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Lin MJ, Iyer S, Chen NC, Langmead B. Measuring, visualizing, and diagnosing reference bias with biastools. Genome Biol 2024; 25:101. [PMID: 38641647 PMCID: PMC11027314 DOI: 10.1186/s13059-024-03240-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Accepted: 04/04/2024] [Indexed: 04/21/2024] Open
Abstract
Many bioinformatics methods seek to reduce reference bias, but no methods exist to comprehensively measure it. Biastools analyzes and categorizes instances of reference bias. It works in various scenarios: when the donor's variants are known and reads are simulated; when donor variants are known and reads are real; and when variants are unknown and reads are real. Using biastools, we observe that more inclusive graph genomes result in fewer biased sites. We find that end-to-end alignment reduces bias at indels relative to local aligners. Finally, we use biastools to characterize how T2T references improve large-scale bias.
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Affiliation(s)
- Mao-Jan Lin
- Department of Computer Science, Johns Hopkins University, Baltimore, USA.
| | - Sheila Iyer
- Department of Computer Science, Johns Hopkins University, Baltimore, USA
| | - Nae-Chyun Chen
- Department of Computer Science, Johns Hopkins University, Baltimore, USA
| | - Ben Langmead
- Department of Computer Science, Johns Hopkins University, Baltimore, USA.
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Lin MJ, Iyer S, Chen NC, Langmead B. Measuring, visualizing and diagnosing reference bias with biastools. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.09.13.557552. [PMID: 37745608 PMCID: PMC10515925 DOI: 10.1101/2023.09.13.557552] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
Many bioinformatics methods seek to reduce reference bias, but no methods exist to comprehensively measure it. Biastools analyzes and categorizes instances of reference bias. It works in various scenarios, i.e. (a) when the donor's variants are known and reads are simulated, (b) when donor variants are known and reads are real, and (c) when variants are unknown and reads are real. Using biastools, we observe that more inclusive graph genomes result in fewer biased sites. We find that end-to-end alignment reduces bias at indels relative to local aligners. Finally, we use biastools to characterize how T2T references improve large-scale bias.
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Affiliation(s)
- Mao-Jan Lin
- Department of Computer Science, Johns Hopkins University
| | - Sheila Iyer
- Department of Computer Science, Johns Hopkins University
| | - Nae-Chyun Chen
- Department of Computer Science, Johns Hopkins University
| | - Ben Langmead
- Department of Computer Science, Johns Hopkins University
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Holmes JB, Moyer E, Phan L, Maglott D, Kattman B. SPDI: data model for variants and applications at NCBI. Bioinformatics 2020; 36:1902-1907. [PMID: 31738401 DOI: 10.1093/bioinformatics/btz856] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2019] [Revised: 11/04/2019] [Accepted: 11/15/2019] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION Normalizing sequence variants on a reference, projecting them across congruent sequences and aggregating their diverse representations are critical to the elucidation of the genetic basis of disease and biological function. Inconsistent representation of variants among variant callers, local databases and tools result in discrepancies that complicate analysis. NCBI's genetic variation resources, dbSNP and ClinVar, require a robust, scalable set of principles to manage asserted sequence variants. RESULTS The SPDI data model defines variants as a sequence of four attributes: sequence, position, deletion and insertion, and can be applied to nucleotide and protein variants. NCBI web services convert representations among HGVS, VCF and SPDI and provide two functions to aggregate variants. One, based on the NCBI Variant Overprecision Correction Algorithm, returns a unique, normalized representation termed the 'Contextual Allele'. The SPDI data model, with its four operations, defines exactly the reference subsequence affected by the variant, even in repeat regions, such as homopolymer and other sequence repeats. The second function projects variants across congruent sequences and depends on an alignment dataset of non-assembly NCBI RefSeq sequences (prefixed NM, NR and NG), as well as inter- and intra-assembly-associated genomic sequences (NCs, NTs and NWs), supporting robust projection of variants across congruent sequences and assembly versions. The variant is projected to all congruent Contextual Alleles. One of these Contextual Alleles, typically the allele based on the latest assembly version, represents the entire set, is designated the unique 'Canonical Allele' and is used directly to aggregate variants across congruent sequences. AVAILABILITY AND IMPLEMENTATION The SPDI services are available for open access at: https://api.ncbi.nlm.nih.gov/variation/v0. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- J Bradley Holmes
- Information Engineering Branch, National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, MD 20894, USA
| | - Eric Moyer
- Information Engineering Branch, National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, MD 20894, USA
| | - Lon Phan
- Information Engineering Branch, National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, MD 20894, USA
| | - Donna Maglott
- Information Engineering Branch, National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, MD 20894, USA
| | - Brandi Kattman
- Information Engineering Branch, National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, MD 20894, USA
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Hunt SE, McLaren W, Gil L, Thormann A, Schuilenburg H, Sheppard D, Parton A, Armean IM, Trevanion SJ, Flicek P, Cunningham F. Ensembl variation resources. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2018; 2018:5255129. [PMID: 30576484 PMCID: PMC6310513 DOI: 10.1093/database/bay119] [Citation(s) in RCA: 280] [Impact Index Per Article: 46.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/31/2018] [Accepted: 10/04/2018] [Indexed: 12/31/2022]
Abstract
The major goal of sequencing humans and many other species is to understand the link between genomic variation, phenotype and disease. There are numerous valuable and well-established variation resources, but collating and making sense of non-homogeneous, often large-scale data sets from disparate sources remains a challenge. Without a systematic catalogue of these data and appropriate query and annotation tools, understanding the genome sequence of an individual and assessing their disease risk is impossible. In Ensembl, we substantially solve this problem: we develop methods to facilitate data integration and broad access; aggregate information in a consistent manner and make it available a variety of standard formats, both visually and programmatically; build analysis pipelines to compare variants to comprehensive genomic annotation sets; and make all tools and data publicly available.
