Dataset Information

TIPP2: metagenomic taxonomic profiling using phylogenetic markers.

ABSTRACT:

Motivation

Metagenomics has revolutionized microbiome research by enabling researchers to characterize the composition of complex microbial communities. Taxonomic profiling is one of the critical steps in metagenomic analyses. Marker genes, which are single-copy and universally found across Bacteria and Archaea, can provide accurate estimates of taxon abundances in the sample.

Results

We present TIPP2, a marker gene-based abundance profiling method, which combines phylogenetic placement with statistical techniques to control classification precision and recall. TIPP2 includes an updated set of reference packages and several algorithmic improvements over the original TIPP method. We find that TIPP2 provides comparable or better estimates of abundance than other profiling methods (including Bracken, mOTUsv2 and MetaPhlAn2), and strictly dominates other methods when there are under-represented (novel) genomes present in the dataset.

Availability and implementation

The code for our method is freely available in open-source form at https://github.com/smirarab/sepp/blob/tipp2/README.TIPP.md. The code and procedure to create new reference packages for TIPP2 are available at https://github.com/shahnidhi/TIPP_reference_package.

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Shah N

PROVIDER: S-EPMC8317105 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature

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Publications

TIPP2: metagenomic taxonomic profiling using phylogenetic markers.

Shah Nidhi N Molloy Erin K EK Pop Mihai M Warnow Tandy T

Bioinformatics (Oxford, England) 20210701 13

<h4>Motivation</h4>Metagenomics has revolutionized microbiome research by enabling researchers to characterize the composition of complex microbial communities. Taxonomic profiling is one of the critical steps in metagenomic analyses. Marker genes, which are single-copy and universally found across Bacteria and Archaea, can provide accurate estimates of taxon abundances in the sample.<h4>Results</h4>We present TIPP2, a marker gene-based abundance profiling method, which combines phylogenetic pla ...[more]

PMID: 33471121

Similar Datasets

Project description:BackgroundLong-read shotgun metagenomic sequencing is gaining in popularity and offers many advantages over short-read sequencing. The higher information content in long reads is useful for a variety of metagenomics analyses, including taxonomic classification and profiling. The development of long-read specific tools for taxonomic classification is accelerating, yet there is a lack of information regarding their relative performance. Here, we perform a critical benchmarking study using 11 methods, including five methods designed specifically for long reads. We applied these tools to several mock community datasets generated using Pacific Biosciences (PacBio) HiFi or Oxford Nanopore Technology sequencing, and evaluated their performance based on read utilization, detection metrics, and relative abundance estimates.ResultsOur results show that long-read classifiers generally performed best. Several short-read classification and profiling methods produced many false positives (particularly at lower abundances), required heavy filtering to achieve acceptable precision (at the cost of reduced recall), and produced inaccurate abundance estimates. By contrast, two long-read methods (BugSeq, MEGAN-LR & DIAMOND) and one generalized method (sourmash) displayed high precision and recall without any filtering required. Furthermore, in the PacBio HiFi datasets these methods detected all species down to the 0.1% abundance level with high precision. Some long-read methods, such as MetaMaps and MMseqs2, required moderate filtering to reduce false positives to resemble the precision and recall of the top-performing methods. We found read quality affected performance for methods relying on protein prediction or exact k-mer matching, and these methods performed better with PacBio HiFi datasets. We also found that long-read datasets with a large proportion of shorter reads (< 2 kb length) resulted in lower precision and worse abundance estimates, relative to length-filtered datasets. Finally, for classification methods, we found that the long-read datasets produced significantly better results than short-read datasets, demonstrating clear advantages for long-read metagenomic sequencing.ConclusionsOur critical assessment of available methods provides best-practice recommendations for current research using long reads and establishes a baseline for future benchmarking studies.

Dataset Information

TIPP2: metagenomic taxonomic profiling using phylogenetic markers.

Motivation

Results

Availability and implementation

Supplementary information

Publications

TIPP2: metagenomic taxonomic profiling using phylogenetic markers.

Similar Datasets

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