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Nguyen AK, Blacksmith MS, Kidd JM. Duplications and Retrogenes Are Numerous and Widespread in Modern Canine Genomic Assemblies. Genome Biol Evol 2024; 16:evae142. [PMID: 38946312 PMCID: PMC11259980 DOI: 10.1093/gbe/evae142] [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: 11/01/2023] [Revised: 05/08/2024] [Accepted: 06/24/2024] [Indexed: 07/02/2024] Open
Abstract
Recent years have seen a dramatic increase in the number of canine genome assemblies available. Duplications are an important source of evolutionary novelty and are also prone to misassembly. We explored the duplication content of nine canine genome assemblies using both genome self-alignment and read-depth approaches. We find that 8.58% of the genome is duplicated in the canFam4 assembly, derived from the German Shepherd Dog Mischka, including 90.15% of unplaced contigs. Highlighting the continued difficulty in properly assembling duplications, less than half of read-depth and assembly alignment duplications overlap, but the mCanLor1.2 Greenland wolf assembly shows greater concordance. Further study shows the presence of multiple segments that have alignments to four or more duplicate copies. These high-recurrence duplications correspond to gene retrocopies. We identified 3,892 candidate retrocopies from 1,316 parental genes in the canFam4 assembly and find that ∼8.82% of duplicated base pairs involve a retrocopy, confirming this mechanism as a major driver of gene duplication in canines. Similar patterns are found across eight other recent canine genome assemblies, with metrics supporting a greater quality of the PacBio HiFi mCanLor1.2 assembly. Comparison between the wolf and other canine assemblies found that 92% of retrocopy insertions are shared between assemblies. By calculating the number of generations since genome divergence, we estimate that new retrocopy insertions appear, on average, in 1 out of 3,514 births. Our analyses illustrate the impact of retrogene formation on canine genomes and highlight the variable representation of duplicated sequences among recently completed canine assemblies.
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Affiliation(s)
- Anthony K Nguyen
- Department of Human Genetics, University of Michigan, Ann Arbor, MI, USA
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Matthew S Blacksmith
- Department of Human Genetics, University of Michigan, Ann Arbor, MI, USA
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Jeffrey M Kidd
- Department of Human Genetics, University of Michigan, Ann Arbor, MI, USA
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
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2
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Hu J, Wang Z, Sun Z, Hu B, Ayoola AO, Liang F, Li J, Sandoval JR, Cooper DN, Ye K, Ruan J, Xiao CL, Wang D, Wu DD, Wang S. NextDenovo: an efficient error correction and accurate assembly tool for noisy long reads. Genome Biol 2024; 25:107. [PMID: 38671502 PMCID: PMC11046930 DOI: 10.1186/s13059-024-03252-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Accepted: 04/17/2024] [Indexed: 04/28/2024] Open
Abstract
Long-read sequencing data, particularly those derived from the Oxford Nanopore sequencing platform, tend to exhibit high error rates. Here, we present NextDenovo, an efficient error correction and assembly tool for noisy long reads, which achieves a high level of accuracy in genome assembly. We apply NextDenovo to assemble 35 diverse human genomes from around the world using Nanopore long-read data. These genomes allow us to identify the landscape of segmental duplication and gene copy number variation in modern human populations. The use of NextDenovo should pave the way for population-scale long-read assembly using Nanopore long-read data.
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Affiliation(s)
- Jiang Hu
- GrandOmics Biosciences, Beijing, 102206, China
- School of Automation Science and Engineering, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, China
| | - Zhuo Wang
- GrandOmics Biosciences, Beijing, 102206, China
| | - Zongyi Sun
- GrandOmics Biosciences, Beijing, 102206, China
| | - Benxia Hu
- Key Laboratory of Genetic Evolution and Animal Models, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China
| | - Adeola Oluwakemi Ayoola
- Key Laboratory of Genetic Evolution and Animal Models, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China
| | - Fan Liang
- GrandOmics Biosciences, Beijing, 102206, China
| | - Jingjing Li
- GrandOmics Biosciences, Beijing, 102206, China
| | - José R Sandoval
- Centro de Investigación de Genética y Biología Molecular (CIGBM), Instituto de Investigación, Facultad de Medicina, Universidad de San Martín de Porres, Lima, 15102, Peru
| | - David N Cooper
- Institute of Medical Genetics, Cardiff University, Heath Park, Cardiff, CF14 4XN, UK
| | - Kai Ye
- School of Automation Science and Engineering, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, China
| | - Jue Ruan
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518120, China
| | - Chuan-Le Xiao
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, #7 Jinsui Road, Tianhe District, Guangzhou, China
| | - Depeng Wang
- GrandOmics Biosciences, Beijing, 102206, China.
| | - Dong-Dong Wu
- Key Laboratory of Genetic Evolution and Animal Models, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China.