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Affiliation(s)
- Sarah E Hunt
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - William McLaren
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Laurent Gil
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Anja Thormann
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Helen Schuilenburg
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Dan Sheppard
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Andrew Parton
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Irina M Armean
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Stephen J Trevanion
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Paul Flicek
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Fiona Cunningham
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
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Abstract
Storing biologically equivalent indels as distinct entries in databases causes data redundancy, and misleads downstream analysis. It is thus desirable to have a unified system for identifying and representing equivalent indels. Moreover, a unified system is also desirable to compare the indel calling results produced by different tools. This paper describes UPS-indel, a utility tool that creates a universal positioning system for indels so that equivalent indels can be uniquely determined by their coordinates in the new system, which also can be used to compare different indel calling results. UPS-indel identifies 15% redundant indels in dbSNP, 29% in COSMIC coding, and 13% in COSMIC noncoding datasets across all human chromosomes, higher than previously reported. Comparing the performance of UPS-indel with existing variant normalization tools vt normalize, BCFtools, and GATK LeftAlignAndTrimVariants shows that UPS-indel is able to identify 456,352 more redundant indels in dbSNP; 2,118 more in COSMIC coding, and 553 more in COSMIC noncoding indel dataset in addition to the ones reported jointly by these tools. Moreover, comparing UPS-indel to state-of-the-art approaches for indel call set comparison demonstrates its clear superiority in finding common indels among call sets. UPS-indel is theoretically proven to find all equivalent indels, and thus exhaustive.
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Jiménez-Barrón LT, O'Rawe JA, Wu Y, Yoon M, Fang H, Iossifov I, Lyon GJ. Genome-wide variant analysis of simplex autism families with an integrative clinical-bioinformatics pipeline. Cold Spring Harb Mol Case Stud 2016; 1:a000422. [PMID: 27148569 PMCID: PMC4850892 DOI: 10.1101/mcs.a000422] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
Autism spectrum disorders (ASDs) are a group of developmental disabilities that affect social interaction and communication and are characterized by repetitive behaviors. There is now a large body of evidence that suggests a complex role of genetics in ASDs, in which many different loci are involved. Although many current population-scale genomic studies have been demonstrably fruitful, these studies generally focus on analyzing a limited part of the genome or use a limited set of bioinformatics tools. These limitations preclude the analysis of genome-wide perturbations that may contribute to the development and severity of ASD-related phenotypes. To overcome these limitations, we have developed and utilized an integrative clinical and bioinformatics pipeline for generating a more complete and reliable set of genomic variants for downstream analyses. Our study focuses on the analysis of three simplex autism families consisting of one affected child, unaffected parents, and one unaffected sibling. All members were clinically evaluated and widely phenotyped. Genotyping arrays and whole-genome sequencing were performed on each member, and the resulting sequencing data were analyzed using a variety of available bioinformatics tools. We searched for rare variants of putative functional impact that were found to be segregating according to de novo, autosomal recessive, X-linked, mitochondrial, and compound heterozygote transmission models. The resulting candidate variants included three small heterozygous copy-number variations (CNVs), a rare heterozygous de novo nonsense mutation in MYBBP1A located within exon 1, and a novel de novo missense variant in LAMB3. Our work demonstrates how more comprehensive analyses that include rich clinical data and whole-genome sequencing data can generate reliable results for use in downstream investigations.
