scholarly journals Current situation of DNA Barcoding data in biodiversity and genomics databases and data integration for museomics

2019 ◽  
Vol 3 ◽  
Author(s):  
Takeru Nakazato
Keyword(s):  
Dna Barcoding ◽  
Dna Sequences ◽  
Life Science ◽  
Current Situation ◽  
Flowering Plants ◽  
Sequencing Technology ◽  
File Transfer ◽  
Science Data ◽  
Ngs Data ◽  
Biodiversity Information

The museomics activity regards museum-preserved specimens as rich resources for DNA studies by extracting and analyzing DNA from these specimens in conjunction with their biodiversity information. Also in biodiversity field, DNA sequence data such as DNA barcoding has become essential as evidence for species identification and phylogenetic analysis as well as occurrence and morphological information. To accelerate biodiversity informatics, it is important to utilize both biodiversity occurrence and morphology data, and bioinformatics sequencing data. There are many databases for biodiversity domain such as GBIF (The Global Biodiversity Information Facility) for species occurrence records, EoL (The Encyclopedia of Life) as a knowledge base of all species, and BOLD (The Barcode of Life Data) for DNA barcoding data. In genomics science, molecular data involving DNA and protein sequences have been captured by the DNA Data Bank in Japan (DDBJ), the European Bioinformatics Institute (EBI, UK), and the National Center for Biotechnology Information (NCBI, US) under the International Nucleotide Sequence Database Collaboration (INSDC) for more than 30 years. Recently, NCBI launched a new database called BioCollections, including 7,930 culture collections, museums, herbaria, and other natural history collections. In addition, we can submit biodiversity information such as specimen voucher IDs, BOLD IDs, and latitude/longitude with DNA sequences. To find out the current situation, I downloaded GenBank (Nucleotide) files (updated at 22 Feb 2019) from the NCBI FTP (file transfer protocol) site and extracted biodiversity features including specimen voucher IDs and BOLD IDs. For Insecta, there are 2,427,343 sequence entries with specimen voucher ID and 1,766,142 entries with BOLD ID of 3,389,495 total entries. The most abundant species with voucher IDs is “Cecidomyiidae sp. BOLD−2016” (Diptera) (35,861 sequence entries). The most frequently referred voucher ID is “USNMENT00921257” (1510 sequence entries), indicating Stenamma megamanni (Hymenoptera, Formicidae, Myrmicinae). For flowering plants (Magnoliophyta), of 3,094,140 total entries, 1,109,420 sequence entries are assigned with voucher IDs and 73,409 entries with BOLD IDs. Additionally, 79,891 matK entries and 63,821 rbcL entries are submitted with voucher IDs, without BOLD IDs. I also retrieved BOLD data for Insecta and flowering plants. The 2,368,801 GenBank entries are referred from 4,176,481 BOLD total entries for Insecta, and the 259,245 GenBank entries from 345,706 BOLD entries for flowering plants. Some DNA barcoding data exist redundantly in BOLD database because BOLD imports sequences from NCBI submitted as DNA barcoding data in BOLD. These entries have different BOLD IDs but same BIN_URL is assigned. Recently, high-throughput sequencing technology, also called next-generation sequencing technology (NGS), has made a great impact in genomic science. Biodiversity researchers became to perform not only DNA barcoding but also RNA-Seq with NGS. NGS also accelerates museomics activity. NGS data are archived to the Sequence Read Archive (SRA) database, and sample information is described in BioSample database in INSDC. To utilize NGS data for biodiversity field, we will need to integrate such databases and other biodiversity databases. We, Database Center for Life Science, tackle to integrate life science data with Semantic Web technology. We held annual meetings to integrate life science data, called BioHackathons, in which researchers from all over the world participated. We began to RDFize BioSample data, but we should import existing schemes used in the biodiversity field including Darwin Core.

scholarly journals Survey of Species Covered by DNA Barcoding Data in BOLD and GenBank for Integration of Data for Museomics