- Kunming Primate Research Center, and National Research Facility for Phenotypic and Genetic Analysis of Model Animals (Primate Facility), National Resource Center for Non-Human Primates, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650107, China.
- Kunming Natural History Museum of Zoology, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China.
| | - Sheng Wang
- Key Laboratory of Genetic Evolution and Animal Models, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China.
- Yunnan Key Laboratory of Biodiversity Information, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China.
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3
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Gozashti L, Hartl DL, Corbett-Detig R. Universal signatures of transposable element compartmentalization across eukaryotic genomes. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.10.17.562820. [PMID: 38585780 PMCID: PMC10996525 DOI: 10.1101/2023.10.17.562820] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
The evolutionary mechanisms that drive the emergence of genome architecture remain poorly understood but can now be assessed with unprecedented power due to the massive accumulation of genome assemblies spanning phylogenetic diversity1,2. Transposable elements (TEs) are a rich source of large-effect mutations since they directly and indirectly drive genomic structural variation and changes in gene expression3. Here, we demonstrate universal patterns of TE compartmentalization across eukaryotic genomes spanning ~1.7 billion years of evolution, in which TEs colocalize with gene families under strong predicted selective pressure for dynamic evolution and involved in specific functions. For non-pathogenic species these genes represent families involved in defense, sensory perception and environmental interaction, whereas for pathogenic species, TE-compartmentalized genes are highly enriched for pathogenic functions. Many TE-compartmentalized gene families display signatures of positive selection at the molecular level. Furthermore, TE-compartmentalized genes exhibit an excess of high-frequency alleles for polymorphic TE insertions in fruit fly populations. We postulate that these patterns reflect selection for adaptive TE insertions as well as TE-associated structural variants. This process may drive the emergence of a shared TE-compartmentalized genome architecture across diverse eukaryotic lineages.
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Affiliation(s)
- Landen Gozashti
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, USA
- Museum of Comparative Zoology, Harvard University, Cambridge, MA, USA
| | - Daniel L. Hartl
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, USA
| | - Russell Corbett-Detig
- Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, CA, USA
- UC Santa Cruz Genomics Institute, University of California Santa Cruz, Santa Cruz, CA, USA
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4
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Brann T, Beltramini A, Chaparro C, Berriman M, Doyle SR, Protasio AV. Subtelomeric plasticity contributes to gene family expansion in the human parasitic flatworm Schistosoma mansoni. BMC Genomics 2024; 25:217. [PMID: 38413905 PMCID: PMC10900676 DOI: 10.1186/s12864-024-10032-8] [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: 10/03/2023] [Accepted: 01/19/2024] [Indexed: 02/29/2024] Open
Abstract
BACKGROUND The genomic region that lies between the telomere and chromosome body, termed the subtelomere, is heterochromatic, repeat-rich, and frequently undergoes rearrangement. Within this region, large-scale structural changes enable gene diversification, and, as such, large multicopy gene families are often found at the subtelomere. In some parasites, genes associated with proliferation, invasion, and survival are often found in these regions, where they benefit from the subtelomere's highly plastic, rapidly changing nature. The increasing availability of complete (or near complete) parasite genomes provides an opportunity to investigate these typically poorly defined and overlooked genomic regions and potentially reveal relevant gene families necessary for the parasite's lifestyle. RESULTS Using the latest chromosome-scale genome assembly and hallmark repeat richness observed at chromosome termini, we have identified and characterised the subtelomeres of Schistosoma mansoni, a metazoan parasitic flatworm that infects over 250 million people worldwide. Approximately 12% of the S. mansoni genome is classified as subtelomeric, and, in line with other organisms, we find these regions to be gene-poor but rich in transposable elements. We find that S. mansoni subtelomeres have undergone extensive interchromosomal recombination and that these sites disproportionately contribute to the 2.3% of the genome derived from segmental duplications. This recombination has led to the expansion of subtelomeric gene clusters containing 103 genes, including the immunomodulatory annexins and other gene families with unknown roles. The largest of these is a 49-copy plexin domain-containing protein cluster, exclusively expressed in the tegument-the tissue located at the host-parasite physical interface-of intramolluscan life stages. CONCLUSIONS We propose that subtelomeric regions act as a genomic playground for trial-and-error of gene duplication and subsequent divergence. Owing to the importance of subtelomeric genes in other parasites, gene families implicated in this subtelomeric expansion within S. mansoni warrant further characterisation for a potential role in parasitism.
