcriptome analysis and gene annotation of young, adult, and old L. stagnalis central nervous system (CNS). RNA sequencing using Illumina NovaSeq 6000 platform produced 56-69 millions of 150 bp paired-end reads, and 74% of these reads were mapped to the draft genome of L. stagnalis. We provide gene annotations for 32,288 coding sequences with a minimum of 100 codons, contributing to the largest number of annotated genes for the L. stagnalis genome to date. Lastly, transcriptomic analyses reveal age-specific differentially expressed genes and enriched pathways in young, adult, and old CNS. These datasets represent the largest and most updated L. stagnalis CNS transcriptomes.

Apis mellifera L., the western honey bee is a major crop pollinator that plays a key role in beekeeping and serves as an important model organism in social behavior studies. Recent efforts have improved on the quality of the honey bee reference genome and developed a chromosome-level assembly of sixteen chromosomes, two of which are gapless. However, the rest suffer from 51 gaps, 160 unplaced/unlocalized scaffolds, and the lack of 2 distal telomeres. The gaps are located at the hard-to-assemble extended highly repetitive chromosomal regions that may contain functional genomic elements. Here, we use de-novo re-assemblies from the most recent reference genome Amel_HAv_3.1 raw reads and other long-read-based assemblies (INRA_AMelMel_1.0, ASM1384120v1, and ASM1384124v1) of the honey bee genome to resolve 13 gaps, five unplaced/unlocalized scaffolds and, the lacking telomeres of the Amel_HAv_3.1. The total length of the resolved gaps is 848,747 bp. The accuracy of the corrected assembly was validated by mapping PacBio reads and performing gene annotation assessment. Comparative analysis suggests that the PacBio-reads-based assemblies of the honey bee genomes failed in the same highly repetitive extended regions of the chromosomes, especially on chromosome 10. To fully resolve these extended repetitive regions, further work using ultra-long Nanopore sequencing would be needed. Our updated assembly facilitates more accurate reference-guided scaffolding and marker/sequence mapping in honey bee genomics studies.

Spatial transcriptomics is an emerging technology requiring costly reagents and considerable skills, limiting the identification of transcriptional markers related to histology. Here, we show that predicted spatial gene-expressions in unmeasured regions and tissues can enhance biologists’ histological interpretations. We developed the Deep learning model for Spatial gene Clusters and Expression, DeepSpaCE and confirmed its performance using the spatial-transcriptome profiles and immunohistochemistry images of consecutive human breast cancer tissue sections. For example, the predicted expression patterns of SPARC, an invasion marker, highlighted a small tumor-invasion region that is difficult to identify using raw data of spatial transcriptome alone because of a lack of measurements. We further developed semi-supervised DeepSpaCE using unlabeled histology images and increased the imputation accuracy of consecutive sections, enhancing applicability for a small sample size. Our method enables users to derive hidden histological characters via spatial transcriptome and gene annotations, leading to accelerated biological discoveries without additional experiments.

Rhodotorula toruloides is an oleaginous yeast with high biotechnological potential. In order to understand the molecular physiology of lipid synthesis in R. toruloides and to advance metabolic engineering, a high-resolution genome is required. We constructed a genome draft of R. toruloides CBS 14, using a hybrid assembly approach, consisting of short and long reads generated by Illumina and Nanopore sequencing, respectively. The genome draft consists of 23 contigs and 3 scaffolds, with a N50 length of 1,529,952 bp, thus largely representing chromosomal organization. The total size is 20,534,857 bp with a GC content of 61.83%. Transcriptomic data from different growth conditions was used to aid species-specific gene annotation. In total we annotated 9,464 genes and identified 11,691 transcripts. Furthermore, we demonstrated the presence of a potential plasmid, an extrachromosomal circular structure of about 11 kb with a copy number about three times as high as the other chromosomes.SignificanceThis obtained high-quality draft genome provides the suitable framework needed for genetic manipulations, and future studies of lipid metabolism and evolution of oleaginous yeasts. The identified extrachromosomal circular DNA may be useful for developing efficient episomal vectors for the manipulation of Rhodotorula yeasts.Data depositionThis project has been deposited at ENA under the accession PRJEB-40807.

The European starling, Sturnus vulgaris, is an ecologically significant, globally invasive avian species that is also suffering from a major decline in its native range. Here, we present the genome assembly and long-read transcriptome of an Australian-sourced European starling (S. vulgaris vAU), and a second North American genome (S. vulgaris vNA), as complementary reference genomes for population genetic and evolutionary characterisation. S. vulgaris vAU combined 10x Genomics linked-reads, low-coverage Nanopore sequencing, and PacBio Iso-Seq full-length transcript scaffolding to generate a 1050 Mb assembly on 1,628 scaffolds (72.5 Mb scaffold N50). Species-specific transcript mapping and gene annotation revealed high structural and functional completeness (94.6% BUSCO completeness). Further scaffolding against the high-quality zebra finch (Taeniopygia guttata) genome assigned 98.6% of the assembly to 32 putative nuclear chromosome scaffolds. Rapid, recent advances in sequencing technologies and bioinformatics software have highlighted the need for evidence-based assessment of assembly decisions on a case-by-case basis. Using S. vulgaris vAU, we demonstrate how the multifunctional use of PacBio Iso-Seq transcript data and complementary homology-based annotation of sequential assembly steps (assessed using a new tool, SAAGA) can be used to assess, inform, and validate assembly workflow decisions. We also highlight some counter-intuitive behaviour in traditional BUSCO metrics, and present Buscomp, a complementary tool for assembly comparison designed to be robust to differences in assembly size and base-calling quality. Finally, we present a second starling assembly, S. vulgaris vNA, to facilitate comparative analysis and global genomic research on this ecologically important species.

The Fathead Minnow (FHM) is one of the most important and widely used model organisms in aquatic toxicology. The lack of a high-quality and well-annotated FHM reference genome, however, has severely hampered the efforts using modem ‘omics approaches with FHM for environmental toxicogenomics studies. We present here a de novo assembled and nearly complete reference of the fathead minnow genome. Compared to the current fragmented and sparsely annotated FHM genome assembly (FHM1), the new highly contiguous and well-annotated FHM reference genome (FHM2) represents a major improvement, having 95.1% of the complete BUSCOs (Benchmarking Universal Single-Copy Orthologs) and a scaffold N50 of 12.0 Mbps. The completeness of gene annotation for the FHM2 reference genome was demonstrated to be comparable to that of the zebrafish (ZF) GRCz11 reference genome. In addition, our comparative genomics analyses between FHM and ZF revealed highly conserved coding regions between two species while discovering much more compact gene structure in FHM than ZF. This study not only provides insights for assembling a highly repetitive AT-rich genome, but also delivers a critical genomic resource essential for toxicogenomics studies in environmental toxicology.

