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The Gene Ontology (GO) knowledgebase (http://geneontology.org) is a comprehensive resource concerning the functions of genes and gene products (proteins and noncoding RNAs). GO annotations cover genes from organisms across the tree of life as well as viruses, though most gene function knowledge currently derives from experiments carried out in a relatively small number of model organisms. Here, we provide an updated overview of the GO knowledgebase, as well as the efforts of the broad, international consortium of scientists that develops, maintains, and updates the GO knowledgebase. The GO knowledgebase consists of three components: (1) the GO—a computational knowledge structure describing the functional characteristics of genes; (2) GO annotations—evidence-supported statements asserting that a specific gene product has a particular functional characteristic; and (3) GO Causal Activity Models (GO-CAMs)—mechanistic models of molecular “pathways” (GO biological processes) created by linking multiple GO annotations using defined relations. Each of these components is continually expanded, revised, and updated in response to newly published discoveries and receives extensive QA checks, reviews, and user feedback. For each of these components, we provide a description of the current contents, recent developments to keep the knowledgebase up to date with new discoveries, and guidance on how users can best make use of the data that we provide. We conclude with future directions for the project.

Long noncoding RNAs (lncRNAs) have been identified in *Plasmodium falciparum*, the parasitic cause for life-threatening malaria, yet their role remains largely undiscovered. Through interactions with nucleic acids and proteins, lncRNAs can modulate gene expression at the transcriptional, post-transcriptional, translational, and post-translational levels. Determining the role of lncRNAs in the regulation of the *P. falciparum* transcriptome and proteome is imperative to further our understanding of gene regulation in the parasite. The characterisation of *P. falciparum* lncRNAs has been hindered by an incomplete annotation and the absence of disruption methods that together would permit high-throughput systematic knockdown of lncRNAs. During my PhD, I addressed these challenges to enable the study of *P. falciparum* lncRNAs *in vitro*. I generated a high-quality lncRNA annotation using manual curation of sequencing data generated at the Sanger Institute, along with supportive datasets from the literature. I evaluated CRISPR-based approaches for *in vitro* disruption of lncRNAs including gene knockout, knockdown, and interference. CRISPR-associated enzymes were explored including commonly used DNA-cutting enzymes (Cas9), inactivated enzymes to block transcription (dCpf1) and enzymes that target RNA directly (Cas13), the latter of which had not been applied to *Plasmodium*. Furthermore, I implemented these tools to demonstrate the feasibility of lncRNA studies in *P. falciparum*. I interrogated a set of lncRNAs that were selected based on predicted biological significance and targetability using dCpf1. LncRNA-depleted parasites were phenotypically characterised by assessing changes in fitness, drug resistance, gametocytogenesis and expression. I identified potential roles for specific lncRNAs in drug resistance and gametocytogenesis. By developing bioinformatics and molecular tools, this work enables future studies elucidating the specific roles of lncRNAs in *P. falciparum*. Understanding the transcriptome and gene regulation will inform the development of novel interventions for the control and eradication of malaria, which remains a serious global health concern.

Publisher Copyright: © 2023 The Authors. Horizontal gene transfer (HGT) and the resulting patterns of gene gain and loss are a fundamental part of bacterial evolution. Investigating these patterns can help us to understand the role of selection in the evolution of bacterial pangenomes and how bacteria adapt to a new niche. Predicting the presence or absence of genes can be a highly error-prone process that can con-found efforts to understand the dynamics of horizontal gene transfer. This review discusses both the challenges in accurately constructing a pangenome and the potential consequences errors can have on downstream analyses. We hope that by sum-marizing these issues researchers will be able to avoid potential pitfalls, leading to improved bacterial pangenome analyses.

The Gene Ontology (GO) knowledgebase (http://geneontology.org) is a comprehensive resource concerning the functions of genes and gene products (proteins and noncoding RNAs). GO annotations cover genes from organisms across the tree of life as well as viruses, though most gene function knowledge currently derives from experiments carried out in a relatively small number of model organisms. Here, we provide an updated overview of the GO knowledgebase, as well as the efforts of the broad, international consortium of scientists that develops, maintains, and updates the GO knowledgebase. The GO knowledgebase consists of three components: (1) the GO-a computational knowledge structure describing the functional characteristics of genes; (2) GO annotations-evidence-supported statements asserting that a specific gene product has a particular functional characteristic; and (3) GO Causal Activity Models (GO-CAMs)-mechanistic models of molecular "pathways" (GO biological processes) created by linking multiple GO annotations using defined relations. Each of these components is continually expanded, revised, and updated in response to newly published discoveries and receives extensive QA checks, reviews, and user feedback. For each of these components, we provide a description of the current contents, recent developments to keep the knowledgebase up to date with new discoveries, and guidance on how users can best make use of the data that we provide. We conclude with future directions for the project.

Functional genomic strategies have become fundamental for annotating gene function and regulatory networks. Here, we combined functional genomics with proteomics by quantifying protein abundances in a genome-scale knockout library in Saccharomyces cerevisiae, using data-independent acquisition mass spectrometry. We find that global protein expression is driven by a complex interplay of (1) general biological properties, including translation rate, protein turnover, the formation of protein complexes, growth rate, and genome architecture, followed by (2) functional properties, such as the connectivity of a protein in genetic, metabolic, and physical interaction networks. Moreover, we show that functional proteomics complements current gene annotation strategies through the assessment of proteome profile similarity, protein covariation, and reverse proteome profiling. Thus, our study reveals principles that govern protein expression and provides a genome-spanning resource for functional annotation.

We present a genome assembly from an individual male Thyatira batis (the peach-blossom moth; Arthropoda; Insecta; Lepidoptera; Drepanidae). The genome sequence is 315 megabases in span. The majority of the assembly (99.68%) is scaffolded into 31 chromosomal pseudomolecules, with the Z sex chromosome assembled. The mitochondrial genome was also assembled and is 15.4 kilobases in length. Gene annotation of this assembly on Ensembl has identified 12,238 protein coding genes.

Background Differential expression (DE) analysis of RNA-seq data typically depends on gene annotations. Different sets of gene annotations are available for the human genome and are continually updated–a process complicated with the development and application of high-throughput sequencing technologies. However, the impact of the complexity of gene annotations on DE analysis remains unclear. Results Using “mappability”, a metric of the complexity of gene annotation, we compared three distinct human gene annotations, GENCODE, RefSeq, and NONCODE, and evaluated how mappability affected DE analysis. We found that mappability was significantly different among the human gene annotations. We also found that increasing mappability improved the performance of DE analysis, and the impact of mappability mainly evident in the quantification step and propagated downstream of DE analysis systematically. Conclusions We assessed how the complexity of gene annotations affects DE analysis using mappability. Our findings indicate that the growth and complexity of gene annotations negatively impact the performance of DE analysis, suggesting that an approach that excludes unnecessary gene models from gene annotations improves the performance of DE analysis.