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Affiliation(s)
- T Brann
- Department of Pathology, University of Cambridge, Cambridge, CB1 2PQ, UK
| | - A Beltramini
- Department of Pathology, University of Cambridge, Cambridge, CB1 2PQ, UK
| | - C Chaparro
- IHPE, CNRS, IFREMER, UPVD, University Montpellier, Perpignan, F-66860, France
| | - M Berriman
- School of Infection and Immunity, University of Glasgow, Glasgow, G12 8TA, UK
| | - S R Doyle
- Wellcome Sanger Institute, Cambridge, CB10 1SA, UK
| | - A V Protasio
- Department of Pathology, University of Cambridge, Cambridge, CB1 2PQ, UK.
- Christ's College, Cambridge, CB2 3BU, UK.
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Chaisson MJP, Sulovari A, Valdmanis PN, Miller DE, Eichler EE. Advances in the discovery and analyses of human tandem repeats. Emerg Top Life Sci 2023; 7:361-381. [PMID: 37905568 PMCID: PMC10806765 DOI: 10.1042/etls20230074] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 10/18/2023] [Accepted: 10/18/2023] [Indexed: 11/02/2023]
Abstract
Long-read sequencing platforms provide unparalleled access to the structure and composition of all classes of tandemly repeated DNA from STRs to satellite arrays. This review summarizes our current understanding of their organization within the human genome, their importance with respect to disease, as well as the advances and challenges in understanding their genetic diversity and functional effects. Novel computational methods are being developed to visualize and associate these complex patterns of human variation with disease, expression, and epigenetic differences. We predict accurate characterization of this repeat-rich form of human variation will become increasingly relevant to both basic and clinical human genetics.
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Affiliation(s)
- Mark J P Chaisson
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA 90089, U.S.A
- The Genomic and Epigenomic Regulation Program, USC Norris Cancer Center, University of Southern California, Los Angeles, CA 90089, U.S.A
| | - Arvis Sulovari
- Computational Biology, Cajal Neuroscience Inc, Seattle, WA 98102, U.S.A
| | - Paul N Valdmanis
- Division of Medical Genetics, Department of Medicine, University of Washington School of Medicine, Seattle, WA 98195, U.S.A
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA 98195, U.S.A
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA 98195, U.S.A
| | - Danny E Miller
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA 98195, U.S.A
- Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, WA 98195, U.S.A
- Department of Pediatrics, University of Washington, Seattle, WA 98195, U.S.A
| | - Evan E Eichler
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA 98195, U.S.A
- Howard Hughes Medical Institute, University of Washington, Seattle, WA 98195, U.S.A
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He Y, Chu Y, Guo S, Hu J, Li R, Zheng Y, Ma X, Du Z, Zhao L, Yu W, Xue J, Bian W, Yang F, Chen X, Zhang P, Wu R, Ma Y, Shao C, Chen J, Wang J, Li J, Wu J, Hu X, Long Q, Jiang M, Ye H, Song S, Li G, Wei Y, Xu Y, Ma Y, Chen Y, Wang K, Bao J, Xi W, Wang F, Ni W, Zhang M, Yu Y, Li S, Kang Y, Gao Z. T2T-YAO: A Telomere-to-telomere Assembled Diploid Reference Genome for Han Chinese. GENOMICS, PROTEOMICS & BIOINFORMATICS 2023; 21:1085-1100. [PMID: 37595788 PMCID: PMC11082261 DOI: 10.1016/j.gpb.2023.08.001] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 08/01/2023] [Accepted: 08/08/2023] [Indexed: 08/20/2023]
Abstract
Since its initial release in 2001, the human reference genome has undergone continuous improvement in quality, and the recently released telomere-to-telomere (T2T) version - T2T-CHM13 - reaches its highest level of continuity and accuracy after 20 years of effort by working on a simplified, nearly homozygous genome of a hydatidiform mole cell line. Here, to provide an authentic complete diploid human genome reference for the Han Chinese, the largest population in the world, we assembled the genome of a male Han Chinese individual, T2T-YAO, which includes T2T assemblies of all the 22 + X + M and 22 + Y chromosomes in both haploids. The