Dataset Information

PALADIN: protein alignment for functional profiling whole metagenome shotgun data.

ABSTRACT:

Motivation

Whole metagenome shotgun sequencing is a powerful approach for assaying the functional potential of microbial communities. We currently lack tools that efficiently and accurately align DNA reads against protein references, the technique necessary for constructing a functional profile. Here, we present PALADIN-a novel modification of the Burrows-Wheeler Aligner that provides accurate alignment, robust reporting capabilities and orders-of-magnitude improved efficiency by directly mapping in protein space.

Results

We compared the accuracy and efficiency of PALADIN against existing tools that employ nucleotide or protein alignment algorithms. Using simulated reads, PALADIN consistently outperformed the popular DNA read mappers BWA and NovoAlign in detected proteins, percentage of reads mapped and ontological similarity. We also compared PALADIN against four existing protein alignment tools: BLASTX, RAPSearch2, DIAMOND and Lambda, using empirically obtained reads. PALADIN yielded results seven times faster than the best performing alternative, DIAMOND and nearly 8000 times faster than BLASTX. PALADIN's accuracy was comparable to all tested solutions.

Availability and implementation

PALADIN was implemented in C, and its source code and documentation are available at https://github.com/twestbrookunh/paladin.

Contact

anthonyw@wildcats.unh.edu.

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Westbrook A

PROVIDER: S-EPMC5423455 | biostudies-literature | 2017 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

PALADIN: protein alignment for functional profiling whole metagenome shotgun data.

Westbrook Anthony A Ramsdell Jordan J Schuelke Taruna T Normington Louisa L Bergeron R Daniel RD Thomas W Kelley WK MacManes Matthew D MD

Bioinformatics (Oxford, England) 20170501 10

<h4>Motivation</h4>Whole metagenome shotgun sequencing is a powerful approach for assaying the functional potential of microbial communities. We currently lack tools that efficiently and accurately align DNA reads against protein references, the technique necessary for constructing a functional profile. Here, we present PALADIN-a novel modification of the Burrows-Wheeler Aligner that provides accurate alignment, robust reporting capabilities and orders-of-magnitude improved efficiency by directl ...[more]

PMID: 28158639

Similar Datasets

Project description:BackgroundMicrobial eukaryotes are found alongside bacteria and archaea in natural microbial systems, including host-associated microbiomes. While microbial eukaryotes are critical to these communities, they are challenging to study with shotgun sequencing techniques and are therefore often excluded.ResultsHere, we present EukDetect, a bioinformatics method to identify eukaryotes in shotgun metagenomic sequencing data. Our approach uses a database of 521,824 universal marker genes from 241 conserved gene families, which we curated from 3713 fungal, protist, non-vertebrate metazoan, and non-streptophyte archaeplastida genomes and transcriptomes. EukDetect has a broad taxonomic coverage of microbial eukaryotes, performs well on low-abundance and closely related species, and is resilient against bacterial contamination in eukaryotic genomes. Using EukDetect, we describe the spatial distribution of eukaryotes along the human gastrointestinal tract, showing that fungi and protists are present in the lumen and mucosa throughout the large intestine. We discover that there is a succession of eukaryotes that colonize the human gut during the first years of life, mirroring patterns of developmental succession observed in gut bacteria. By comparing DNA and RNA sequencing of paired samples from human stool, we find that many eukaryotes continue active transcription after passage through the gut, though some do not, suggesting they are dormant or nonviable. We analyze metagenomic data from the Baltic Sea and find that eukaryotes differ across locations and salinity gradients. Finally, we observe eukaryotes in Arabidopsis leaf samples, many of which are not identifiable from public protein databases.ConclusionsEukDetect provides an automated and reliable way to characterize eukaryotes in shotgun sequencing datasets from diverse microbiomes. We demonstrate that it enables discoveries that would be missed or clouded by false positives with standard shotgun sequence analysis. EukDetect will greatly advance our understanding of how microbial eukaryotes contribute to microbiomes. Video abstract.