Ustilaginoidea virens is the fungal pathogen causing rice false smut, resulting in not only yield lost but also grain pollution with toxic mycotoxins. Here we deployed PacBio Sequel II HIFI-read sequencing technology to generate a near-complete genome assembly for the U. virens isolate UV-FJ-1 (38.48 Mb), which was isolated from Fujian province, China. The genome assembly contains 116 contigs with N50 of 0.65 Mb and a maximum length of 2.10 Mb, and the genome completeness is ≥98% assessed by benchmarking universal single-copy orthologs (BUSCOs) and the mapping rate of Illumina short reads. Excluding 35.78% repeat sequences, we identified a total of 7,164 protein-coding genes, of which 5,818 were functionally annotated and 223 encode putative effector proteins. Moreover, 21 secondary metabolite biosynthesis gene clusters were found in UV-FJ-1 genome. Taken together, this high-quality genome assembly and gene annotation resource will provide a better insight for characterizing the biological and pathogenic mechanisms of U. virens.

Background Paralogs formed through gene duplication and isoforms formed through alternative splicing have been important processes for increasing protein diversity and maintaining cellular homeostasis. Despite their recognized importance and the advent of large-scale genomic and transcriptomic analyses, paradoxically, accurate annotations of all gene loci to allow the identification of paralogs and isoforms remain surprisingly incomplete. In particular, the global analysis of the transcriptome of a non-model organism for which there is no reference genome is especially challenging. Results To reliably discriminate between the paralogs and isoforms in RNA-seq data, we redefined the pre-existing sequence features (sequence similarity, inverse count of consecutive identical or non-identical blocks, and match-mismatch fraction) previously derived from full-length cDNAs and EST sequences and described newly discovered genomic and transcriptomic features (twilight zone of protein sequence alignment and expression level difference). In addition, the effectiveness and relevance of the proposed features were verified with two widely used support vector machine (SVM) and random forest (RF) models. From nine RNA-seq datasets, all AUC (area under the curve) scores of ROC (receiver operating characteristic) curves were over 0.9 in the RF model and significantly higher than those in the SVM model. Conclusions In this study, using an RF model with five proposed RNA-seq features, we implemented our method called Paralogs and Isoforms Classifier based on Machine-learning approaches (PIC-Me) and showed that it outperformed an existing method. Finally, we envision that our tool will be a valuable computational resource for the genomics community to help with gene annotation and will aid in comparative transcriptomics and evolutionary genomics studies, especially those on non-model organisms.

The European rabbit (Oryctolagus cuniculus) is important as a biomedical model given its unique features in immunity and metabolism. The current reference genome OryCun2.0 established with whole-genome shotgun sequencing was quite fragmented and had not been updated for ten years. In this work, we provided a new rabbit genome assembly UM_NZW_1.0 to improve OryCun2.0 by leveraging the contig lengths based on long-read sequencing and a wealth of available Illumina paired-end sequence data. UM_NZW_1.0 showed a remarkable increase of continuity compared with OryCun2.0, with 5 times longer contig N50 and approximately 75% gaps closed. Many of the closed gaps were overlapped with protein-coding genes or transcriptional features, resulting in an enhancement of gene annotations. In particular, UM_NZW_1.0 presented a more complete landscape of the MHC region and the IGH locus, therefore provided a valuable resource for future researches on rabbits.

Possvm (Phylogenetic Ortholog Sorting with Species oVerlap and MCL [Markov clustering algorithm]) is a tool that automates the process of identifying clusters of orthologous genes from precomputed phylogenetic trees and classifying gene families. It identifies orthology relationships between genes using the species overlap algorithm to infer taxonomic information from the gene tree topology, and then uses the MCL to identify orthology clusters and provide annotated gene families. Our benchmarking shows that this approach, when provided with accurate phylogenies, is able to identify manually curated orthogroups with very high precision and recall. Overall, Possvm automates the routine process of gene tree inspection and annotation in a highly interpretable manner, and provides reusable outputs and phylogeny-aware gene annotations that can be used to inform comparative genomics and gene family evolution analyses. Research in A.S.-P. group was supported by the European Research Council (ERC) under the European Union’s Horizon 2020 Research and Innovation Programme Grant Agreement (851647), the Spanish Ministry of Science and Innovation (PGC2018-098210-A-I00), the Centro de Excelencia Severo Ochoa scheme (SEV-2016-0571), and the Agencia Estatal de Investigación. X.G.-B. was supported by a Juan de la Cierva fellowship (FJC2018-036282-I) from the Spanish Ministry of Economy, Industry, and Competitiveness.

Clonostachys rosea is a necrotrophic mycoparasitic fungus with excellent biological control ability against numerous fungal plant pathogens. Here, we performed genomic sequencing of C. rosea strain CanS41 using Oxford Nanopore sequencing technology. We generated a high-quality genome assembly (>99.99% accuracy), which comprised 26 contigs containing 60.68 Mb sequences with a GC content of 48.55% and a repeat content of 8.38%. The N50 contig length is 3.02 Mb. In total, 20,818 protein-coding genes were identified and functionally annotated. Genes encoding secreted proteins and carbohydrate-active enzymes as well as secondary metabolic gene clusters were also identified and analyzed. In summary, the high-quality genome assembly and gene annotation provided here will allow further exploration of biological functions and enhance biological control ability of C. rosea.

Motivation Accurate assembly of RNA-seq is a crucial step in many analytic tasks such as gene annotation or expression studies. Despite ongoing research, progress on traditional single sample assembly has brought no major breakthrough. Multi-sample RNA-Seq experiments provide more information than single sample datasets and thus constitute a promising area of research. Yet, this advantage is challenging to utilize due to the large amount of accumulating errors. Results We present an extension to Ryūtō enabling the reconstruction of consensus transcriptomes from multiple RNA-seq datasets, incorporating consensus calling at low level features. We report stable improvements already at three replicates. Ryūtō outperforms competing approaches, providing a better and user-adjustable sensitivity-precision trade-off. Ryūtō’s unique ability to utilize a (incomplete) reference for multi sample assemblies greatly increases precision. We demonstrate benefits for differential expression analysis. Ryūtō consistently improves assembly on replicates of the same tissue independent of filter settings, even when mixing conditions or time series. Consensus voting in Ryūtō is especially effective at high precision assembly, while Ryūtō’s conventional mode can reach higher recall. Availability and implementation Ryūtō is available at https://github.com/studla/RYUTO. Supplementary information Supplementary data are available at Bioinformatics online.

Objective The objective of this study was identification of the transcription factor binding sites (TFBS) in the promoter of HOX genes and elucidation of the comprehensive interaction of transcription factors (TFs)/genes with HOX. Methodology Promoter sequences of HOXA3, HOXA5, HOXA9, HOXA10, HOXA13, HOXB5, HOXC10, HOXC12, and HOXD10 were analyzed to predict the TFBS and their targets using TRANSFAC, TRRUST, and Harmonizome. Functional analysis of the processed data sets was carried out using DAVID and GATHER gene annotation tools. A network of regulatory interactions was constructed using NetworkAnalyst and a comprehensive illustration of the TF-gene network was constructed with HOX as a central hub using the Encyclopedia of DNA Elements chromatin immunoprecipitation sequencing data. Further, the enriched network was constructed to elucidate the roles of these genes in the various pathways. Results Binding sites for E2F1, HNF3α, SP3, and KLF6 were common to promoter regions of all of the HOX genes. The functional annotation and pathway analysis elucidated the regulatory activity of a distinct set of TF-genes in interaction with HOX. A P value ≤.05 and false discovery rate ≤0.01 were considered statistically significant. Conclusion We have confirmed that the predicted TFBSs in the HOX gene promoters function in transcriptional regulation by modulating target gene activity. TF-gene interactions are crucial to understanding oral carcinogenesis.

