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Type 1 diabetes (T1D) is a chronic autoimmune metabolic disorder with onset in pediatric/adolescent age, characterized by insufficient insulin production, due to a progressive destruction of pancreatic β-cells. Evidence on the correlation between the human gut microbiota (GM) composition and T1D insurgence has been recently reported. In particular, 16S rRNA-based metagenomics has been intensively employed in the last decade in a number of investigations focused on GM representation in relation to a pre-disease state or to a response to clinical treatments. On the other hand, few works have been published using alternative functional omics, which is more suitable to provide a different interpretation of such a relationship. In this work, we pursued a comprehensive metaproteomic investigation on T1D children compared with a group of siblings (SIBL) and a reference control group (CTRL) composed of aged matched healthy subjects, with the aim of finding features in the T1D patients’ GM to be related with the onset of the disease. Modulated metaproteins were found either by comparing T1D with CTRL and SIBL or by stratifying T1D by insulin need (IN), as a proxy of β-cells damage, showing some functional and taxonomic traits of the GM, possibly related to the disease onset at different stages of severity.

In support of the international effort to obtain a reference sequence of the bread wheat genome and to provide plant communities dealing with large and complex genomes with a versatile, easy-to-use online automated tool for annotation, we have developed the TriAnnot pipeline. Its modular architecture allows for the annotation and masking of transposable elements, the structural, and functional annotation of protein-coding genes with an evidence-based quality indexing, and the identification of conserved non-coding sequences and molecular markers. The TriAnnot pipeline is parallelized on a 712 CPU computing cluster that can run a 1-Gb sequence annotation in less than 5 days. It is accessible through a web interface for small scale analyses or through a server for large scale annotations. The performance of TriAnnot was evaluated in terms of sensitivity, specificity, and general fitness using curated reference sequence sets from rice and wheat. In less than 8 h, TriAnnot was able to predict more than 83% of the 3,748 CDS from rice chromosome 1 with a fitness of 67.4%. On a set of 12 reference Mb-sized contigs from wheat chromosome 3B, TriAnnot predicted and annotated 93.3% of the genes among which 54% were perfectly identified in accordance with the reference annotation. It also allowed the curation of 12 genes based on new biological evidences, increasing the percentage of perfect gene prediction to 63%. TriAnnot systematically showed a higher fitness than other annotation pipelines that are not improved for wheat. As it is easily adaptable to the annotation of other plant genomes, TriAnnot should become a useful resource for the annotation of large and complex genomes in the future.

Abstract Even though automated functional annotation of genes represents a fundamental step in most genomic and metagenomic workflows, it remains challenging at large scales. Here, we describe a major upgrade to eggNOG-mapper, a tool for functional annotation based on precomputed orthology assignments, now optimized for vast (meta)genomic data sets. Improvements in version 2 include a full update of both the genomes and functional databases to those from eggNOG v5, as well as several efficiency enhancements and new features. Most notably, eggNOG-mapper v2 now allows for: 1) de novo gene prediction from raw contigs, 2) built-in pairwise orthology prediction, 3) fast protein domain discovery, and 4) automated GFF decoration. eggNOG-mapper v2 is available as a standalone tool or as an online service at http://eggnog-mapper.embl.de.

Orthology assignment is ideally suited for functional inference. However, because predicting orthology is computationally intensive at large scale, and most pipelines are relatively inaccessible (e.g., new assignments only available through database updates), less precise homology-based functional transfer is still the default for (meta-)genome annotation. We, therefore, developed eggNOG-mapper, a tool for functional annotation of large sets of sequences based on fast orthology assignments using precomputed clusters and phylogenies from the eggNOG database. To validate our method, we benchmarked Gene Ontology (GO) predictions against two widely used homology-based approaches: BLAST and InterProScan. Orthology filters applied to BLAST results reduced the rate of false positive assignments by 11%, and increased the ratio of experimentally validated terms recovered over all terms assigned per protein by 15%. Compared with InterProScan, eggNOG-mapper achieved similar proteome coverage and precision while predicting, on average, 41 more terms per protein and increasing the rate of experimentally validated terms recovered over total term assignments per protein by 35%. EggNOG-mapper predictions scored within the top-5 methods in the three GO categories using the CAFA2 NK-partial benchmark. Finally, we evaluated eggNOG-mapper for functional annotation of metagenomics data, yielding better performance than interProScan. eggNOG-mapper runs ∼15× faster than BLAST and at least 2.5× faster than InterProScan. The tool is available standalone and as an online service at http://eggnog-mapper.embl.de.

Whole exome sequencing has been increasingly used in human disease studies. Prioritization based on appropriate functional annotations has been used as an indispensable step to select candidate variants. Here we present the latest updates to dbNSFP (version 4.1), a database designed to facilitate this step by providing deleteriousness prediction and functional annotation for all potential nonsynonymous and splice-site SNVs (a total of 84,013,093) in the human genome. The current version compiled 36 deleteriousness prediction scores, including 12 transcript-specific scores, and other variant and gene-level functional annotations. The database is available at http://database.liulab.science/dbNSFP with a downloadable version and a web-service.

Background HH-suite is a widely used open source software suite for sensitive sequence similarity searches and protein fold recognition. It is based on pairwise alignment of profile Hidden Markov models (HMMs), which represent multiple sequence alignments of homologous proteins. Results We developed a single-instruction multiple-data (SIMD) vectorized implementation of the Viterbi algorithm for profile HMM alignment and introduced various other speed-ups. These accelerated the search methods HHsearch by a factor 4 and HHblits by a factor 2 over the previous version 2.0.16. HHblits3 is ∼10× faster than PSI-BLAST and ∼20× faster than HMMER3. Jobs to perform HHsearch and HHblits searches with many query profile HMMs can be parallelized over cores and over cluster servers using OpenMP and message passing interface (MPI). The free, open-source, GPLv3-licensed software is available at https://github.com/soedinglab/hh-suite . Conclusion The added functionalities and increased speed of HHsearch and HHblits should facilitate their use in large-scale protein structure and function prediction, e.g. in metagenomics and genomics projects.

