The RefSeq project at the National Center for Biotechnology Information (NCBI) maintains and curates a publicly available database of annotated genomic, transcript, and protein sequence records (http://www.ncbi.nlm.nih.gov/refseq/). The RefSeq project leverages the data submitted to the International Nucleotide Sequence Database Collaboration (INSDC) against a combination of computation, manual curation, and collaboration to produce a standard set of stable, non-redundant reference sequences. The RefSeq project augments these reference sequences with current knowledge including publications, functional features and informative nomenclature. The database currently represents sequences from more than 55,000 organisms (>4800 viruses, >40,000 prokaryotes and >10,000 eukaryotes; RefSeq release 71), ranging from a single record to complete genomes. This paper summarizes the current status of the viral, prokaryotic, and eukaryotic branches of the RefSeq project, reports on improvements to data access and details efforts to further expand the taxonomic representation of the collection. We also highlight diverse functional curation initiatives that support multiple uses of RefSeq data including taxonomic validation, genome annotation, comparative genomics, and clinical testing. We summarize our approach to utilizing available RNA-Seq and other data types in our manual curation process for vertebrate, plant, and other species, and describe a new direction for prokaryotic genomes and protein name management.

Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation

The RAST (Rapid Annotation using Subsystem Technology) annotation engine was built in 2008 to annotate bacterial and archaeal genomes. It works by offering a standard software pipeline for identifying genomic features (i.e., protein-encoding genes and RNA) and annotating their functions. Recently, in order to make RAST a more useful research tool and to keep pace with advancements in bioinformatics, it has become desirable to build a version of RAST that is both customizable and extensible. In this paper, we describe the RAST tool kit (RASTtk), a modular version of RAST that enables researchers to build custom annotation pipelines. RASTtk offers a choice of software for identifying and annotating genomic features as well as the ability to add custom features to an annotation job. RASTtk also accommodates the batch submission of genomes and the ability to customize annotation protocols for batch submissions. This is the first major software restructuring of RAST since its inception.

/pdf/rasttk-a-modular-and-extensible-implementation-of-the-rast-3ckj95nwgy.pdf

RASTtk: A modular and extensible implementation of the RAST algorithm for building custom annotation pipelines and annotating batches of genomes

Genomics promises comprehensive surveying of genomes and metagenomes, but rapidly changing technologies and expanding data volumes make evaluation of completeness a challenging task. Technical sequencing quality metrics can be complemented by quantifying completeness of genomic data sets in terms of the expected gene content of Benchmarking Universal Single-Copy Orthologs (BUSCO, http://busco.ezlab.org). The latest software release implements a complete refactoring of the code to make it more flexible and extendable to facilitate high-throughput assessments. The original six lineage assessment data sets have been updated with improved species sampling, 34 new subsets have been built for vertebrates, arthropods, fungi, and prokaryotes that greatly enhance resolution, and data sets are now also available for nematodes, protists, and plants. Here, we present BUSCO v3 with example analyses that highlight the wide-ranging utility of BUSCO assessments, which extend beyond quality control of genomics data sets to applications in comparative genomics analyses, gene predictor training, metagenomics, and phylogenomics.

https://serval.unil.ch/resource/serval:BIB_2A4C7F8DA305.P001/REF.pdf

BUSCO Applications from Quality Assessments to Gene Prediction and Phylogenomics.

Genomics drives the current progress in molecular biology, generating unprecedented volumes of data. The scientific value of these sequences depends on the ability to evaluate their completeness using a biologically meaningful approach. Here, we describe the use of the BUSCO tool suite to assess the completeness of genomes, gene sets, and transcriptomes, using their gene content as a complementary method to common technical metrics. The chapter introduces the concept of universal single-copy genes, which underlies the BUSCO methodology, covers the basic requirements to set up the tool, and provides guidelines to properly design the analyses, run the assessments, and interpret and utilize the results.

BUSCO: Assessing Genome Assembly and Annotation Completeness.

