Promotion of rice seedlings growth and enhancement of cadmium immobilization under cadmium stress with two types of organic fertilizer

Cadmium (Cd)-contaminated soil poses a severe threat to crop production and human health, while also resulting in a waste of land resources. In this study, two types of organic fertilizer (ZCK: Low-content available iron; Z2: High-content available iron) were applied to Cd-contaminated soil for rice cultivation, and the effects of the fertilizer on rice growth and Cd passivation were investigated in conjunction with soil microbial analysis. Results showed that Z2 could alter the composition, structure, and diversity of microbial communities, as well as enhance the complexity and stability of the microbial network. Both 2% and 5% Z2 significantly increased the fresh weight and dry weight of rice plants while suppressing Cd absorption. The 2% Z2 exhibited the best Cd passivation effect. Gene predictions suggested that Z2 may promote plant growth by regulating microbial production of organic acids that dissolve phosphorus and potassium. Furthermore, it is suggested that Z2 may facilitate the absorption and immobilization of soil cadmium through the regulation of microbial cadmium efflux and uptake systems, as well as via the secretion of extracellular polysaccharides. In summary, Z2 can promote rice growth, suppress Cd absorption by rice, and passivate soil Cd by regulating soil microbial communities.

Effect of nitrogen retention composite additives Ca(H2PO4)2 and MgSO4 on the degradation of lignocellulose, compost maturation, and fungal communities in compost

This study investigated the effects of the nitrogen retention composite additives Ca(H2PO4)2 and MgSO4 on lignocellulose degradation, maturation, and fungal communities in composts. The study included control (C, without Ca(H2PO4)2 and MgSO4), 1% Ca(H2PO4)2 + 2% MgSO4 (CaPM1), 1.5% Ca(H2PO4)2 + 3% MgSO4 (CaPM2). The results showed that Ca(H2PO4)2 and MgSO4 enhanced the degradation of total organic carbon (TOC) and promoted the degradation of lignocellulose in compost, with CaPM2 showing the highest TOC and lignocellulose degradation. Changes in the three-dimensional excitation-emission matrix fluorescence spectroscopy (3D-EEM) of dissolved organic matter (DOM) components in compost indicated that the treatment group with the addition of Ca(H2PO4)2 and MgSO4 promoted the production of humic acids (HAs) and increased the degree of compost decomposition, with CaPM2 demonstrating the highest degree of decomposition. The addition of Ca(H2PO4)2 and MgSO4 modified the composition of the fungal community. Ca(H2PO4)2 and MgSO4 increased the relative abundance of Ascomycota, decreased unclassified_Fungi, and Glomeromycota, and activated the fungal genera Thermomyces and Aspergillus, which can degrade lignin and cellulose during the thermophilic stage of composting. Ca(H2PO4)2 and MgSO4 also increased the abundance of Saprotroph, particularly undefined Saprotroph. In conclusion, the addition of Ca(H2PO4)2 and MgSO4 in composting activated fungal communities involved in lignocellulose degradation, promoted the degradation of lignocellulose, and enhanced the maturation degree of compost.

Draft genome of Brasenia schreberi, a worldwide distributed and endangered aquatic plant

OBJECTIVES

Brasenia is a monotypic genus in the family of Cabombaceae. The only species, B. schreberi, is a macrophyte distributed worldwide. Because it requires good water quality, it is endangered in China and other countries due to the deterioration of aquatic habitats. The young leaves and stems of B. schreberi are covered by thick mucilage, which has high medical value. As an allelopathic aquatic plant, it can also be used in the management of aquatic weeds. Here, we present its assembled and annotated genome to help shed light on medial and allelopathic substrates and facilitate their conservation.

DATA DESCRIPTION

Genomic DNA and RNA extracted from B. schreberi leaf tissues were used for whole genome and RNA sequencing using a Nanopore and/or MGI sequencer. The assembly was 1,055,148,839 bp in length, with 92 contigs and an N50 of 22,379,495 bp. The repetitive elements in the assembly were 555,442,205 bp. A completeness assessment of the assembly with BUSCO and compleasm indicated 88.4 and 90.9% completeness in the Eudicots database and 95.4 and 96.6% completeness in the Embryphyta database. Gene annotation revealed 67,747 genes that coded for 73,344 proteins.

Bioinformatics for Prohormone and Neuropeptide Discovery

Neuropeptides and peptide hormones are signaling molecules produced via complex posttranslational modifications of precursor proteins known as prohormones. Neuropeptides activate specific receptors and are associated with the regulation of physiological systems and behaviors. The identification of prohormones-and the neuropeptides created by these prohormones-from genomic assemblies has become essential to support the annotation and use of the rapidly growing number of sequenced genomes. Here we describe a well-validated methodology for identifying the prohormone complement from genomic assemblies that employs widely available public toolsets and databases. The uncovered prohormone sequences can then be screened for putative neuropeptides to enable accurate proteomic discovery and validation.

A hybrid Stacking-SMOTE model for optimizing the prediction of autistic genes

PURPOSE

Autism spectrum disorder(ASD) is a disease associated with the neurodevelopment of the brain. The autism spectrum can be observed in early childhood, where the symptoms of the disease usually appear in children within the first year of their life. Currently, ASD can only be diagnosed based on the apparent symptoms due to the lack of information on genes related to the disease. Therefore, in this paper, we need to predict the largest number of disease-causing genes for a better diagnosis.

METHODS

A hybrid stacking ensemble model with Synthetic Minority Oversampling TEchnique (Stack-SMOTE) is proposed to predict the genes associated with ASD. The proposed model uses the gene ontology database to measure the similarities between the genes using a hybrid gene similarity function(HGS). HGS is effective in measuring the similarity as it combines the features of information gain-based methods and graph-based methods. The proposed model solves the imbalanced ASD dataset problem using the Synthetic Minority Oversampling Technique (SMOTE), which generates synthetic data rather than duplicates the data to reduce the overfitting. Sequentially, a gradient boosting-based random forest classifier (GBBRF) is introduced as a new combination technique to enhance the prediction of ASD genes. Moreover, the GBBRF classifier combined with random forest(RF), k-nearest neighbor, support vector machine(SVM), and logistic regression(LR) to form the proposed Stacking-SMOTE model to optimize the prediction of ASD genes.

RESULTS

The proposed Stacking-SMOTE model is evaluated using the Simons Foundation Autism Research Initiative (SFARI) gene database and a set of candidates ASD genes.The results of the proposed model-based SMOTE outperform other reported undersampling and oversampling techniques. Sequentially, the results of GBBRF achieve higher accuracy than using the basic classifiers. Moreover, the experimental results show that the proposed Stacking-SMOTE model outperforms the existing ASD prediction models with approximately 95.5% accuracy.