quality of T2T-YAO is much better than those of all currently available diploid assemblies, and its haploid version, T2T-YAO-hp, generated by selecting the better assembly for each autosome, reaches the top quality of fewer than one error per 29.5 Mb, even higher than that of T2T-CHM13. Derived from an individual living in the aboriginal region of the Han population, T2T-YAO shows clear ancestry and potential genetic continuity from the ancient ancestors. Each haplotype of T2T-YAO possesses ∼ 330-Mb exclusive sequences, ∼ 3100 unique genes, and tens of thousands of nucleotide and structural variations as compared with CHM13, highlighting the necessity of a population-stratified reference genome. The construction of T2T-YAO, an accurate and authentic representative of the Chinese population, would enable precise delineation of genomic variations and advance our understandings in the hereditability of diseases and phenotypes, especially within the context of the unique variations of the Chinese population.
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Affiliation(s)
- Yukun He
- Department of Respiratory and Critical Care Medicine, Peking University People's Hospital, Beijing 100044, China
| | - Yanan Chu
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
| | - Shuming Guo
- Linfen Clinical Medicine Research Center, Linfen 041000, China; Institute of Chest and Lung Diseases, Shanxi Medical University, Taiyuan 030001, China
| | - Jiang Hu
- GrandOmics Biosciences Co., Ltd, Wuhan 430076, China
| | - Ran Li
- Department of Respiratory and Critical Care Medicine, Peking University People's Hospital, Beijing 100044, China; Beijing Key Laboratory of Genome and Precision Medicine Technologies, Beijing 100101, China
| | - Yali Zheng
- Department of Respiratory, Critical Care and Sleep Medicine, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen 361101, China
| | - Xinqian Ma
- Department of Respiratory and Critical Care Medicine, Peking University People's Hospital, Beijing 100044, China; Beijing Key Laboratory of Genome and Precision Medicine Technologies, Beijing 100101, China
| | - Zhenglin Du
- Institute of PSI Genomics, Wenzhou 325024, China
| | - Lili Zhao
- Department of Respiratory and Critical Care Medicine, Peking University People's Hospital, Beijing 100044, China; Beijing Key Laboratory of Genome and Precision Medicine Technologies, Beijing 100101, China
| | - Wenyi Yu
- Department of Respiratory and Critical Care Medicine, Peking University People's Hospital, Beijing 100044, China; Beijing Key Laboratory of Genome and Precision Medicine Technologies, Beijing 100101, China
| | - Jianbo Xue
- Department of Respiratory and Critical Care Medicine, Peking University People's Hospital, Beijing 100044, China; Beijing Key Laboratory of Genome and Precision Medicine Technologies, Beijing 100101, China
| | - Wenjie Bian
- Department of Respiratory and Critical Care Medicine, Peking University People's Hospital, Beijing 100044, China; Beijing Key Laboratory of Genome and Precision Medicine Technologies, Beijing 100101, China
| | - Feifei Yang
- Department of Respiratory and Critical Care Medicine, Peking University People's Hospital, Beijing 100044, China; Beijing Key Laboratory of Genome and Precision Medicine Technologies, Beijing 100101, China
| | - Xi Chen
- Department of Respiratory and Critical Care Medicine, Peking University People's Hospital, Beijing 100044, China; Beijing Key Laboratory of Genome and Precision Medicine Technologies, Beijing 100101, China
| | - Pingan Zhang
- Department of Respiratory and Critical Care Medicine, Peking University People's Hospital, Beijing 100044, China; Beijing Key Laboratory of Genome and Precision Medicine Technologies, Beijing 100101, China
| | - Rihan Wu
- Department of Respiratory and Critical Care Medicine, Peking University People's Hospital, Beijing 100044, China; Beijing Key Laboratory of Genome and Precision Medicine Technologies, Beijing 100101, China
| | - Yifan Ma
- Department of Respiratory and Critical Care Medicine, Peking University People's Hospital, Beijing 100044, China; Beijing Key Laboratory of Genome and Precision Medicine Technologies, Beijing 100101, China