Annotating the genomes of multiple organisms allows us to study their genes as well as the evolution of those genes. While many eukaryotic genome assemblies already include computational gene predictions, these predictions can benefit from review and refinement through manual gene annotation. The Genomics Education Partnership (GEP; thegep.org) has developed an annotation protocol for protein-coding genes that enables undergraduate students and other researchers to create high-quality gene annotations that can be utilized in subsequent scientific investigations. For example, this protocol has been utilized by the GEP faculty to engage undergraduate students in the comparative annotation of genes involved in the insulin signaling pathway in 28 Drosophila species, using D. melanogaster as the informant genome. Students construct gene models using multiple lines of computational and experimental evidence including expression data (e.g., RNA-Seq), sequence similarity (e.g., BLAST, multiple sequence alignments), and computational gene predictions. For quality control, each gene is annotated by at least two students working independently, followed by reconciliation of the submitted gene models by a more experienced student. This article provides an overview of the annotation protocol and describes how discrepancies in student submitted gene models are resolved to produce a final, high-quality gene set suitable for subsequent analyses. This annotation protocol can be adapted to other scientific questions (e.g., expansion of the Drosophila Muller F element) and other species (e.g., parasitoid wasps) to provide additional opportunities for undergraduate students to participate in genomics research. These student annotation efforts can substantially improve the quality of gene annotations in publicly available genomic databases.

Discovering genes involved in complex human genetic disorders is a major challenge. Many have suggested that machine learning (ML) algorithms using gene networks can be used to supplement traditional genetic association-based approaches to predict or prioritize disease genes. However, questions have been raised about the utility of ML methods for this type of task due to biases within the data, and poor real-world performance. Using autism spectrum disorder (ASD) as a test case, we sought to investigate the question: Can machine learning aid in the discovery of disease genes? We collected thirteen published ASD gene prioritization studies and evaluated their performance using known and novel high-confidence ASD genes. We also investigated their biases towards generic gene annotations, like number of association publications. We found that ML methods which do not incorporate genetics information have limited utility for prioritization of ASD risk genes. These studies perform at a comparable level to generic measures of likelihood for the involvement of genes in any condition, and do not out-perform genetic association studies. Future efforts to discover disease genes should be focused on developing and validating statistical models for genetic association, specifically for association between rare variants and disease, rather than developing complex machine learning methods using complex heterogeneous biological data with unknown reliability.

In 2019, a novel coronavirus, SARS-CoV-2/nCoV-19, emerged in Wuhan, China, and has been responsible for the current COVID-19 pandemic. The evolutionary origins of the virus remain elusive and understanding its complex mutational signatures could guide vaccine design and development. As part of the international “CoronaHack” in April 2020 (https://www.coronahack.co.uk/), we employed a collection of contemporary methodologies to compare the genomic sequences of coronaviruses isolated from human (SARS-CoV-2;n=163), bat (bat-CoV;n=215) and pangolin (pangolin-CoV;n=7) available in public repositories. Following de novo gene annotation prediction, analyses of gene-gene similarity network, codon usage bias and variant discovery were undertaken. Strong host-associated divergences were noted in ORF3a, ORF6, ORF7a, ORF8 and S, and in codon usage bias profiles. Lastly, we have characterised several high impact variants (inframe insertion/deletion or stop gain) in bat-CoV and pangolin-CoV populations, some of which are found in the same amino acid position and may be highlighting loci of potential functional relevance.

INTRODUCTION: The NIAGADS Alzheimer9s Genomics Database is an interactive knowledgebase for AD genetics that provides access to GWAS summary statistics datasets deposited at NIAGADS, a national genetics data repository for AD and related dementia (ADRD). METHODS: The website makes available >70 genome-wide summary statistics datasets from GWAS and genome sequencing analysis for AD/ADRD. Variants identified from these datasets are mapped to up-to-date variant and gene annotations from a variety of resources and linked to functional genomics data. The database is powered by a big-data optimized relational database and uses ontologies to consistently annotate study designs and phenotypes, facilitating data harmonization and efficient real-time data analysis and variant or gene report generation. RESULTS: Detailed variant reports provide tabular and interactive graphical summaries of known ADRD associations, as well as highlight variants flagged by the Alzheimer9s Disease Sequencing Project (ADSP). Gene reports provide summaries of co-located ADRD risk-associated variants and have been expanded to include meta-analysis results from aggregate association tests performed by the ADSP allowing us to flag genes with genetic-evidence for AD. DISCUSSION: The GenomicsDB makes available >100 million variant annotations, including ~30 million (5 million novel) variants identified as AD-relevant by ADSP, for browsing and real-time mining via the website or programmatically through a REST API. With a newly redesigned, efficient, search interface and comprehensive record pages linking summary statistics to variant and gene annotations, this resource makes these data both accessible and interpretable, establishing itself as valuable tool for AD research.

The monarch butterfly epitomizes insect biodiversity decline. Understanding the genetic basis of the adaptation of the monarch to a changing environment requires genomic and transcriptomic resources that better reflect its genetic diversity while being informative about gene functionality during life cycle. We report a reference-quality genome assembly from an individual resident at a nonmigratory colony in Mexico, and a new gene annotation and expression atlas for 14,865 genes, including 492 unreported long noncoding RNA (lncRNA) genes, based on RNA-seq data from 14 larval and pupal stages, plus adult morphological sections. Two thirds of the genes show significant expression changes associated with a life stage or section, with lncRNAs being more finely regulated during adulthood than protein-coding genes, and male-biased expression being four times more common than female-biased. The two portions of the heterochromosome Z display distinct patterns of differential expression between the sexes, reflecting that dosage compensation is either absent or incomplete –depending on the sample– in the ancestral but not in the novel portion of the Z. This study represents a major advance in the genomic and transcriptome resources available for D. plexippus while providing the first systematic analysis of its transcriptional program across most of its life cycle.