CONCLUSION

The proposed Stacking-SMOTE model demonstrates that SMOTE is effective in handling the autism imbalanced data. Sequentially, the integration between the gradient boosting and random forest classifier (GBBRF) support to build a robust stacking ensemble model(Stacking-SMOTE).

Galba: genome annotation with miniprot and AUGUSTUS

BACKGROUND

The Earth Biogenome Project has rapidly increased the number of available eukaryotic genomes, but most released genomes continue to lack annotation of protein-coding genes. In addition, no transcriptome data is available for some genomes.

RESULTS

Various gene annotation tools have been developed but each has its limitations. Here, we introduce GALBA, a fully automated pipeline that utilizes miniprot, a rapid protein-to-genome aligner, in combination with AUGUSTUS to predict genes with high accuracy. Accuracy results indicate that GALBA is particularly strong in the annotation of large vertebrate genomes. We also present use cases in insects, vertebrates, and a land plant. GALBA is fully open source and available as a docker image for easy execution with Singularity in high-performance computing environments.

CONCLUSIONS

Our pipeline addresses the critical need for accurate gene annotation in newly sequenced genomes, and we believe that GALBA will greatly facilitate genome annotation for diverse organisms.

HEC-ASD: a hybrid ensemble-based classification model for predicting autism spectrum disorder disease genes

PURPOSE

Autism spectrum disorder (ASD) is the most prevalent disease today. The causes of its infection may be attributed to genetic causes by 80% and environmental causes by 20%. In spite of this, the majority of the current research is concerned with environmental causes, and the least proportion with the genetic causes of the disease. Autism is a complex disease, which makes it difficult to identify the genes that cause the disease.

METHODS

Hybrid ensemble-based classification (HEC-ASD) model for predicting ASD genes using gradient boosting machines is proposed. The proposed model utilizes gene ontology (GO) to construct a gene functional similarity matrix using hybrid gene similarity (HGS) method. HGS measures the semantic similarity between genes effectively. It combines the graph-based method, such as Wang method with the number of directed children's nodes of gene term from GO. Moreover, an ensemble gradient boosting classifier is adapted to enhance the prediction of genes forming a robust classification model.

RESULTS

The proposed model is evaluated using the Simons Foundation Autism Research Initiative (SFARI) gene database. The experimental results are promising as they improve the classification performance for predicting ASD genes. The results are compared with other approaches that used gene regulatory network (GRN), protein to protein interaction network (PPI), or GO. The HEC-ASD model reaches the highest prediction accuracy of 0.88% using ensemble learning classifiers.

CONCLUSION

The proposed model demonstrates that ensemble learning technique using gradient boosting is effective in predicting autism spectrum disorder genes. Moreover, the HEC-ASD model utilized GO rather than using PPI network and GRN.

Leaf thickness of barley: genetic dissection, candidate genes prediction and its relationship with yield-related traits

In this first genetic study on assessing leaf thickness directly in cereals, major and environmentally stable QTL were detected in barley and candidate genes underlying a major locus were identified. Leaf thickness (LT) is an important characteristic affecting leaf functions which have been intensively studied. However, as LT has a small dimension in many plant species and technically difficult to measure, previous studies on this characteristic are often based on indirect estimations. In the first study of detecting QTL controlling LT by directly measuring the characteristic in barley, large and stable loci were detected from both field and glasshouse trials conducted in different cropping seasons by assessing a population of 201 recombinant inbred lines. Four loci (locating on chromosome arms 2H, 3H, 5H and 6H, respectively) were consistently detected for flag leaf thickness (FLT) in each of these trials. The one on 6H had the largest effect, with a maximum LOD 9.8 explaining up to 20.9% of phenotypic variance. FLT does not only show strong interactions with flag leaf width and flag leaf area but has also strong correlations with fertile tiller number, spike row types, kernel number per spike and heading date. Though with reduced efficiency, these loci were also detectable from assessing second last leaf of fully grown plants or even from assessing the third leaves of seedlings. Taking advantage of the high-quality genome assemblies for both parents of the mapping population used in this study, three candidate genes underlying the 6H QTL were predicted based on orthologous analysis. These results do not only broaden our understanding on genetic basis of LT and its relationship with other traits in cereal crops but also form the bases for cloning and functional analysis of genes regulating LT in barley.

Annotation of Protein-Coding Genes in Plant Genomes

Advances in next-generation sequencing technologies and the lower sequencing costs are paving the way to more plant genome sequencing, assembly, and annotation projects. While genome assembly is the first step toward elucidating the genome structure of a species, it is the annotation of the protein-coding genes that provide meaningful information to biologists. However, genome annotation is not a trivial task. Therefore, the aim of this chapter is to provide a detailed view of this important process, including tools and commands that can be used to carry out such a process.

A Hybrid Protocol for Finding Novel Gene Targets for Various Diseases Using Microarray Expression Data Analysis and Text Mining

The advancement in technology for various scientific experiments and the amount of raw data produced from that is enormous, thus giving rise to various subsets of biologists working with genome, proteome, transcriptome, expression, pathway, and so on. This has led to exponential growth in scientific literature which is becoming beyond the means of manual curation and annotation for extracting information of importance. Microarray data are expression data, analysis of which results in a set of up/downregulated lists of genes that are functionally annotated to ascertain the biological meaning of genes. These genes are represented as vocabularies and/or Gene Ontology terms when associated with pathway enrichment analysis need relational and conceptual understanding to a disease. The chapter deals with a hybrid approach we designed for identifying novel drug-disease targets. Microarray data for muscular dystrophy is explored here as an example and text mining approaches are utilized with an aim to identify promisingly novel drug targets. Our main objective is to give a basic overview from a biologist's perspective for whom text mining approaches of data mining and information retrieval is fairly a new concept. The chapter aims to bridge the gap between biologist and computational text miners and bring about unison for a more informative research in a fast and time efficient manner.

Gene prediction of aging-related diseases based on DNN and Mashup

Background

At present, the bioinformatics research on the relationship between aging-related diseases and genes is mainly through the establishment of a machine learning multi-label model to classify each gene. Most of the existing methods for predicting pathogenic genes mainly rely on specific types of gene features, or directly encode multiple features with different dimensions, use the same encoder to concatenate and predict the final results, which will be subject to many limitations in the applicability of the algorithm. Possible shortcomings of the above include: incomplete coverage of gene features by a single type of biomics data, overfitting of small dimensional datasets by a single encoder, or underfitting of larger dimensional datasets.

