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Article ; Online: Faster SARS-CoV-2 sequence validation and annotation for GenBank using VADR.

Nawrocki, Eric P

NAR genomics and bioinformatics

2023 Volume 5, Issue 1, Page(s) lqad002

Abstract: In 2020 and 2021, >1.5 million SARS-CoV-2 sequences were submitted to GenBank. The initial version (v1.0) of the VADR (Viral Annotation DefineR) software package that GenBank uses to automatically validate and annotate incoming viral sequences is too ... ...

Abstract	In 2020 and 2021, >1.5 million SARS-CoV-2 sequences were submitted to GenBank. The initial version (v1.0) of the VADR (Viral Annotation DefineR) software package that GenBank uses to automatically validate and annotate incoming viral sequences is too slow and memory intensive to process many thousands of SARS-CoV-2 sequences in a reasonable amount of time. Additionally, long stretches of ambiguous N nucleotides, which are common in many SARS-CoV-2 sequences, prevent VADR from accurate validation and annotation. VADR has been updated to more accurately and rapidly annotate SARS-CoV-2 sequences. Stretches of consecutive Ns are now identified and temporarily replaced with expected nucleotides to facilitate processing, and the slowest steps have been overhauled using
Language	English
Publishing date	2023-01-20
Publishing country	England
Document type	Journal Article
ISSN	2631-9268
ISSN (online)	2631-9268
DOI	10.1093/nargab/lqad002
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Faster SARS-CoV-2 sequence validation and annotation for GenBank using VADR.

Nawrocki, Eric P

bioRxiv : the preprint server for biology

2022

Abstract: Background: In 2020 and 2021, more than 1.5 million SARS-CoV-2 sequences were submitted to GenBank. The initial version (v1.0) of the VADR (Viral Annotation DefineR) software package that GenBank uses to automatically validate and annotate incoming ... ...

Abstract	Background: In 2020 and 2021, more than 1.5 million SARS-CoV-2 sequences were submitted to GenBank. The initial version (v1.0) of the VADR (Viral Annotation DefineR) software package that GenBank uses to automatically validate and annotate incoming viral sequences is too slow and memory intensive to process many thousands of SARS-CoV-2 sequences in a reasonable amount of time. Additionally, long stretches of ambiguous N nucleotides, which are common in many SARS-CoV-2 sequences, prevent VADR from accurate validation and annotation. Results: VADR has been updated to more accurately and rapidly annotate SARS-CoV-2 sequences. Stretches of consecutive Ns are now identified and temporarily replaced with expected nucleotides to facilitate processing, and the slowest steps have been overhauled using Conclusion: VADR is now nearly 1000 times faster than it was in early 2020 for processing SARS-CoV-2 sequences submitted to GenBank. It has been used to screen and annotate more than 1.5 million SARS-CoV-2 sequences since June 2020, and it is now efficient enough to cope with the current rate of hundreds of thousands of submitted sequences per month. Version 1.4.1 is freely available ( https://github.com/ncbi/vadr ) for local installation and use.
Language	English
Publishing date	2022-04-27
Publishing country	United States
Document type	Preprint
DOI	10.1101/2022.04.25.489427
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Article ; Online: Faster SARS-CoV-2 sequence validation and annotation for GenBank using VADR

Nawrocki, Eric P

bioRxiv

Abstract	Background: In 2020 and 2021, more than 1.5 million SARS-CoV-2 sequences were submitted to GenBank. The initial version (v1.0) of the VADR (Viral Annotation DefineR) software package that GenBank uses to automatically validate and annotate incoming viral sequences is too slow and memory intensive to process many thousands of SARS-CoV-2 sequences in a reasonable amount of time. Additionally, long stretches of ambiguous N nucleotides, which are common in many SARS-CoV-2 sequences, prevent VADR from accurate validation and annotation. Results: VADR has been updated to more accurately and rapidly annotate SARS-CoV-2 sequences. Stretches of consecutive Ns are now identified and temporarily replaced with expected nucleotides to facilitate processing, and the slowest steps have been overhauled using blastn and glsearch, increasing speed, reducing the memory requirement from 64Gb to 2Gb per thread, and allowing simple, coarse-grained parallelization on multiple processors per host. Conclusion: VADR is now nearly 1000 times faster than it was in early 2020 for processing SARS-CoV-2 sequences submitted to GenBank. It has been used to screen and annotate more than 1.5 million SARS-CoV-2 sequences since June 2020, and it is now efficient enough to cope with the current rate of hundreds of thousands of submitted sequences per month. Version 1.4.1 is freely available (https://github.com/ncbi/vadr) for local installation and use.
Keywords	covid19
Language	English
Publishing date	2022-04-27
Publisher	Cold Spring Harbor Laboratory
Document type	Article ; Online
DOI	10.1101/2022.04.25.489427
Database	COVID19

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Article: Influenza sequence validation and annotation using VADR.