A contiguous assembly of the inbred ‘EL10’ sugar beet (Beta vulgaris ssp. vulgaris) genome was constructed using PacBio long read sequencing, BioNano optical mapping, Hi-C scaffolding, and Illumina short read error correction. The EL10.1 assembly was 540 Mb, of which 96.7% was contained in nine chromosome-sized pseudomolecules with lengths from 52 to 65 Mb, and 31 contigs with a median size of 282 kb that remained unassembled. Gene annotation incorporating RNAseq data and curated sequences via the MAKER annotation pipeline generated 24,255 gene models. Results indicated that the EL10.1 genome assembly is a contiguous genome assembly highly congruent with the published sugar beet reference genome. Gross duplicate gene analyses of EL10.1 revealed little large-scale intra-genome duplication. Reduced gene copy number for well-annotated gene families relative to other core eudicots was observed, especially for transcription factors. Variation in genome size in B. vulgaris was investigated by flow cytometry among 50 individuals drawn from EL10 progeny and three unrelated germplasm accessions, producing estimates from 633 to 875 Mb/1C. Read depth mapping with short-read whole genome sequences from other sugar beet germplasm suggested that relatively few regions of the sugar beet genome appeared associated with high-copy number variation.

BackgroundExploratory analysis of complex transcriptomic data presents multiple challenges. Many methods often rely on preexisting gene annotations, impeding identification and characterization of new transcripts. Even for a single cell type, comprehending the diversity of RNA species transcribed at each genomic region requires combining multiple datasets, each enriched for specific types of RNA. Currently, examining combinatorial patterns in these data requires time-consuming visual inspection using a genome browser.MethodWe developed a new segmentation and genome annotation (SAGA) method, SegRNA, that integrates data from multiple transcriptome profiling assays. SegRNA identifies recurring combinations of signals across multiple datasets measuring the abundance of transcribed RNAs. Using complementary techniques, SegRNA builds on the Segway SAGA framework by learning parameters from both the forward and reverse DNA strands. SegRNA’s unsupervised approach allows exploring patterns in these data without relying on pre-existing transcript models.ResultsWe used SegRNA to generate the first unsupervised transcriptome annotation of the K562 chronic myeloid leukemia cell line, integrating multiple types of RNA data. Combining RNA-seq, CAGE, and PRO-seq experiments together captured a diverse population of RNAs throughout the genome. As expected, SegRNA annotated patterns associated with gene components such as promoters, exons, and introns. Additionally, we identified a pattern enriched for novel small RNAs transcribed within intergenic, intronic, and exonic regions. We applied SegRNA to FANTOM6 CAGE data characterizing 285 lncRNA knockdowns. Overall, SegRNA efficiently summarizes diverse multi-experiment data.

Improvements in DNA sequencing technology and computational methods have led to a substantial increase in the creation of high-quality genome assemblies of many species. To understand the biology of these genomes, annotation of gene features and other functional elements is essential; however for most species, only the reference genome is well-annotated. One strategy to annotate new or improved genome assemblies is to map or ‘lift over’ the genes from a previously-annotated reference genome. Here we describe Liftoff, a new genome annotation lift-over tool capable of mapping genes between two assemblies of the same or closely-related species. Liftoff aligns genes from a reference genome to a target genome and finds the mapping that maximizes sequence identity while preserving the structure of each exon, transcript, and gene. We show that Liftoff can accurately map 99.9% of genes between two versions of the human reference genome with an average sequence identity >99.9%. We also show that Liftoff can map genes across species by successfully lifting over 98.4% of human protein-coding genes to a chimpanzee genome assembly with 98.7% sequence identity.AvailabilityThe source code for Liftoff is available athttps://github.com/agshumate/Liftoff

The domestic cat (Felis catus) is one of the most popular companion animals in the world. Comprehensive genomic resources will aid the development and application of veterinary medicine including to improve feline health, in particular, to enable precision medicine which is promising in human application. However, currently available cat genome assemblies were mostly built based on the Abyssinian cat breed which is highly inbred and has limited power in representing the vast diversity of the cat population. Moreover, the current reference assembly remains fragmented with sequences contained in thousands of scaffolds. We constructed a reference-grade chromosome-scale genome assembly of a domestic cat, Felis catus genome of American Shorthair breed, Anicom American shorthair 1.0 (AnAms1.0) with high contiguity (scaffold N50 > 120 Mb), by combining multiple advanced genomic technologies, including PacBio long-read sequencing as well as sequence scaffolding by long-range genomic information obtained from Hi-C and optical mapping data. Homology-based and ab initio gene annotation was performed with the Iso-Seq data. Analyzed data is be publicly accessible on Cats genome informatics (Cats-I, https://cat.annotation.jp/), a cat genome database established as a platform to facilitate the accumulation and sharing of genomic resources to improve veterinary care.

Bread wheat (Triticum aestivum) is a major food crop and an important plant system for agricultural genetics research. However, due to the complexity and size of its allohexaploid genome, genomic resources are limited compared to other major crops. The IWGSC recently published a reference genome and associated annotation (IWGSC v1.0, Chinese Spring) that has been widely adopted and utilized by the wheat community. Although this reference assembly represents all 3 wheat subgenomes at chromosome scale, it was derived from short reads, and thus is missing a substantial portion of the expected 16 gigabases of genomic sequence. We earlier published an independent wheat assembly (Triticum 3.1, Chinese Spring) that came much closer in length to the expected genome size, although it was only a contig-level assembly lacking gene annotations. Here, we describe a reference-guided effort to scaffold those contigs into chromosome-length pseudomolecules, add in any missing sequence that was unique to the IWGSC 1.0 assembly, and annotate the resulting pseudomolecules with genes. Our updated assembly, Triticum 4.0, contains 15.07 gigabases of non-gap sequence anchored to chromosomes, which is 1.2 gigabases more than the previous reference assembly. It includes 108,639 genes unambiguously localized to chromosomes, including over 2000 genes that were previously unplaced. We also discovered more than 5700 new genes, all of them duplications in the Chinese Spring genome that are missing from the IWGSC assembly and annotation. The Triticum 4.0 assembly and annotations are freely available at www.ncbi.nlm.nih.gov/bioproject/PRJNA392179.

