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Wu EY, Singh NP, Choi K, Zakeri M, Vincent M, Churchill GA, Ackert-Bicknell CL, Patro R, Love MI. SEESAW: detecting isoform-level allelic imbalance accounting for inferential uncertainty. Genome Biol 2023; 24:165. [PMID: 37438847 PMCID: PMC10337143 DOI: 10.1186/s13059-023-03003-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Accepted: 06/29/2023] [Indexed: 07/14/2023] Open
Abstract
Detecting allelic imbalance at the isoform level requires accounting for inferential uncertainty, caused by multi-mapping of RNA-seq reads. Our proposed method, SEESAW, uses Salmon and Swish to offer analysis at various levels of resolution, including gene, isoform, and aggregating isoforms to groups by transcription start site. The aggregation strategies strengthen the signal for transcripts with high uncertainty. The SEESAW suite of methods is shown to have higher power than other allelic imbalance methods when there is isoform-level allelic imbalance. We also introduce a new test for detecting imbalance that varies across a covariate, such as time.
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Affiliation(s)
- Euphy Y Wu
- Department of Biostatistics, University of North Carolina-Chapel Hill, Chapel Hill, NC, USA
| | - Noor P Singh
- Department of Computer Science, University of Maryland, College Park, MD, USA
| | | | - Mohsen Zakeri
- Department of Computer Science, University of Maryland, College Park, MD, USA
| | | | | | - Cheryl L Ackert-Bicknell
- Department of Orthopedics, School of Medicine, University of Colorado, Anschutz Campus, Aurora, CO, USA
| | - Rob Patro
- Department of Computer Science, University of Maryland, College Park, MD, USA
| | - Michael I Love
- Department of Biostatistics, University of North Carolina-Chapel Hill, Chapel Hill, NC, USA.
- Department of Genetics, University of North Carolina-Chapel Hill, Chapel Hill, NC, USA.
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Guarracino A, Heumos S, Nahnsen S, Prins P, Garrison E. ODGI: understanding pangenome graphs. BIOINFORMATICS (OXFORD, ENGLAND) 2022; 38:3319-3326. [PMID: 35552372 DOI: 10.1101/2021.11.10.467921] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Revised: 03/18/2022] [Indexed: 05/24/2023]
Abstract
MOTIVATION Pangenome graphs provide a complete representation of the mutual alignment of collections of genomes. These models offer the opportunity to study the entire genomic diversity of a population, including structurally complex regions. Nevertheless, analyzing hundreds of gigabase-scale genomes using pangenome graphs is difficult as it is not well-supported by existing tools. Hence, fast and versatile software is required to ask advanced questions to such data in an efficient way. RESULTS We wrote Optimized Dynamic Genome/Graph Implementation (ODGI), a novel suite of tools that implements scalable algorithms and has an efficient in-memory representation of DNA pangenome graphs in the form of variation graphs. ODGI supports pre-built graphs in the Graphical Fragment Assembly format. ODGI includes tools for detecting complex regions, extracting pangenomic loci, removing artifacts, exploratory analysis, manipulation, validation and visualization. Its fast parallel execution facilitates routine pangenomic tasks, as well as pipelines that can quickly answer complex biological questions of gigabase-scale pangenome graphs. AVAILABILITY AND IMPLEMENTATION ODGI is published as free software under the MIT open source license. Source code can be downloaded from https://github.com/pangenome/odgi and documentation is available at https://odgi.readthedocs.io. ODGI can be installed via Bioconda https://bioconda.github.io/recipes/odgi/README.html or GNU Guix https://github.com/pangenome/odgi/blob/master/guix.scm. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
| | - Simon Heumos
- Quantitative Biology Center (QBiC), University of Tübingen, Tübingen 72076, Germany
- Biomedical Data Science, Department of Computer Science, University of Tübingen, Tübingen 72076, Germany
| | - Sven Nahnsen
- Quantitative Biology Center (QBiC), University of Tübingen, Tübingen 72076, Germany
- Biomedical Data Science, Department of Computer Science, University of Tübingen, Tübingen 72076, Germany
| | - Pjotr Prins
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN 38163, USA
| | - Erik Garrison
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN 38163, USA
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Li W, Liu J, Zhang H, Liu Z, Wang Y, Xing L, He Q, Du H. Plant pan-genomics: recent advances, new challenges, and roads ahead. J Genet Genomics 2022; 49:833-846. [PMID: 35750315 DOI: 10.1016/j.jgg.2022.06.004] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Revised: 06/09/2022] [Accepted: 06/10/2022] [Indexed: 10/18/2022]