Calhoun, Vincent C / Hatcher, Eneida L / Yankie, Linda / Nawrocki, Eric P

bioRxiv : the preprint server for biology

2024

Abstract: Tens of thousands of influenza sequences are deposited into the GenBank database each year. The software tool FLAN has been used by GenBank since 2007 to validate and annotate incoming influenza sequence submissions, and has been publicly available as a ... ...

Abstract	Tens of thousands of influenza sequences are deposited into the GenBank database each year. The software tool FLAN has been used by GenBank since 2007 to validate and annotate incoming influenza sequence submissions, and has been publicly available as a webserver but not as a standalone tool. VADR is a general sequence validation and annotation software package used by GenBank for Norovirus, Dengue virus and SARS-CoV-2 virus sequence processing that is available as a standalone tool. We have created VADR influenza models based on the FLAN reference sequences and adapted VADR to accurately annotate influenza sequences. VADR and FLAN show consistent results on the vast majority of influenza sequences, and when they disagree VADR is usually correct. VADR can also accurately process influenza D sequences as well as influenza A H17, H18, H19, N10 and N11 subtype sequences, which FLAN cannot. VADR 1.6.3 and the associated influenza models are now freely available for users to download and use.
Language	English
Publishing date	2024-03-25
Publishing country	United States
Document type	Preprint
DOI	10.1101/2024.03.21.585980
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Annotating functional RNAs in genomes using Infernal.

Nawrocki, Eric P

Methods in molecular biology (Clifton, N.J.)

2014 Volume 1097, Page(s) 163–197

Abstract: Many different types of functional non-coding RNAs participate in a wide range of important cellular functions but the large majority of these RNAs are not routinely annotated in published genomes. Several programs have been developed for identifying ... ...

Abstract	Many different types of functional non-coding RNAs participate in a wide range of important cellular functions but the large majority of these RNAs are not routinely annotated in published genomes. Several programs have been developed for identifying RNAs, including specific tools tailored to a particular RNA family as well as more general ones designed to work for any family. Many of these tools utilize covariance models (CMs), statistical models of the conserved sequence, and structure of an RNA family. In this chapter, as an illustrative example, the Infernal software package and CMs from the Rfam database are used to identify RNAs in the genome of the archaeon Methanobrevibacter ruminantium, uncovering some additional RNAs not present in the genome's initial annotation. Analysis of the results and comparison with family-specific methods demonstrate some important strengths and weaknesses of this general approach.
MeSH term(s)	Computational Biology/methods ; Genomics/methods ; Molecular Sequence Annotation/methods ; RNA/chemistry ; RNA/genetics ; Sequence Analysis, RNA/methods ; Software
Chemical Substances	RNA (63231-63-0)
Language	English
Publishing date	2014
Publishing country	United States
Document type	Journal Article
ISSN	1940-6029
ISSN (online)	1940-6029
DOI	10.1007/978-1-62703-709-9_9
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Article ; Online: Group I introns are widespread in archaea.

Nawrocki, Eric P / Jones, Thomas A / Eddy, Sean R

Nucleic acids research

2018 Volume 46, Issue 15, Page(s) 7970–7976

Abstract: Group I catalytic introns have been found in bacterial, viral, organellar, and some eukaryotic genomes, but not in archaea. All known archaeal introns are bulge-helix-bulge (BHB) introns, with the exception of a few group II introns. It has been proposed ...