2020 ◽  
Vol 4 ◽  
Author(s):  
Takeru Nakazato
Keyword(s):  
Dna Barcoding ◽  
Fish Species ◽  
Dna Sequences ◽  
Dna Barcode ◽  
Database Systems ◽  
Species Level ◽  
Ncbi Taxonomy ◽  
Dna Barcodes ◽  
Voucher Specimen ◽  
Biodiversity Information

DNA barcoding technology has become employed widely for biodiversity and molecular biology researchers to identify species and analyze their phylogeny. Recently, DNA metabarcoding and environmental DNA (eDNA) technology have developed by expanding the concept of DNA barcoding. These techniques analyze the diversity and quantity of organisms within an environment by detecting biogenic DNA in water and soil. It is particularly popular for monitoring fish species living in rivers and lakes (Takahara et al. 2012). BOLD Systems (Barcode of Life Database systems, Ratnasingham and Hebert 2007) is a database for DNA barcoding, archiving 8.5 million of barcodes (as of August 2020) along with the voucher specimen, from which the DNA barcode sequence is derived, including taxonomy, collected country, and museum vouchered as metadata (e.g. https://www.boldsystems.org/index.php/Public_RecordView?processid=TRIBS054-16). Also, many barcoding data are submitted to GenBank (Sayers et al. 2020), which is a database for DNA sequences managed by NCBI (National Center for Biotechnology Information, US). The number of the records of DNA barcodes, i.e. COI (cytochrome c oxidase I) gene for animal, has grown significantly (Porter and Hajibabaei 2018). BOLD imports DNA barcoding data from GenBank, and lots of DNA barcoding data in GenBank are also assigned BOLD IDs. However, we have to refer to both BOLD and GenBank data when performing DNA barcoding. I have previously investigated the registration of DNA barcoding data in GenBank, especially the association with BOLD, using insects and flowering plants as examples (Nakazato 2019). Here, I surveyed the number of species covered by BOLD and GenBank. I used fish data as an example because eDNA research is particularly focused on fish. I downloaded all GenBank files for vertebrates from NCBI FTP (File Transfer Protocol) sites (as of November 2019). Of the GenBank fish entries, 86,958 (7.3%) were assigned BOLD identifiers (IDs). The NCBI taxonomy database has registrations for 39,127 species of fish, and 20,987 scientific names at the species level (i.e., excluding names that included sp., cf. or aff.). GenBank entries with BOLD IDs covered 11,784 species (30.1%) and 8,665 species-level names (41.3%). I also obtained whole "specimens and sequences combined data" for fish from BOLD systems (as of November 2019). In the BOLD, there are 273,426 entries that are registered as fish. Of these entries, 211,589 BOLD entries were assigned GenBank IDs, i.e. with values in “genbank_accession” column, and 121,748 entries were imported from GenBank, i.e. with "Mined from GenBank, NCBI" description in "institution_storing" column. The BOLD data covered 18,952 fish species and 15,063 species-level names, but 35,500 entries were assigned no species-level names and 22,123 entries were not even filled with family-level names. At the species level, 8,067 names co-occurred in GenBank and BOLD, with 6,997 BOLD-specific names and 599 GenBank-specific names. GenBank has 425,732 fish entries with voucher IDs, of which 340,386 were not assigned a BOLD ID. Of these 340,386 entries, 43,872 entries are registrations for COI genes, which could be candidates for DNA barcodes. These candidates include 4,201 species that are not included in BOLD, thus adding these data will enable us to identify 19,863 fish to the species level. For researchers, it would be very useful if both BOLD and GenBank DNA barcoding data could be searched in one place. For this purpose, it is necessary to integrate data from the two databases. A lot of biodiversity data are recorded based on the Darwin Core standard while DNA sequencing data are sometimes integrated or cross-linked by RDF (Resource Description Framework). It may not be technically difficult to integrate these data, but the species data referenced differ from the EoL (The Encyclopedia of Life) for BOLD and the NCBI taxonomy for GenBank, and the differences in taxonomic systems make it difficult to match by scientific name description. GenBank has fields for the latitude and longitude of the specimens sampled, and Porter and Hajibabaei 2018 argue that this information should be enhanced. However, this information may be better described in the specimen and occurrence databases. The integration of barcoding data with the specimen and occurrence data will solve these problems. Most importantly, it will save the researcher from having to register the same information in multiple databases. In the field of biodiversity, only DNA barcode sequences may have been focused on and used as gene sequences. The museomics community regards museum-preserved specimens as rich resources for DNA studies because their biodiversity information can accompany the extraction and analysis of their DNA (Nakazato 2018). GenBank is useful for biodiversity studies due to its low rate of mislabelling (Leray et al. 2019). In the future, we will be working with a variety of DNA, including genomes from museum specimens as well as DNA barcoding. This will require more integrated use of biodiversity information and DNA sequence data. This integration is also of interest to molecular biologists and bioinformaticians.