Both the annotation and identification of genes in pathogenic parasites are still challenging. Although, as a survival factor, nitric oxide (NO) has been proven to be synthesized in Trichomonas vaginalis (TV), nitric oxide synthase (NOS) has not yet been annotated in the TV genome. We developed a witness-to-suspect strategy to identify incorrectly annotated genes in TV via the Smith–Waterman and Needleman–Wunsch algorithms through in-depth and repeated alignment of whole coding sequences of TV against thousands of sequences of known proteins from other organisms. A novel NOS of TV (TV NOS), which was annotated as hydrogenase in the NCBI database, was successfully identified; this TV NOS had a high witness-to-suspect ratio and contained all the NOS cofactor-binding motifs (NADPH, tetrahydrobiopterin (BH4), heme and flavin adenine dinucleotide (FAD) motifs). To confirm this identification, we performed in silico modeling of the protein structure and cofactor docking, cloned the gene, expressed and purified the protein, performed mass spectrometry analysis, and ultimately performed an assay to measure enzymatic activity. Our data showed that although the predicted structure of the TV NOS protein was not similar to the structure of NOSs of other species, all cofactor-binding motifs could interact with their ligands with high affinities. We clearly showed that the purified protein had high enzymatic activity for generating NO in vitro. This study provides an innovative approach to identify incorrectly annotated genes in TV and highlights a novel NOS that might serve as a virulence factor of TV.

Background The Gram-negative intracellular bacterium Mycoplasma anatis is a pathogen of respiratory infectious diseases in ducks and has caused significant economic losses in the poultry industry. Objective This study, as the first report of the structure and function of the pan-genome of Mycoplasma anatis, may provide a valuable genetic basis for many aspects of future research on the pathogens of waterfowl. Methods We sequenced the whole genomes of 15 Mycoplasma anatis isolated from ducks in China. Draft genome sequencing was carried out and whole-genome sequencing was performed by the sequencers of the PacBio Sequel and an IonTorrent Personal Genome Machine (PGM). Then the common genic elements of protein-coding genes, tRNAs, and rRNAs of Mycoplasma anatis genomes were predicted by using the pipeline Prokka v1.13.7. To investigate homologous protein clusters across Mycoplasma anatis genomes, we adopted Roary v3.13.0 to cluster orthologous genes (OGs) based on the following criteria. Results We obtained one complete genome and 14 genome sketches. Microbial mobile genetic element analysis revealed the distribution of insertion sequences (IS30, IS3, and IS1634), prophage regions, and CRISPR arrays in the genome of Mycoplasma anatis. Comparative genomic analysis decoded the genetic components and functional classification of the pan-genome of Mycoplasma anatis that comprised 646 core genes, 231 dispensable genes and among them 110 was strain-specific. Virulence-related gene profiles of Mycoplasma anatis were systematically identified, and the products of these genes included bacterial ABC transporter systems, iron transport proteins, toxins, and secretion systems. Conclusion A complete virulence-related gene profile of Mycoplasma anatis has been identified, most of the genes are highly conserved in all strains. Sequencing results are relevant to the molecular mechanisms of drug resistance, adaptive evolution of pathogens, population structure, and vaccine development. Supplementary Information The online version contains supplementary material available at 10.1007/s13258-021-01129-5.

Mammographic breast density (MBD) is a marker of increased breast cancer (BC) risk, yet much remains to be clarified about the underlying mechanisms. We investigated whether DNA methylation patterns differ between high- vs. low-MBD women who developed BC during an 8.9-year median follow-up in the Florence section of the European Prospective Investigation into Cancer and Nutrition. We analysed 96 pairs of women with BC arising on high- vs. low-MBD breasts (BI-RADS category III–IV vs. I). DNA methylation was determined on pre-diagnostic blood samples using the Illumina Infinium MethylationEPIC BeadChip assay. The statistical analysis was conducted by performing an epigenome-wide association study (EWAS), by searching differentially methylated regions (DMRs) in gene promoters (followed by functional enrichment and gene annotation analysis); and through a “candidate pathways” approach focusing on pre-defined inflammation-related pathways. In EWAS, no single CpG site was differentially methylated between high- and low-MBD women after correction for multiple testing. A total of 140 DMRs were identified, of which 131 were hyper- and 9 hypo-methylated amongst high-MBD women. These DMRs encompassed an annotation cluster of 35 genes coding for proteins implicated in transcription regulation and DNA binding. The “apoptosis signalling” was the only inflammation-related candidate pathway differentially methylated between high- and low-MBD women. Pre-diagnostic methylation patterns differ between high- vs. low-MBD women who subsequently develop BC, particularly, in genes involved in the regulation of DNA transcription and cell apoptosis. Our study provides novel clues about the mechanisms linking MBD and BC.

Objectives Arachnids have fascinating and unique biology, particularly for questions on sex differences and behavior, creating the potential for development of powerful emerging models in this group. Recent advances in genomic techniques have paved the way for a significant increase in the breadth of genomic studies in non-model organisms. One growing area of research is comparative transcriptomics. When phylogenetic relationships to model organisms are known, comparative genomic studies provide context for analysis of homologous genes and pathways. The goal of this study was to lay the groundwork for comparative transcriptomics of sex differences in the brain of wolf spiders, a non-model organism of the pyhlum Euarthropoda, by generating transcriptomes and analyzing gene expression. Data description To examine sex-differential gene expression, short read transcript sequencing and de novo transcriptome assembly were performed. Messenger RNA was isolated from brain tissue of male and female subadult and mature wolf spiders (Schizocosa ocreata). The raw data consist of sequences for the two different life stages in each sex. Computational analyses on these data include de novo transcriptome assembly and differential expression analyses. Sample-specific and combined transcriptomes, gene annotations, and differential expression results are described in this data note and are available from publicly-available databases.

Motivation 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. Results 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 98.3% of human protein-coding genes to a chimpanzee genome assembly with 98.2% sequence identity. Availability and implementation Liftoff can be installed via bioconda and PyPI. In addition, the source code for Liftoff is available at https://github.com/agshumate/Liftoff. Supplementary information Supplementary data are available at Bioinformatics online.