Methods

We use the known gene disease association data and gene descriptors, such as gene ontology terms (GO), protein interaction data (PPI), PathDIP, Kyoto Encyclopedia of genes and genomes Genes (KEGG), etc, as input for deep learning to predict the association between genes and diseases. Our innovation is to use Mashup algorithm to reduce the dimensionality of PPI, GO and other large biological networks, and add new pathway data in KEGG database, and then combine a variety of biological information sources through modular Deep Neural Network (DNN) to predict the genes related to aging diseases.

Result and conclusion

The results show that our algorithm is more effective than the standard neural network algorithm (the Area Under the ROC curve from 0.8795 to 0.9153), gradient enhanced tree classifier and logistic regression classifier. In this paper, we firstly use DNN to learn the similar genes associated with the known diseases from the complex multi-dimensional feature space, and then provide the evidence that the assumed genes are associated with a certain disease.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04518-5.

TSEBRA: transcript selector for BRAKER

BACKGROUND

BRAKER is a suite of automatic pipelines, BRAKER1 and BRAKER2, for the accurate annotation of protein-coding genes in eukaryotic genomes. Each pipeline trains statistical models of protein-coding genes based on provided evidence and, then predicts protein-coding genes in genomic sequences using both the extrinsic evidence and statistical models. For training and prediction, BRAKER1 and BRAKER2 incorporate complementary extrinsic evidence: BRAKER1 uses only RNA-seq data while BRAKER2 uses only a database of cross-species proteins. The BRAKER suite has so far not been able to reliably exceed the accuracy of BRAKER1 and BRAKER2 when incorporating both types of evidence simultaneously. Currently, for a novel genome project where both RNA-seq and protein data are available, the best option is to run both pipelines independently, and to pick one, likely better output. Therefore, one or another type of the extrinsic evidence would remain unexploited.

RESULTS

We present TSEBRA, a software that selects gene predictions (transcripts) from the sets generated by BRAKER1 and BRAKER2. TSEBRA uses a set of rules to compare scores of overlapping transcripts based on their support by RNA-seq and homologous protein evidence. We show in computational experiments on genomes of 11 species that TSEBRA achieves higher accuracy than either BRAKER1 or BRAKER2 running alone and that TSEBRA compares favorably with the combiner tool EVidenceModeler.

CONCLUSION

TSEBRA is an easy-to-use and fast software tool. It can be used in concert with the BRAKER pipeline to generate a gene prediction set supported by both RNA-seq and homologous protein evidence.

A review of computational tools for generating metagenome-assembled genomes from metagenomic sequencing data

Metagenomic sequencing provides a culture-independent avenue to investigate the complex microbial communities by constructing metagenome-assembled genomes (MAGs). A MAG represents a microbial genome by a group of sequences from genome assembly with similar characteristics. It enables us to identify novel species and understand their potential functions in a dynamic ecosystem. Many computational tools have been developed to construct and annotate MAGs from metagenomic sequencing, however, there is a prominent gap to comprehensively introduce their background and practical performance. In this paper, we have thoroughly investigated the computational tools designed for both upstream and downstream analyses, including metagenome assembly, metagenome binning, gene prediction, functional annotation, taxonomic classification, and profiling. We have categorized the commonly used tools into unique groups based on their functional background and introduced the underlying core algorithms and associated information to demonstrate a comparative outlook. Furthermore, we have emphasized the computational requisition and offered guidance to the users to select the most efficient tools. Finally, we have indicated current limitations, potential solutions, and future perspectives for further improving the tools of MAG construction and annotation. We believe that our work provides a consolidated resource for the current stage of MAG studies and shed light on the future development of more effective MAG analysis tools on metagenomic sequencing.

Prokaryotic Genome Annotation

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

Comprehensive evaluation of protein-coding sORFs prediction based on a random sequence strategy

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

Simple, Reliable, and Time-Efficient Manual Annotation of Bacterial Genomes with MAISEN

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

Robustness analysis of metabolic predictions in algal microbial communities based on different annotation pipelines

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

FINDER: an automated software package to annotate eukaryotic genes from RNA-Seq data and associated protein sequences

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.

Parallel analysis of global garlic gene expression and alliin content following leaf wounding

BACKGROUND

Allium sativum (garlic) is an economically important food source and medicinal plant rich in sulfides and other protective substances such as alliin, the precursor of allicin biosynthesis. Cysteine, serine and sulfur is the precursor of alliin biosynthesis. However, little is known about the alliin content under abiotic stress or the mechanism by which it is synthesized.

RESULTS

The findings revealed that the content of alliin was lowest in the garlic roots, and highest in the buds. Furthermore, alliin levels decreased in mature leaves following wounding. Transcriptome data generated over time after wounding further revealed significant up-regulation of genes integral to the biosynthetic pathways of cysteine and serine in mature garlic leaves.

CONCLUSIONS

The findings suggest that differential expression of cysteine, serine and sulfide-related genes underlies the accumulation of alliin and its precursors in garlic, providing a basis for further analyses of alliin biosynthesis.

geneRFinder: gene finding in distinct metagenomic data complexities

Background

Microbes perform a fundamental economic, social, and environmental role in our society. Metagenomics makes it possible to investigate microbes in their natural environments (the complex communities) and their interactions. The way they act is usually estimated by looking at the functions they play in those environments and their responsibility is measured by their genes. The advances of next-generation sequencing technology have facilitated metagenomics research however it also creates a heavy computational burden. Large and complex biological datasets are available as never before. There are many gene predictors available that can aid the gene annotation process though they lack handling appropriately metagenomic data complexities. There is no standard metagenomic benchmark data for gene prediction. Thus, gene predictors may inflate their results by obfuscating low false discovery rates.

Results

We introduce geneRFinder, an ML-based gene predictor able to outperform state-of-the-art gene prediction tools across this benchmark by using only one pre-trained Random Forest model. Average prediction rates of geneRFinder differed in percentage terms by 54% and 64%, respectively, against Prodigal and FragGeneScan while handling high complexity metagenomes. The specificity rate of geneRFinder had the largest distance against FragGeneScan, 79 percentage points, and 66 more than Prodigal. According to McNemar’s test, all percentual differences between predictors performances are statistically significant for all datasets with a 99% confidence interval.

Conclusions

We provide geneRFinder, an approach for gene prediction in distinct metagenomic complexities, available at gitlab.com/r.lorenna/generfinder and https://osf.io/w2yd6/, and also we provide a novel, comprehensive benchmark data for gene prediction—which is based on The Critical Assessment of Metagenome Interpretation (CAMI) challenge, and contains labeled data from gene regions—available at https://sourceforge.net/p/generfinder-benchmark.

