Dataset Information

Phylogenetic Heatmaps Highlight Composition Biases in Sequenced Reads.

ABSTRACT: 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.

SUBMITTER: Choudhari S

PROVIDER: S-EPMC5374381 | biostudies-literature | 2017 Jan

REPOSITORIES: biostudies-literature

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Phylogenetic Heatmaps Highlight Composition Biases in Sequenced Reads.

Choudhari Sulbha S Grigoriev Andrey A

Microorganisms 20170124 1

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 compac ...[more]

PMID: 28125031

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Project description:The rugged protein sequence-function landscape complicates efforts, both in nature and in the laboratory, to evolve protein function. Protein library diversification must strike a balance between sufficient variegation to thoroughly sample alternative functionality versus the probability of mutant destabilization below an expressible threshold. In this work, we explore the sequence-function landscape in the context of screening for molecular recognition from an Ig scaffold library. The fibronectin type III domain is used to explore the impact of two sequence diversification strategies: (a) partial wild-type conservation at structurally important positions within the paratope region and (b) tailored amino acid composition mimicking antibody binding-site composition at putative paratope positions. Structurally important positions within the paratope region were identified through stability, structural, and phylogenetic analyses and partially or fully conserved in sequence. To achieve tailored antibody-like diversity, we designed a set of skewed nucleotide mixtures yielding codons approximately matching the distribution observed in antibody complementarity-determining regions without incurring the expense of triphosphoramidite-based construction. These design elements were explored via comparison of three library designs: a random library, a library with wild-type bias in the DE loop only and tyrosine-serine diversity elsewhere, and a library with wild-type bias at 11 positions and the antibody-inspired amino acid distribution. Using pooled libraries for direct competition in a single tube, selection and maturation of binders to seven targets yielded 19 of 21 clones that originated from the structurally biased, tailored-diversity library design. Sequence analysis of the selected clones supports the importance of both tailored compositional diversity and structural bias. In addition, selection of both well and poorly expressed clones from two libraries further elucidated the impact of structural bias.

Project description:Recent years have seen an increasing effort to incorporate phylogenetic hypotheses to the study of community assembly processes. The incorporation of such evolutionary information has been eased by the emergence of specialized software for the automatic estimation of partially resolved supertrees based on published phylogenies. Despite this growing interest in the use of phylogenies in ecological research, very few studies have attempted to quantify the potential biases related to the use of partially resolved phylogenies and to branch length accuracy, and no work has examined how tree shape may affect inference of community phylogenetic metrics. In this study, using a large plant community and elevational dataset, we tested the influence of phylogenetic resolution and branch length information on the quantification of phylogenetic structure; and also explored the impact of tree shape (stemminess) on the loss of accuracy in phylogenetic structure quantification due to phylogenetic resolution. For this purpose, we used 9 sets of phylogenetic hypotheses of varying resolution and branch lengths to calculate three indices of phylogenetic structure: the mean phylogenetic distance (NRI), the mean nearest taxon distance (NTI) and phylogenetic diversity (stdPD) metrics. The NRI metric was the less sensitive to phylogenetic resolution, stdPD showed an intermediate sensitivity, and NTI was the most sensitive one; NRI was also less sensitive to branch length accuracy than NTI and stdPD, the degree of sensitivity being strongly dependent on the dating method and the sample size. Directional biases were generally towards type II errors. Interestingly, we detected that tree shape influenced the accuracy loss derived from the lack of phylogenetic resolution, particularly for NRI and stdPD. We conclude that well-resolved molecular phylogenies with accurate branch length information are needed to identify the underlying phylogenetic structure of communities, and also that sensitivity of phylogenetic structure measures to low phylogenetic resolution can strongly differ depending on phylogenetic tree shape.

Project description:BACKGROUND:Illumina sequencing of a marker gene is popular in metagenomic studies. However, Illumina paired-end (PE) reads sometimes cannot be merged into single reads for subsequent analysis. When mergeable PE reads are limited, one can simply use only first reads for taxonomy annotation, but that wastes information in the second reads. Presumably, including second reads should improve taxonomy annotation. However, a rigorous investigation of how best to do this and how much can be gained has not been reported. RESULTS:We evaluated two methods of joining as opposed to merging PE reads into single reads for taxonomy annotation using simulated data with sequencing errors. Our rigorous evaluation involved several top classifiers (RDP classifier, SINTAX, and two alignment-based methods) and realistic benchmark datasets. For most classifiers, read joining ameliorated the impact of sequencing errors and improved the accuracy of taxonomy predictions. For alignment-based top-hit classifiers, rearranging the reference sequences is recommended to avoid improper alignments of joined reads. For word-counting classifiers, joined reads could be compared to the original reference for classification. We also applied read joining to our own real MiSeq PE data of nasal microbiota of asthmatic children. Before joining, trimming low quality bases was necessary for optimizing taxonomy annotation and sequence clustering. We then showed that read joining increased the amount of effective data for taxonomy annotation. Using these joined trimmed reads, we were able to identify two promising bacterial genera that might be associated with asthma exacerbation. CONCLUSIONS:When mergeable PE reads are limited, joining them into single reads for taxonomy annotation is always recommended. Reference sequences may need to be rearranged accordingly depending on the classifier. Read joining also relaxes the constraint on primer selection, and thus may unleash the full capacity of Illumina PE data for taxonomy annotation. Our work provides guidance for fully utilizing PE data of a marker gene when mergeable reads are limited.

Dataset Information

Phylogenetic Heatmaps Highlight Composition Biases in Sequenced Reads.

Publications

Phylogenetic Heatmaps Highlight Composition Biases in Sequenced Reads.

Similar Datasets

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