Background Peppers (Capsicum annuum L.) containing distinct capsaicinoids are the most widely cultivated spices in the world. However, extreme genomic diversity among species represents an obstacle to breeding pepper. Results Here, we report de novo genome assemblies of Capsicum annuum ‘Early Calwonder (non-pungent, ECW)’ and ‘Small Fruit (pungent, SF)’ along with their annotations. In total, we assembled 2.9 Gb of ECW and SF genome sequences, representing over 91% of the estimated genome sizes. Structural and functional annotation of the two pepper genomes generated about 35,000 protein-coding genes each, of which 93% were assigned putative functions. Comparison between newly and publicly available pepper gene annotations revealed both shared and specific gene content. In addition, a comprehensive analysis of nucleotide-binding and leucine-rich repeat (NLR) genes through whole-genome alignment identified five significant regions of NLR copy number variation (CNV). Detailed comparisons of those regions revealed that these CNVs were generated by intra-specific genomic variations that accelerated diversification of NLRs among peppers. Conclusions Our analyses unveil an evolutionary mechanism responsible for generating CNVs of NLRs among pepper accessions, and provide novel genomic resources for functional genomics and molecular breeding of disease resistance in Capsicum species.

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.

Conventional scRNA-seq expression analyses rely on the availability of a high quality genome annotation. Yet, as we show here with scRNA-seq experiments and analyses spanning human, mouse, chicken, mole rat, lemur and sea urchin, genome annotations are often incomplete, in particular for organisms that are not routinely studied. To overcome this hurdle, we created a scRNA-seq analysis routine that recovers biologically relevant transcriptional activity beyond the scope of the best available genome annotation by performing scRNA-seq analysis on any region in the genome for which transcriptional products are detected. Our tool generates a single-cell expression matrix for all transcriptionally active regions (TARs), performs single-cell TAR expression analysis to identify biologically significant TARs, and then annotates TARs using gene homology analysis. This procedure uses single-cell expression analyses as a filter to direct annotation efforts to biologically significant transcripts and thereby uncovers biology to which scRNA-seq would otherwise be in the dark., Conventional single-cell RNA sequencing analysis rely on genome annotations that may be incomplete or inaccurate especially for understudied organisms. Here the authors present a bioinformatic tool that leverages single-cell data to uncover biologically relevant transcripts beyond the best available genome annotation.

Background The mitochondrial (mt) genomes of Sarcoptiformes mites typically contain 37 genes. Although the loss of genes is rare in Sarcoptiformes mite mitogenomes, two of the six previously reported oribatid mites (Acariforms: Sarcoptiformes) are reported to have lost parts of their tRNA genes. To confirm whether the tRNA genes were indeed lost and whether the loss is universal, we re-annotated the available oribatid mite sequences and sequenced the mitogenome of Oribatula sakamorii. Methods The mitogenome of O. sakamorii was sequenced using an Illumina HiSeq sequencer. The mt tRNA gene was annotated using multi-software combined with a manual annotation approach. Phylogenetic analyses were performed using the maximum likelihood and Bayesian inference methods with concatenated nucleotide and amino acid sequences. Results The mitogenomes of O. sakamorii contained 37 genes, including 22 tRNA genes. We identified all mt tRNA genes that were reported as “lost” in Steganacarus magnus and Paraleius leontonychus and revealed certain atypical tRNA annotation errors in oribatid mite sequences. Oribatid mite mitogenomes are characterized by low rates of genetic rearrangement, with six or seven gene blocks conserved between the mitogenome of all species and that of ancestral arthropods. Considering the relative order of the major genes (protein-coding genes and rRNAs), only one or two genes were rearranged with respect to their positions in the ancestral genome. We explored the phylogenetic relationships among the available oribatid mites, and the results confirmed the systematic position of Hermannia in the Crotonioidea superfamily. This was also supported by the synapomorphic gene-derived boundaries. Conclusions The tRNA “lost” phenomenon is not universal in oribatid mites. Rather, highly atypical secondary structure of the inferred mt tRNA genes made them unidentifiable using a single type of tRNA search program. The use of multi-software combined with a manual annotation approach can improve the accuracy of tRNA gene annotation. In addition, we identified the precise systematic position of Hermannia and validated that Astigmata is nested in Oribatida. Graphical Abstract

Black spot needle blight is a serious conifer disease of Pinus sylvestris var. mongolica occurring in Northeast China, which is usually caused by the plant pathogenic fungus Pestalotiopsis neglecta. From the diseased pine needles collected in Honghuaerji, the P. neglecta strain YJ-3 was isolated and identified as the phytopathogen, and its culture characteristics were studied. Then, we generated a highly contiguous 48.36-Mbp genome assembly (N50 = 6.62 Mbp) of the P. neglecta strain YJ-3 by combining the PacBio RS II Single Molecule Real Time (SMRT) and Illumina HiSeq X Ten sequencing platforms. The results showed that a total of 13,667 protein-coding genes were predicted and annotated using multiple bioinformatics databases. The genome assembly and annotation resource reported here will be useful for the study of fungal infection mechanisms and pathogen–host interaction.

Branch number on the main stem (BNMS) is an important factor that affects crop plant architecture and yield in soybean [Glycine max (L.) Merr.]. With the aim of elucidating the genetic basis of BNMS, 10 consensus quantitative trait loci (QTLs) were identified, which were on 9 chromosomes and explained 0.3–33.3% of the phenotypic variance. One new QTL-qBN.N (R2 = 19.6%) was detected in two populations (F2:3-Taiyuan 2017 and F2:4-Taiyuan 2018), which occur only in Taiyuan. Thus, the interaction analysis of QTL × environment confirmed that QTL-qBN.N was greatly affected by the environment. Of these, QTL-qBN.C2 (R2 = 33.3%) was defined as a major QTL, and was also verified and fine-mapped in the recombinant inbred line population. The high-coverage re-sequencing of two parental lines and newly developed InDel PCR-based markers allowed the region of qBN.C2 to be narrowed down to 304.9 kb. According to the gene annotation of the QTL interval, a total of 24 genes were obtained. Three potential candidate genes were identified by quantitative real-time PCR, and only Glyma.06G188400 had the highest relative expression at the axillary meristem, and thus it may potentially participate in branching development. The successful and rapid fine-mapping and discovery of candidate genes for BNMS in soybean were achieved in this study.

The goal of this research was to develop a new genetic database of simple sequence repetition (SSR) primers for faba and classify them according to their target genes and respective biological processes. Approximately 75,605 and 148,196 previously published genomic and transcriptomic faba sequences, respectively, have been used to detect possible SSRs. The numbers of identified SSRs from each dataset were 25,502 and 12,319, respectively. The distribution of different repeat classes indicated that trinucleotides represent the largest number of repeat counts, followed by dinucleotides. The extracted genic SSR sequences were used to design 1091 polymerase chain reaction (PCR) primers, of which only 238 (21.8%) primers target genomic sequences and the other 853 PCR primers targeted transcriptomic sequences. The annotation of gene-targeted SSRs showed that approximately 897 genes were targeted by our SSR primers. Approximately 1890 gene ontology (GO) identification codes have been obtained. The GO keywords were distributed among distinct molecular cell features. The highest redundancies involved 554 technical words, 196 domains, and 160 molecular feature phrases. These GO codes belonged to the general level of GO and included molecular function, cellular component, and biological process (544, 670, and 676 GOs, respectively). Twenty-seven SSR PCR primers were synthesized to 12 Egyptian faba bean genotypes. Approximately 11 SSR provided one to two PCR bands, whereas other SSRs provided only one sharp band with polymorphic band size. There were 13 polymorphic primers. The polymorphism information content was 0.3, which implied moderate informativeness.