Abstract
Pan-genomics can encompass most of the genetic diversity of a species or population and has proved to be a powerful tool for studying genomic evolution and the origin and domestication of species, and for providing information for plant improvement. Plant genomics has greatly progressed because of improvements in sequencing technologies and the rapid reduction of sequencing costs. Nevertheless, pan-genomics still presents many challenges, including computationally intensive assembly methods, high costs with large numbers of samples, ineffective integration of big data, and difficulty in applying it to downstream multi-omics analysis and breeding research. In this review, we summarize the definition and recent achievements of plant pan-genomics, computational technologies used for pan-genome construction, and the applications of pan-genomes in plant genomics and molecular breeding. We also discuss challenges and perspectives for future pan-genomics studies and provide a detailed pipeline for sample selection, genome assembly and annotation, structural variation identification, and construction and application of graph-based pan-genomes. The aim is to provide important guidance for plant pan-genome research and a better understanding of the genetic basis of genome evolution, crop domestication, and phenotypic diversity for future studies.
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Affiliation(s)
- Wei Li
- School of Life Sciences, Institute of Life Sciences and Green Development, Hebei University, Baoding, Hebei 071000, China
| | - Jianan Liu
- School of Life Sciences, Institute of Life Sciences and Green Development, Hebei University, Baoding, Hebei 071000, China
| | - Hongyu Zhang
- School of Life Sciences, Institute of Life Sciences and Green Development, Hebei University, Baoding, Hebei 071000, China
| | - Ze Liu
- School of Life Sciences, Institute of Life Sciences and Green Development, Hebei University, Baoding, Hebei 071000, China
| | - Yu Wang
- School of Life Sciences, Institute of Life Sciences and Green Development, Hebei University, Baoding, Hebei 071000, China
| | - Longsheng Xing
- School of Life Sciences, Institute of Life Sciences and Green Development, Hebei University, Baoding, Hebei 071000, China
| | - Qiang He
- School of Life Sciences, Institute of Life Sciences and Green Development, Hebei University, Baoding, Hebei 071000, China
| | - Huilong Du
- School of Life Sciences, Institute of Life Sciences and Green Development, Hebei University, Baoding, Hebei 071000, China.
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Baaijens JA, Bonizzoni P, Boucher C, Della Vedova G, Pirola Y, Rizzi R, Sirén J. Computational graph pangenomics: a tutorial on data structures and their applications. NATURAL COMPUTING 2022; 21:81-108. [PMID: 36969737 PMCID: PMC10038355 DOI: 10.1007/s11047-022-09882-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2023]
Abstract
Computational pangenomics is an emerging research field that is changing the way computer scientists are facing challenges in biological sequence analysis. In past decades, contributions from combinatorics, stringology, graph theory and data structures were essential in the development of a plethora of software tools for the analysis of the human genome. These tools allowed computational biologists to approach ambitious projects at population scale, such as the 1000 Genomes Project. A major contribution of the 1000 Genomes Project is the characterization of a broad spectrum of genetic variations in the human genome, including the discovery of novel variations in the South Asian, African and European populations-thus enhancing the catalogue of variability within the reference genome. Currently, the need to take into account the high variability in population genomes as well as the specificity of an individual genome in a personalized approach to medicine is rapidly pushing the abandonment of the traditional paradigm of using a single reference genome. A graph-based representation of multiple genomes, or a graph pangenome, is replacing the linear reference genome. This means completely rethinking well-established procedures to analyze, store, and access information from genome representations. Properly addressing these challenges is crucial to face the computational tasks of ambitious healthcare projects aiming to characterize human diversity by sequencing 1M individuals (Stark et al. 2019). This tutorial aims to introduce readers to the most recent advances in the theory of data structures for the representation of graph pangenomes. We discuss efficient representations of haplotypes and the variability of genotypes in graph pangenomes, and highlight applications in solving computational problems in human and microbial (viral) pangenomes.