Disease diagnosis and treatment is challenging in part due to the misalignment of diagnostic categories with the underlying biology of disease. The evaluation of large-scale genomic experimental datasets is a compelling approach to refining the classification of biological concepts, such as disease. Well-established approaches, some of which rely on information theory or network analysis, quantitatively assess relationships among biological entities using gene annotations, structured vocabularies, and curated data sources. However, the gene annotations used in these evaluations are often sparse, potentially biased due to uneven study and representation in the literature, and constrained to the single species from which they were derived. In order to overcome these deficiencies inherent in the structure and sparsity of these annotated datasets, we developed a novel Network Enhanced Similarity Search (NESS) tool which takes advantage of multi-species networks of heterogeneous data to bridge sparsely populated datasets.NESS employs a random walk with restart algorithm across harmonized multi-species data, effectively compensating for sparsely populated and noisy genomic studies. We further demonstrate that it is highly resistant to spurious or sparse datasets and generates significantly better recapitulation of ground truth biological pathways than other similarity metrics alone. Furthermore, since NESS has been deployed as an embedded tool in the GeneWeaver environment, it can rapidly take advantage of curated multi-species networks to provide informative assertions of relatedness of any pair of biological entities or concepts, e.g., gene-gene, gene-disease, or phenotype-disease associations. NESS ultimately enables multi-species analysis applications to leverage model organism data to overcome the challenge of data sparsity in the study of human disease.Availability and ImplementationImplementation available at https://geneweaver.org/ness. Source code freely available at https://github.com/treynr/ness.Author summaryFinding consensus among large-scale genomic datasets is an ongoing challenge in the biomedical sciences. Harmonizing and analyzing such data is important because it allows researchers to mitigate the idiosyncrasies of experimental systems, alleviate study biases, and augment sparse datasets. Additionally, it allows researchers to utilize animal model studies and cross-species experiments to better understand biological function in health and disease. Here we provide a tool for integrating and analyzing heterogeneous functional genomics data using a graph-based model. We show how this type of analysis can be used to identify similar relationships among biological entities such as genes, processes, and disease through shared genomic associations. Our results indicate this approach is effective at reducing biases caused by sparse and noisy datasets. We show how this type of analysis can be used to aid the classification gene function and prioritization of genes involved in substance use disorders. In addition, our analysis reveals genes and biological pathways with shared association to multiple, co-occurring substance use disorders.

Many prokaryotic RNAs are transcribed from loci outside of annotated protein coding genes. Across bacterial species hundreds of short open reading frames antisense to annotated genes show evidence of both transcription and translation, for instance in ribosome profiling data. Determining the functional fraction of these protein products awaits further research, including insights from studies of molecular interactions and detailed evolutionary analysis. There are multiple lines of evidence however that many of these newly discovered proteins are of use to the organism. Condition-specific phenotypes have been characterised for a few. These proteins should be added to genome annotations, and the methods for predicting them standardised. Evolutionary analysis of these typically young sequences also may provide important insights into gene evolution. This research should be prioritised for its exciting potential to uncover large numbers of novel proteins with extremely diverse potential practical uses, including applications in synthetic biology and responding to pathogens.

Background The eukaryotic genome is capable of producing multiple isoforms from a gene by alternative polyadenylation (APA) during pre-mRNA processing. APA in the 3′-untranslated region (3′-UTR) of mRNA produces transcripts with shorter or longer 3′-UTR. Often, 3′-UTR serves as a binding platform for microRNAs and RNA-binding proteins, which affect the fate of the mRNA transcript. Thus, 3′-UTR APA is known to modulate translation and provides a mean to regulate gene expression at the post-transcriptional level. Current bioinformatics pipelines have limited capability in profiling 3′-UTR APA events due to incomplete annotations and a low-resolution analyzing power: widely available bioinformatics pipelines do not reference actionable polyadenylation (cleavage) sites but simulate 3′-UTR APA only using RNA-seq read coverage, causing false positive identifications. To overcome these limitations, we developed APA-Scan, a robust program that identifies 3′-UTR APA events and visualizes the RNA-seq short-read coverage with gene annotations. Methods APA-Scan utilizes either predicted or experimentally validated actionable polyadenylation signals as a reference for polyadenylation sites and calculates the quantity of long and short 3′-UTR transcripts in the RNA-seq data. APA-Scan works in three major steps: (i) calculate the read coverage of the 3′-UTR regions of genes; (ii) identify the potential APA sites and evaluate the significance of the events among two biological conditions; (iii) graphical representation of user specific event with 3′-UTR annotation and read coverage on the 3′-UTR regions. APA-Scan is implemented in Python3. Source code and a comprehensive user’s manual are freely available at https://github.com/compbiolabucf/APA-Scan. Result APA-Scan was applied to both simulated and real RNA-seq datasets and compared with two widely used baselines DaPars and APAtrap. In simulation APA-Scan significantly improved the accuracy of 3′-UTR APA identification compared to the other baselines. The performance of APA-Scan was also validated by 3′-end-seq data and qPCR on mouse embryonic fibroblast cells. The experiments confirm that APA-Scan can detect unannotated 3′-UTR APA events and improve genome annotation. Conclusion APA-Scan is a comprehensive computational pipeline to detect transcriptome-wide 3′-UTR APA events. The pipeline integrates both RNA-seq and 3′-end-seq data information and can efficiently identify the significant events with a high-resolution short reads coverage plots.

A simplistic understanding of the central dogma falls short in correlating the number of genes in the genome to the number of proteins in the proteome. Post-transcriptional alternative splicing contributes to the complexity of proteome and are critical in understanding gene expression. mRNA-sequencing (RNA-seq) has been widely used to study the transcriptome and provides opportunity to detect alternative splicing events among different biological conditions. Despite the popularity of studying transcriptome variants with RNA-seq, few efficient and user-friendly bioinformatics tools have been developed for the genome-wide detection and visualization of alternative splicing events. We have developed AS-Quant (Alternative Splicing Quantitation), a robust program to identify alternative splicing events and visualize the short-read coverage with gene annotations. AS-Quant works in three steps: (i) calculate the read coverage of the potential splicing exons and the corresponding gene; (ii) categorize the splicing events into five different types based on annotation, and assess the significance of the events between two biological conditions; (iii) generate the short reads coverage plot with a complete gene annotation for user specified splicing events. To evaluate the performance, two significant alternative splicing events identified by AS-Quant between two biological contexts were validated by RT-PCR.ImplementationAS-Quant is implemented in Python. Source code and a comprehensive user’s manual are freely available at https://github.com/CompbioLabUCF/AS-Quant

The availability of reference genomes has revolutionized the study of biology. Multiple competing technologies have been developed to improve the quality and robustness of genome assemblies during the last decade. The two widely-used long read sequencing providers – Pacbio (PB) and Oxford Nanopore Technologies (ONT) – have recently updated their platforms: PB enable high throughput HiFi reads with base-level resolution with >99% and ONT generated reads as long as 2 Mb. We applied the two up-to-date platforms to one single rice individual, and then compared the two assemblies to investigate the advantages and limitations of each. The results showed that ONT ultralong reads delivered higher contiguity producing a total of 18 contigs of which 10 were assembled into a single chromosome compared to that of 394 contigs and three chromosome-level contigs for the PB assembly. The ONT ultralong reads also prevented assembly errors caused by long repetitive regions for which we observed a total 44 genes of false redundancies and 10 genes of false losses in the PB assembly leading to over/under-estimations of the gene families in those long repetitive regions. We also noted that the PB HiFi reads generated assemblies with considerably less errors at the level of single nucleotide and small InDels than that of the ONT assembly which generated an average 1.06 errors per Kb assembly and finally engendered 1,475 incorrect gene annotations via altered or truncated protein predictions.