Draft genome assembly and annotation of the masked birch caterpillar, Drepana arcuata (Lepidoptera: Drepanoidea)

The masked birch caterpillar, Drepana arcuata Walker (Lepidoptera: Drepanidae), and other Drepanoidea (Lepidoptera) species are excellent organisms for investigating the function and evolution of vibratory communication and sociality in caterpillars. We present a de novo assembled draft genome and functional annotation for D. arcuata, using a combination of short and long sequencing reads generated by Illumina HiSeq X and Oxford Nanopore Technologies (ONT) MinION sequencing platforms, respectively. A total of 460,694,612 150bp paired-end Illumina and 395,890 ONT raw reads were assembled into 11,493 scaffolds spanning a genome size of 270.5Mb. The resulting D. arcuata genome has a GC content of 38.79%, repeat content of 8.26%, is 86.5% complete based on Benchmarking Universal Single-Copy Orthologs (BUSCO) assessment, and comprises 10,398 predicted protein-coding genes. These data represent the first genomic resources for the lepidopteran superfamily Drepanoidea. Although the order Lepidoptera comprises numerous ecologically and economically important species, assembled genomes and annotations are available for < 1% of the total species. These data can be further utilized for research on Lepidoptera genomics as well as on the function and evolution of vibratory communication and sociality in larval insects.

Understanding the causes of errors in eukaryotic protein-coding gene prediction: a case study of primate proteomes

Background

Recent advances in sequencing technologies have led to an explosion in the number of genomes available, but accurate genome annotation remains a major challenge. The prediction of protein-coding genes in eukaryotic genomes is especially problematic, due to their complex exon–intron structures. Even the best eukaryotic gene prediction algorithms can make serious errors that will significantly affect subsequent analyses.

Results

We first investigated the prevalence of gene prediction errors in a large set of 176,478 proteins from ten primate proteomes available in public databases. Using the well-studied human proteins as a reference, a total of 82,305 potential errors were detected, including 44,001 deletions, 27,289 insertions and 11,015 mismatched segments where part of the correct protein sequence is replaced with an alternative erroneous sequence. We then focused on the mismatched sequence errors that cause particular problems for downstream applications. A detailed characterization allowed us to identify the potential causes for the gene misprediction in approximately half (5446) of these cases. As a proof-of-concept, we also developed a simple method which allowed us to propose improved sequences for 603 primate proteins.

Conclusions

Gene prediction errors in primate proteomes affect up to 50% of the sequences. Major causes of errors include undetermined genome regions, genome sequencing or assembly issues, and limitations in the models used to represent gene exon–intron structures. Nevertheless, existing genome sequences can still be exploited to improve protein sequence quality. Perspectives of the work include the characterization of other types of gene prediction errors, as well as the development of a more comprehensive algorithm for protein sequence error correction.

Predicting and clustering plant CLE genes with a new method developed specifically for short amino acid sequences

Background

The CLV3/ESR-RELATED (CLE) gene family encodes small secreted peptides (SSPs) and plays vital roles in plant growth and development by promoting cell-to-cell communication. The prediction and classification of CLE genes is challenging because of their low sequence similarity.

Results

We developed a machine learning-aided method for predicting CLE genes by using a CLE motif-specific residual score matrix and a novel clustering method based on the Euclidean distance of 12 amino acid residues from the CLE motif in a site-weight dependent manner. In total, 2156 CLE candidates—including 627 novel candidates—were predicted from 69 plant species. The results from our CLE motif-based clustering are consistent with previous reports using the entire pre-propeptide. Characterization of CLE candidates provided systematic statistics on protein lengths, signal peptides, relative motif positions, amino acid compositions of different parts of the CLE precursor proteins, and decisive factors of CLE prediction. The approach taken here provides information on the evolution of the CLE gene family and provides evidence that the CLE and IDA/IDL genes share a common ancestor.

Conclusions

Our new approach is applicable to SSPs or other proteins with short conserved domains and hence, provides a useful tool for gene prediction, classification and evolutionary analysis.

Global sequence features based translation initiation site prediction in human genomic sequences

Gene prediction has been increasingly important in genome annotation due to advancements in sequencing technology. Genome annotation further helps in determining the structure and function of these genes. Translation initiation site prediction (TIS) in human genomic sequences is one of the fundamental and essential steps in gene prediction. Thus, accurate prediction of TIS in these sequences is highly desirable. Although many computational methods were developed for this problem, none of them focused on finding these sites in human genomic sequences. In this paper, a new TIS prediction method is proposed by incorporating global sequence based features. Support vector machine is used to assess the prediction power of these features. The proposed method achieved accuracy of above 90% when tested for genomic as well as cDNA sequences. The experimental results indicate that the method works well for both genomic and cDNA sequences. The method can be integrated into gene prediction system in future.

BMT: Bioinformatics mini toolbox for comprehensive DNA and protein analysis

Background Bioinformatics tools are of great significance and are used in different spheres of life sciences. There are wide variety of tools available to perform primary analysis of DNA and protein but most of them are available on different platforms and many remain undetected. Accessing these tools separately to perform individual task is uneconomical and inefficient. Objective Our aim is to bring different bioinformatics models on a single platform to ameliorate scientific research. Hence, our objective is to make a tool for comprehensive DNA and protein analysis. Methods To develop a reliable, straight-forward and standalone desktop application we used state of the art python packages and libraries. Bioinformatics Mini Toolbox (BMT) is combination of seven tools including FastqTrimmer, Gene Prediction, DNA Analysis, Translation, Protein analysis and Pairwise and Multiple alignment. Results FastqTrimmer assists in quality assurance of NGS data. Gene prediction predicts the genes by homology from novel genome on the basis of reference sequence. Protein analysis and DNA analysis calculates physiochemical properties of nucleotide and protein sequences, respectively. Translation translates the DNA sequence into six open reading frames. Pairwise alignment performs pairwise global and local alignment of DNA and protein sequences on the basis or multiple matrices. Multiple alignment aligns multiple sequences and generates a phylogenetic tree. Conclusion We developed a tool for comprehensive DNA and protein analysis. The link to download BMT is https://github.com/nasiriqbal012/BMT_SETUP.git.

Genotype-phenotype correlations of Berardinelli-Seip congenital lipodystrophy and novel candidate genes prediction

BACKGROUND

Berardinelli-Seip congenital lipodystrophy (BSCL) is a heterogeneous autosomal recessive disorder characterized by an almost total lack of adipose tissue in the body. Mutations in the AGPAT2, BSCL2, CAV1 and PTRF genes define I-IV subtype of BSLC respectively and clinical data indicate that new causative genes remain to be discovered. Here, we retrieved 341 cases from 60 BSCL-related studies worldwide and aimed to explore genotype-phenotype correlations based on mutations of AGPAT2 and BSCL2 genes from 251 cases. We also inferred new candidate genes for BSCL through protein-protein interaction and phenotype-similarity.