Following the elucidation of the critical roles they play in numerous important biological processes, long noncoding RNAs (lncRNAs) have gained vast attention in recent years. Manual annotation of lncRNAs is restricted by known gene annotations and is prone to false prediction due to the incompleteness of available data. However, with the advent of high-throughput sequencing technologies, a magnitude of high-quality data has become available for annotation, especially for plant species such as wheat. Here, we compared prediction accuracies of several machine learning algorithms using a 10-fold cross-validation. This study includes a comprehensive feature selection step to refine irrelevant and repeated features. We present a crop-specific, alignment-free coding potential prediction tool, LncMachine, that performs at higher prediction accuracies than the currently available popular tools (CPC2, CPAT, and CNIT) when used with the Random Forest algorithm. Further, LncMachine with Random Forest performed well on human and mouse data, with an average accuracy of 92.67%. LncMachine only requires either a FASTA file or a TAB separated CSV file containing features as input files. LncMachine can deploy several user-provided algorithms in real time and therefore be effortlessly applied to a wide range of studies.

Genome analysis of Halomonas shambharensis, a novel species, was performed to understand the osmoprotectant strategies used by the strain to overcome the salinity stress and to explore the prospective industrial uses. It will also help to better understand the ecological roles of Halomonas species in hypersaline habitats. Ultrastructure of the cell was determined by using transmission electron microscopy. Standard microbiological methods were used to find out growth parameters and heterotrophic mode of nutrition. For Genome analysis, complete bacterial genome sequencing was performed using the Oxford Nanopore MinION DNA Sequencer. Assembly, annotation and finishing of the obtained sequence were done by using a Prokaryotic Genome Annotation Pipeline (PGAP) (SPAdes v. 3.10.1). Predicted Coading sequences (CDSs) obtained through the PGAP were used for functional annotation using Clusters of Orthologous Groups and Kyoto Encyclopedia of Genes and Genomes (KEGG) platforms. The H. shambharensis was found to be a Gram-stain-negative, rod-shaped bacterium, motile with a peritrichous flagella. The H. shambharensis bacterium can grow in a wide range of temperature (from 25 to 65 °C), pH (pH 4 to pH 12.0) and salt concentration (5.0% NaCl to 30.0% NaCl). After annotation and assembly, the total genome size obtained was 1,533,947 bp, which revealed 146 subsystems, 3847 coding sequences, and 19RNAs with G+C content of 63.6%. Gene annotation identified the genes related to various metabolic pathways, including carbohydrate metabolism, fatty acid metabolism and stress tolerance. The genomic dataset of H. shambharensis will be useful for analysis of protein-coding gene families and how these coding genes are significant for the survival and metabolism among the different species of Halomonas. The complete genome sequence presented here will help to unravel the biotechnological potential of H. shambharensis for production of the high-value products such as betaine, or as a source of gene-mining for individual enzymes.

Background Improving grain yield in cereals especially in wheat is a main objective for plant breeders. One of the main constrains for improving this trait is the G × E interaction (GEI) which affects the performance of wheat genotypes in different environments. Selecting high yielding genotypes that can be used for a target set of environments is needed. Phenotypic selection can be misleading due to the environmental conditions. Incorporating information from phenotypic and genomic analyses can be useful in selecting the higher yielding genotypes for a group of environments. Results A set of 270 F3:6 wheat genotypes in the Nebraska winter wheat breeding program was tested for grain yield in nine environments. High genetic variation for grain yield was found among the genotypes. G × E interaction was also highly significant. The highest yielding genotype differed in each environment. The correlation for grain yield among the nine environments was low (0 to 0.43). Genome-wide association study revealed 70 marker traits association (MTAs) associated with increased grain yield. The analysis of linkage disequilibrium revealed 16 genomic regions with a highly significant linkage disequilibrium (LD). The candidate parents’ genotypes for improving grain yield in a group of environments were selected based on three criteria; number of alleles associated with increased grain yield in each selected genotype, genetic distance among the selected genotypes, and number of different alleles between each two selected parents. Conclusion Although G × E interaction was present, the advances in DNA technology provided very useful tools and analyzes. Such features helped to genetically select the highest yielding genotypes that can be used to cross grain production in a group of environments.

As the most widely consumed beverage in the world, tea has various nutritional, economic, and global cultural values. With the development of the third-generation sequencing technology, several genome sequences of tea plants have been published. These genomic data have pivotal information that is of benefit to tea plant breeders and biologists in advancing tea plant improvement and the final quality of tea products. We hereby present the integrative online database, Tea Plant Genome Database (TeaPGDB; http://eplant.njau.edu.cn/tea ), which incorporates the published genome sequences of tea plants. The current release of TeaPGDB hosts published tea plant genome data with various online tools, including JBrowse, gene search, SSR search, BLAST. TeaPGDB also contains a download server, which provides access for the download of genome-related data and rich annotation files. TeaPGDB is committed to collecting, integrating, and annotating published tea plant genome data, providing data support for research on tea plant heredity, evolution, breeding for resistance, plant improvement, and facilitating the characterization of important traits or flavor related genes in the community. Compared with other tea plant databases, this database not only contains more complete genome data and gene annotation information, but also has a user-friendly interface for researchers in the field.

Gene prioritisation at GWAS loci necessities careful assembly and examination of different types of molecular evidence to arrive at a set of plausible candidates. In many human traits, common small-effect mutations may subtly dysregulate the function of the very same genes which are impacted by rare, large-effect mutations causing Mendelian disease of similar phenotype. However, information on gene-Mendelian disease associations, rare pathogenic mutations driving the disease, and the disease phenotype ontology is dispersed across many data sources and does not integrate easily with enrichment analysis.MendelVar is a new webserver facilitating transfer of knowledge from Mendelian disease research into interpretation of genetic associations from GWAS of complex traits. MendelVar allows querying of pre-defined or LD-determined genomic intervals against a comprehensive integrated database to find overlap with genes linked to Mendelian disease. Next, MendelVar looks for enrichment of any Human Phenotype Ontology, Disease Ontology and other ontology/pathway terms associated with identified Mendelian genes. In addition, MendelVar provides a list of all overlapping pathogenic and likely pathogenic variants for Mendelian disease sourced from ClinVar.Inclusion of information obtained from MendelVar in post-GWAS gene annotation pipelines can strengthen the case for causal importance of some genes. Moreover, as genes with Mendelian disease evidence may make for more successful drug targets, this may be particularly useful in drug discovery pipelines. Taking GWAS summary statistics for male-pattern baldness, intelligence and atopic dermatitis, we demonstrate the use of MendelVar in prioritizing candidate genes at these loci which are linked to relevant enriched ontology terms. MendelVar is freely available athttps://mendelvar.mrcieu.ac.uk/

Background Eukaryotic protein-coding genes consist of exons and introns. Exon–intron borders are conserved between species and thus their changes might be observed only on quite long evolutionary distances. One of the rarest types of change, in which intron relocates over a short distance, is called "intron sliding", but the reality of this event has been debated for a long time. The main idea of a search for intron sliding is to use the most accurate genome annotation and genome sequence, as well as high-quality transcriptome data. We applied them in a search for sliding introns in mammals in order to widen knowledge about the presence or absence of such phenomena in this group. Results We didn’t find any significant evidence of intron sliding in the primate group (human, chimpanzee, rhesus macaque, crab-eating macaque, green monkey, marmoset). Only one possible intron sliding event supported by a set of high quality transcriptomes was observed between EIF1AX human and sheep gene orthologs. Also, we checked a list of previously observed intron sliding events in mammals and showed that most likely they are artifacts of genome annotations and are not shown in subsequent annotation versions as well as are not supported by transcriptomic data. Conclusions We assume that intron sliding is indeed a very rare evolutionary event if it exists at all. Every case of intron sliding needs a lot of supportive data for detection and confirmation.