BackgroundThe King Scallop, Pecten maximus, is distributed in shallow waters along the Atlantic coast of Europe. It forms the basis of a valuable commercial fishery and its ubiquity means that it plays a key role in coastal ecosystems and food webs. Like other filter feeding bivalves it can accumulate potent phytotoxins, to which it has evolved some immunity. The molecular origins of this immunity are of interest to evolutionary biologists, pharmaceutical companies and fisheries management.FindingsHere we report the genome sequencing of this species, conducted as part of the Wellcome Sanger 25 Genomes Project. This genome was assembled from PacBio reads and scaffolded with 10x Chromium and Hi-C data, and its 3,983 scaffolds have an N50 of 44.8 Mb (longest scaffold 60.1 Mb), with 92% of the assembly sequence contained in 19 scaffolds, corresponding to the 19 chromosomes found in this species. The total assembly spans 918.3 Mb, and is the best-scaffolded marine bivalve genome published to date, exhibiting 95.5% recovery of the metazoan BUSCO set. Gene annotation resulted in 67,741 gene models. Analysis of gene content revealed large numbers of gene duplicates, as previously seen in bivalves, with little gene loss, in comparison with the sequenced genomes of other marine bivalve species.ConclusionsThe genome assembly of Pecten maximus and its annotated gene set provide a high-quality platform for a wide range of investigations, including studies on such disparate topics as shell biomineralization, pigmentation, vision and resistance to algal toxins. As a result of our findings we highlight the sodium channel gene Nav1, known as a gene conferring resistance to saxitoxin and tetrodotoxin, as a candidate for further studies investigating immunity to domoic acid.

The Microbe Directory (TMD) is a comprehensive database of annotations for microbial species collating features such as gram-stain, capsid-symmetry, resistance to antibiotics and more. This work presents a significant improvement to the original Microbe Directory (2018). This update adds 68,852 taxa, many new annotation features, an interface for the statistical analysis of microbiomes based on TMD features, and presents a portal for the broad community to add or correct entries. This update also adds curated lists of gene annotations which are useful for characterizing microbial genomes. Much of the new data in TMD is sourced from a set of databases and independent studies collating these data into a single quality controlled and curated source. This will allow researchers and clinicians to have easier access to microbial data and provide for the possibility of serendipitous discovery of otherwise unexpected trends.

Prokaryotic genome annotation is heavily dependent on automated gene annotation pipelines that are prone to propagate errors and underestimate genome complexity. We describe an optimized proteogenomic workflow that uses ribo-seq and proteomic data of Salmonella Typhiumurium to identify unannotated proteins or alternative protein forms raised upon alternative translation initiation (i.e. N-terminal proteoforms). This data analysis encompasses the searching of co-fragmenting peptides and post-processing with extended peptide-to-spectrum quality features including comparison to predicted fragment ion intensities. When applying this strategy, an enhanced proteome-depth is achieved as well as greater confidence for unannotated peptide hits. We demonstrate the general applicability of our pipeline by re-analyzing public Deinococcus radiodurans datasets. Taken together, systematic re-analysis using available prokaryotic (proteome) datasets holds great promise to assist in experimentally-based genome annotation.

The evolutionary rapid emergence of new genes gives rise to "orphan genes" that share no sequence homology to genes in closely related genomes. These genes provide organisms with a reservoir of genetic elements to quickly respond to changing selection pressures. Gene annotation pipelines that combine ab initio machine-learning with sequence homology-based searches are efficient in identifying basal genes with a long evolutionary history. However, their ability to identify orphan genes and other young genes has not been systematically evaluated. Here, we classify the phylostrata of curated Arabidopsis thaliana genes and use these to assess the ability of two of the most prevalent annotation pipelines, MAKER and BRAKER, to predict orphans and other young genes. MAKER predictions are highly dependent on the RNA-Seq evidence, predicting between 11% and 60% of the orphan-genes and 95% to 98% of basal-genes in the annotated genome of Arabidopsis. In contrast, BRAKER consistently predicts 33% of orphan-genes and 98% of basal-genes. A less used method to identify genes is by directly aligning RNA-Seq data to the genome sequence. We present a Findable, Accessible, Interoperable and Reusable (FAIR) approach, called BIND, that mitigates the under-prediction of orphan genes. BIND combines BRAKER predictions with direct evidence-based inference of transcripts based on RNA-Seq alignments to the genome. BIND increases the number and accuracy of orphan gene predictions, identifying 68% of Araport11-annotated orphan genes and 99% of the conserved genes.

BackgroundUnderstanding transcriptome is critical for explaining functional as well as regulatory roles of genomic regions. Current methods for the identification of transcription unit (TU) uses RNA-seq which, however, requires large quantities of mRNA limiting the identification of inherently unstable TUs e.g. for miRNA precursors. This problem can be resolved by chromatin based approaches due to a correlation between histone modifications and transcription.ResultsHere we introduce EPIGENE, a novel chromatin segmentation method for the identification of active TUs using transcription associated histone modifications. Unlike existing chromatin segmentation approaches, EPIGENE uses a constrained, semi-supervised multivariate hidden markov model (HMM) that models the observed combination of histone modifications using a product of independent Bernoulli random variables, to identify active TUs. Our results show that EPIGENE can identify genome-wide TUs unbiasedly. EPIGENE predicted TUs showed an enrichment of RNA Polymerase II in transcription start site and gene body indicating that they have been transcribed. Comprehensive validation with existing annotations revealed that 93% of EPIGENE TUs can be explained by existing gene annotations and 5% of EPIGENE TUs in HepG2 can be explained by microRNA annotations. EPIGENE outperforms existing RNA-Seq based approaches in TU prediction precision across human cell lines. Finally, we identify 381 novel TUs in K562 and 43 novel cell-specific TUs all of which are supported by RNA Polymerase II data.ConclusionsWe demonstrate the applicability of HMM to identify genome-wide active TUs and provides valuable information about unannotated TUs. EPIGENE is an open-source method and is freely available at: https://github.com/imbeLab/EPIGENE.

Ribosome profiling (RIBO-seq) in prokaryotes has the potential to facilitate accurate detection of translation initiation sites, to increase understanding of translational dynamics, and has already allowed detection of many unannotated genes. However, protocols for ribosome profiling and corresponding data analysis are not yet standardized. To better understand the influencing factors, we analysed 48 ribosome profiling samples from 9 studies on E. coli K12 grown in LB medium. We particularly investigated the size selection step in each experiment since the selection for ribosome-protected footprints (RPFs) has been performed at various read lengths. We suggest choosing a size range between 22-30 nucleotides in order to obtain protein-coding fragments. In order to use RIBO-seq data for improving gene annotation of weakly expressed genes, the total amount of reads mapping to protein-coding sequences and not rRNA or tRNA is important, but no consensus about the appropriate sequencing depth has been reached. Again, this causes significant variation between studies. Our analysis suggests that 20 million non rRNA/tRNA mapping reads are required for global detection of translated annotated genes. Further, we highlight the influence of drug induced ribosome stalling, causing bias at translation start sites. Drug induced stalling may be especially useful for detecting weakly expressed genes. These suggestions should improve both gene detection and the comparability of resulting ribosome profiling datasets.

MotivationEffective and efficient exploration of numeric data and annotations as a function of genomic position requires specialized software.ResultsWe present G-Graph, an interactive genomic scatter plot viewer. G-Graph stacks or tiles multiple data series in one graph using different colors and markers. It displays gene annotation and other metadata, allows easy changes to the appearance of data series, implements stack-based undo functionality, and saves user-selected application views as image and pdf files. G-Graph delivers smooth and rapid scrolling and zooming even for datasets with millions of points and line segments. The primary target user is a researcher examining many copy number profiles to identify potentially deleterious variants. G-Graph runs under Linux, Mac OSX and Windows.Availabilityhttps://github.com/docpaa/mumdex/ or https://mumdex.com/ggraph/Contactandrewsp@cshl.edu (or paa@drpa.us)

Gene losses provide an insightful route for studying the morphological and physiological adaptations of species, but their discovery is challenging. Existing genome annotation tools and protein databases focus on annotating intact genes and do not attempt to distinguish nonfunctional genes from genes missing annotation due to sequencing and assembly artifacts. Previous attempts to annotate gene losses have required significant manual curation, which hampers their scalability for the ever-increasing deluge of newly sequenced genomes. Using extreme sequence erosion (deletion and non-synonymous substitution) as an unambiguous signature of loss, we developed an automated approach for detecting high-confidence protein-coding gene loss events across a species tree. Our approach relies solely on gene annotation in a single reference genome, raw assemblies for the remaining species to analyze, and the associated phylogenetic tree for all organisms involved. Using the hg38 human assembly as a reference, we discovered over 500 unique human genes affected by such high-confidence erosion events in different clades across 58 mammals. While most of these events likely have benign consequences, we also found dozens of clade-specific gene losses that result in early lethality in outgroup mammals or are associated with severe congenital diseases in humans. Our discoveries yield intriguing potential for translational medical genetics and for evolutionary biology, and our approach is readily applicable to large-scale genome sequencing efforts across the tree of life.

Transcriptional profiling of hematopoietic cell subpopulations has helped characterize the developmental stages of the hematopoietic system and the molecular basis of malignant and non-malignant blood diseases for the past three decades. The introduction of high-throughput RNA sequencing has increased knowledge of the full repertoire of RNA molecules in hematopoietic cells of different types, without relying on prior gene annotation. Here, we introduce the analysis of the BLUEPRINT consortium gene expression data for mature hematopoietic cells, comprising 90 total RNA and 32 small RNA sequencing experiments, from 27 different cell types. We used these data to describe the transcriptional profile of each we used guided transcriptome assembly to extend the annotation of the transcribed genome, which led to the identification of hundreds of novel non-coding RNA genes, which display a high degree of cell type specificity. We also characterized the expression of circular RNAs and found that these are also highly cell type specific. This resource refines the active transcriptional landscape of mature hematopoietic cells, highlights abundant genes and transcriptional isoforms for each cell type, and provides valuable data and visualisation tools for the scientific community working on hematological development and diseases.

We present WhichTF, a novel computational method to identify dominant transcription factors (TFs) from chromatin accessibility measurements. To rank TFs, WhichTF integrates high-confidence genome-wide computational prediction of TF binding sites based on evolutionary sequence conservation, putative gene-regulatory models, and ontology-based gene annotations. Applying WhichTF, we find that the identified dominant TFs have been implicated as functionally important in well-studied cell types, such as NF-κB family members in lymphocytes and GATA factors in cardiac tissue. To distinguish the transcriptional regulatory landscape in closely related samples, we devise a differential analysis framework and demonstrate its utility in lymphocyte, mesoderm developmental, and disease cells. We also find TFs known for stress response in multiple samples, suggesting routine experimental caveats that warrant careful consideration. WhichTF yields biological insight into known and novel molecular mechanisms of TF-mediated transcriptional regulation in diverse contexts, including human and mouse cell types, cell fate trajectories, and disease-associated tissues.

Motivation Rapid development in long read sequencing and scaffolding technologies is accelerating the production of reference-quality assemblies for large eukaryotic genomes. However, haplotype divergence in regions of high heterozygosity often results in assemblers creating two copies rather than one copy of a region, leading to breaks in contiguity and compromising downstream steps such as gene annotation. Several tools have been developed to resolve this problem. However, they either only focus on removing contained duplicate regions, also known as haplotigs, or fail to use all the relevant information and hence make errors. Results Here we present a novel tool “purge_dups” that uses sequence similarity and read depth to automatically identify and remove both haplotigs and heterozygous overlaps. In comparison with the current standard, purge_haplotigs, we demonstrate that purge_dups can reduce heterozygous duplication and increase assembly continuity while maintaining completeness of the primary assembly. Moreover, purge_dups is fully automatic and can be easy integrated into assembly pipelines. Availability The source code is written in C and is available at https://github.com/dfguan/purge_dups . Contact ydwang@hit.edu.cn , rd109@cam.ac.uk, This work was supported by the National Key Research and Development Program of China [2017YFC0907503, 2018YFC0910504 and 2017YFC1201201 to D.G. and Y.W.]; China Scholarship Council to D.G.; Wellcome Trust [WT207492 to S.A.M. and R.D., and WT206194 to J.W. and K.H.].

With the proceedings in the genome study in rubber tree, more and more data were produced. However, these data are still deposited in public databases as raw format, and the utilization of these NGS data is hindered for the researchers in rubber tree community. HeveaDB was therefore constructed and maintained under the organization of International Rubber Research and Development Board. The HeveaDB 1.0 (http://hevea.catas.cn) mainly stores 4 versions of Hevea draft genome, 99 NGS transcriptomes, 18451annotated EST sequences, 12 curated gene families, 30200 gene annotations, 5049 wild germplasm phenotype data, and 18328 Wickham rubber clonal information. Several bioinformatic tools are integrated in the database to facilitate the utilization of the data for the users who are not skilled in bioinformatics. With the periodical update and functional improvement, the HeveaDB will serve as a potential platform for genomic studies and breeding technology in rubber tree.