| | - Changjun Shao
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
| | - Jing Chen
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
| | - Jian Wang
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
| | - Jiwei Li
- Department of Respiratory, Critical Care and Sleep Medicine, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen 361101, China
| | - Jing Wu
- Department of Respiratory, Critical Care and Sleep Medicine, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen 361101, China
| | - Xiaoyi Hu
- Department of Respiratory, Critical Care and Sleep Medicine, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen 361101, China
| | - Qiuyue Long
- Department of Respiratory, Critical Care and Sleep Medicine, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen 361101, China
| | - Mingzheng Jiang
- Department of Respiratory, Critical Care and Sleep Medicine, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen 361101, China
| | - Hongli Ye
- Department of Respiratory, Critical Care and Sleep Medicine, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen 361101, China
| | - Shixu Song
- Department of Respiratory, Critical Care and Sleep Medicine, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen 361101, China
| | - Guangyao Li
- Linfen Clinical Medicine Research Center, Linfen 041000, China
| | - Yue Wei
- Linfen Clinical Medicine Research Center, Linfen 041000, China
| | - Yu Xu
- Beijing Jishuitan Hospital, Capital Medical University, Beijing 100035, China
| | - Yanliang Ma
- Department of Respiratory and Critical Care Medicine, Peking University People's Hospital, Beijing 100044, China; Beijing Key Laboratory of Genome and Precision Medicine Technologies, Beijing 100101, China
| | - Yanwen Chen
- Department of Respiratory and Critical Care Medicine, Peking University People's Hospital, Beijing 100044, China; Beijing Key Laboratory of Genome and Precision Medicine Technologies, Beijing 100101, China
| | - Keqiang Wang
- Department of Respiratory and Critical Care Medicine, Peking University People's Hospital, Beijing 100044, China; Beijing Key Laboratory of Genome and Precision Medicine Technologies, Beijing 100101, China
| | - Jing Bao
- Department of Respiratory and Critical Care Medicine, Peking University People's Hospital, Beijing 100044, China; Beijing Key Laboratory of Genome and Precision Medicine Technologies, Beijing 100101, China
| | - Wen Xi
- Department of Respiratory and Critical Care Medicine, Peking University People's Hospital, Beijing 100044, China; Beijing Key Laboratory of Genome and Precision Medicine Technologies, Beijing 100101, China
| | - Fang Wang
- Department of Respiratory and Critical Care Medicine, Peking University People's Hospital, Beijing 100044, China; Beijing Key Laboratory of Genome and Precision Medicine Technologies, Beijing 100101, China
| | - Wentao Ni
- Department of Respiratory and Critical Care Medicine, Peking University People's Hospital, Beijing 100044, China; Beijing Key Laboratory of Genome and Precision Medicine Technologies, Beijing 100101, China
| | - Moqin Zhang
- Department of Respiratory and Critical Care Medicine, Peking University People's Hospital, Beijing 100044, China; Beijing Key Laboratory of Genome and Precision Medicine Technologies, Beijing 100101, China
| | - Yan Yu
- Department of Respiratory and Critical Care Medicine, Peking University People's Hospital, Beijing 100044, China; Beijing Key Laboratory of Genome and Precision Medicine Technologies, Beijing 100101, China
| | - Shengnan Li
- Department of Respiratory and Critical Care Medicine, Peking University People's Hospital, Beijing 100044, China; Beijing Key Laboratory of Genome and Precision Medicine Technologies, Beijing 100101, China
| | - Yu Kang
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100490, China.
| | - Zhancheng Gao
- Department of Respiratory and Critical Care Medicine, Peking University People's Hospital, Beijing 100044, China; Institute of Chest and Lung Diseases, Shanxi Medical University, Taiyuan 030001, China; Beijing Key Laboratory of Genome and Precision Medicine Technologies, Beijing 100101, China.