RESULTS

Analysis results show that BSCL type II with earlier age of onset of diabetes mellitus, higher risk to suffer from premature death and mental retardation, is a more severe disorder than BSCL type I, but BSCL type I patients are more likely to have bone cysts. In BSCL type I, females are at higher risk of developing diabetes mellitus and acanthosis nigricans than males, while in BSCL type II, males suffer from diabetes mellitus earlier than females. In addition, some significant correlations among BSCL-related phenotypes were identified. New candidate genes prediction through protein-protein interaction and phenotype-similarity was conducted and we found that CAV3, EBP, SNAP29, HK1, CHRM3, OBSL1 and DNAJC13 genes could be the pathogenic factors for BSCL. Particularly, CAV3 and EBP could be high-priority candidate genes contributing to pathogenesis of BSCL.

CONCLUSIONS

Our study largely enhances the current knowledge of phenotypic and genotypic heterogeneity of BSCL and promotes the more comprehensive understanding of pathogenic mechanisms for BSCL.

A benchmark study of ab initio gene prediction methods in diverse eukaryotic organisms

Background

The draft genome assemblies produced by new sequencing technologies present important challenges for automatic gene prediction pipelines, leading to less accurate gene models. New benchmark methods are needed to evaluate the accuracy of gene prediction methods in the face of incomplete genome assemblies, low genome coverage and quality, complex gene structures, or a lack of suitable sequences for evidence-based annotations.

Results

We describe the construction of a new benchmark, called G3PO (benchmark for Gene and Protein Prediction PrOgrams), designed to represent many of the typical challenges faced by current genome annotation projects. The benchmark is based on a carefully validated and curated set of real eukaryotic genes from 147 phylogenetically disperse organisms, and a number of test sets are defined to evaluate the effects of different features, including genome sequence quality, gene structure complexity, protein length, etc. We used the benchmark to perform an independent comparative analysis of the most widely used ab initio gene prediction programs and identified the main strengths and weaknesses of the programs. More importantly, we highlight a number of features that could be exploited in order to improve the accuracy of current prediction tools.

Conclusions

The experiments showed that ab initio gene structure prediction is a very challenging task, which should be further investigated. We believe that the baseline results associated with the complex gene test sets in G3PO provide useful guidelines for future studies.

Comparative genomics and association analysis identifies virulence genes of Cercospora sojina in soybean

BACKGROUND

Recently, a new strain of Cercospora sojina (Race15) has been identified, which has caused the breakdown of resistance in most soybean cultivars in China. Despite this serious yield reduction, little is known about why this strain is more virulent than others. Therefore, we sequenced the Race15 genome and compared it to the Race1 genome sequence, as its virulence is significantly lower. We then re-sequenced 30 isolates of C. sojina from different regions to identifying differential virulence genes using genome-wide association analysis (GWAS).

RESULTS

The 40.12-Mb Race15 genome encodes 12,607 predicated genes and contains large numbers of gene clusters that have annotations in 11 different common databases. Comparative genomics revealed that although these two genomes had a large number of homologous genes, their genome structures have evolved to introduce 245 specific genes. The most important 5 candidate virulence genes were located on Contig 3 and Contig 1 and were mainly related to the regulation of metabolic mechanisms and the biosynthesis of bioactive metabolites, thereby putatively affecting fungi self-toxicity and reducing host resistance. Our study provides insight into the genomic basis of C. sojina pathogenicity and its infection mechanism, enabling future studies of this disease.

CONCLUSIONS

Via GWAS, we identified five candidate genes using three different methods, and these candidate genes are speculated to be related to metabolic mechanisms and the biosynthesis of bioactive metabolites. Meanwhile, Race15 specific genes may be linked with high virulence. The genes highly prevalent in virulent isolates should also be proposed as candidates, even though they were not found in our SNP analysis. Future work should focus on using a larger sample size to confirm and refine candidate gene identifications and should study the functional roles of these candidates, in order to investigate their potential roles in C. sojina pathogenicity.

Enhanced genome assembly and a new official gene set for Tribolium castaneum

Background

The red flour beetle Tribolium castaneum has emerged as an important model organism for the study of gene function in development and physiology, for ecological and evolutionary genomics, for pest control and a plethora of other topics. RNA interference (RNAi), transgenesis and genome editing are well established and the resources for genome-wide RNAi screening have become available in this model. All these techniques depend on a high quality genome assembly and precise gene models. However, the first version of the genome assembly was generated by Sanger sequencing, and with a small set of RNA sequence data limiting annotation quality.

Results

Here, we present an improved genome assembly (Tcas5.2) and an enhanced genome annotation resulting in a new official gene set (OGS3) for Tribolium castaneum, which significantly increase the quality of the genomic resources. By adding large-distance jumping library DNA sequencing to join scaffolds and fill small gaps, the gaps in the genome assembly were reduced and the N50 increased to 4753kbp. The precision of the gene models was enhanced by the use of a large body of RNA-Seq reads of different life history stages and tissue types, leading to the discovery of 1452 novel gene sequences. We also added new features such as alternative splicing, well defined UTRs and microRNA target predictions. For quality control, 399 gene models were evaluated by manual inspection. The current gene set was submitted to Genbank and accepted as a RefSeq genome by NCBI.

Conclusions

The new genome assembly (Tcas5.2) and the official gene set (OGS3) provide enhanced genomic resources for genetic work in Tribolium castaneum. The much improved information on transcription start sites supports transgenic and gene editing approaches. Further, novel types of information such as splice variants and microRNA target genes open additional possibilities for analysis.

CNN-MGP: Convolutional Neural Networks for Metagenomics Gene Prediction

Accurate gene prediction in metagenomics fragments is a computationally challenging task due to the short-read length, incomplete, and fragmented nature of the data. Most gene-prediction programs are based on extracting a large number of features and then applying statistical approaches or supervised classification approaches to predict genes. In our study, we introduce a convolutional neural network for metagenomics gene prediction (CNN-MGP) program that predicts genes in metagenomics fragments directly from raw DNA sequences, without the need for manual feature extraction and feature selection stages. CNN-MGP is able to learn the characteristics of coding and non-coding regions and distinguish coding and non-coding open reading frames (ORFs). We train 10 CNN models on 10 mutually exclusive datasets based on pre-defined GC content ranges. We extract ORFs from each fragment; then, the ORFs are encoded numerically and inputted into an appropriate CNN model based on the fragment-GC content. The output from the CNN is the probability that an ORF will encode a gene. Finally, a greedy algorithm is used to select the final gene list. Overall, CNN-MGP is effective and achieves a 91% accuracy on testing dataset. CNN-MGP shows the ability of deep learning to predict genes in metagenomics fragments, and it achieves an accuracy higher than or comparable to state-of-the-art gene-prediction programs that use pre-defined features.