Long non-coding RNAs (lncRNAs) have emerged as key coordinators of biological and cellular processes. Characterizing lncRNA expression across cells and tissues is key to understanding their role in determining phenotypes including human diseases. We present here FC-R2, a comprehensive expression atlas across a broadly-defined human transcriptome, inclusive of over 109,000 coding and non-coding genes, as described in the FANTOM CAGE-Associated Transcriptome (FANTOM-CAT) study. This atlas greatly extends the gene annotation used in the original recount2 resource. We demonstrate the utility of the FC-R2 atlas by reproducing key findings from published large studies and by generating new results across normal and diseased human samples. In particular, we (a) identify tissue specific transcription profiles for distinct classes of coding and non-coding genes, (b) perform differential expression analyses across thirteen cancer types, providing new insights linking promoter and enhancer lncRNAs expression to tumor pathogenesis, and (c) confirm the prognostic value of several enhancers in cancer. Comprised of over 70,000 samples, the FC-R2 atlas will empower other researchers to investigate functions and biological roles of both known coding genes and novel lncRNAs. Most importantly, access to the FC-R2 atlas is available from https://jhubiostatistics.shinyapps.io/recount/, the recount Bioconductor package, and http://marchionnilab.org/fcr2.html.

The early infantile epileptic encephalopathies (EIEE) are a group of rare, severe neurodevelopmental disorders, where even the most thorough sequencing studies leave 60-65% of patients without a molecular diagnosis. Here, we explore the incompleteness of transcript models used for exome and genome analysis as one potential explanation for lack of current diagnoses. Therefore, we have updated the GENCODE gene annotation for 191 epilepsy-associated genes, using human brain-derived transcriptomic libraries and other data to build 3,550 novel putative transcript models. The extended transcriptional footprint of these genes allowed for 294 intronic or intergenic variants, found in human mutation databases, to be reclassified as exonic, while a further 70 intronic variants were reclassified as splice-site proximal. Using SCN1A as a case study due to its close phenotype/genotype correlation with Dravet syndrome, we screened 122 people with Dravet syndrome, or a similar phenotype, with a panel of novel exon sequences representing eight established genes and identified two de novo SCN1A variants that now, through improved gene annotation can be ascribed to residing among novel exons. These two (from 122 screened patients, 1.6%) new molecular diagnoses carry significant clinical implications. Furthermore, we identified a previously-classified SCN1A intronic Dravet-associated variant that now lies within a deeply conserved novel exon. Our findings illustrate the potential gains of thorough gene annotation in improving diagnostic yields for genetic disorders. We would expect to find new molecular diagnoses in our 191 genes that were originally suspected by clinicians for patients, with a negative diagnosis.

The sequence and assembly of human genomes using long-read sequencing technologies has revolutionized our understanding of structural variation and genome organization. We compared the accuracy, continuity, and gene annotation of genome assemblies generated from either high-fidelity (HiFi) or continuous long-read (CLR) datasets from the same complete hydatidiform mole human genome. We find that the HiFi sequence data assemble an additional 10% of duplicated regions and more accurately represent the structure of tandem repeats, as validated with orthogonal analyses. As a result, an additional 5 Mbp of pericentromeric sequences are recovered in the HiFi assembly, resulting in a 2.5-fold increase in the NG50 within 1 Mbp of the centromere (HiFi 480.6 kbp, CLR 191.5 kbp). Additionally, the HiFi genome assembly was generated in significantly less time with fewer computational resources than the CLR assembly. Although the HiFi assembly has significantly improved continuity and accuracy in many complex regions of the genome, it still falls short of the assembly of centromeric DNA and the largest regions of segmental duplication using existing assemblers. Despite these shortcomings, our results suggest that HiFi may be the most effective stand-alone technology for de novo assembly of human genomes.

High throughput assays allow researchers to identify sets of genes related to experimental conditions or phenotypes of interest. These gene sets are frequently subjected to functional interpretation using databases of gene annotations. Recent approaches have extended this approach to also consider networks of gene-gene relationships and interactions when attempting to characterize properties of a gene set. We present here a supervised learning algorithm for gene set analysis, called ‘GeneSet MAPR’, that for the first time explicitly considers the patterns of direct as well as indirect relationships present in the network to quantify gene-gene similarities and then report shared properties of the gene set. Our extensive evaluations show that GeneSet MAPR performs better than other network-based methods for the task of identifying genes related to a given gene set, enabling more reliable functional characterizations of the gene set. When applied to the set of response-associated genes from a triple negative breast cancer study, GeneSet MAPR uncovers gene families such as claudins, kallikreins, and collagen type alpha chains related to patient’s response to treatment, and which are not uncovered with traditional analysis.

The recent explosion of transcriptomics and proteomic data have resulted in vast amounts of datasets without connection and sometime too large to be easily analysed. Integration between datasets and analysis of an extracted datasets are limiting factors which need to be solved in order to make full use of the data and to connect data.ExpressWeb is an online web tool that combines a Taylor clustering of expressed data sets to extract gene network with gene annotations to visualise the co-expression network. Data sets can become from personal or publically experiments. ExpressWeb allows to easily compute clustering on expression data and provides friendly and useful visualisation tools as heatmaps, graphs and networks, generating output images which can be used for scientific publications.

ObjectivesBlack Bengal goat (Capra hircus), a member of the Bovidae family with the unique traits of high prolificacy, skin quality and low demand for food is the most socioeconomically significant goat breed in Bangladesh. Furthermore, the aptitude of adaptation and disease resistance capacity of it is highly notable which makes its whole genome information an area of research interest.Data descriptionThe genomic DNA of local (Chittagong, Bangladesh) healthy Black Bengal goat (Capra hircus) was extracted and then sequenced. The de novo assembly and structural annotations are being presented here. Sequencing was done using Illumina sequencing platform and the draft genome assembled is about 3.04 Gb. 26458 Genes were annotated using Maker gene annotations tool which predicted BUSCO Gene models. Universal Single Copy Orthologs refer 82.5% completeness of the assembled genome.

The Integration of known nucleotide variations in breeding programs and medical assessments demands the ability to scan and validate thousands of gene-affected SNPs. Designing PCR primers targeting these SNPs require computational tools could generate accurate and target-specific primers using multidisciplinary data types with high accuracy. KASP-Hedgehog is a local-installation, simple and routine tool for allele-specific PCR primer design. KASP-Hedgehog gives user the ability to design KASP primer from a variety of genomic data, extract bi-allelic SNPs for genomic sequences with unknown nucleotide variations and select SNPs with potential effect depending on gene annotation. This tool can use user-provided or self-produced SNPs database to create allele-specific primers with degenerate structure in order to enhance PCR assay selectivity and increase amplification reaction. Additionally, it run an in silico PCR test for designed primers against provided FASTA sequences in order to increase primers selectivity and provide user more information. Moreover, it has interactive and user-friendly graphical user interface (GUI) in addition to a command-line package could be integrated in different bioinformatics pipelines.

