A signal processing method for alignment-free metagenomic binning: multi-resolution genomic binary patterns
Algorithms in bioinformatics use textual representations of genetic information, sequences of the characters A, T, G and C represented computationally as strings or sub-strings. Signal and related image processing methods offer a rich source of alternative descriptors as they are designed to work in...
| Автори: | , , |
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| Формат: | Journal article |
| Мова: | English |
| Опубліковано: |
Springer Nature
2019
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| _version_ | 1826279429853151232 |
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| author | Kouchaki, S Tapinos, A Robertson, D |
| author_facet | Kouchaki, S Tapinos, A Robertson, D |
| author_sort | Kouchaki, S |
| collection | OXFORD |
| description | Algorithms in bioinformatics use textual representations of genetic information, sequences of the characters A, T, G and C represented computationally as strings or sub-strings. Signal and related image processing methods offer a rich source of alternative descriptors as they are designed to work in the presence of noisy data without the need for exact matching. Here we introduce a method, multi-resolution local binary patterns (MLBP) adapted from image processing to extract local 'texture' changes from nucleotide sequence data. We apply this feature space to the alignment-free binning of metagenomic data. The effectiveness of MLBP is demonstrated using both simulated and real human gut microbial communities. Sequence reads or contigs can be represented as vectors and their 'texture' compared efficiently using machine learning algorithms to perform dimensionality reduction to capture eigengenome information and perform clustering (here using randomized singular value decomposition and BH-tSNE). The intuition behind our method is the MLBP feature vectors permit sequence comparisons without the need for explicit pairwise matching. We demonstrate this approach outperforms existing methods based on k-mer frequencies. The signal processing method, MLBP, thus offers a viable alternative feature space to textual representations of sequence data. The source code for our Multi-resolution Genomic Binary Patterns method can be found at https://github.com/skouchaki/MrGBP . |
| first_indexed | 2024-03-06T23:58:36Z |
| format | Journal article |
| id | oxford-uuid:751c79b2-7d48-420d-9da7-3bac9b8d3b81 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T23:58:36Z |
| publishDate | 2019 |
| publisher | Springer Nature |
| record_format | dspace |
| spelling | oxford-uuid:751c79b2-7d48-420d-9da7-3bac9b8d3b812022-03-26T20:07:24ZA signal processing method for alignment-free metagenomic binning: multi-resolution genomic binary patternsJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:751c79b2-7d48-420d-9da7-3bac9b8d3b81EnglishSymplectic Elements at OxfordSpringer Nature2019Kouchaki, STapinos, ARobertson, DAlgorithms in bioinformatics use textual representations of genetic information, sequences of the characters A, T, G and C represented computationally as strings or sub-strings. Signal and related image processing methods offer a rich source of alternative descriptors as they are designed to work in the presence of noisy data without the need for exact matching. Here we introduce a method, multi-resolution local binary patterns (MLBP) adapted from image processing to extract local 'texture' changes from nucleotide sequence data. We apply this feature space to the alignment-free binning of metagenomic data. The effectiveness of MLBP is demonstrated using both simulated and real human gut microbial communities. Sequence reads or contigs can be represented as vectors and their 'texture' compared efficiently using machine learning algorithms to perform dimensionality reduction to capture eigengenome information and perform clustering (here using randomized singular value decomposition and BH-tSNE). The intuition behind our method is the MLBP feature vectors permit sequence comparisons without the need for explicit pairwise matching. We demonstrate this approach outperforms existing methods based on k-mer frequencies. The signal processing method, MLBP, thus offers a viable alternative feature space to textual representations of sequence data. The source code for our Multi-resolution Genomic Binary Patterns method can be found at https://github.com/skouchaki/MrGBP . |
| spellingShingle | Kouchaki, S Tapinos, A Robertson, D A signal processing method for alignment-free metagenomic binning: multi-resolution genomic binary patterns |
| title | A signal processing method for alignment-free metagenomic binning: multi-resolution genomic binary patterns |
| title_full | A signal processing method for alignment-free metagenomic binning: multi-resolution genomic binary patterns |
| title_fullStr | A signal processing method for alignment-free metagenomic binning: multi-resolution genomic binary patterns |
| title_full_unstemmed | A signal processing method for alignment-free metagenomic binning: multi-resolution genomic binary patterns |
| title_short | A signal processing method for alignment-free metagenomic binning: multi-resolution genomic binary patterns |
| title_sort | signal processing method for alignment free metagenomic binning multi resolution genomic binary patterns |
| work_keys_str_mv | AT kouchakis asignalprocessingmethodforalignmentfreemetagenomicbinningmultiresolutiongenomicbinarypatterns AT tapinosa asignalprocessingmethodforalignmentfreemetagenomicbinningmultiresolutiongenomicbinarypatterns AT robertsond asignalprocessingmethodforalignmentfreemetagenomicbinningmultiresolutiongenomicbinarypatterns AT kouchakis signalprocessingmethodforalignmentfreemetagenomicbinningmultiresolutiongenomicbinarypatterns AT tapinosa signalprocessingmethodforalignmentfreemetagenomicbinningmultiresolutiongenomicbinarypatterns AT robertsond signalprocessingmethodforalignmentfreemetagenomicbinningmultiresolutiongenomicbinarypatterns |