Phylogenetic Heatmaps Highlight Composition Biases in Sequenced Reads
Due to advancements in sequencing technology, sequence data production is no longer a constraint in the field of microbiology and has made it possible to study uncultured microbes or whole environments using metagenomics. However, these new technologies introduce different biases in metagenomic sequ...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2017-01-01
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| Series: | Microorganisms |
| Subjects: | |
| Online Access: | http://www.mdpi.com/2076-2607/5/1/4 |
| _version_ | 1818272445189062656 |
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| author | Sulbha Choudhari Andrey Grigoriev |
| author_facet | Sulbha Choudhari Andrey Grigoriev |
| author_sort | Sulbha Choudhari |
| collection | DOAJ |
| description | Due to advancements in sequencing technology, sequence data production is no longer a constraint in the field of microbiology and has made it possible to study uncultured microbes or whole environments using metagenomics. However, these new technologies introduce different biases in metagenomic sequencing, affecting the nucleotide distribution of resulting sequence reads. Here, we illustrate such biases using two methods. One is based on phylogenetic heatmaps (PGHMs), a novel approach for compact visualization of sequence composition differences between two groups of sequences containing the same phylogenetic groups. This method is well suited for finding noise and biases when comparing metagenomics samples. We apply PGHMs to detect noise and bias in the data produced with different DNA extraction protocols, different sequencing platforms and different experimental frameworks. In parallel, we use principal component analysis displaying different clustering of sequences from each sample to support our findings and illustrate the utility of PGHMs. We considered contributions of the read length and GC-content variation and observed that in most cases biases were generally due to the GC-content of the reads. |
| first_indexed | 2024-12-12T21:42:11Z |
| format | Article |
| id | doaj.art-72b8d2de60364fa7aeec34c253ce9f58 |
| institution | Directory Open Access Journal |
| issn | 2076-2607 |
| language | English |
| last_indexed | 2024-12-12T21:42:11Z |
| publishDate | 2017-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Microorganisms |
| spelling | doaj.art-72b8d2de60364fa7aeec34c253ce9f582022-12-22T00:11:01ZengMDPI AGMicroorganisms2076-26072017-01-0151410.3390/microorganisms5010004microorganisms5010004Phylogenetic Heatmaps Highlight Composition Biases in Sequenced ReadsSulbha Choudhari0Andrey Grigoriev1Department of Biology, Center for Computational and Integrative Biology, Rutgers University, Camden, NJ 08102, USADepartment of Biology, Center for Computational and Integrative Biology, Rutgers University, Camden, NJ 08102, USADue to advancements in sequencing technology, sequence data production is no longer a constraint in the field of microbiology and has made it possible to study uncultured microbes or whole environments using metagenomics. However, these new technologies introduce different biases in metagenomic sequencing, affecting the nucleotide distribution of resulting sequence reads. Here, we illustrate such biases using two methods. One is based on phylogenetic heatmaps (PGHMs), a novel approach for compact visualization of sequence composition differences between two groups of sequences containing the same phylogenetic groups. This method is well suited for finding noise and biases when comparing metagenomics samples. We apply PGHMs to detect noise and bias in the data produced with different DNA extraction protocols, different sequencing platforms and different experimental frameworks. In parallel, we use principal component analysis displaying different clustering of sequences from each sample to support our findings and illustrate the utility of PGHMs. We considered contributions of the read length and GC-content variation and observed that in most cases biases were generally due to the GC-content of the reads.http://www.mdpi.com/2076-2607/5/1/4nucleotide compositionmetagenomicssequencing biascomputational analysisgenome sequencing |
| spellingShingle | Sulbha Choudhari Andrey Grigoriev Phylogenetic Heatmaps Highlight Composition Biases in Sequenced Reads Microorganisms nucleotide composition metagenomics sequencing bias computational analysis genome sequencing |
| title | Phylogenetic Heatmaps Highlight Composition Biases in Sequenced Reads |
| title_full | Phylogenetic Heatmaps Highlight Composition Biases in Sequenced Reads |
| title_fullStr | Phylogenetic Heatmaps Highlight Composition Biases in Sequenced Reads |
| title_full_unstemmed | Phylogenetic Heatmaps Highlight Composition Biases in Sequenced Reads |
| title_short | Phylogenetic Heatmaps Highlight Composition Biases in Sequenced Reads |
| title_sort | phylogenetic heatmaps highlight composition biases in sequenced reads |
| topic | nucleotide composition metagenomics sequencing bias computational analysis genome sequencing |
| url | http://www.mdpi.com/2076-2607/5/1/4 |
| work_keys_str_mv | AT sulbhachoudhari phylogeneticheatmapshighlightcompositionbiasesinsequencedreads AT andreygrigoriev phylogeneticheatmapshighlightcompositionbiasesinsequencedreads |