Project description:BackgroundThe mycobiome, representing the fungal component of microbial communities, is increasingly acknowledged as an integral part of the gut microbiome. However, research in this area remains relatively limited. The characterization of mycobiome taxa from metagenomic data is heavily reliant on the quality of the software and databases. In this study, we evaluated the feasibility of mycobiome profiling using existing bioinformatics tools on simulated fungal metagenomic data.ResultsWe identified seven tools claiming to perform taxonomic assignment of fungal shotgun metagenomic sequences. One of these was outdated and required substantial modifications of the code to be functional and was thus excluded. To evaluate the accuracy of identification and relative abundance of the remaining tools (Kraken2, MetaPhlAn4, EukDetect, FunOMIC, MiCoP, and HumanMycobiomeScan), we constructed 18 mock communities of varying species richness and abundance levels. The mock communities comprised up to 165 fungal species belonging to the phyla Ascomycota and Basidiomycota, commonly found in gut microbiomes. Of the tools, FunOMIC and HumanMycobiomeScan needed source code modifications to run. Notably, only one species, Candida orthopsilosis, was consistently identified by all tools across all communities where it was included. Increasing community richness improved precision of Kraken2 and the relative abundance accuracy of all tools on species, genus, and family levels. MetaPhlAn4 accurately identified all genera present in the communities and FunOMIC identified most species. The top three tools for overall accuracy in both identification and relative abundance estimation were EukDetect, MiCoP, and FunOMIC, respectively. Adding 90% and 99% bacterial background did not significantly impact these tools' performance. Among the whole genome reference tools (Kraken2, HMS, and MiCoP), MiCoP exhibited the highest accuracy when the same reference database was used.ConclusionOur survey of mycobiome-specific software revealed a very limited selection of such tools and their poor robustness due to error-prone software, along with a significant lack of comprehensive databases enabling characterization of the mycobiome. None of the implemented tools fully agreed on the mock community profiles. FunOMIC recognized most of the species, but EukDetect and MiCoP provided predictions that were closest to the correct compositions. The bacterial background did not impact these tools' performance. Video Abstract.

Project description:BackgroundVariation of microorganism communities in the rumen of cattle (Bos taurus) is of great interest because of possible links to economically or environmentally important traits, such as feed conversion efficiency or methane emission levels. The resolution of studies investigating this variation may be improved by utilizing untargeted massively parallel sequencing (MPS), that is, sequencing without targeted amplification of genes. The objective of this study was to develop a method which used MPS to generate "rumen metagenome profiles", and to investigate if these profiles were repeatable among samples taken from the same cow. Given faecal samples are much easier to obtain than rumen fluid samples; we also investigated whether rumen metagenome profiles were predictive of faecal metagenome profiles.ResultsRather than focusing on individual organisms within the rumen, our method used MPS data to generate quantitative rumen micro-biome profiles, regardless of taxonomic classifications. The method requires a previously assembled reference metagenome. A number of such reference metagenomes were considered, including two rumen derived metagenomes, a human faecal microflora metagenome and a reference metagenome made up of publically available prokaryote sequences. Sequence reads from each test sample were aligned to these references. The "rumen metagenome profile" was generated from the number of the reads that aligned to each contig in the database. We used this method to test the hypothesis that rumen fluid microbial community profiles vary more between cows than within multiple samples from the same cow. Rumen fluid samples were taken from three cows, at three locations within the rumen. DNA from the samples was sequenced on the Illumina GAIIx. When the reads were aligned to a rumen metagenome reference, the rumen metagenome profiles were repeatable (P?<?0.00001) by cow regardless of location of sampling rumen fluid. The repeatability was estimated at 9%, albeit with a high standard error, reflecting the small number of animals in the study. Finally, we compared rumen microbial profiles to faecal microbial profiles. Our hypothesis, that there would be a stronger correlation between faeces and rumen fluid from the same cow than between faeces and rumen fluid from different cows, was not supported by our data (with much greater significance of rumen versus faeces effect than animal effect in mixed linear model).ConclusionsWe have presented a simple and high throughput method of metagenome profiling to assess the similarity of whole metagenomes, and illustrated its use on two novel datasets. This method utilises widely used freeware. The method should be useful in the exploration and comparison of metagenomes.

Dataset Information

PALADIN: protein alignment for functional profiling whole metagenome shotgun data.

Motivation

Results

Availability and implementation

Contact

Supplementary information

Publications

PALADIN: protein alignment for functional profiling whole metagenome shotgun data.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets