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Caliskan A, Dangwal S, Dandekar T. Metadata integrity in bioinformatics: Bridging the gap between data and knowledge. Comput Struct Biotechnol J 2023; 21:4895-4913. [PMID: 37860229 PMCID: PMC10582761 DOI: 10.1016/j.csbj.2023.10.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 10/04/2023] [Accepted: 10/04/2023] [Indexed: 10/21/2023] Open
Abstract
In the fast-evolving landscape of biomedical research, the emergence of big data has presented researchers with extraordinary opportunities to explore biological complexities. In biomedical research, big data imply also a big responsibility. This is not only due to genomics data being sensitive information but also due to genomics data being shared and re-analysed among the scientific community. This saves valuable resources and can even help to find new insights in silico. To fully use these opportunities, detailed and correct metadata are imperative. This includes not only the availability of metadata but also their correctness. Metadata integrity serves as a fundamental determinant of research credibility, supporting the reliability and reproducibility of data-driven findings. Ensuring metadata availability, curation, and accuracy are therefore essential for bioinformatic research. Not only must metadata be readily available, but they must also be meticulously curated and ideally error-free. Motivated by an accidental discovery of a critical metadata error in patient data published in two high-impact journals, we aim to raise awareness for the need of correct, complete, and curated metadata. We describe how the metadata error was found, addressed, and present examples for metadata-related challenges in omics research, along with supporting measures, including tools for checking metadata and software to facilitate various steps from data analysis to published research.
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Affiliation(s)
- Aylin Caliskan
- Department of Bioinformatics, Biocenter, University of Würzburg, 97074 Würzburg, Germany
| | - Seema Dangwal
- Stanford Cardiovascular Institute, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305-5101, United States
| | - Thomas Dandekar
- Department of Bioinformatics, Biocenter, University of Würzburg, 97074 Würzburg, Germany
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Sapoval N, Liu Y, Lou EG, Hopkins L, Ensor KB, Schneider R, Stadler LB, Treangen TJ. QuaID: Enabling Earlier Detection of Recently Emerged SARS-CoV-2 Variants of Concern in Wastewater. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2022:2021.09.08.21263279. [PMID: 35898338 PMCID: PMC9327636 DOI: 10.1101/2021.09.08.21263279] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
As clinical testing declines, wastewater monitoring can provide crucial surveillance on the emergence of SARS-CoV-2 variants of concern (VoC) in communities. Multiple recent studies support that wastewater-based SARS-CoV-2 detection of circulating VoC can precede clinical cases by up to two weeks. Furthermore, wastewater based epidemiology enables wide population-based screening and study of viral evolutionary dynamics. However, highly sensitive detection of emerging variants remains a complex task due to the pooled nature of environmental samples and genetic material degradation. In this paper we propose quasi-unique mutations for VoC identification, implemented in a novel bioinformatics tool (QuaID) for VoC detection based on quasi-unique mutations. The benefits of QuaID are three-fold: (i) provides up to 3 week earlier VoC detection compared to existing approaches, (ii) enables more sensitive VoC detection, which is shown to be tolerant of >50% mutation drop-out, and (iii) leverages all mutational signatures, including insertions & deletions.
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Affiliation(s)
- Nicolae Sapoval
- Department of Computer Science, Rice University, 6100 Main Street, Houston, TX 77005, USA
| | - Yunxi Liu
- Department of Computer Science, Rice University, 6100 Main Street, Houston, TX 77005, USA
| | - Esther G Lou
- Department of Civil and Environmental Engineering, Rice University, 6100 Main Street, Houston, TX 77005, USA
| | - Loren Hopkins
- Houston Health Department, 8000 N. Stadium Dr., Houston, TX 77054
- Department of Statistics, Rice University, 6100 Main Street, Houston, TX 77005, USA
| | - Katherine B Ensor
- Department of Statistics, Rice University, 6100 Main Street, Houston, TX 77005, USA
| | | | - Lauren B Stadler
- Department of Civil and Environmental Engineering, Rice University, 6100 Main Street, Houston, TX 77005, USA
| | - Todd J Treangen
- Department of Computer Science, Rice University, 6100 Main Street, Houston, TX 77005, USA
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Abstract
The availability of public genomics data has become essential for modern life sciences research, yet the quality, traceability, and curation of these data have significant impacts on a broad range of microbial genomics research. While microbial genome databases such as NCBI’s RefSeq database leverage the scalability of crowd sourcing for growth, genomics data provenance and authenticity of the source materials used to produce data are not strict requirements. Here, we describe the de novo assembly of 1,113 bacterial genome references produced from authenticated materials sourced from the American Type Culture Collection (ATCC), each with full genomics data provenance relating to bioinformatics methods, quality control, and passage history. Comparative genomics analysis of ATCC standard reference genomes (ASRGs) revealed significant issues with regard to NCBI’s RefSeq bacterial genome assemblies related to completeness, mutations, structure, strain metadata, and gaps in traceability to the original biological source materials. Nearly half of RefSeq assemblies lack details on sample source information, sequencing technology, or bioinformatics methods. Deep curation of these records is not within the scope of NCBI’s core mission in supporting open science, which aims to collect sequence records that are submitted by the public. Nonetheless, we propose that gaps in metadata accuracy and data provenance represent an “elephant in the room” for microbial genomics research. Effectively addressing these issues will require raising the level of accountability for data depositors and acknowledging the need for higher expectations of quality among the researchers whose research depends on accurate and attributable reference genome data. IMPORTANCE The traceability of microbial genomics data to authenticated physical biological materials is not a requirement for depositing these data into public genome databases. This creates significant risks for the reliability and data provenance of these important genomics research resources, the impact of which is not well understood. We sought to investigate this by carrying out a comparative genomics study of 1,113 ATCC standard reference genomes (ASRGs) produced by ATCC from authenticated and traceable materials using the latest sequencing technologies. We found widespread discrepancies in genome assembly quality, genetic variability, and the quality and completeness of the associated metadata among hundreds of reference genomes for ATCC strains found in NCBI’s RefSeq database. We present a comparative analysis of de novo-assembled ASRGs, their respective metadata, and variant analysis using RefSeq genomes as a reference. Although assembly quality in RefSeq has generally improved over time, we found that significant quality issues remain, especially as related to genomic data and metadata provenance. Our work highlights the importance of data authentication and provenance for the microbial genomics community, and underscores the risks of ignoring this issue in the future.
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Bussi Y, Kapon R, Reich Z. Large-scale k-mer-based analysis of the informational properties of genomes, comparative genomics and taxonomy. PLoS One 2021; 16:e0258693. [PMID: 34648558 PMCID: PMC8516232 DOI: 10.1371/journal.pone.0258693] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Accepted: 10/02/2021] [Indexed: 12/24/2022] Open
Abstract
Information theoretic approaches are ubiquitous and effective in a wide variety of bioinformatics applications. In comparative genomics, alignment-free methods, based on short DNA words, or k-mers, are particularly powerful. We evaluated the utility of varying k-mer lengths for genome comparisons by analyzing their sequence space coverage of 5805 genomes in the KEGG GENOME database. In subsequent analyses on four k-mer lengths spanning the relevant range (11, 21, 31, 41), hierarchical clustering of 1634 genus-level representative genomes using pairwise 21- and 31-mer Jaccard similarities best recapitulated a phylogenetic/taxonomic tree of life with clear boundaries for superkingdom domains and high subtree similarity for named taxons at lower levels (family through phylum). By analyzing ~14.2M prokaryotic genome comparisons by their lowest-common-ancestor taxon levels, we detected many potential misclassification errors in a curated database, further demonstrating the need for wide-scale adoption of quantitative taxonomic classifications based on whole-genome similarity.