There is growing evidence to suggest that human gene annotation remains incomplete, with a disproportionate impact on the brain transcriptome. We used RNA-sequencing data from GTEx to detect novel transcription in an annotation-agnostic manner across 13 human brain regions and 28 human tissues. We found that genes highly expressed in brain are significantly more likely to be re-annotated, as are genes associated with Mendelian and complex neurodegenerative disorders. We improved the annotation of 63% of known OMIM-morbid genes and 65% of those with a neurological phenotype. We determined that novel transcribed regions, particularly those identified in brain, tend to be poorly conserved across mammals but are significantly depleted for genetic variation within humans. As exemplified by SNCA, we explored the implications of re-annotation for Mendelian and complex Parkinson’s disease. We validated in silico and experimentally a novel, brain-specific, potentially protein-coding exon of SNCA. We release our findings as tissue-specific transcriptomes in BED format and via vizER: http://rytenlab.com/browser/app/vizER. Together these resources will facilitate basic genomics research with the greatest impact on neurogenetics.

Despite the reduced read length, the so-called Next-Generation Sequencing (NGS) of second-generation has allowed rapid and complete genome characterization of many species. MinION (Oxford Nanopore Technologies), a portable third-generation NGS device, enables sequencing of long DNA fragments at low cost. Here we used a low-coverage MinION sequencing in combination with short-read NGS to improve genome assembly. We tested this possibility by using MinION R9.0 with Rapid 1D kit and MiSeq with >300X paired-end 300 bp reads (Illumina, Inc.) for the genome assembly of a Streptococcus agalactiae clinical isolate (2.2 Mb). With as few as 1,171 MinION reads that covered the genome at 2.4X (the longest read being 186 Kb long), the hybrid assembly combining MinION and Illumina reads increased the N50 by 4.9-fold compared to the assembly using Illumina data alone. Almost 50% of the genome was represented into a single contig (1.02 Mb). Besides, this allowed the full reconstruction of mobile elements, including a plasmid, and improved gene annotation. Taken together, our results support that shallow MinION sequencing combined with high-throughput second-generation NGS constitutes a cost-efficient strategy for the assembly of whole genomes.

SummaryHayai-Annotation Plants is a browser-based interface for an ultra-fast and accurate gene annotation system for plant species using R. The pipeline combines the sequence-similarity searches, using USEARCH against UniProtKB (taxonomy Embryophyta), with a functional annotation step. Hayai-Annotation Plants provides five layers of annotation: 1) gene name; 2) gene ontology terms consisting of its three main domains (Biological Process, Molecular Function, and Cellular Component); 3) enzyme commission number; 4) protein existence level; 5) and evidence type. In regard to speed and accuracy, Hayai-Annotation Plants annotated Arabidopsis thaliana (Araport11, representative peptide sequences) within five minutes with an accuracy of 96.4 %.Availability and ImplementationThe software is implemented in R and runs on Macintosh and Linux systems. It is freely available at https://github.com/kdri-genomics/Hayai-Annotation-Plants under the GPLv3 license.

The initial characterization and clustering of biological samples is a critical step in the analysis of any transcriptomic study. In many studies, principal component analysis (PCA) is the clustering algorithm of choice to predict the relationship of samples or cells based solely on differential gene expression. In addition to the pure quality evaluation of the data, a PCA can also provide initial insights into the biological background of an experiment and help researchers to interpret the data and design the subsequent computational steps accordingly. However, to avoid misleading clusterings and interpretations, an appropriate selection of the underlying gene sets to build the PCA and the choice of the most fitting principal components for the visualization are crucial parts. Here, we present PCAGO, an easy-to-use and interactive web service to analyze gene quantification data derived from RNA sequencing (RNA-Seq) experiments with PCA. The tool includes features such as read-count normalization, filtering of read counts by gene annotation, and various visualization options. Additionally, PCAGO helps to select appropriate parameters such as the number of genes and principal components to create meaningful visualizations.Availability and implementationThe web service is implemented in R and freely available at pcago.bioinf.uni-jena.de.Contactmartin.hoelzer@uni-jena.de

Motivation: Published genome annotations are filled with erroneous gene models that represent issues associated with frame, start side identification, splice sites, and related structural features. The source of these inconsistencies can often be traced to translated text file formats designed to describe long read alignments and predicted gene structures. The majority of gene prediction frameworks do not provide downstream filtering to remove problematic gene annotations, nor do they represent these annotations in a format consistent with current file standards. In addition, these frameworks lack consideration for functional attributes, such as the presence or absence of protein domains which can be used for gene model validation. Summary: To provide oversight to the increasing number of published genome annotations, we present gFACs as a software package to filter, analyze, and convert predicted gene models and alignments. gFACs operates across a wide range of alignment, analysis, and gene prediction software inputs 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. Availability and Implementation: gFACs is freely available and implemented in Perl with support from BioPerl libraries: https://gitlab.com/PlantGenomicsLab/gFACs

Disruptive innovations in long-range, cost-effective direct template nucleic acid sequencing are transforming clinical and diagnostic medicine. A multidrug resistant strain and a pan-susceptible strain ofMannheimia haemolytica, isolated from pneumonic bovine lung samples, were respectively sequenced at 146x and 111x coverage with Oxford Nanopore Technologies MinION.De novoassembly produced a complete genome for the non-resistant strain and a nearly complete assembly for the drug resistant strain. Functional annotation using RAST (Rapid Annotations using Subsystems Technology), CARD (Comprehensive Antibiotic Resistance Database) and ResFinder databases identified genes conferring resistance to different classes of antibiotics including beta lactams, tetracyclines, lincosamides, phenicols, aminoglycosides, sulfonamides and macrolides. Antibiotic resistance phenotypes of theM. haemolyticastrains were confirmed with minimum inhibitory concentration (MIC) assays. The sequencing capacity of highly portable MinION devices was verified by sub-sampling sequencing reads; potential for antimicrobial resistance determined by identification of resistance genes in the draft assemblies with as little as 5,437 MinION reads corresponded to all classes of MIC assays. The resulting quality assemblies and AMR gene annotation highlight efficiency of ultra long-read, whole-genome sequencing (WGS) as a valuable tool in diagnostic veterinary medicine.