scholarly journals ELIXIR: Data for Life – Coordinating life science data and services across Europe 

2019 ◽  
Vol 3 ◽  
Author(s):  
Jerry Lanfear
Keyword(s):  
Life Science ◽  
Publicly Funded ◽  
Health Food ◽  
Science Data ◽  
Global Biodiversity Information Facility ◽  
Occurrence Data ◽  
Development Goals ◽  
Biology Laboratory ◽  
Access Services ◽  
Biodiversity Information

ELIXIR unites Europe’s leading life science organisations in managing and safeguarding the increasing volume of data being generated by publicly funded research. It coordinates, integrates and sustains bioinformatics resources across its 22 member states, plus EMBL-EBI (European Molecular Biology Laboratory - European Bioinformatics Institute), and enables end users to access services and data that are vital for their research. ELIXIR's remit spans the full breadth of life science data, including data related to human health, food production (agriculture, farming, aquaculture) and the environment (e.g. pollution remediation, ecology), all of clear socio-economic benefit. As a result, ELIXIR contributes to the delivery of several sustainable development goals. This poster will introduce ELIXIR and describe the contribution it can make to coordinating data and services relevant to biodiversity. The poster will set the context for how molecularly-derived biodiversity occurrence data can significantly enhance resources such as the Global Biodiversity Information Facility (GBIF) and the Ocean Biogeographic Information System (OBIS), e.g. by filling in acute gaps in our knowledge of species across realms.

scholarly journals A Challenge to Integrate Bioinformatics and Biodiversity Informatics Data as Museomics

2018 ◽  
Vol 2 ◽  
pp. e26102 ◽  
Author(s):  
Takeru Nakazato
Keyword(s):  
Gene Expression ◽  
Phylogenetic Analysis ◽  
Molecular Biology ◽  
Life Science ◽  
Sequence Data ◽  
Molecular Data ◽  
Biodiversity Informatics ◽  
Science Data ◽  
Dna Sequence Data ◽  
Ngs Data

