Recurrent use of evolutionary importance for functional annotation of proteins based on local structural similarity

The annotation of protein function has not kept pace with the exponential growth of raw sequence and structure data. An emerging solution to this problem is to identify 3D motifs or templates in protein structures that are necessary and sufficient determinants of function. Here, we demonstrate the recurrent use of evolutionary trace information to construct such 3D templates for enzymes, search for them in other structures, and distinguish true from spurious matches. Serine protease templates built from evolutionarily important residues distinguish between proteases and other proteins nearly as well as the classic Ser-His-Asp catalytic triad. In 53 enzymes spanning 33 distinct functions, an automated pipeline identifies functionally related proteins with an average positive predictive power of 62%, including correct matches to proteins with the same function but with low sequence identity (the average identity for some templates is only 17%). Although these template building, searching, and match classification strategies are not yet optimized, their sequential implementation demonstrates a functional annotation pipeline which does not require experimental information, but only local molecular mimicry among a small number of evolutionarily important residues.     

TheViral MetaGenome Annotation Pipeline(VMGAP):an automated tool for the functional annotation of viral Metagenomic shotgun sequencing data

In the past few years, the field of metagenomics has been growing at an accelerated pace, particularly in response to advancements in new sequencing technologies. The large volume of sequence data from novel organisms generated by metagenomic projects has triggered the development of specialized databases and tools focused on particular groups of organisms or data types. Here we describe a pipeline for the functional annotation of viral metagenomic sequence data. The Viral MetaGenome Annotation Pipeline (VMGAP) pipeline takes advantage of a number of specialized databases, such as collections of mobile genetic elements and environmental metagenomes to improve the classification and functional prediction of viral gene products. The pipeline assigns a functional term to each predicted protein sequence following a suite of comprehensive analyses whose results are ranked according to a priority rules hierarchy. Additional annotation is provided in the form of enzyme commission (EC) numbers, GO/MeGO terms and Hidden Markov Models together with supporting evidence.     

RAPYD--rapid annotation platform for yeast data

Lower eukaryotes of the kingdom Fungi include a variety of biotechnologically important yeast species that are in the focus of genome research for more than a decade. Due to the rapid progress in ultra-fast sequencing technologies, the amount of available yeast genome data increases steadily. Thus, an efficient bioinformatics platform is required that covers genome assembly, eukaryotic gene prediction, genome annotation, comparative yeast genomics, and metabolic pathway reconstruction. Here, we present a bioinformatics platform for yeast genomics named RAPYD addressing the key requirements of extensive yeast sequence data analysis. The first step is a comprehensive regional and functional annotation of a yeast genome. A region prediction pipeline was implemented to obtain reliable and high-quality predictions of coding sequences and further genome features. Functions of coding sequences are automatically determined using a configurable prediction pipeline. Based on the resulting functional annotations, a metabolic pathway reconstruction module can be utilized to rapidly generate an overview of organism-specific features and metabolic blueprints. In a final analysis step shared and divergent features of closely related yeast strains can be explored using the comparative genomics module. An in-depth application example of the yeast Meyerozyma guilliermondii illustrates the functionality of RAPYD. A user-friendly web interface is available at https://rapyd.cebitec.uni-bielefeld.de.     

eggNOG-mapper v2: Functional Annotation,Orthology Assignments,and Domain Prediction at the Metagenomic Scale

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.     

Mercator: a fast and simple web server for genome scale functional annotation of plant sequence data

Next‐generation technologies generate an overwhelming amount of gene sequence data. Efficient annotation tools are required to make these data amenable to functional genomics analyses. The Mercator pipeline automatically assigns functional terms to protein or nucleotide sequences. It uses the MapMan ‘BIN’ ontology, which is tailored for functional annotation of plant ‘omics’ data. The classification procedure performs parallel sequence searches against reference databases, compiles the results and computes the most likely MapMan BINs for each query. In the current version, the pipeline relies on manually curated reference classifications originating from the three reference organisms (Arabidopsis, Chlamydomonas, rice), various other plant species that have a reviewed SwissProt annotation, and more than 2000 protein domain and family profiles at InterPro, CDD and KOG. Functional annotations predicted by Mercator achieve accuracies above 90% when benchmarked against manual annotation. In addition to mapping files for direct use in the visualization software MapMan, Mercator provides graphical overview charts, detailed annotation information in a convenient web browser interface and a MapMan‐to‐GO translation table to export results as GO terms. Mercator is available free of charge via http://mapman.gabipd.org/web/guest/app/Mercator .     

Xylella fastidiosa comparative genomic database is an information resource to explore the annotation, genomic features, and biology of different strains

The Xylella fastidiosa comparative genomic database is a scientific resource with the aim to provide a user-friendly interface for accessing high-quality manually curated genomic annotation and comparative sequence analysis, as well as for identifying and mapping prophage-like elements, a marked feature of Xylella genomes. Here we describe a database and tools for exploring the biology of this important plant pathogen. The hallmarks of this database are the high quality genomic annotation, the functional and comparative genomic analysis and the identification and mapping of prophage-like elements. It is available from web site http://www.xylella.lncc.br.     