The eutherian interferons were implicated as paradigmatic effector proteins in innate and acquired immunity. Yet, familiar interferon classification and nomenclature disagreed with functional genomics, phylogenetic and evolutionary analyses. Using eutherian comparative genomic analysis protocol and 35 public eutherian reference genomic sequence data sets, the present analysis attempted to resolve major disagreements in descriptions of eutherian interferons. Among 836 eutherian interferon potential coding sequences, the tests of reliability of eutherian public genomic sequences annotated, in aggregate, 495 complete coding sequences that were partitioned into supercluster IF1 and supercluster IF2 gene data sets. There were 29 human class 2 cytokine genes described, including initially described human IF1IC2 gene. There was convergence between nonhomologous eutherian supercluster IF1 and supercluster IF2 protein primary structures including common cysteine amino acid residues implicated in disulfide bonding. The integrated gene annotations, phylogenetic analysis and protein molecular analysis proposed revised and updated classification and nomenclature of eutherian interferons.

Objective: Compound Xuanju has good effects in treating rheumatoid arthritis (RA), but its composition is complex, and its active ingredients and mechanism have not been fully defined. In this study, the active ingredients and mechanism of compound Xuanju for the treatment of RA were explored through network pharmacological methods. Methods: TCMSP and TCMID, Pubmed, CNKI, Wanfang, and VIP databases were used to screen, select active pharmaceutical ingredients and targets; Drugbank disease target screening database, GeneCards database, Therapeutic The Target Database (TTD) database and DisGeNET database were used to collect RA targets, and OmicShare was used to screen compound Xuanju and RA for common targets and construct a Venn diagram. A protein target database String was used to construct a common target interaction network. OmicShare mapping software builds a "drug-active ingredient-target" network and analyzes their associations. DAVID online software performs gene annotation (GO) and KEGG pathway enrichment analysis on key targets. Results: A total of 73 effective ingredients of compound Xuanju were obtained, and corresponding to 229 targets; 2337 targets for RA. 155 key targets for potential active ingredients of compound Xuanju predicted therapeutic effect of RA, the key targets map 55 active ingredients of compound Xuanju capsules. These targets mainly involve signaling pathways such as Toll-like receptor signaling pathway, PI3K-Akt signaling pathway, HIF-1 signaling pathway, and TNF signaling pathway acting on RA. Conclusion: Compound Xuanju may via its potential 55 active ingredients act on 155 targets to treat RA through Toll-like receptor signaling pathway, PI3K-Akt signaling pathway, NF-κB signaling pathway, HIF-1 signaling pathway and TNF signaling pathway. This study lays the theoretical basis for the widespread application of compound Xuanju in clinical practice.

Populus alba × P. glandulosa clone 84 K, derived from South Korea, is widely cultivated in China and used as a model in the molecular research of woody plants because of high gene transformation efficiency. Here, we combined 63-fold coverage Illumina short reads and 126-fold coverage PacBio long reads to assemble the genome. Due to the high heterozygosity level at 2.1% estimated by k-mer analysis, we exploited TrioCanu for genome assembly. The PacBio clean subreads of P. alba × P. glandulosa were separated into two parts according to the similarities, compared with the parental genomes of P. alba and P. glandulosa. The two parts of the subreads were assembled to two sets of subgenomes comprising subgenome A (405.31 Mb, from P. alba) and subgenome G (376.05 Mb, from P. glandulosa) with the contig N50 size of 5.43 Mb and 2.15 Mb, respectively. A high-quality P. alba × P. glandulosa genome assembly was obtained. The genome size was 781.36 Mb with the contig N50 size of 3.66 Mb and the longest contig was 19.47 Mb. In addition, a total of 176.95 Mb (43.7%), 152.37 Mb (40.5%) of repetitive elements were identified and a total of 38,701 and 38,449 protein-coding genes were predicted in subgenomes A and G, respectively. For functional annotation, 96.98% of subgenome A and 96.96% of subgenome G genes were annotated with public databases. This de novo assembled genome will facilitate systematic and comprehensive study, such as multi-omics analysis, in the model tree P. alba × P. glandulosa.

This article was the first attempt to analyze the biological activity of ursodeoxycholic acid in the context of the in silico prediction of changes in activity of genes. Prediction of the dynamics of changes in gene activity was carried out using the DIGEP-Pred algorithm. The obtained gene arrays were analyzed using the GeneMANIA application. Gene annotation was obtained from The Human Protein Atlas. The results showed that the initial sample of genes is distributed according to the localization of expression into several groups: testes, cerebral cortex, placenta, and parathyroid glands. Groups of genes differed among themselves both in the number of members and in the number of relationships between them. The largest group included 25 genes. The activity of the genes of this group was localized to the testes. According to the prediction, all members of this group will be repressed after exposure to ursodeoxycholic acid. The next group consisted of 14 genes located in the cerebral cortex. Within this group, both an increase and a decrease in the activity of genes when exposed to ursodeoxycholic acid are predicted. Groups of the placenta and parathyroid glands contained relatively few genes (nine genes each) and were characterized by a low number of relationships between members. An analysis of the specialized literature revealed the effects of taking ursodeoxycholic acid, which may be associated with the changes in gene activity, which were predicted in silico. There was no unambiguous relationship between the groups of identified in silico genes and the effects described in the literature. It was revealed that 45 genes of the entire array of genes (81 genes) are prognostic markers of oncological processes.

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 CS v1.0, Chinese Spring) that has been widely adopted and utilized by the wheat community. Although this reference assembly represents all three wheat subgenomes at chromosome-scale, it was derived from short reads, and thus is missing a substantial portion of the expected 16 Gbp of genomic sequence. We earlier published an independent wheat assembly (Triticum_aestivum_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 CS v1.0 assembly, and annotate the resulting pseudomolecules with genes. Our updated assembly, Triticum_aestivum_4.0, contains 15.07 Gbp of nongap sequence anchored to chromosomes, which is 1.2 Gbps 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 >5700 additional gene copies, facilitating the accurate annotation of functional gene duplications including at the Ppd-B1 photoperiod response locus.

