1
|
Neums L, Koestler DC, Xia Q, Hu J, Patel S, Bell-Glenn S, Pei D, Zhang B, Boyd S, Chalise P, Thompson JA. Assessing equivalent and inverse change in genes between diverse experiments. FRONTIERS IN BIOINFORMATICS 2022; 2:893032. [PMID: 36304274 PMCID: PMC9580844 DOI: 10.3389/fbinf.2022.893032] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Accepted: 08/22/2022] [Indexed: 05/26/2024] Open
Abstract
Background: It is important to identify when two exposures impact a molecular marker (e.g., a gene's expression) in similar ways, for example, to learn that a new drug has a similar effect to an existing drug. Currently, statistically robust approaches for making comparisons of equivalence of effect sizes obtained from two independently run treatment vs. control comparisons have not been developed. Results: Here, we propose two approaches for evaluating the question of equivalence between effect sizes of two independent studies: a bootstrap test of the Equivalent Change Index (ECI), which we previously developed, and performing Two One-Sided t-Tests (TOST) on the difference in log-fold changes directly. The ECI of a gene is computed by taking the ratio of the effect size estimates obtained from the two different studies, weighted by the maximum of the two p-values and giving it a sign indicating if the effects are in the same or opposite directions, whereas TOST is a test of whether the difference in log-fold changes lies outside a region of equivalence. We used a series of simulation studies to compare the two tests on the basis of sensitivity, specificity, balanced accuracy, and F1-score. We found that TOST is not efficient for identifying equivalently changed gene expression values (F1-score = 0) because it is too conservative, while the ECI bootstrap test shows good performance (F1-score = 0.95). Furthermore, applying the ECI bootstrap test and TOST to publicly available microarray expression data from pancreatic cancer showed that, while TOST was not able to identify any equivalently or inversely changed genes, the ECI bootstrap test identified genes associated with pancreatic cancer. Additionally, when investigating publicly available RNAseq data of smoking vs. vaping, no equivalently changed genes were identified by TOST, but ECI bootstrap test identified genes associated with smoking. Conclusion: A bootstrap test of the ECI is a promising new statistical approach for determining if two diverse studies show similarity in the differential expression of genes and can help to identify genes which are similarly influenced by a specific treatment or exposure. The R package for the ECI bootstrap test is available at https://github.com/Hecate08/ECIbootstrap.
Collapse
Affiliation(s)
- Lisa Neums
- Department of Biostatistics and Data Science, University of Kansas Medical Center, Kansas City, KS, United States
- University of Kansas Cancer Center, Kansas City, KS, United States
| | - Devin C. Koestler
- Department of Biostatistics and Data Science, University of Kansas Medical Center, Kansas City, KS, United States
- University of Kansas Cancer Center, Kansas City, KS, United States
| | - Qing Xia
- Department of Biostatistics and Data Science, University of Kansas Medical Center, Kansas City, KS, United States
- University of Kansas Cancer Center, Kansas City, KS, United States
| | - Jinxiang Hu
- Department of Biostatistics and Data Science, University of Kansas Medical Center, Kansas City, KS, United States
- University of Kansas Cancer Center, Kansas City, KS, United States
| | - Shachi Patel
- Department of Biostatistics and Data Science, University of Kansas Medical Center, Kansas City, KS, United States
- University of Kansas Cancer Center, Kansas City, KS, United States
| | - Shelby Bell-Glenn
- Department of Biostatistics and Data Science, University of Kansas Medical Center, Kansas City, KS, United States
- University of Kansas Cancer Center, Kansas City, KS, United States
| | - Dong Pei
- Department of Biostatistics and Data Science, University of Kansas Medical Center, Kansas City, KS, United States
- University of Kansas Cancer Center, Kansas City, KS, United States
| | - Bo Zhang
- Department of Biostatistics and Data Science, University of Kansas Medical Center, Kansas City, KS, United States
| | - Samuel Boyd
- Department of Biostatistics and Data Science, University of Kansas Medical Center, Kansas City, KS, United States