Repertoire-wide gene structure analyses: a case study comparing automatically predicted and manually annotated gene models

Background

The location and modular structure of eukaryotic protein-coding genes in genomic sequences can be automatically predicted by gene annotation algorithms. These predictions are often used for comparative studies on gene structure, gene repertoires, and genome evolution. However, automatic annotation algorithms do not yet correctly identify all genes within a genome, and manual annotation is often necessary to obtain accurate gene models and gene sets. As manual annotation is time-consuming, only a fraction of the gene models in a genome is typically manually annotated, and this fraction often differs between species. To assess the impact of manual annotation efforts on genome-wide analyses of gene structural properties, we compared the structural properties of protein-coding genes in seven diverse insect species sequenced by the i5k initiative.

Results

Our results show that the subset of genes chosen for manual annotation by a research community (3.5–7% of gene models) may have structural properties (e.g., lengths and exon counts) that are not necessarily representative for a species’ gene set as a whole. Nonetheless, the structural properties of automatically generated gene models are only altered marginally (if at all) through manual annotation. Major correlative trends, for example a negative correlation between genome size and exonic proportion, can be inferred from either the automatically predicted or manually annotated gene models alike. Vice versa, some previously reported trends did not appear in either the automatic or manually annotated gene sets, pointing towards insect-specific gene structural peculiarities.

Conclusions

In our analysis of gene structural properties, automatically predicted gene models proved to be sufficiently reliable to recover the same gene-repertoire-wide correlative trends that we found when focusing on manually annotated gene models only. We acknowledge that analyses on the individual gene level clearly benefit from manual curation. However, as genome sequencing and annotation projects often differ in the extent of their manual annotation and curation efforts, our results indicate that comparative studies analyzing gene structural properties in these genomes can nonetheless be justifiable and informative.

Electronic supplementary material

The online version of this article (10.1186/s12864-019-6064-8) contains supplementary material, which is available to authorized users.

Soil bacterial community dynamics following surfactant addition and bioaugmentation in pyrene-contaminated soils

Because of their toxic properties, polycyclic aromatic hydrocarbons (PAHs) are designated as priority pollutants. The low solubility and strong sorption of PAHs in soil often limits bioremediation. To increase PAH bioavailability and enhance microbial degradation, surfactants are often added to contaminated soils. However, the effects of surfactants on the PAH degradation capacities of soil microbes are generally neglected. In this study, 16S rRNA gene high-throughput sequencing was used to evaluate changes in the soil microbial community after the application of rhamnolipid biosurfactant or Brij-35 surfactant and Mycobacterium vanbaalenii PYR-1 bioaugmentation over a 50-d mineralization study in two soils contaminated with pyrene at 10 mg kg^-1. The introduction of pyrene in both soils resulted in an increase in Firmicutes and a decrease in microbial richness and Shannon diversity index. Amendment of rhamnolipid at 1,400 μg g^-1 to the native clay soil resulted in a decrease in Bacillus from 48% to 2%, which was accompanied with an increase in Mycoplana that accounted for 67% of the total genera relative abundance. Phylogenetic investigation of communities by reconstruction of unobserved states was used to predict the activity of functional genes involved in the PAH degradation KEGG pathway and determined that M. vanbaalenii PYR-1 bioaugmentation resulted in an increased number of functional genes utilized in PAH biodegradation. Results of this study provide a better understanding of the soil microbial dynamics in response to surfactant amendments in addition to bioaugmentation of a PAH-degrading microbe. This knowledge contributes to successful and efficient surfactant-enhanced bioremediation of PAH-contaminated soils.

gFACs: Gene Filtering, Analysis, and Conversion to Unify Genome Annotations Across Alignment and Gene Prediction Frameworks

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

Genome and transcriptome characterization of the glycoengineered Nicotiana benthamiana line ΔXT/FT

BACKGROUND

The allotetraploid tobacco species Nicotiana benthamiana native to Australia has become a popular host for recombinant protein production. Although its usage grows every year, little is known on this plant's genomic and transcriptomic features. Most N. benthamiana accessions currently used in research lack proper documentation of their breeding history and provenance. One of these, the glycoengineered N. benthamiana line ΔXT/FT is increasingly used for the production of biopharmaceutical proteins.

RESULTS

Based on an existing draft assembly of the N. benthamiana genome we predict 50,516 protein -encoding genes (62,216 transcripts) supported by expression data derived from 2.35 billion mRNA-seq reads. Using single-copy core genes we show high completeness of the predicted gene set. We functionally annotate more than two thirds of the gene set through sequence homology to genes from other Nicotiana species. We demonstrate that the expression profiles from leaf tissue of ΔXT/FT and its wild type progenitor only show minimal differences. We identify the transgene insertion sites in ΔXT/FT and show that one of the transgenes was inserted inside another predicted gene that most likely lost its function upon insertion. Based on publicly available mRNA-seq data, we confirm that the N. benthamiana accessions used by different research institutions most likely derive from a single source.

CONCLUSIONS

This work provides gene annotation of the N. benthamiana genome, a genomic and transcriptomic characterization of a transgenic N. benthamiana line in comparison to its wild-type progenitor, and sheds light onto the relatedness of N. benthamiana accessions that are used in laboratories around the world.

Evaluating the performance of sequence encoding schemes and machine learning methods for splice sites recognition

Identification of splice sites is imperative for prediction of gene structure. Machine learning-based approaches (MLAs) have been reported to be more successful than the rule-based methods for identification of splice sites. However, the strings of alphabets should be transformed into numeric features through sequence encoding before using them as input in MLAs. In this study, we evaluated the performances of 8 different sequence encoding schemes i.e., Bayes kernel, density and sparse (DS), distribution of tri-nucleotide and 1st order Markov model (DM), frequency difference distance measure (FDDM), paired-nucleotide frequency difference between true and false sites (FDTF), 1st order Markov model (MM1), combination of both 1st and 2nd order Markov model (MM1 + MM2) and 2nd order Markov model (MM2) in respect of predicting donor and acceptor splice sites using 5 supervised learning methods (ANN, Bagging, Boosting, RF and SVM). The encoding schemes and machine learning methods were first evaluated in 4 species i.e., A. thaliana, C. elegans, D. melanogaster and H. sapiens, and then performances were validated with another four species i.e., Ciona intestinalis, Dictyostelium discoideum, Phaeodactylum tricornutum and Trypanosoma brucei. In terms of ROC (receiver-operating-characteristics) and PR (precision-recall) curves, FDTF encoding approach achieved higher accuracy followed by either MM2 or FDDM. Further, SVM was found to achieve higher accuracy (in terms of ROC and PR curves) followed by RF across encoding schemes and species. In terms of prediction accuracy across species, the SVM-FDTF combination was optimum than other combinations of classifiers and encoding schemes. Further, splice site prediction accuracies were observed higher for the species with low intron density. To our limited knowledge, this is the first attempt as far as comprehensive evaluation of sequence encoding schemes for prediction of splice sites is concerned. We have also developed an R-package EncDNA (https://cran.r-project.org/web/packages/EncDNA/index.html) for encoding of splice site motifs with different encoding schemes, which is expected to supplement the existing nucleotide sequence encoding approaches. This study is believed to be useful for the computational biologists for predicting different functional elements on the genomic DNA.