Phages infecting bacteria of the genus Staphylococcus play an important role in their host’s ecology and evolution. On one hand, horizontal gene transfer from phage can encourage the rapid adaptation of pathogenic Staphylococcus enabling them to escape host immunity or access novel environments. On the other hand, lytic phages are promising agents for the treatment of bacterial infections, especially those resistant to antibiotics. As part of an ongoing effort to gain novel insights into bacteriophage diversity, we characterized the complete genome of the Staphylococcus bacteriophage Metroid, a cluster C phage with a genome size of 151kb, encompassing 254 predicted protein-coding genes as well as 4 tRNAs. A comparative genomic analysis highlights strong similarities – including a conservation of the lysis cassette – with other Staphylococcus cluster C1 bacteriophages, several of which were previously characterized for therapeutic applications.

There is now abundant evidence of rapid evolution in natural populations, but the genetic mechanisms of these changes remain unclear. One possible route to rapid evolution is through changes in the expression of genes that influence traits under selection. We examined contemporary evolutionary gene expression changes in plant populations responding to environmental fluctuations. We compared genome-wide gene expression, using RNA-seq, in two populations of Brassica rapa collected over four time points between 1997 and 2014, during which precipitation in southern California fluctuated dramatically and phenotypic and genotypic changes occurred. By combining transcriptome profiling with the resurrection approach, we directly examined evolutionary changes in gene expression over time. For both populations, we found a substantial number of differentially expressed genes between generations, indicating rapid evolution in the expression of many genes. Using existing gene annotations, we found that many changes occurred in genes involved in regulating stress responses and flowering time. These appeared related to the fluctuations in precipitation and were potentially adaptive. However, the evolutionary changes in gene expression differed across generations within and between populations, indicating largely independent evolutionary trajectories across populations and over time. Our study provides strong evidence for rapid evolution in gene expression, and indicates that changes in gene expression can be one mechanism of rapid evolutionary responses to selection episodes. This study also illustrates that combining resurrection studies with transcriptomics is a powerful approach for investigating evolutionary changes at the gene regulatory level, and will provide new insights into the genetic basis of contemporary evolution.

Phytophthora infestans is a devastating pathogen causing potato late blight (Solanum tuberosum). Here we report the sequencing, assembly and genome annotation for two Phytophthora infestans isolates sampled in Republic of Korea. Genome sequencing was carried out using long read (Oxford Nanopore) and short read (Illumina Nextseq) sequencing technologies that significantly improved the contiguity and quality of P. infestans genome assembly. Our resources would help researchers better understand the molecular mechanisms by which P. infestans causes late blight disease in the future.

Pseudomonas phages PaGz-1 and PaZq-1, two new phages infecting Pseudomonas aeruginosa, were isolated from fresh water in Guangdong province, China. The genomes of these two phages consist of 93,975 bp and 94,315 bp and contain 175 and 172 open reading frames (ORFs), respectively. The genome sequences of PaGz-1 and PaZq-1 share 95.8% identity with a query coverage of 94%, suggesting that these two phages belong to two different species. Based on results of nucleotide sequence alignment, gene annotation, and phylogenetic analysis, we propose PaGz-1 and PaZq-1 as representative isolates of two species in the genus Pakpunavirus within the family Myoviridae.

Phenotypes are the result of the complex interplay between environmental and genetic factors. To better understand the interactions between chemical compounds and human phenotypes, and further exposome research we have developed “phexpo,” a tool to perform and explore bidirectional chemical and phenotype interactions using enrichment analyses. Phexpo utilizes gene annotations from 2 curated public repositories, the Comparative Toxicogenomics Database and the Human Phenotype Ontology. We have applied phexpo in 3 case studies linking: (1) individual chemicals (a drug, warfarin, and an industrial chemical, chloroform) with phenotypes, (2) individual phenotypes (left ventricular dysfunction) with chemicals, and (3) multiple phenotypes (covering polycystic ovary syndrome) with chemicals. The results of these analyses demonstrated successful identification of relevant chemicals or phenotypes supported by bibliographic references. The phexpo R package (https://github.com/GHLCLab/phexpo) provides a new bidirectional analyses approach covering relationships from chemicals to phenotypes and from phenotypes to chemicals.

Gene annotation is a critical bottleneck in genomic research, especially for the comprehensive study of very large gene families in the genomes of nonmodel organisms. Despite the recent progress in automatic methods, state‐of‐the‐art tools used for this task often produce inaccurate annotations, such as fused, chimeric, partial or even completely absent gene models for many family copies, errors that require considerable extra efforts to be corrected. Here we present bitacora , a bioinformatics solution that integrates popular sequence similarity‐based search tools and Perl scripts to facilitate both the curation of these inaccurate annotations and the identification of previously undetected gene family copies directly in genomic DNA sequences. We tested the performance of bitacora in annotating the members of two chemosensory gene families with different repertoire size in seven available genome sequences, and compared its performance with that of augustus‐ppx , a tool also designed to improve automatic annotations using a sequence similarity‐based approach. Despite the relatively high fragmentation of some of these drafts, bitacora was able to improve the annotation of many members of these families and detected thousands of new chemoreceptors encoded in genome sequences. The program creates general feature format (GFF) files, with both curated and newly identified gene models, and FASTA files with the predicted proteins. These outputs can be easily integrated in genomic annotation editors, greatly facilitating subsequent manual annotation and downstream evolutionary analyses.

Background : Bipolar disorder (BD) is a type of chronic emotional disorder with a complex genetic structure. However, its genetic molecular mechanism is still unclear, which makes it insufficient to be diagnosed and treated. Methods and Results: In this paper, we proposed a model for predicting BD based on single nucleotide polymorphisms (SNPs) screening by genome-wide association study (GWAS), which was constructed by a convolutional neural network (CNN) that predicted the probability of the disease. According to the difference of GWAS threshold, two sets of data were named: group P001 and group P005. And different convolutional neural networks are set for the two sets of data. The training accuracy of the model trained with group P001 data is 96%, and the test accuracy is 91%. The training accuracy of the model trained with group P005 data is 94.5%, and the test accuracy is 92%. At the same time, we used gradient weighted class activation mapping (Grad-CAM) to interpret the prediction model, indirectly to identify high-risk SNPs of BD. In the end, we compared these high-risk SNPs with human gene annotation information. Conclusion: The model prediction results of the group P001 yielded 137 risk genes, of which 22 were reported to be associated with the occurrence of BD. The model prediction results of the group P005 yielded 407 risk genes, of which 51 were reported to be associated with the occurrence of BD.

Nowadays, manipulating and analyzing publicly available genomic datasets become a daily task in bioinformatics and genomics laboratories. The release of several genome sequencing projects prompts bioinformaticians to develop automated scripts and pipelines which analyze genomic datasets in particular gene annotation pipelines. Handling genome annotation files with fully-featured programs used by non-developers is necessary, furthermore, accelerating genomic data analysis with a focus on diminishing the genome annotation and sequence files based on specific features is required. Consequently, to extract genome features from GTF or GFF3 in a precise manner, GAD script (https://github.com/bio-projects/GAD) provides a simple graphical user interface which interpreted by all python versions installed in different operating systems. GAD script contains unique entry widgets which are capable to analyze multiple genome sequence and annotation files by a click. With highly influential coded functions, genome features such upstream genes, downstream genes, intergenic regions, genes, transcripts, exons, introns, coding sequences, five prime untranslated regions, and three prime untranslated regions and other ambiguous sequence ontology terms will be extracted. GAD script outputs the results in diverse file formats such as BED, GTF/GFF3 and FASTA files which supported by other bioinformatics programs. Our script could be incorporated into various pipelines in all genomics laboratories with the aim of accelerating data analysis.