Abstract	Group I catalytic introns have been found in bacterial, viral, organellar, and some eukaryotic genomes, but not in archaea. All known archaeal introns are bulge-helix-bulge (BHB) introns, with the exception of a few group II introns. It has been proposed that BHB introns arose from extinct group I intron ancestors, much like eukaryotic spliceosomal introns are thought to have descended from group II introns. However, group I introns have little sequence conservation, making them difficult to detect with standard sequence similarity searches. Taking advantage of recent improvements in a computational homology search method that accounts for both conserved sequence and RNA secondary structure, we have identified 39 group I introns in a wide range of archaeal phyla, including examples of group I introns and BHB introns in the same host gene.
MeSH term(s)	Archaea/classification ; Archaea/enzymology ; Archaea/genetics ; Base Sequence ; Introns/genetics ; Nucleic Acid Conformation ; Phylogeny ; RNA, Archaeal/chemistry ; RNA, Archaeal/classification ; RNA, Archaeal/genetics ; RNA, Catalytic/chemistry ; RNA, Catalytic/classification ; RNA, Catalytic/genetics ; Species Specificity
Chemical Substances	RNA, Archaeal ; RNA, Catalytic
Language	English
Publishing date	2018-05-22
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, N.I.H., Intramural ; Research Support, Non-U.S. Gov't
ZDB-ID	186809-3
ISSN	1362-4962 ; 1362-4954 ; 0301-5610 ; 0305-1048
ISSN (online)	1362-4962 ; 1362-4954
ISSN	0301-5610 ; 0305-1048
DOI	10.1093/nar/gky414
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Article ; Online: Ribovore: ribosomal RNA sequence analysis for GenBank submissions and database curation.

Schäffer, Alejandro A / McVeigh, Richard / Robbertse, Barbara / Schoch, Conrad L / Johnston, Anjanette / Underwood, Beverly A / Karsch-Mizrachi, Ilene / Nawrocki, Eric P

BMC bioinformatics

2021 Volume 22, Issue 1, Page(s) 400

Abstract: Background: The DNA sequences encoding ribosomal RNA genes (rRNAs) are commonly used as markers to identify species, including in metagenomics samples that may combine many organismal communities. The 16S small subunit ribosomal RNA (SSU rRNA) gene is ... ...

Abstract	Background: The DNA sequences encoding ribosomal RNA genes (rRNAs) are commonly used as markers to identify species, including in metagenomics samples that may combine many organismal communities. The 16S small subunit ribosomal RNA (SSU rRNA) gene is typically used to identify bacterial and archaeal species. The nuclear 18S SSU rRNA gene, and 28S large subunit (LSU) rRNA gene have been used as DNA barcodes and for phylogenetic studies in different eukaryote taxonomic groups. Because of their popularity, the National Center for Biotechnology Information (NCBI) receives a disproportionate number of rRNA sequence submissions and BLAST queries. These sequences vary in quality, length, origin (nuclear, mitochondria, plastid), and organism source and can represent any region of the ribosomal cistron. Results: To improve the timely verification of quality, origin and loci boundaries, we developed Ribovore, a software package for sequence analysis of rRNA sequences. The ribotyper and ribosensor programs are used to validate incoming sequences of bacterial and archaeal SSU rRNA. The ribodbmaker program is used to create high-quality datasets of rRNAs from different taxonomic groups. Key algorithmic steps include comparing candidate sequences against rRNA sequence profile hidden Markov models (HMMs) and covariance models of rRNA sequence and secondary-structure conservation, as well as other tests. Nine freely available blastn rRNA databases created and maintained with Ribovore are used for checking incoming GenBank submissions and used by the blastn browser interface at NCBI. Since 2018, Ribovore has been used to analyze more than 50 million prokaryotic SSU rRNA sequences submitted to GenBank, and to select at least 10,435 fungal rRNA RefSeq records from type material of 8350 taxa. Conclusion: Ribovore combines single-sequence and profile-based methods to improve GenBank processing and analysis of rRNA sequences. It is a standalone, portable, and extensible software package for the alignment, classification and validation of rRNA sequences. Researchers planning on submitting SSU rRNA sequences to GenBank are encouraged to download and use Ribovore to analyze their sequences prior to submission to determine which sequences are likely to be automatically accepted into GenBank.
MeSH term(s)	DNA, Ribosomal ; Databases, Nucleic Acid ; Phylogeny ; RNA, Ribosomal ; RNA, Ribosomal, 16S/genetics ; RNA, Ribosomal, 18S/genetics ; Sequence Analysis, RNA
Chemical Substances	DNA, Ribosomal ; RNA, Ribosomal ; RNA, Ribosomal, 16S ; RNA, Ribosomal, 18S
Language	English
Publishing date	2021-08-12
Publishing country	England
Document type	Journal Article
ZDB-ID	2041484-5
ISSN	1471-2105 ; 1471-2105
ISSN (online)	1471-2105
ISSN	1471-2105
DOI	10.1186/s12859-021-04316-z
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Infernal 1.1: 100-fold faster RNA homology searches.