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Affiliation(s)
- Jasmijn A. Baaijens
- Department of Intelligent Systems, Delft University of Technology, Van Mourik Broekmanweg 6, 2628XE Delft, The Netherlands
- Department of Biomedical Informatics, Harvard University, 10 Shattuck St, Boston, MA 02115, USA
| | - Paola Bonizzoni
- Department of Informatics, Systems and Communication (DISCo), University of Milano-Bicocca, V.le Sarca, 336, 20126 Milan, Italy
| | - Christina Boucher
- Department of Computer and Information Science and Engineering, University of Florida, 432 Newell Dr, Gainesville, FL 32603, USA
| | - Gianluca Della Vedova
- Department of Informatics, Systems and Communication (DISCo), University of Milano-Bicocca, V.le Sarca, 336, 20126 Milan, Italy
| | - Yuri Pirola
- Department of Informatics, Systems and Communication (DISCo), University of Milano-Bicocca, V.le Sarca, 336, 20126 Milan, Italy
| | - Raffaella Rizzi
- Department of Informatics, Systems and Communication (DISCo), University of Milano-Bicocca, V.le Sarca, 336, 20126 Milan, Italy
| | - Jouni Sirén
- Genomics Institute, University of California, 1156 High St., Santa Cruz, CA 95064, USA
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Sirén J, Monlong J, Chang X, Novak AM, Eizenga JM, Markello C, Sibbesen JA, Hickey G, Chang PC, Carroll A, Gupta N, Gabriel S, Blackwell TW, Ratan A, Taylor KD, Rich SS, Rotter JI, Haussler D, Garrison E, Paten B. Pangenomics enables genotyping of known structural variants in 5202 diverse genomes. Science 2021; 374:abg8871. [PMID: 34914532 PMCID: PMC9365333 DOI: 10.1126/science.abg8871] [Citation(s) in RCA: 94] [Impact Index Per Article: 31.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
We introduce Giraffe, a pangenome short-read mapper that can efficiently map to a collection of haplotypes threaded through a sequence graph. Giraffe maps sequencing reads to thousands of human genomes at a speed comparable to that of standard methods mapping to a single reference genome. The increased mapping accuracy enables downstream improvements in genome-wide genotyping pipelines for both small variants and larger structural variants. We used Giraffe to genotype 167,000 structural variants, discovered in long-read studies, in 5202 diverse human genomes that were sequenced using short reads. We conclude that pangenomics facilitates a more comprehensive characterization of variation and, as a result, has the potential to improve many genomic analyses.
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Affiliation(s)
- Jouni Sirén
- UC Santa Cruz Genomics Institute, Santa Cruz, CA, USA
| | - Jean Monlong
- UC Santa Cruz Genomics Institute, Santa Cruz, CA, USA
| | - Xian Chang
- UC Santa Cruz Genomics Institute, Santa Cruz, CA, USA
| | - Adam M. Novak
- UC Santa Cruz Genomics Institute, Santa Cruz, CA, USA
| | | | | | | | - Glenn Hickey
- UC Santa Cruz Genomics Institute, Santa Cruz, CA, USA
| | - Pi-Chuan Chang
- Google Inc, 1600 Amphitheatre Pkwy, Mountain View, CA, USA
| | - Andrew Carroll
- Google Inc, 1600 Amphitheatre Pkwy, Mountain View, CA, USA
| | - Namrata Gupta
- Genomics Platform, Broad Institute, Cambridge, MA, USA
| | - Stacey Gabriel
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA
| | | | - Aakrosh Ratan
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | - Kent D. Taylor
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Stephen S. Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | - Jerome I. Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - David Haussler
- UC Santa Cruz Genomics Institute, Santa Cruz, CA, USA
- Howard Hughes Medical Institute, University of California, Santa Cruz, CA, USA
| | - Erik Garrison
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN, USA
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