Circular RNAs (circRNAs) are formed by joining the 3′ and 5′ ends of RNA molecules. Identification of circRNAs is an important part of circRNA research. The circRNA prediction methods can predict the circRNAs with start and end positions in the chromosome but cannot identify the full-length circRNA sequences. We present an R package FcircSEC (Full Length circRNA Sequence Extraction and Classification) to extract the full-length circRNA sequences based on gene annotation and the output of any circRNA prediction tools whose output has a chromosome, start and end positions, and a strand for each circRNA. To validate FcircSEC, we have used three databases, circbase, circRNAdb, and plantcircbase. With information such as the chromosome and strand of each circRNA as the input, the identified sequences by FcircSEC are consistent with the databases. The novelty of FcircSEC is that it can take the output of state-of-the-art circRNA prediction tools as input and is applicable for human and other species. We also classify the circRNAs as exonic, intronic, and others. The R package FcircSEC is freely available.

BACKGROUND: Handedness in plants introduced by helical growth of organs is frequently observed, and it has fascinated plant scientists for decades. However, the genetic control of natural handedness has not been revealed. In the model legume Medicago truncatula, pods can be coiled in a clockwise or anti-clockwise manner, providing a model for genetic analysis of plant handedness. OBJECTIVE: We aimed to localize the Sense of Pod Coiling (SPC) gene controlling pod coiling direction in M. truncatula. METHODS: Linkage analysis was used with a biparental population for fine mapping of the SPC gene. The genome sequence of M. truncatula Mt4.0 was used for marker identification and physical mapping. Single nucleotide polymorphisms (SNPs) between the parental lines were converted to CAPS (cleaved amplified polymorphic sequences) markers. Genetic map was constructed using the software JoinMap version 3.0. Gene predication and annotation provided by the M. truncatula genome database (http://www.medicagogenome.org) was confirmed with the programs of FGENESH and Pfam 32.0, respectively. Quantitative reverse transcription PCR (qRT-PCR) was used to analyze the relative expression levels of candidate genes. RESULTS: The genetic analysis indicated that the anti-clockwise coiling is dominant to clockwise and is controlled by the single gene, SPC. The SPC gene was delimited to a 250 kb-region on Chromosome 7. Total of 15 protein-coding genes were identified in the SPC locus through gene annotation and sequence analysis. Of those, two genes, potentially encoding a receptor-like kinase and a vacuolar cation/proton exchanger respectively, were selected as candidates for the SPC gene. CONCLUSIONS: The result presented here lay a foundation for gene cloning of SPC, which will help us to understand the molecular mechanisms underlying helical growth in plant organs.

To provide insight into molecular diagnosis and individualized treatment of ischemic stroke (IS), several available datasets in IS were analyzed to identify the differentially expressed genes and microRNAs (miRNAs). Series matrix files from GSE22255 and GSE16561 (mRNA profiles), a well as GSE110993 (miRNA profile) were downloaded from the Gene Expression Omnibus database. System‑level clustering was performed with GeneCluster 3.0 software, and gene annotation and pathway enrichment were performed with gene ontology analysis and Database for Annotation, Visualization and Integrated Discovery software. For a protein‑protein interaction (PPI) network, Biological General Repository for Interaction Datasets and IntAct interaction information were integrated to determine the interaction of differentially expressed genes. The selected miRNA candidates were imported into the TargetScan, miRDB and miRecords databases for the prediction of target genes. The present study identified 128 upregulated and 231 downregulated genes in female stroke patients, and 604 upregulated and 337 downregulated genes in male stroke patients compared with sex‑ and age‑matched controls. The construction of a PPI network demonstrated that male stroke patients exhibited YWHAE, CUL3 and JUN as network center nodes, and in female patients CYLD, FOS and PIK3R1 interactions were the strongest. Notably, these interactions are mainly involved in immune inflammatory response, apoptosis and other biological pathways, such as blood coagulation. Female and male upregulated genes were cross‑validated with another set of Illumina HumanRef‑8 v3.0 expression beadchip (GSE16561). Functional item association networks, gene function networks and transcriptional regulatory networks were successfully constructed, and the relationships between miRNAs and target genes were successfully predicted. The present study identified a number of transcription factors, including DEFA1, PDK4, SDPR, TCN1 and MMP9, and miRNAs, including miRNA (miR)‑21, miR‑143/145, miR‑125‑5p and miR‑122, which may serve important roles in the development of cerebral stroke and may be important molecular indicators for the treatment of IS.

High-Throughput Sequencing technologies are transforming many research fields, including the analysis of phage display libraries. The phage display technology coupled with deep sequencing was introduced more than a decade ago and holds the potential to circumvent the traditional laborious picking and testing of individual phage rescued clones. However, from a bioinformatics point of view, the analysis of this kind of data was always performed by adapting tools designed for other purposes, thus not considering the noise background typical of the ‘interactome sequencing’ approach and the heterogeneity of the data. InteractomeSeq is a web server allowing data analysis of protein domains (‘domainome’) or epitopes (‘epitome’) from either Eukaryotic or Prokaryotic genomic phage libraries generated and selected by following an Interactome sequencing approach. InteractomeSeq allows users to upload raw sequencing data and to obtain an accurate characterization of domainome/epitome profiles after setting the parameters required to tune the analysis. The release of this tool is relevant for the scientific and clinical community, because InteractomeSeq will fill an existing gap in the field of large-scale biomarkers profiling, reverse vaccinology, and structural/functional studies, thus contributing essential information for gene annotation or antigen identification. InteractomeSeq is freely available at https://InteractomeSeq.ba.itb.cnr.it/

The extensive phenotypic diversity within natural populations of Arabidopsis is associated with differences in gene expression. Transcript levels can be considered as inheritable quantitative traits, and used to map expression quantitative trait loci (eQTL) in genome-wide association studies (GWASs). In order to identify putative genetic determinants for variations in gene expression, we used publicly available genomic and transcript variation data from 665 Arabidopsis accessions and applied the single nucleotide polymorphism-set (Sequence) Kernel Association Test (SKAT) method for the identification of eQTL. Moreover, we used the penalized orthogonal-components regression (POCRE) method to increase the power of statistical tests. Then, gene annotations were used as test units to identify genes that are associated with natural variations in transcript accumulation, which correspond to candidate regulators, some of which may have a broad impact on gene expression. Besides increasing the chances to identify real associations, the analysis using POCRE and SKAT significantly reduced the computational cost required to analyze large datasets. As a proof of concept, we used this approach to identify eQTL that represent novel candidate regulators of immune responses. The versatility of this approach allows its application to any process that is subjected to natural variation among Arabidopsis accessions.