- University of Kansas Cancer Center, Kansas City, KS, United States
| | - Prabhakar Chalise
- Department of Biostatistics and Data Science, University of Kansas Medical Center, Kansas City, KS, United States
- University of Kansas Cancer Center, Kansas City, KS, United States
| | - Jeffrey A. Thompson
- Department of Biostatistics and Data Science, University of Kansas Medical Center, Kansas City, KS, United States
- University of Kansas Cancer Center, Kansas City, KS, United States
| |
Collapse
|
2
|
Nagy I, Veeckman E, Liu C, Bel MV, Vandepoele K, Jensen CS, Ruttink T, Asp T. Chromosome-scale assembly and annotation of the perennial ryegrass genome. BMC Genomics 2022; 23:505. [PMID: 35831814 PMCID: PMC9281035 DOI: 10.1186/s12864-022-08697-0] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Accepted: 06/14/2022] [Indexed: 12/29/2022] Open
Abstract
BACKGROUND The availability of chromosome-scale genome assemblies is fundamentally important to advance genetics and breeding in crops, as well as for evolutionary and comparative genomics. The improvement of long-read sequencing technologies and the advent of optical mapping and chromosome conformation capture technologies in the last few years, significantly promoted the development of chromosome-scale genome assemblies of model plants and crop species. In grasses, chromosome-scale genome assemblies recently became available for cultivated and wild species of the Triticeae subfamily. Development of state-of-the-art genomic resources in species of the Poeae subfamily, which includes important crops like fescues and ryegrasses, is lagging behind the progress in the cereal species. RESULTS Here, we report a new chromosome-scale genome sequence assembly for perennial ryegrass, obtained by combining PacBio long-read sequencing, Illumina short-read polishing, BioNano optical mapping and Hi-C scaffolding. More than 90% of the total genome size of perennial ryegrass (approximately 2.55 Gb) is covered by seven pseudo-chromosomes that show high levels of collinearity to the orthologous chromosomes of Triticeae species. The transposon fraction of perennial ryegrass was found to be relatively low, approximately 35% of the total genome content, which is less than half of the genome repeat content of cultivated cereal species. We predicted 54,629 high-confidence gene models, 10,287 long non-coding RNAs and a total of 8,393 short non-coding RNAs in the perennial ryegrass genome. CONCLUSIONS The new reference genome sequence and annotation presented here are valuable resources for comparative genomic studies in grasses, as well as for breeding applications and will expedite the development of productive varieties in perennial ryegrass and related species.
Collapse
Affiliation(s)
- Istvan Nagy
- Center for Quantitative Genetics and Genomics, Aarhus University, Forsøgsvej 1, Slagelse, DK-4200 Denmark
| | - Elisabeth Veeckman
- Flanders Research Institute for Agriculture, Fisheries and Food (ILVO), Plant Sciences Unit, Caritasstraat 39, Melle, B-9090 Belgium
- Bioinformatics Institute Ghent, Ghent University, Technologiepark 71, Ghent, B-9052 Belgium
- Present address: DLF Seeds A/S, Denmark, Højerupvej 31, Store Heddinge, DK-4660 Denmark
| | - Chang Liu
- Zentrum für Molekularbiologie der Pflanzen (ZMBP), Eberhard Karls Universität, Auf der Morgenstelle 32, Tübingen, 72076 Germany
- Present address: Institut für Biologie, Universität Hohenheim, Garbenstr. 30, Stuttgart, 70599 Germany
| | - Michiel Van Bel
- Bioinformatics Institute Ghent, Ghent University, Technologiepark 71, Ghent, B-9052 Belgium
- VIB Center for Plant Systems Biology, Technologiepark 71, Ghent, B-9052 Belgium
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Technologiepark 71, Ghent, B-9052 Belgium
| | - Klaas Vandepoele
- Bioinformatics Institute Ghent, Ghent University, Technologiepark 71, Ghent, B-9052 Belgium
- VIB Center for Plant Systems Biology, Technologiepark 71, Ghent, B-9052 Belgium
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Technologiepark 71, Ghent, B-9052 Belgium
| | | | - Tom Ruttink
- Flanders Research Institute for Agriculture, Fisheries and Food (ILVO), Plant Sciences Unit, Caritasstraat 39, Melle, B-9090 Belgium
| | - Torben Asp
- Center for Quantitative Genetics and Genomics, Aarhus University, Forsøgsvej 1, Slagelse, DK-4200 Denmark
| |
Collapse
|