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Affiliation(s)
- Yuval Bussi
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Ruti Kapon
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
| | - Ziv Reich
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
- * E-mail:
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Gozashti L, Corbett-Detig R. Shortcomings of SARS-CoV-2 genomic metadata. BMC Res Notes 2021; 14:189. [PMID: 34001211 PMCID: PMC8128092 DOI: 10.1186/s13104-021-05605-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Accepted: 05/06/2021] [Indexed: 11/10/2022] Open
Abstract
OBJECTIVE The SARS-CoV-2 pandemic has prompted one of the most extensive and expeditious genomic sequencing efforts in history. Each viral genome is accompanied by a set of metadata which supplies important information such as the geographic origin of the sample, age of the host, and the lab at which the sample was sequenced, and is integral to epidemiological efforts and public health direction. Here, we interrogate some shortcomings of metadata within the GISAID database to raise awareness of common errors and inconsistencies that may affect data-driven analyses and provide possible avenues for resolutions. RESULTS Our analysis reveals a startling prevalence of spelling errors and inconsistent naming conventions, which together occur in an estimated ~ 9.8% and ~ 11.6% of "originating lab" and "submitting lab" GISAID metadata entries respectively. We also find numerous ambiguous entries which provide very little information about the actual source of a sample and could easily associate with multiple sources worldwide. Importantly, all of these issues can impair the ability and accuracy of association studies by deceptively causing a group of samples to identify with multiple sources when they truly all identify with one source, or vice versa.
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Affiliation(s)
- Landen Gozashti
- Department of Organismic and Evolutionary Biology and Museum of Comparative Zoology, Harvard University, Cambridge, MA, 02138, USA. .,Department of Biomolecular Engineering and Genomics Institute, University of California Santa Cruz, Santa Cruz, CA, 95064, USA.
| | - Russell Corbett-Detig
- Department of Biomolecular Engineering and Genomics Institute, University of California Santa Cruz, Santa Cruz, CA, 95064, USA
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Wang H, Drake SK, Yong C, Gucek M, Lyes MA, Rosenberg AZ, Soderblom E, Arthur Moseley M, Dekker JP, Suffredini AF. A Genoproteomic Approach to Detect Peptide Markers of Bacterial Respiratory Pathogens. Clin Chem 2017; 63:1398-1408. [PMID: 28588123 PMCID: PMC10863334 DOI: 10.1373/clinchem.2016.269647] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2016] [Accepted: 05/02/2017] [Indexed: 12/16/2022]
Abstract
BACKGROUND Rapid identification of respiratory pathogens may facilitate targeted antimicrobial therapy. Direct identification of bacteria in bronchoalveolar lavage (BAL) by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry is confounded by interfering substances. We describe a method to identify unique peptide markers of 5 gram-negative bacteria by liquid chromatography-tandem mass spectrometry (LC-MS/MS) for direct pathogen identification in BAL. METHODS In silico translation and digestion were performed on 14-25 whole genomes representing strains of Acinetobacter baumannii, Moraxella catarrhalis, Pseudomonas aeruginosa, Stenotrophomonas maltophilia, and Klebsiella pneumoniae. Peptides constituting theoretical core peptidomes in each were identified. Rapid tryptic digestion was performed; peptides were analyzed by LC-MS/MS and compared with the theoretical core peptidomes. High-confidence core peptides (false discovery rate <1%) were identified and analyzed with the lowest common ancestor search to yield potential species-specific peptide markers. The species specificity of each peptide was verified with protein BLAST. Further, 1 or 2 pathogens were serially diluted into pooled inflamed BAL, and a targeted LC-MS/MS assay was used to detect 25 peptides simultaneously. RESULTS Five unique peptides with the highest abundance for each pathogen distinguished these pathogens with varied detection sensitivities. Peptide markers for A. baumannii and P. aeruginosa, when spiked simultaneously into inflamed BAL, were detected with as few as 3.6 (0.2) × 103 and 2.2 (0.6) × 103 colony-forming units, respectively, by targeted LC-MS/MS. CONCLUSIONS This proof-of-concept study shows the feasibility of identifying unique peptides in BAL for 5 gram-negative bacterial pathogens, and it may provide a novel approach for rapid direct identification of bacterial pathogens in BAL.