Background Cotton ( Gossypium spp.) is the most important fiber and oil crop in the world. With the emergence of huge -omics data sets, it is essential to have an integrated functional genomics database that allows worldwide users to quickly and easily fetch and visualize genomic information. Currently available cotton-related databases have some weakness in integrating multiple kinds of -omics data from multiple Gossypium species. Therefore, it is necessary to establish an integrated functional genomics database for cotton. Description We developed CottonFGD (Cotton Functional Genomic Database, https://cottonfgd.org ), an integrated database that includes genomic sequences, gene structural and functional annotations, genetic marker data, transcriptome data, and population genome resequencing data for all four of the sequenced Gossypium species. It consists of three interconnected modules: search, profile, and analysis. These modules make CottonFGD enable both single gene review and batch analysis with multiple kinds of -omics data and multiple species. CottonFGD also includes additional pages for data statistics, bulk data download, and a detailed user manual. Conclusion Equipped with specialized functional modules and modernized visualization tools, and populated with multiple kinds of -omics data, CottonFGD provides a quick and easy-to-use data analysis platform for cotton researchers worldwide.

Background Metagenomes can be analysed using different approaches and tools. One of the most important distinctions is the way to perform taxonomic and functional assignment, choosing between the use of assembly algorithms or the direct analysis of raw sequence reads instead by homology searching, k-mer analysys, or detection of marker genes. Many instances of each approach can be found in the literature, but to the best of our knowledge no evaluation of their different performances has been carried on, and we question if their results are comparable. Results We have analysed several real and mock metagenomes using different methodologies and tools, and compared the resulting taxonomic and functional profiles. Our results show that database completeness (the representation of diverse organisms and taxa in it) is the main factor determining the performance of the methods relying on direct read assignment either by homology, k-mer composition or similarity to marker genes, while methods relying on assembly and assignment of predicted genes are most influenced by metagenomic size, that in turn determines the completeness of the assembly (the percentage of read that were assembled). Conclusions Although differences exist, taxonomic profiles are rather similar between raw read assignment and assembly assignment methods, while they are more divergent for methods based on k-mers and marker genes. Regarding functional annotation, analysis of raw reads retrieves more functions, but it also makes a substantial number of over-predictions. Assembly methods are more advantageous as the size of the metagenome grows bigger.

Background Finger millet ( Eleusine coracana (L.) Gaertn.) is an important staple food crop widely grown in Africa and South Asia. Among the millets, finger millet has high amount of calcium, methionine, tryptophan, fiber, and sulphur containing amino acids. In addition, it has C4 photosynthetic carbon assimilation mechanism, which helps to utilize water and nitrogen efficiently under hot and arid conditions without severely affecting yield. Therefore, development and utilization of genomic resources for genetic improvement of this crop is immensely useful. Results Experimental results from whole genome sequencing and assembling process of ML-365 finger millet cultivar yielded 1196 Mb covering approximately 82% of total estimated genome size. Genome analysis showed the presence of 85,243 genes and one half of the genome is repetitive in nature. The finger millet genome was found to have higher colinearity with foxtail millet and rice as compared to other Poaceae species. Mining of simple sequence repeats (SSRs) yielded abundance of SSRs within the finger millet genome. Functional annotation and mining of transcription factors revealed finger millet genome harbors large number of drought tolerance related genes. Transcriptome analysis of low moisture stress and non-stress samples revealed the identification of several drought-induced candidate genes, which could be used in drought tolerance breeding. Conclusions This genome sequencing effort will strengthen plant breeders for allele discovery, genetic mapping, and identification of candidate genes for agronomically important traits. Availability of genomic resources of finger millet will enhance the novel breeding possibilities to address potential challenges of finger millet improvement.

FAPROTAX is a promising tool for predicting ecological relevant functions of bacterial and archaeal taxa derived from 16S rRNA amplicon sequencing. The database was initially developed to predict the function of marine species using standard microbiological references. This study, however, has attempted to access the application of FAPROTAX in soil environments. We hypothesized that FAPROTAX was compatible with terrestrial ecosystems. The potential use of FAPROTAX to assign ecological functions of soil bacteria was investigated using meta-analysis and our newly designed experiments. Soil samples from two major terrestrial ecosystems, including agricultural land and forest, were collected. Bacterial taxonomy was analyzed using Illumina sequencing of the 16S rRNA gene and ecological functions of the soil bacteria were assigned by FAPROTAX. The presence of all functionally assigned OTUs (Operation Taxonomic Units) in soil were manually checked using peer-reviewed articles as well as standard microbiology books. Overall, we showed that sample source was not a predominant factor that limited the application of FAPROTAX, but poor taxonomic identification was. The proportion of assigned taxa between aquatic and non-aquatic ecosystems was not significantly different (p > 0.05). There were strong and significant correlations (σ = 0.90–0.95, p < 0.01) between the number of OTUs assigned to genus or order level and the number of functionally assigned OTUs. After manual verification, we found that more than 97% of the FAPROTAX assigned OTUs have previously been detected and potentially performed functions in agricultural and forest soils. We further provided information regarding taxa capable of N-fixation, P and K solubilization, which are three main important elements in soil systems and can be integrated with FAPROTAX to increase the proportion of functionally assigned OTUs. Consequently, we concluded that FAPROTAX can be used for a fast-functional screening or grouping of 16S derived bacterial data from terrestrial ecosystems and its performance could be enhanced through improving the taxonomic and functional reference databases.