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Affiliation(s)
- Laura T Jiménez-Barrón
- Stanley Institute for Cognitive Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York 11724, USA;; Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Cuernavaca, Morelos 62210, Mexico
| | - Jason A O'Rawe
- Stanley Institute for Cognitive Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York 11724, USA;; Graduate Genetics Program, Stony Brook University, Stony Brook, New York 11794, USA
| | - Yiyang Wu
- Stanley Institute for Cognitive Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York 11724, USA;; Graduate Genetics Program, Stony Brook University, Stony Brook, New York 11794, USA
| | - Margaret Yoon
- Stanley Institute for Cognitive Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York 11724, USA
| | - Han Fang
- Stanley Institute for Cognitive Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York 11724, USA
| | - Ivan Iossifov
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York 11724, USA;; New York Genome Center, New York, New York 10013, USA
| | - Gholson J Lyon
- Stanley Institute for Cognitive Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York 11724, USA;; Graduate Genetics Program, Stony Brook University, Stony Brook, New York 11794, USA;; Utah Foundation for Biomedical Research, Salt Lake City, Utah 84107, USA
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Wittler R, Marschall T, Schönhuth A, Mäkinen V. Repeat- and error-aware comparison of deletions. Bioinformatics 2015; 31:2947-54. [PMID: 25979471 DOI: 10.1093/bioinformatics/btv304] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2014] [Accepted: 05/08/2015] [Indexed: 12/22/2022] Open
Abstract
MOTIVATION The number of reported genetic variants is rapidly growing, empowered by ever faster accumulation of next-generation sequencing data. A major issue is comparability. Standards that address the combined problem of inaccurately predicted breakpoints and repeat-induced ambiguities are missing. This decisively lowers the quality of 'consensus' callsets and hampers the removal of duplicate entries in variant databases, which can have deleterious effects in downstream analyses. RESULTS We introduce a sound framework for comparison of deletions that captures both tool-induced inaccuracies and repeat-induced ambiguities. We present a maximum matching algorithm that outputs virtual duplicates among two sets of predictions/annotations. We demonstrate that our approach is clearly superior over ad hoc criteria, like overlap, and that it can reduce the redundancy among callsets substantially. We also identify large amounts of duplicate entries in the Database of Genomic Variants, which points out the immediate relevance of our approach. AVAILABILITY AND IMPLEMENTATION Implementation is open source and available from https://bitbucket.org/readdi/readdi CONTACT roland.wittler@uni-bielefeld.de or t.marschall@mpi-inf.mpg.de SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Roland Wittler
- Genome Informatics, Faculty of Technology and Center for Biotechnology (CeBiTec), Bielefeld University, Germany, Center for Bioinformatics, Saarland University and Department of Computational Biology and Applied Algorithmics, Max Planck Institute for Informatics, Saarbrücken, Germany, Centrum Wiskunde & Informatica (CWI), Life Sciences Group, Amsterdam, The Netherlands and Helsinki Institute for Information Technology (HIIT), Department of Computer Science, University of Helsinki, Finland
| | - Tobias Marschall
- Genome Informatics, Faculty of Technology and Center for Biotechnology (CeBiTec), Bielefeld University, Germany, Center for Bioinformatics, Saarland University and Department of Computational Biology and Applied Algorithmics, Max Planck Institute for Informatics, Saarbrücken, Germany, Centrum Wiskunde & Informatica (CWI), Life Sciences Group, Amsterdam, The Netherlands and Helsinki Institute for Information Technology (HIIT), Department of Computer Science, University of Helsinki, Finland
| | - Alexander Schönhuth
- Genome Informatics, Faculty of Technology and Center for Biotechnology (CeBiTec), Bielefeld University, Germany, Center for Bioinformatics, Saarland University and Department of Computational Biology and Applied Algorithmics, Max Planck Institute for Informatics, Saarbrücken, Germany, Centrum Wiskunde & Informatica (CWI), Life Sciences Group, Amsterdam, The Netherlands and Helsinki Institute for Information Technology (HIIT), Department of Computer Science, University of Helsinki, Finland
| | - Veli Mäkinen
- Genome Informatics, Faculty of Technology and Center for Biotechnology (CeBiTec), Bielefeld University, Germany, Center for Bioinformatics, Saarland University and Department of Computational Biology and Applied Algorithmics, Max Planck Institute for Informatics, Saarbrücken, Germany, Centrum Wiskunde & Informatica (CWI), Life Sciences Group, Amsterdam, The Netherlands and Helsinki Institute for Information Technology (HIIT), Department of Computer Science, University of Helsinki, Finland
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