High-fidelity (repeat) consensus sequences from short reads using combined read clustering and assembly
Abstract Background Despite the many cheap and fast ways to generate genomic data, good and exact genome assembly is still a problem, with especially the repeats being vastly underrepresented and often misassembled. As short reads in low coverage are already sufficient to represent the repeat landsc...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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BMC
2024-01-01
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| Series: | BMC Genomics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12864-023-09948-4 |
| _version_ | 1797340261745950720 |
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| author | Ludwig Mann Kristin Balasch Nicola Schmidt Tony Heitkam |
| author_facet | Ludwig Mann Kristin Balasch Nicola Schmidt Tony Heitkam |
| author_sort | Ludwig Mann |
| collection | DOAJ |
| description | Abstract Background Despite the many cheap and fast ways to generate genomic data, good and exact genome assembly is still a problem, with especially the repeats being vastly underrepresented and often misassembled. As short reads in low coverage are already sufficient to represent the repeat landscape of any given genome, many read cluster algorithms were brought forward that provide repeat identification and classification. But how can trustworthy, reliable and representative repeat consensuses be derived from unassembled genomes? Results Here, we combine methods from repeat identification and genome assembly to derive these robust consensuses. We test several use cases, such as (1) consensus building from clustered short reads of non-model genomes, (2) from genome-wide amplification setups, and (3) specific repeat-centred questions, such as the linked vs. unlinked arrangement of ribosomal genes. In all our use cases, the derived consensuses are robust and representative. To evaluate overall performance, we compare our high-fidelity repeat consensuses to RepeatExplorer2-derived contigs and check, if they represent real transposable elements as found in long reads. Our results demonstrate that it is possible to generate useful, reliable and trustworthy consensuses from short reads by a combination from read cluster and genome assembly methods in an automatable way. Conclusion We anticipate that our workflow opens the way towards more efficient and less manual repeat characterization and annotation, benefitting all genome studies, but especially those of non-model organisms. |
| first_indexed | 2024-03-08T10:00:27Z |
| format | Article |
| id | doaj.art-084f541f62754da790d613dd07f27aaa |
| institution | Directory Open Access Journal |
| issn | 1471-2164 |
| language | English |
| last_indexed | 2024-03-08T10:00:27Z |
| publishDate | 2024-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Genomics |
| spelling | doaj.art-084f541f62754da790d613dd07f27aaa2024-01-29T10:59:56ZengBMCBMC Genomics1471-21642024-01-0125111110.1186/s12864-023-09948-4High-fidelity (repeat) consensus sequences from short reads using combined read clustering and assemblyLudwig Mann0Kristin Balasch1Nicola Schmidt2Tony Heitkam3Faculty of Biology, Technische Universität DresdenFaculty of Biology, Technische Universität DresdenFaculty of Biology, Technische Universität DresdenFaculty of Biology, Technische Universität DresdenAbstract Background Despite the many cheap and fast ways to generate genomic data, good and exact genome assembly is still a problem, with especially the repeats being vastly underrepresented and often misassembled. As short reads in low coverage are already sufficient to represent the repeat landscape of any given genome, many read cluster algorithms were brought forward that provide repeat identification and classification. But how can trustworthy, reliable and representative repeat consensuses be derived from unassembled genomes? Results Here, we combine methods from repeat identification and genome assembly to derive these robust consensuses. We test several use cases, such as (1) consensus building from clustered short reads of non-model genomes, (2) from genome-wide amplification setups, and (3) specific repeat-centred questions, such as the linked vs. unlinked arrangement of ribosomal genes. In all our use cases, the derived consensuses are robust and representative. To evaluate overall performance, we compare our high-fidelity repeat consensuses to RepeatExplorer2-derived contigs and check, if they represent real transposable elements as found in long reads. Our results demonstrate that it is possible to generate useful, reliable and trustworthy consensuses from short reads by a combination from read cluster and genome assembly methods in an automatable way. Conclusion We anticipate that our workflow opens the way towards more efficient and less manual repeat characterization and annotation, benefitting all genome studies, but especially those of non-model organisms.https://doi.org/10.1186/s12864-023-09948-4Repetitive DNATransposable elementsConsensus sequencesRepeat assemblyRepeat clusteringeccDNA |
| spellingShingle | Ludwig Mann Kristin Balasch Nicola Schmidt Tony Heitkam High-fidelity (repeat) consensus sequences from short reads using combined read clustering and assembly BMC Genomics Repetitive DNA Transposable elements Consensus sequences Repeat assembly Repeat clustering eccDNA |
| title | High-fidelity (repeat) consensus sequences from short reads using combined read clustering and assembly |
| title_full | High-fidelity (repeat) consensus sequences from short reads using combined read clustering and assembly |
| title_fullStr | High-fidelity (repeat) consensus sequences from short reads using combined read clustering and assembly |
| title_full_unstemmed | High-fidelity (repeat) consensus sequences from short reads using combined read clustering and assembly |
| title_short | High-fidelity (repeat) consensus sequences from short reads using combined read clustering and assembly |
| title_sort | high fidelity repeat consensus sequences from short reads using combined read clustering and assembly |
| topic | Repetitive DNA Transposable elements Consensus sequences Repeat assembly Repeat clustering eccDNA |
| url | https://doi.org/10.1186/s12864-023-09948-4 |
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