The Reference Sequence (RefSeq) project at the National Center for Biotechnology Information (NCBI) provides annotation for over 95 000 prokaryotic genomes that meet standards for sequence quality, completeness, and freedom from contamination. Genomes are annotated by a single Prokaryotic Genome Annotation Pipeline (PGAP) to provide users with a resource that is as consistent and accurate as possible. Notable recent changes include the development of a hierarchical evidence scheme, a new focus on curating annotation evidence sources, the addition and curation of protein profile hidden Markov models (HMMs), release of an updated pipeline (PGAP-4), and comprehensive re-annotation of RefSeq prokaryotic genomes. Antimicrobial resistance proteins have been reannotated comprehensively, improved structural annotation of insertion sequence transposases and selenoproteins is provided, curated complex domain architectures have given upgraded names to millions of multidomain proteins, and we introduce a new kind of annotation rule-BlastRules. Continual curation of supporting evidence, and propagation of improved names onto RefSeq proteins ensures that the functional annotation of genomes is kept current. An increasing share of our annotation now derives from HMMs and other sets of annotation rules that are portable by nature, and available for download and for reuse by other investigators. RefSeq is found at https://www.ncbi.nlm.nih.gov/refseq/.

RefSeq: an update on prokaryotic genome annotation and curation.

This unit describes how to use the gene-finding programs GeneMark.hmm-E and GeneMark-ES for finding protein-coding genes in the genomic DNA of eukaryotic organisms. These bioinformatics tools have been demonstrated to have state-of-the-art accuracy for many fungal, plant, and animal genomes, and have frequently been used for gene annotation in novel genomic sequences. An additional advantage of GeneMark-ES is that the problem of algorithm parameterization is solved automatically, with parameters estimated by iterative self-training (unsupervised training).

Eukaryotic Gene Prediction Using GeneMark.hmm-E and GeneMark-ES

In this unit, the GeneMark and GeneMark.hmm programs are presented as two different methods for the in silico prediction of genes in prokaryotes. GeneMark can be used for whole genome analysis as well as for the local analysis of a particular gene and its surrounding regions. GeneMark.hmm makes use of Hidden Markov models to find the transition points (boundaries) between protein coding states and noncoding states and can be efficiently used for larger genome sequences. These methods can be used in conjunction with each other for a higher sensitivity of gene detection.

Prokaryotic gene prediction using GeneMark and GeneMark.hmm.

This unit describes how to use several gene-finding programs from the GeneMark line developed for finding protein-coding ORFs in genomic DNA of prokaryotic species, in genomic DNA of eukaryotic species with intronless genes, in genomes of viruses and phages, and in prokaryotic metagenomic sequences, as well as in EST sequences with spliced-out introns. These bioinformatics tools were demonstrated to have state-of-the-art accuracy and have been frequently used for gene annotation in novel nucleotide sequences. An additional advantage of these sequence-analysis tools is that the problem of algorithm parameterization is solved automatically, with parameters estimated by iterative self-training (unsupervised training).

Gene Identification in Prokaryotic Genomes, Phages, Metagenomes, and EST Sequences with GeneMarkS Suite

This unit describes how to use several gene-finding programs from the GeneMark line developed for finding protein-coding ORFs in genomic DNA of prokaryotic species, in genomic DNA of eukaryotic species with intronless genes, in genomes of viruses and phages, and in prokaryotic metagenomic sequences, as well as in EST sequences with spliced-out introns. These bioinformatics tools were demonstrated to have state-of-the-art accuracy, and have been frequently used for gene annotation in novel nucleotide sequences. An additional advantage of these sequence-analysis tools is that the problem of algorithm parameterization is solved automatically, with parameters estimated by iterative self-training (unsupervised training). Curr. Protoc. Microbiol. 32:1E.7.1-1E.7.17. © 2014 by John Wiley & Sons, Inc.


Keywords:

gene finding;
hidden Markov model;
metagenome

Gene identification in prokaryotic genomes, phages, metagenomes, and EST sequences with GeneMarkS suite.

In this unit, eukaryotic GeneMark.hmm is presented as a method for detecting genes in eukaryotic DNA sequences. The eukaryotic GeneMark.hmm uses Markov models of protein coding and noncoding sequences, as well as positional nucleotide frequency matrices for prediction of the translational start, translational termination and splice sites. All these models along with length distributions of exons, introns and intergenic regions are integrated into one Hidden Markov model. The unit describes running the program over the Internet and locally on a Unix machine. It also discusses GeneMarkS EV, which can be used to detect genes in eukaryotic viruses.

Alex Lomsadze

Papers

Eukaryotic Gene Prediction Using GeneMark.hmm-E and GeneMark-ES

Prokaryotic gene prediction using GeneMark and GeneMark.hmm.

Gene Identification in Prokaryotic Genomes, Phages, Metagenomes, and EST Sequences with GeneMarkS Suite

Gene identification in prokaryotic genomes, phages, metagenomes, and EST sequences with GeneMarkS suite.

Eukaryotic gene prediction using GeneMark.hmm.