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Affiliation(s)
- Honghui Wang
- Critical Care Medicine Department, Clinical Center, National Institutes of Health, Bethesda, MD
| | - Steven K Drake
- Critical Care Medicine Department, Clinical Center, National Institutes of Health, Bethesda, MD
| | - Chen Yong
- Proteomic Core Facility, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD
| | - Marjan Gucek
- Proteomic Core Facility, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD
| | - Matthew A Lyes
- Critical Care Medicine Department, Clinical Center, National Institutes of Health, Bethesda, MD
| | - Avi Z Rosenberg
- Kidney Disease Section, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD
| | - Erik Soderblom
- Proteomics and Metabolomics Shared Resource, Duke University School of Medicine, Durham, NC
| | - M Arthur Moseley
- Proteomics and Metabolomics Shared Resource, Duke University School of Medicine, Durham, NC
| | - John P Dekker
- Microbiology Service, Department of Laboratory Medicine, Clinical Center, National Institutes of Health, Bethesda, MD
| | - Anthony F Suffredini
- Critical Care Medicine Department, Clinical Center, National Institutes of Health, Bethesda, MD;
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Thai QK, Chung DA, Tran HD. Canis mtDNA HV1 database: a web-based tool for collecting and surveying Canis mtDNA HV1 haplotype in public database. BMC Genet 2017; 18:60. [PMID: 28651548 PMCID: PMC5485557 DOI: 10.1186/s12863-017-0528-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2017] [Accepted: 06/21/2017] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Canine and wolf mitochondrial DNA haplotypes, which can be used for forensic or phylogenetic analyses, have been defined in various schemes depending on the region analyzed. In recent studies, the 582 bp fragment of the HV1 region is most commonly used. 317 different canine HV1 haplotypes have been reported in the rapidly growing public database GenBank. These reported haplotypes contain several inconsistencies in their haplotype information. To overcome this issue, we have developed a Canis mtDNA HV1 database. This database collects data on the HV1 582 bp region in dog mitochondrial DNA from the GenBank to screen and correct the inconsistencies. It also supports users in detection of new novel mutation profiles and assignment of new haplotypes. DESCRIPTION The Canis mtDNA HV1 database (CHD) contains 5567 nucleotide entries originating from 15 subspecies in the species Canis lupus. Of these entries, 3646 were haplotypes and grouped into 804 distinct sequences. 319 sequences were recognized as previously assigned haplotypes, while the remaining 485 sequences had new mutation profiles and were marked as new haplotype candidates awaiting further analysis for haplotype assignment. Of the 3646 nucleotide entries, only 414 were annotated with correct haplotype information, while 3232 had insufficient or lacked haplotype information and were corrected or modified before storing in the CHD. The CHD can be accessed at http://chd.vnbiology.com . It provides sequences, haplotype information, and a web-based tool for mtDNA HV1 haplotyping. The CHD is updated monthly and supplies all data for download. CONCLUSIONS The Canis mtDNA HV1 database contains information about canine mitochondrial DNA HV1 sequences with reconciled annotation. It serves as a tool for detection of inconsistencies in GenBank and helps identifying new HV1 haplotypes. Thus, it supports the scientific community in naming new HV1 haplotypes and to reconcile existing annotation of HV1 582 bp sequences.
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Affiliation(s)
- Quan Ke Thai
- Saigon University, 273 An Duong Vuong street, District 5, Ho Chi Minh city, Vietnam
| | - Dung Anh Chung
- Institute of Agricultural science for Southern Vietnam, 121 Nguyen Binh Khiem street, District 1, Ho Chi Minh city, Vietnam
| | - Hoang-Dung Tran
- Nguyen Tat Thanh University, 300A Nguyen Tat Thanh street, District 4, Ho Chi Minh city, Vietnam
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