Museum-preserved samples are attracting attention as a rich resource for DNA studies. Museomics aims to link DNA sequence data back to the museum collection. Molecular biologists are interested in morphological information including body size, pattern, and colors, and sequence data have also become essential for biodiversity research as evidence for species identification and phylogenetic analysis. For more than 30 years, molecular data, such as DNA and protein sequences, have been captured by the DNA Data Bank of Japan (DDBJ), the European Bioinformatics Institute (EBI, UK), and the National Center for Biotechnology Information (NCBI, US) under the International Nucleotide Sequence Database Collaboration (INSDC). INSDC provides collected molecular data to researchers as public databases including GenBank for DNA sequences and Gene Expression Omnibus (GEO) for gene expression. These three institutes synchronize archived data and publish all data on an FTP (File Transfer Protocol) site so that it is available for big data analysis. In recent years, high-throughput sequencing technology, also called next-generation sequencing (NGS) technology, has been widely utilized for molecular biology including genomics, transcriptomics, and metagenomics. Biodiversity researchers also focus on NGS data for DNA barcoding and phylogenetic analysis as well as molecular biology. Additionally, a portable NGS platform, MinION (Oxford Nanopore Technologies), has been launched, enabling biodiversity researchers to perform DNA sequencing in the field. Along with GenBank and GEO data, INSDC accepts NGS data and provides a public primary database, called the Sequence Read Archive (SRA). As of March 2018, 6.4 Peta Bases of NGS data is freely available under more than 130,000 projects in SRA. The Database Center for Life Science (DBCLS) provides a search engine for public NGS data, called DBCLS SRA (http://sra.dbcls.jp/) in collaboration with DDBJ. SRA contains not only raw sequence reads or processed data mapped to genome, but also information on the experimental design, including project types, sequencing platforms, and sample species. Researchers can use this data to refine their search results. We also linked publications referring to NGS data to the corresponding SRA entries. The mission of DBCLS is to accelerate the accessibility of life science data. Collected data used to be described in the Excel-readable tabular format, but these formats are difficult to merge with other databases because of the ambiguity of labels. To overcome this difficulty, we recently integrated life science data with Semantic Web technology. We held annual meetings to integrate life science data, called BioHackathons, in which researchers from all over the world participated. UniProt and Ensembl databases currently provide an RDF (Resource Description Framework) version of curated genome and protein data, respectively. In the biodiversity domain, there are many databases such as GBIF (The Global Biodiversity Information Facility) for species occurrence records, EoL (The Encyclopedia of Life) as a knowledge base of all species, and BoL (The Barcode of Life) for DNA barcoding data. RDF is utilized to describe Darwin Core based data so that bioinformatics and biodiversity informatics researchers can technically merge both types of data. Currently, specimen data and DNA sequence data are not linked. Museomics starts with cross-referencing specimen and sequence IDs and by making data sources comply with an existing standard.

DNA barcoding of bats (Chiroptera) from the Colombian northern region

Mammalia ◽  
2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Álvaro J. Benítez ◽  
Dina Ricardo-Caldera ◽  
María Atencia-Pineda ◽  
Jesús Ballesteros-Correa ◽  
Julio Chacón-Pacheco ◽  
...  
Keyword(s):  
Dna Barcoding ◽  
Species Identification ◽  
Dna Sequences ◽  
Genetic Distances ◽  
Northern Region ◽  
Coi Gene ◽  
Individual Species ◽  
Species Variation ◽  
Molossus Molossus ◽  
Artibeus Lituratus

Abstract Bats are mammals of great ecological and medical importance, which have associations with different pathogenic microorganisms. DNA barcoding is a tool that can expedite species identification using short DNA sequences. In this study, we assess the DNA barcoding methodology in bats from the Colombian Northern region, specifically in the Córdoba department. Cytochrome oxidase subunit I (COI) gene sequences of nine bat species were typified, and their comparison with other Neotropic samples revealed that this marker is suitable for individual species identification, with ranges of intra-species variation from 0.1 to 0.9%. Bat species clusters are well supported and differentiated, showing average genetic distances ranging from 3% between Artibeus lituratus and Artibeus planirostris, up to 27% between Carollia castanea and Molossus molossus. C. castanea and Glossophaga soricina show geographical structuring in the Neotropic. The findings reported in this study confirm DNA barcoding usefulness for fast species identification of bats in the region.

scholarly journals FCC – An automated rule-based processing tool for life science data

2013 ◽  
Vol 8 (1) ◽  
pp. 3 ◽  
Author(s):  
Simon Barkow-Oesterreicher ◽  
Can Türker ◽  
Christian Panse
Keyword(s):  
Life Science ◽  
Science Data ◽  
Rule Based

scholarly journals Benchmarking DNA barcodes: an assessment using available primate sequences

Genome ◽  
2006 ◽  
Vol 49 (7) ◽  
pp. 851-854 ◽  
Author(s):  
Mehrdad Hajibabaei ◽  
Gregory AC Singer ◽  
Donal A Hickey
Keyword(s):  
New Species ◽  
Cryptic Species ◽  
Dna Barcoding ◽  
Species Identification ◽  
Dna Sequences ◽  
Phylogenetic Relationships ◽  
Primate Species ◽  
Dna Barcodes ◽  
Global Biodiversity ◽  
Efficient Sequence