High-quality genome assembly of the silkworm, Bombyx mori

In 2008, the genome assembly and gene models for the domestic silkworm, Bombyx mori, were published by a Japanese and Chinese collaboration group. However, the genome assembly contains a non-negligible number of misassembled and gap regions due to the presence of many repetitive sequences within the silkworm genome. The erroneous genome assembly occasionally causes incorrect gene prediction. Here we performed hybrid assembly based on 140 × deep sequencing of long (PacBio) and short (Illumina) reads. The remaining gaps in the initial genome assembly were closed using BAC and Fosmid sequences, giving a new total length of 460.3 Mb, with 30 gap regions and an N50 comprising 16.8 Mb in scaffolds and 12.2 Mb in contigs. More RNA-seq and piRNA-seq reads were mapped on the new genome assembly compared with the previous version, indicating that the new genome assembly covers more transcribed regions, including repetitive elements. We performed gene prediction based on the new genome assembly using available mRNA and protein sequence data. The number of gene models was 16,880 with an N50 of 2154 bp. The new gene models reflected more accurate coding sequences and gene sets than old ones. The proportion of repetitive elements was also reestimated using the new genome assembly, and was calculated to be 46.8% in the silkworm genome. The new genome assembly and gene models are provided in SilkBase (http://silkbase.ab.a.u-tokyo.ac.jp).

Whole-Genome Annotation with BRAKER

BRAKER is a pipeline for highly accurate and fully automated gene prediction in novel eukaryotic genomes. It combines two major tools: GeneMark-ES/ET and AUGUSTUS. GeneMark-ES/ET learns its parameters from a novel genomic sequence in a fully automated fashion; if available, it uses extrinsic evidence for model refinement. From the protein-coding genes predicted by GeneMark-ES/ET, we select a set for training AUGUSTUS, one of the most accurate gene finding tools that, in contrast to GeneMark-ES/ET, integrates extrinsic evidence already into the gene prediction step. The first published version, BRAKER1, integrated genomic footprints of unassembled RNA-Seq reads into the training as well as into the prediction steps. The pipeline has since been extended to the integration of data on mapped cross-species proteins, and to the usage of heterogeneous extrinsic evidence, both RNA-Seq and protein alignments. In this book chapter, we briefly summarize the pipeline methodology and describe how to apply BRAKER in environments characterized by various combinations of external evidence.

tRNAscan-SE: Searching for tRNA Genes in Genomic Sequences

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

GeMoMa: Homology-Based Gene Prediction Utilizing Intron Position Conservation and RNA-seq Data

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

Multi-Genome Annotation with AUGUSTUS

Comparing multiple related genomes can help to improve their structural annotation. The accuracy and consistency of the predicted exon-intron structures of the protein coding genes can be higher when considering all genomes at once rather than annotating one genome at a time.The comparative gene prediction algorithm of AUGUSTUS performs such a multi-genome annotation. A multiple alignment of genomes is used to exploit evolutionary clues to conservation and negative selection. Further, AUGUSTUS exploits the fact that orthologous genes typically have congruent exon-intron structures. Comparative AUGUSTUS simultaneously predicts the genes in all input genomes. In this chapter we walk the reader through a small example from eight vertebrate species, including the construction of an alignment of the input genomes and how to integrate RNA-Seq evidence from multiple species for gene finding.

Comparative Genome Annotation

Newly sequenced genomes are being added to the tree of life at an unprecedented fast pace. Increasingly, such new genomes are phylogenetically close to previously sequenced and annotated genomes. In other cases, whole clades of closely related species or strains ought to be annotated simultaneously. Often, in subsequent studies differences between the closely related species or strains are in the focus of research when the shared gene structures prevail. We here review methods for comparative structural genome annotation. The reviewed methods include classical approaches such as the alignment of protein sequences or protein profiles against the genome and comparative gene prediction methods that exploit a genome alignment to annotate a target genome. Newer approaches such as the simultaneous annotation of multiple genomes are also reviewed. We discuss how the methods depend on the phylogenetic placement of genomes, give advice on the choice of methods, and examine the consistency between gene structure annotations in an example. Further, we provide practical advice on genome annotation in general.

Whole-genome sequence data and analysis of type strains 'Pusillimonas nitritireducens' and 'Pusillimonas subterraneus' isolated from nitrate- and radionuclide-contaminated groundwater in Russia

Two strains, 'Pusillimonas nitritireducens' JR1/69-2-13^T and 'Pusillimonas subterraneus' JR1/69-3-13^T, of aerobic, motile, Gram-negative, non-spore-forming, organotrophic, psychrotolerant bacteria were isolated from a sample of nitrate- and radionuclide-contaminated groundwater in Russia. Here we describe the draft genomes of these strains. The sequenced and annotated genome of the strain JR1/69-2-13^T contained 4.3 Mbp with 4108 protein-coding genes. The genome of the strain JR1/69-3-13^T contained 4.5 Mbp with 4260 protein-coding genes. Genome analysis of both strains provides an insight into the genomic basis of their resistance to nitrate, heavy metals and metalloids. The draft genome sequences of strains 'Pusillimonas nitritireducens' JR1/69-2-13^T and 'Pusillimonas subterraneus' JR1/69-3-13^T are available at DDBJ/EMBL/GenBank under the accession nos. https://www.ncbi.nlm.nih.gov/nuccore/PDNV00000000 and https://www.ncbi.nlm.nih.gov/nuccore/PDNW00000000, respectively.

Feature selection for gene prediction in metagenomic fragments

BACKGROUND

Computational approaches, specifically machine-learning techniques, play an important role in many metagenomic analysis algorithms, such as gene prediction. Due to the large feature space, current de novo gene prediction algorithms use different combinations of classification algorithms to distinguish between coding and non-coding sequences.

