Sketching and sampling approaches for fast and accurate long read classification
Abstract Background In modern sequencing experiments, quickly and accurately identifying the sources of the reads is a crucial need. In metagenomics, where each read comes from one of potentially many members of a community, it can be important to identify the exact species the read is from. In othe...
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| Format: | Article |
| Language: | English |
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BMC
2022-10-01
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| Series: | BMC Bioinformatics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12859-022-05014-0 |
| _version_ | 1811223635178815488 |
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| author | Arun Das Michael C. Schatz |
| author_facet | Arun Das Michael C. Schatz |
| author_sort | Arun Das |
| collection | DOAJ |
| description | Abstract Background In modern sequencing experiments, quickly and accurately identifying the sources of the reads is a crucial need. In metagenomics, where each read comes from one of potentially many members of a community, it can be important to identify the exact species the read is from. In other settings, it is important to distinguish which reads are from the targeted sample and which are from potential contaminants. In both cases, identification of the correct source of a read enables further investigation of relevant reads, while minimizing wasted work. This task is particularly challenging for long reads, which can have a substantial error rate that obscures the origins of each read. Results Existing tools for the read classification problem are often alignment or index-based, but such methods can have large time and/or space overheads. In this work, we investigate the effectiveness of several sampling and sketching-based approaches for read classification. In these approaches, a chosen sampling or sketching algorithm is used to generate a reduced representation (a “screen”) of potential source genomes for a query readset before reads are streamed in and compared against this screen. Using a query read’s similarity to the elements of the screen, the methods predict the source of the read. Such an approach requires limited pre-processing, stores and works with only a subset of the input data, and is able to perform classification with a high degree of accuracy. Conclusions The sampling and sketching approaches investigated include uniform sampling, methods based on MinHash and its weighted and order variants, a minimizer-based technique, and a novel clustering-based sketching approach. We demonstrate the effectiveness of these techniques both in identifying the source microbial genomes for reads from a metagenomic long read sequencing experiment, and in distinguishing between long reads from organisms of interest and potential contaminant reads. We then compare these approaches to existing alignment, index and sketching-based tools for read classification, and demonstrate how such a method is a viable alternative for determining the source of query reads. Finally, we present a reference implementation of these approaches at https://github.com/arun96/sketching . |
| first_indexed | 2024-04-12T08:36:10Z |
| format | Article |
| id | doaj.art-4863253d5464448d8c1fc312bfd8f73d |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-04-12T08:36:10Z |
| publishDate | 2022-10-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-4863253d5464448d8c1fc312bfd8f73d2022-12-22T03:40:00ZengBMCBMC Bioinformatics1471-21052022-10-0123112310.1186/s12859-022-05014-0Sketching and sampling approaches for fast and accurate long read classificationArun Das0Michael C. Schatz1Department of Computer Science, Johns Hopkins UniversityDepartment of Computer Science, Johns Hopkins UniversityAbstract Background In modern sequencing experiments, quickly and accurately identifying the sources of the reads is a crucial need. In metagenomics, where each read comes from one of potentially many members of a community, it can be important to identify the exact species the read is from. In other settings, it is important to distinguish which reads are from the targeted sample and which are from potential contaminants. In both cases, identification of the correct source of a read enables further investigation of relevant reads, while minimizing wasted work. This task is particularly challenging for long reads, which can have a substantial error rate that obscures the origins of each read. Results Existing tools for the read classification problem are often alignment or index-based, but such methods can have large time and/or space overheads. In this work, we investigate the effectiveness of several sampling and sketching-based approaches for read classification. In these approaches, a chosen sampling or sketching algorithm is used to generate a reduced representation (a “screen”) of potential source genomes for a query readset before reads are streamed in and compared against this screen. Using a query read’s similarity to the elements of the screen, the methods predict the source of the read. Such an approach requires limited pre-processing, stores and works with only a subset of the input data, and is able to perform classification with a high degree of accuracy. Conclusions The sampling and sketching approaches investigated include uniform sampling, methods based on MinHash and its weighted and order variants, a minimizer-based technique, and a novel clustering-based sketching approach. We demonstrate the effectiveness of these techniques both in identifying the source microbial genomes for reads from a metagenomic long read sequencing experiment, and in distinguishing between long reads from organisms of interest and potential contaminant reads. We then compare these approaches to existing alignment, index and sketching-based tools for read classification, and demonstrate how such a method is a viable alternative for determining the source of query reads. Finally, we present a reference implementation of these approaches at https://github.com/arun96/sketching .https://doi.org/10.1186/s12859-022-05014-0SketchingSamplingClassificationMinHashMetagenomics |
| spellingShingle | Arun Das Michael C. Schatz Sketching and sampling approaches for fast and accurate long read classification BMC Bioinformatics Sketching Sampling Classification MinHash Metagenomics |
| title | Sketching and sampling approaches for fast and accurate long read classification |
| title_full | Sketching and sampling approaches for fast and accurate long read classification |
| title_fullStr | Sketching and sampling approaches for fast and accurate long read classification |
| title_full_unstemmed | Sketching and sampling approaches for fast and accurate long read classification |
| title_short | Sketching and sampling approaches for fast and accurate long read classification |
| title_sort | sketching and sampling approaches for fast and accurate long read classification |
| topic | Sketching Sampling Classification MinHash Metagenomics |
| url | https://doi.org/10.1186/s12859-022-05014-0 |
| work_keys_str_mv | AT arundas sketchingandsamplingapproachesforfastandaccuratelongreadclassification AT michaelcschatz sketchingandsamplingapproachesforfastandaccuratelongreadclassification |