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7
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Silva JM, Qi W, Pinho AJ, Pratas D. AlcoR: alignment-free simulation, mapping, and visualization of low-complexity regions in biological data. Gigascience 2022; 12:giad101. [PMID: 38091509 PMCID: PMC10716826 DOI: 10.1093/gigascience/giad101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Revised: 09/29/2023] [Accepted: 11/07/2023] [Indexed: 12/18/2023] Open
Abstract
BACKGROUND Low-complexity data analysis is the area that addresses the search and quantification of regions in sequences of elements that contain low-complexity or repetitive elements. For example, these can be tandem repeats, inverted repeats, homopolymer tails, GC-biased regions, similar genes, and hairpins, among many others. Identifying these regions is crucial because of their association with regulatory and structural characteristics. Moreover, their identification provides positional and quantity information where standard assembly methodologies face significant difficulties because of substantial higher depth coverage (mountains), ambiguous read mapping, or where sequencing or reconstruction defects may occur. However, the capability to distinguish low-complexity regions (LCRs) in genomic and proteomic sequences is a challenge that depends on the model's ability to find them automatically. Low-complexity patterns can be implicit through specific or combined sources, such as algorithmic or probabilistic, and recurring to different spatial distances-namely, local, medium, or distant associations. FINDINGS This article addresses the challenge of automatically modeling and distinguishing LCRs, providing a new method and tool (AlcoR) for efficient and accurate segmentation and visualization of these regions in genomic and proteomic sequences. The method enables the use of models with different memories, providing the ability to distinguish local from distant low-complexity patterns. The method is reference and alignment free, providing additional methodologies for testing, including a highly flexible simulation method for generating biological sequences (DNA or protein) with different complexity levels, sequence masking, and a visualization tool for automatic computation of the LCR maps into an ideogram style. We provide illustrative demonstrations using synthetic, nearly synthetic, and natural sequences showing the high efficiency and accuracy of AlcoR. As large-scale results, we use AlcoR to unprecedentedly provide a whole-chromosome low-complexity map of a recent complete human genome and the haplotype-resolved chromosome pairs of a heterozygous diploid African cassava cultivar. CONCLUSIONS The AlcoR method provides the ability of fast sequence characterization through data complexity analysis, ideally for scenarios entangling the presence of new or unknown sequences. AlcoR is implemented in C language using multithreading to increase the computational speed, is flexible for multiple applications, and does not contain external dependencies. The tool accepts any sequence in FASTA format. The source code is freely provided at https://github.com/cobilab/alcor.
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Affiliation(s)
- Jorge M Silva
- IEETA, Institute of Electronics and Informatics Engineering of Aveiro, and LASI, Intelligent Systems Associate Laboratory, University of Aveiro, Campus Universitário de Santiago, 3810-193 Aveiro, Portugal
- Department of Electronics Telecommunications and Informatics, University of Aveiro, Campus Universitario de Santiago, 3810-193, Aveiro, Portugal
| | - Weihong Qi
- Functional Genomics Center Zurich, ETH Zurich and University of Zurich, Winterthurerstrasse, 190, 8057, Zurich, Switzerland
- SIB, Swiss Institute of Bioinformatics, 1202, Geneva, Switzerland
| | - Armando J Pinho
- IEETA, Institute of Electronics and Informatics Engineering of Aveiro, and LASI, Intelligent Systems Associate Laboratory, University of Aveiro, Campus Universitário de Santiago, 3810-193 Aveiro, Portugal
- Department of Electronics Telecommunications and Informatics, University of Aveiro, Campus Universitario de Santiago, 3810-193, Aveiro, Portugal
| | - Diogo Pratas
- IEETA, Institute of Electronics and Informatics Engineering of Aveiro, and LASI, Intelligent Systems Associate Laboratory, University of Aveiro, Campus Universitário de Santiago, 3810-193 Aveiro, Portugal
- Department of Electronics Telecommunications and Informatics, University of Aveiro, Campus Universitario de Santiago, 3810-193, Aveiro, Portugal
- Department of Virology, University of Helsinki, Haartmaninkatu, 3, 00014 Helsinki, Finland
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