Estimating population diversity with unreliable low frequency counts.
(3/360)
We consider the classical population diversity estimation scenario based on frequency count data (the number of classes or taxa represented once, twice, etc. in the sample), but with the proviso that the lowest frequency counts, especially the singletons, may not be reliably observed. This arises especially in data derived from modern high-throughput DNA sequencing, where errors may cause sequences to be incorrectly assigned to new taxa instead of being matched to existing, observed taxa. We look at a spectrum of methods for addressing this issue, focusing in particular on fitting a parametric mixture model and deleting the highest-diversity component; we also consider regarding the data as left-censored and effectively pooling two or more low frequency counts. We find that these purely statistical "downstream" corrections will depend strongly on their underlying assumptions, but that such methods can be useful nonetheless. (+info)
Comparisons of distance methods for combining covariates and abundances in microbiome studies.
(4/360)
This article compares different methods for combining abundance data, phylogenetic trees and clinical covariates in a nonparametric setting. In particular we study the output from the principal coordinates analysis on UNIFRAC and WEIGHTED UNIFRAC distances and the output from a double principal coordinate analyses DPCOA using distances computed on the phylogenetic tree. We also present power comparisons for some of the standard tests of phylogenetic signal between different types of samples. These methods are compared both on simulated and real data sets. Our study shows that DPCoA is less robust to outliers, and more robust to small noisy fluctuations around zero. (+info)
Proteotyping of microbial communities by optimization of tandem mass spectrometry data interpretation.
(5/360)
We report the development of a novel high performance computing method for the identification of proteins from unknown (environmental) samples. The method uses computational optimization to provide an effective way to control the false discovery rate for environmental samples and complements de novo peptide sequencing. Furthermore, the method provides information based on the expressed protein in a microbial community, and thus complements DNA-based identification methods. Testing on blind samples demonstrates that the method provides 79-95% overlap with analogous results from searches involving only the correct genomes. We provide scaling and performance evaluations for the software that demonstrate the ability to carry out large-scale optimizations on 1258 genomes containing 4.2M proteins. (+info)
Phyloseq: a bioconductor package for handling and analysis of high-throughput phylogenetic sequence data.
(6/360)
We present a detailed description of a new Bioconductor package, phyloseq, for integrated data and analysis of taxonomically-clustered phylogenetic sequencing data in conjunction with related data types. The phyloseq package integrates abundance data, phylogenetic information and covariates so that exploratory transformations, plots, and confirmatory testing and diagnostic plots can be carried out seamlessly. The package is built following the S4 object-oriented framework of the R language so that once the data have been input the user can easily transform, plot and analyze the data. We present some examples that highlight the methods and the ease with which we can leverage existing packages. (+info)
Artificial functional difference between microbial communities caused by length difference of sequencing reads.
(7/360)
Homology-based approaches are often used for the annotation of microbial communities, providing functional profiles that are used to characterize and compare the content and the functionality of microbial communities. Metagenomic reads are the starting data for these studies, however considerable differences are observed between the functional profiles-built from sequencing reads produced by different sequencing techniques-for even the same microbial community. Using simulation experiments, we show that such functional differences are likely to be caused by the actual difference in read lengths, and are not the results of a sampling bias of the sequencing techniques. Furthermore, the functional differences derived from different sequencing techniques cannot be fully explained by the read-count bias, i.e. 1) the higher fraction of unannotated shorter reads (i.e., "read length matters"), and 2) the different lengths of proteins in different functional categories. Instead, we show here that specific functional categories are under-annotated, because similarity-search-based functional annotation tools tend to miss more reads from functional categories that contain less conserved genes/proteins. In addition, the accuracy of functional annotation of short reads for different functions varies, further skewing the functional profiles. To address these issues, we present a simple yet efficient method to improve the frequency estimates of different functional categories in the functional profiles of metagenomes, based on the functional annotation of simulated reads from complete microbial genomes. (+info)
MetaDomain: a profile HMM-based protein domain classification tool for short sequences.
(8/360)
Protein homology search provides basis for functional profiling in metagenomic annotation. Profile HMM-based methods classify reads into annotated protein domain families and can achieve better sensitivity for remote protein homology search than pairwise sequence alignment. However, their sensitivity deteriorates with the decrease of read length. As a result, a large number of short reads cannot be classified into their native domain families. In this work, we introduce MetaDomain, a protein domain classification tool designed for short reads generated by next-generation sequencing technologies. MetaDomain uses relaxed position-specific score thresholds to align more reads to a profile HMM while using the distribution of alignment positions as an additional constraint to control false positive matches. In this work MetaDomain is applied to the transcriptomic data of a bacterial genome and a soil metagenomic data set. The experimental results show that it can achieve better sensitivity than the state-of-the-art profile HMM alignment tool in identifying encoded domains from short sequences. The source codes of MetaDomain are available at http://sourceforge.net/projects/metadomain/. (+info)