DNA barcoding has been recently promoted as a method for both assigning specimens to known species and for discovering new and cryptic species. Here we test both the potential and the limitations of DNA barcodes by analysing a group of well-studied organisms—the primates. Our results show that DNA barcodes provide enough information to efficiently identify and delineate primate species, but that they cannot reliably uncover many of the deeper phylogenetic relationships. Our conclusion is that these short DNA sequences do not contain enough information to build reliable molecular phylogenies or define new species, but that they can provide efficient sequence tags for assigning unknown specimens to known species. As such, DNA barcoding provides enormous potential for use in global biodiversity studies.Key words: DNA barcoding, species identification, primate, biodiversity.

scholarly journals DNA barcoding the flowering plants from the tropical coral islands of Xisha (China)

2018 ◽  
Vol 8 (21) ◽  
pp. 10587-10593 ◽  
Author(s):  
Shengchun Li ◽  
Xin Qian ◽  
Zexin Zheng ◽  
Miaomiao Shi ◽  
Xiaoyu Chang ◽  
...  
Keyword(s):  
Dna Barcoding ◽  
Flowering Plants ◽  
Tropical Coral ◽  
Coral Islands

scholarly journals SHI7 Is a Self-Learning Pipeline for Multipurpose Short-Read DNA Quality Control

mSystems ◽  
2018 ◽  
Vol 3 (3) ◽  
Author(s):  
Gabriel A. Al-Ghalith ◽  
Benjamin Hillmann ◽  
Kaiwei Ang ◽  
Robin Shields-Cutler ◽  
Dan Knights
Keyword(s):  
Quality Control ◽  
Dna Sequences ◽  
Sequence Data ◽  
Background Knowledge ◽  
Sequencing Technology ◽  
Data Set ◽  
Short Read ◽  
Dna Quality ◽  
Public Data ◽  
User Friendly

ABSTRACT Next-generation sequencing technology is of great importance for many biological disciplines; however, due to technical and biological limitations, the short DNA sequences produced by modern sequencers require numerous quality control (QC) measures to reduce errors, remove technical contaminants, or merge paired-end reads together into longer or higher-quality contigs. Many tools for each step exist, but choosing the appropriate methods and usage parameters can be challenging because the parameterization of each step depends on the particularities of the sequencing technology used, the type of samples being analyzed, and the stochasticity of the instrumentation and sample preparation. Furthermore, end users may not know all of the relevant information about how their data were generated, such as the expected overlap for paired-end sequences or type of adaptors used to make informed choices. This increasing complexity and nuance demand a pipeline that combines existing steps together in a user-friendly way and, when possible, learns reasonable quality parameters from the data automatically. We propose a user-friendly quality control pipeline called SHI7 (canonically pronounced “shizen”), which aims to simplify quality control of short-read data for the end user by predicting presence and/or type of common sequencing adaptors, what quality scores to trim, whether the data set is shotgun or amplicon sequencing, whether reads are paired end or single end, and whether pairs are stitchable, including the expected amount of pair overlap. We hope that SHI7 will make it easier for all researchers, expert and novice alike, to follow reasonable practices for short-read data quality control. IMPORTANCE Quality control of high-throughput DNA sequencing data is an important but sometimes laborious task requiring background knowledge of the sequencing protocol used (such as adaptor type, sequencing technology, insert size/stitchability, paired-endedness, etc.). Quality control protocols typically require applying this background knowledge to selecting and executing numerous quality control steps with the appropriate parameters, which is especially difficult when working with public data or data from collaborators who use different protocols. We have created a streamlined quality control pipeline intended to substantially simplify the process of DNA quality control from raw machine output files to actionable sequence data. In contrast to other methods, our proposed pipeline is easy to install and use and attempts to learn the necessary parameters from the data automatically with a single command.

Refined JST Thesaurus Extended with Data from Other Open Life Science Data Sources

2017 ◽  
pp. 35-48 ◽  
Author(s):  
Tatsuya Kushida ◽  
Yuka Tateisi ◽  
Takeshi Masuda ◽  
Katsutaro Watanabe ◽  
Katsuji Matsumura ◽  
...  
Keyword(s):  
Life Science ◽  
Data Sources ◽  
Science Data
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