RESULTS

In this study, we apply a filter method to select relevant features from a large set of known features instead of combining them using linear classifiers or ignoring their individual coding potential. We use minimum redundancy maximum relevance (mRMR) to select the most relevant features. Support vector machines (SVM) are trained using these features, and the classification score is transformed into the posterior probability of the coding class. A greedy algorithm uses the probability of overlapped candidate genes to select the final genes. Instead of using one model for all sequences, we train an ensemble of SVM models on mutually exclusive datasets based on GC content and use the appropriated model to classify candidate genes based on their read's GC content.

CONCLUSION

Our proposed algorithm achieves an improvement over some existing algorithms. mRMR produces promising results in gene prediction. It improves classification performance and feature interpretation. Our research serves as a basis for future studies on feature selection for gene prediction.

De novo sequencing and initial annotation of the Mongolian gerbil (Meriones unguiculatus) genome

The Mongolian gerbil (Meriones unguiculatus) is a member of the rodent family that displays several features not found in mice or rats, including sensory specializations and social patterns more similar to those in humans. These features have made gerbils a valuable animal for research studies of auditory and visual processing, brain development, learning and memory, and neurological disorders. Here, we report the whole gerbil annotated genome sequence, and identify important similarities and differences to the human and mouse genomes. We further analyze the chromosomal structure of eight genes with high relevance for controlling neural signaling and demonstrate a high degree of homology between these genes in mouse and gerbil. This homology increases the likelihood that individual genes can be rapidly identified in gerbil and used for genetic manipulations. The availability of the gerbil genome provides a foundation for advancing our knowledge towards understanding evolution, behavior and neural function in mammals. ACCESSION NUMBER: The Whole Genome Shotgun sequence data from this project has been deposited at DDBJ/ENA/GenBank under the accession NHTI00000000. The version described in this paper is version NHTI01000000. The fragment reads, and mate pair reads have been deposited in the Sequence Read Archive under BioSample accession SAMN06897401.

Gene Unprediction with Spurio: A tool to identify spurious protein sequences

We now have access to the sequences of tens of millions of proteins. These protein sequences are essential for modern molecular biology and computational biology. The vast majority of protein sequences are derived from gene prediction tools and have no experimental supporting evidence for their translation.  Despite the increasing accuracy of gene prediction tools there likely exists a large number of spurious protein predictions in the sequence databases.  We have developed the Spurio tool to help identify spurious protein predictions in prokaryotes.  Spurio searches the query protein sequence against a prokaryotic nucleotide database using tblastn and identifies homologous sequences. The tblastn matches are used to score the query sequence's likelihood of being a spurious protein prediction using a Gaussian process model. The most informative feature is the appearance of stop codons within the presumed translation of homologous DNA sequences. Benchmarking shows that the Spurio tool is able to distinguish spurious from true proteins. However, transposon proteins are prone to be predicted as spurious because of the frequency of degraded homologs found in the DNA sequence databases. Our initial experiments suggest that less than 1% of the proteins in the UniProtKB sequence database are likely to be spurious and that Spurio is able to identify over 60 times more spurious proteins than the AntiFam resource. The Spurio software and source code is available under an MIT license at the following URL: https://bitbucket.org/bateman-group/spurio.

De novo genome and transcriptome resources of the Adzuki bean borer Ostrinia scapulalis (Lepidoptera: Crambidae)

We present a draft genome assembly with a de novo prediction and automated functional annotation of coding genes, and a reference transcriptome of the Adzuki bean borer, Ostrinia scapulalis, based on RNA sequencing of various tissues and developmental stages. The genome assembly spans 419 Mb, has a GC content of 37.4% and includes 26,120 predicted coding genes. The reference transcriptome holds 33,080 unigenes and contains a high proportion of a set of genes conserved in eukaryotes and arthropods, used as quality assessment of the reconstructed transcripts. The new genomic and transcriptomic data presented here significantly enrich the public sequence databases for the Crambidae and Lepidoptera, and represent useful resources for future researches related to the evolution and the adaptation of phytophagous moths. The genome and transcriptome assemblies have been deposited and made accessible via a NCBI BioProject (id PRJNA390510) and the LepidoDB database (http://bipaa.genouest.org/sp/ostrinia_scapulalis/).

A dual transcript-discovery approach to improve the delimitation of gene features from RNA-seq data in the chicken model

The sequence of the chicken genome, like several other draft genome sequences, is presently not fully covered. Gaps, contigs assigned with low confidence and uncharacterized chromosomes result in gene fragmentation and imprecise gene annotation. Transcript abundance estimation from RNA sequencing (RNA-seq) data relies on read quality, library complexity and expression normalization. In addition, the quality of the genome sequence used to map sequencing reads, and the gene annotation that defines gene features, must also be taken into account. A partially covered genome sequence causes the loss of sequencing reads from the mapping step, while an inaccurate definition of gene features induces imprecise read counts from the assignment step. Both steps can significantly bias interpretation of RNA-seq data. Here, we describe a dual transcript-discovery approach combining a genome-guided gene prediction and a de novo transcriptome assembly. This dual approach enabled us to increase the assignment rate of RNA-seq data by nearly 20% as compared to when using only the chicken reference annotation, contributing therefore to a more accurate estimation of transcript abundance. More generally, this strategy could be applied to any organism with partial genome sequence and/or lacking a manually-curated reference annotation in order to improve the accuracy of gene expression studies.

Evaluating Programs for Predicting Genes and Transcripts with RNA-Seq Support in Fungal Genomes

The steps needed to computationally predict genes and transcripts in fungal genomes with support from RNA-Seq data are described in detail for three prediction programs: CodingQuarry, BRAKER1, and Harfang. These programs predicted from 86% to 92% (Harfang) of the genes in a manually curated reference set for Aspergillus niger strain NRRL3. Genes with little or no RNA-Seq read coverage were predicted less successfully than genes with adequate coverage.

Bioinformatics for Prohormone and Neuropeptide Discovery

Neuropeptides and peptide hormones are signaling molecules produced via complex post-translational modifications of precursor proteins known as prohormones. Neuropeptides activate specific receptors and are associated with the regulation of physiological systems and behaviors. The identification of prohormones-and the neuropeptides created by these prohormones-from genomic assemblies has become essential to support the annotation and use of the rapidly growing number of sequenced genomes. Here we describe a methodology for identifying the prohormone complement from genomic assemblies that employs widely available public toolsets and databases. The uncovered prohormone sequences can then be screened for putative neuropeptides to enable accurate proteomic discovery and validation.

Fungal Genome Annotation

The term "genome annotation" includes identification of protein-coding and noncoding sequences (e.g., repeats, rDNA, and ncRNA) in genome assemblies and attaching functional information (metadata) to these annotated features. Here, we describe the basic outline of fungal nuclear and mitochondrial genome annotation as performed at the US Department of Energy Joint Genome Institute (JGI).