Annotated genes and nonannotated genomes: cross-species use of Gene Ontology in ecology and evolution research

Recent advances in molecular technologies have opened up unprecedented opportunities for molecular ecologists to better understand the molecular basis of traits of ecological and evolutionary importance in almost any organism. Nevertheless, reliable and systematic inference of functionally relevant information from these masses of data remains challenging. The aim of this review is to highlight how the Gene Ontology (GO) database can be of use in resolving this challenge. The GO provides a largely species-neutral source of information on the molecular function, biological role and cellular location of tens of thousands of gene products. As it is designed to be species-neutral, the GO is well suited for cross-species use, meaning that, functional annotation derived from model organisms can be transferred to inferred orthologues in newly sequenced species. In other words, the GO can provide gene annotation information for species with nonannotated genomes. In this review, we describe the GO database, how functional information is linked with genes/gene products in model organisms, and how molecular ecologists can utilize this information to annotate their own data. Then, we outline various applications of GO for enhancing the understanding of molecular basis of traits in ecologically relevant species. We also highlight potential pitfalls, provide step-by-step recommendations for conducting a sound study in nonmodel organisms, suggest avenues for future research and outline a strategy for maximizing the benefits of a more ecological and evolutionary genomics-oriented ontology by ensuring its compatibility with the GO.     

An automated protein annotation filter for integrating web-based annotation tools

A wide range of web based prediction and annotation tools are frequently used for determining protein function from sequence. However, parallel processing of sequences for annotation through web tools is not possible due to several constraints in functional programming for multiple queries. Here, we propose the development of APAF as an automated protein annotation filter to overcome some of these difficulties through an integrated approach.     

The prediction of protein subcellular localization from sequence: a shortcut to functional genome annotation.

Automated sequence annotation is a major goal of post-genomic era with hundreds of genomes in the databases, from both prokaryotes and eukaryotes. While the number of fully sequenced chromosomes from microbial organisms exponentially increased in the last decade above 600, presently we know the whole DNA content of only 25 eukaryotic organisms, including Homo sapiens. However, the process of genome annotation is far from being completed. This is particularly relevant in eukaryotes, whose cells contain several subcellular compartments, or organelles, enclosed by membranes, where different relevant functions are performed. Translocation across the membrane into the organelles is a highly regulated and complex cellular process. Indeed different proteins and/or protein isoforms, originated from genes by alternative splicing, may be conveyed to different cell compartments, depending on their specific role in the cell. During recent years the prediction of subcellular localization (SL) by computational means has been an active research area. Several methods are presently available based on different notions and addressing different aspects of SL. This review provides a short overview of the most well performing methods described in the literature, highlighting their predictive capabilities and different applications.     

Genome browsing with Ensembl: a practical overview.

A wealth of gene information is accruing in public databases. Genome browsers such as Ensembl are needed to organize and depict this information in the context of the genome. Ensembl provides an open source gene set based on experimental evidence for over 30 species, the majority of which are vertebrates. Genes and annotation are accessible through the Ensembl browser (http://www.ensembl.org), and through direct queries of its databases using the Perl API (Application Programme Interface), MySQL or BioMart.     

An automated annotation tool for genomic DNA sequences using GeneScan and BLAST

Genomic sequence data are often available well before the annotated sequence is published. We present a method for analysis of genomic DNA to identify coding sequences using the GeneScan algorithm and characterize these resultant sequences by BLAST. The routines are used to develop a system for automated annotation of genome DNA sequences.     

MakeHub:Fully Automated Generation of UCSC Genome Browser Assembly Hubs

Novel genomes are today often annotated by small consortia or individuals whose background is not from bioinformatics.This audience requires tools that are easy to use.Such need has been addressed by several genome annotation tools and pipelines.Visualizing resulting annotation is a crucial step of quality control.The UCSC Genome Browser is a powerful and popular genome visualization tool.Assembly Hubs,which can be hosted on any publicly available web server,allow browsing genomes via UCSC Genome Browser servers.The steps for creating custom Assembly Hubs are well documented and the required tools are publicly available.However,the number of steps for creating a novel Assembly Hub is large.In some cases,the format of input files needs to be adapted,which is a difficult task for scientists without programming background.Here,we describe Make Hub,a novel command line tool that generates Assembly Hubs for the UCSC Genome Browser in a fully automated fashion.The pipeline also allows extending previously created Hubs by additional tracks.Make Hub is freely available for downloading at https://github.com/Gaius-Augustus/Make Hub.     

Rapid identification and interpretation of gene–environment associations using the new R.SamBada landscape genomics pipeline

sam βada is a genome–environment association software, designed to search for signatures of local adaptation. However, pre‐ and postprocessing of data can be labour‐intensive, preventing wider uptake of the method. We have now developed R.SamBada, an r ‐package providing a pipeline for landscape genomic analysis based on sam βada , spanning from the retrieval of environmental conditions at sampling locations to gene annotation using the Ensembl genome browser. As a result, R.SamBada standardizes the landscape genomics pipeline and eases the search for candidate genes of local adaptation, enhancing reproducibility of landscape genomic studies. The efficiency and power of the pipeline is illustrated using two examples: sheep populations from Morocco with no evident population structure and Lidia cattle from Spain displaying population substructuring. In both cases, R.SamBada enabled rapid identification and interpretation of candidate genes, which are further discussed in the light of local adaptation. The package is available in the r CRAN package repository and on GitHub (github.com/SolangeD/R.SamBada).