We have a new and improved search experience for viral genes from select human pathogens. When you search for a virus such as HIV-1 (more examples below), you now get an interactive graphical representation of the viral genome where you can see all the annotated viral proteins in context. Clicking on the gene / protein objects allows you to access sequences, publications, and analysis tools for the selected protein. This new feature is designed to help you quickly find information relevant to your research on clinically important viruses.Figure 1. Top: The virus genome graphic result for a search with HIV-1 with access to analysis tools, downloads, and relevant results in the Genome and Virus resources. Bottom: The result obtained by clicking the env gene graphic, which provides links to protein and nucleotide sequences, the literature, analysis tools, and downloads.
Try it out using the following example searches and let us know what you think!
How does it work? Download PGAP from GitHub, provide some basic information and the FASTA sequences for your genome sequence, and run the pipeline on your own machine, compute farm or the cloud. PGAP will produce annotation consistent with NCBI’s internal PGAP. Submit the resulting annotated genome to GenBank through the genome submission portal, and get an accession back.
As with any other submitted assembly, PGAP-annotated genomes will be screened for foreign contaminants and vector sequences at submission. Any annotated assemblies that don’t pass may need to be modified. We are developing an automated process to handle these edits!
We are also working on other improvements to stand-alone PGAP such as a module for calculating Average Nucleotide Identity (ANI) to confirm the assembly’s taxonomic assignment. Stay tuned for new developments!
If you are a consumer or producer of AGP (A Golden Path) files for genome assemblies, please read on. We’d like your feedback on the proposed changes described here.
As you know, AGP files are used to describe the structure of certain genome assemblies. The AGP file format has not kept up with changes in sequencing technology or International Sequence Database Collaboration (INSDC) feature usage. NCBI is therefore proposing to extend the current AGP v2.0 specification to add new linkage evidence types and a gap type of “contamination” as detailed below and described in the AGP v2.1 proposed specification.
NCBI announces Annotation Release 100 of the Pacific white shrimp (Penaeus vannamei) genome in RefSeq, based on the assembly (GCF_003789085.1) submitted by the Institute of Oceanology, Chinese Academy of Sciences. The Pacific white shrimp is one of the most important shrimp species in fisheries and aquaculture and represents the first decapod to have its genome annotated by NCBI. We predicted 24,987 protein coding genes with evidence from alignment of six billion RNA-Seq reads and homology with invertebrate proteins. This annotation will enable genomic research in this commercially important species.
If you’ve been searching in Gene, Nucleotide, Protein, Genome or Assembly databases, you’ve probably noticed the new search experience we introduced in September to interpret several common language searches and offer improved results. We’re excited to announce we’ve added as-you-type suggestions to the search bar in these databases.
Here’s a peek at the new menu in the NCBI Gene database.
Figure 1. Typing into the search box brings up automatic suggestions of the most popular queries.
Earlier this year, we announced the release of a new and improved search feature that interprets plain language to give better results for common searches. This feature, originally developed in NCBI Labs and later released on the NCBI All Databases search, is now available across several NCBI resources: Nucleotide, Protein, Gene, Genome, and Assembly. Whether you are searching for a specific gene or for a whole genome, you will now retrieve NCBI’s best results regardless of the database you search.
The image below shows the results for a search for human INS in the Nucleotide database. Even though this is a Nucleotide search, the results include relevant information from Gene, Protein, Taxonomy, plus links to the NCBI reference sequences (RefSeq) as well as access to BLAST and the insulin gene region in NCBI’s genome browser, the Genome Data Viewer.Figure 1. The new natural language search result in the Nucleotide database from a search for human INS.
Try out this new search capability and let us know what you think. And keep visiting the NCBI Labs search page to try our latest experiments, which we’ll also announce here on NCBI Insights.
addition of new file types, including a feature_count.txt file with counts of gene, RNA, and CDS features of specific types and a translated_cds.faa file with conceptual translations of each CDS feature on the genome
improvements to the Sequence Ontology feature types used in GFF3, including identification of pseudogene gene features as “pseudogene” instead of “gene” in column 3
improvements to the gene_biotype calculation to categorize transcribed pseudogenes as transcribed_pseudogene instead of misc_RNA
addition of the #!annotation-source unofficial pragma to GFF3 files with the annotation name, for assemblies where that information is available
expanded the UCSC sequence name mapping provided in the assembly report files to provide mappings between GenBank or RefSeq sequence accessions, chromosome or scaffold names, and the UCSC sequence name for most of the recent assemblies in the UCSC Genome Browser
Nearly complete set of translation-related genes lends support to hypothesis that giant viruses evolved from smaller viruses
An international team of researchers, including NCBI’s Eugene Koonin and Natalya Yutin, has discovered a novel group of giant viruses (dubbed “Klosneuviruses”) with a more complete set of translation machinery genes than any virus that has been described to date. “This discovery significantly expands our understanding of viral evolution,” said Koonin. “These are the most ‘cell-like’ viruses ever identified. However, the computational analysis of the virus genomes shows that these viruses have not evolved from cells by reductive evolution but rather have evolved from smaller viruses, gradually acquiring genes from their hosts at different stages of their evolution.”