Nawrocki, Eric P / Eddy, Sean R

Bioinformatics (Oxford, England)

2013 Volume 29, Issue 22, Page(s) 2933–2935

Abstract: Summary: Infernal builds probabilistic profiles of the sequence and secondary structure of an RNA family called covariance models (CMs) from structurally annotated multiple sequence alignments given as input. Infernal uses CMs to search for new family ... ...

Abstract	Summary: Infernal builds probabilistic profiles of the sequence and secondary structure of an RNA family called covariance models (CMs) from structurally annotated multiple sequence alignments given as input. Infernal uses CMs to search for new family members in sequence databases and to create potentially large multiple sequence alignments. Version 1.1 of Infernal introduces a new filter pipeline for RNA homology search based on accelerated profile hidden Markov model (HMM) methods and HMM-banded CM alignment methods. This enables ∼100-fold acceleration over the previous version and ∼10 000-fold acceleration over exhaustive non-filtered CM searches. Availability: Source code, documentation and the benchmark are downloadable from http://infernal.janelia.org. Infernal is freely licensed under the GNU GPLv3 and should be portable to any POSIX-compliant operating system, including Linux and Mac OS/X. Documentation includes a user's guide with a tutorial, a discussion of file formats and user options and additional details on methods implemented in the software. Contact: nawrockie@janelia.hhmi.org
MeSH term(s)	Algorithms ; Nucleic Acid Conformation ; RNA/chemistry ; Sequence Alignment/methods ; Sequence Analysis, RNA ; Sequence Homology, Nucleic Acid ; Software
Chemical Substances	RNA (63231-63-0)
Language	English
Publishing date	2013-09-04
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	1422668-6
ISSN	1367-4811 ; 1367-4803
ISSN (online)	1367-4811
ISSN	1367-4803
DOI	10.1093/bioinformatics/btt509
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Special focus: bioinformatics.

Nawrocki, Eric P / Burge, Sarah W

RNA biology

2013 Volume 10, Issue 7, Page(s) 1160

MeSH term(s)	Computational Biology/methods ; RNA/chemistry ; RNA/physiology
Chemical Substances	RNA (63231-63-0)
Language	English
Publishing date	2013-08-17
Publishing country	United States
Document type	Editorial
ISSN	1555-8584
ISSN (online)	1555-8584
DOI	10.4161/rna.25606
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Computational identification of functional RNA homologs in metagenomic data.

Nawrocki, Eric P / Eddy, Sean R

RNA biology

2013 Volume 10, Issue 7, Page(s) 1170–1179

Abstract: A key step toward understanding a metagenomics data set is the identification of functional sequence elements within it, such as protein coding genes and structural RNAs. Relative to protein coding genes, structural RNAs are more difficult to identify ... ...

Abstract	A key step toward understanding a metagenomics data set is the identification of functional sequence elements within it, such as protein coding genes and structural RNAs. Relative to protein coding genes, structural RNAs are more difficult to identify because of their reduced alphabet size, lack of open reading frames, and short length. Infernal is a software package that implements "covariance models" (CMs) for RNA homology search, which harness both sequence and structural conservation when searching for RNA homologs. Thanks to the added statistical signal inherent in the secondary structure conservation of many RNA families, Infernal is more powerful than sequence-only based methods such as BLAST and profile HMMs. Together with the Rfam database of CMs, Infernal is a useful tool for identifying RNAs in metagenomics data sets.
MeSH term(s)	Algorithms ; Computational Biology/methods ; Databases, Nucleic Acid ; Metagenomics ; Nucleic Acid Conformation ; RNA/chemistry ; RNA/genetics ; Search Engine ; Sequence Homology, Nucleic Acid ; Software
Chemical Substances	RNA (63231-63-0)
Language	English
Publishing date	2013-05-20
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Review
ISSN	1555-8584
ISSN (online)	1555-8584
DOI	10.4161/rna.25038
Database	MEDical Literature Analysis and Retrieval System OnLINE

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