Taxonomic Classification of Bacterial 16S rRNA Genes Using Short Sequencing Reads: Evaluation of Effective Study Designs

Massively parallel high throughput sequencing technologies allow us to interrogate the microbial composition of biological samples at unprecedented resolution. The typical approach is to perform high-throughout sequencing of 16S rRNA genes, which are then taxonomically classified based on similarity to known sequences in existing databases. Current technologies cause a predicament though, because although they enable deep coverage of samples, they are limited in the length of sequence they can produce. As a result, high-throughout studies of microbial communities often do not sequence the entire 16S rRNA gene. The challenge is to obtain reliable representation of bacterial communities through taxonomic classification of short 16S rRNA gene sequences. In this study we explored properties of different study designs and developed specific recommendations for effective use of short-read sequencing technologies for the purpose of interrogating bacterial communities, with a focus on classification using naïve Bayesian classifiers. To assess precision and coverage of each design, we used a collection of ∼8,500 manually curated 16S rRNA gene sequences from cultured bacteria and a set of over one million bacterial 16S rRNA gene sequences retrieved from environmental samples, respectively. We also tested different configurations of taxonomic classification approaches using short read sequencing data, and provide recommendations for optimal choice of the relevant parameters. We conclude that with a judicious selection of the sequenced region and the corresponding choice of a suitable training set for taxonomic classification, it is possible to explore bacterial communities at great depth using current technologies, with only a minimal loss of taxonomic resolution.</p

A reanalysis of mouse ENCODE comparative gene expression data [v1; ref status: indexed, http://f1000r.es/5ez]

Recently, the Mouse ENCODE Consortium reported that comparative gene expression data from human and mouse tend to cluster more by species rather than by tissue. This observation was surprising, as it contradicted much of the comparative gene regulatory data collected previously, as well as the common notion that major developmental pathways are highly conserved across a wide range of species, in particular across mammals. Here we show that the Mouse ENCODE gene expression data were collected using a flawed study design, which confounded sequencing batch (namely, the assignment of samples to sequencing flowcells and lanes) with species. When we account for the batch effect, the corrected comparative gene expression data from human and mouse tend to cluster by tissue, not by species

Functional Characterization of Variations on Regulatory Motifs

Transcription factors (TFs) regulate gene expression through specific interactions with short promoter elements. The same regulatory protein may recognize a variety of related sequences. Moreover, once they are detected it is hard to predict whether highly similar sequence motifs will be recognized by the same TF and regulate similar gene expression patterns, or serve as binding sites for distinct regulatory factors. We developed computational measures to assess the functional implications of variations on regulatory motifs and to compare the functions of related sites. We have developed computational means for estimating the functional outcome of substituting a single position within a binding site and applied them to a collection of putative regulatory motifs. We predict the effects of nucleotide variations within motifs on gene expression patterns. In cases where such predictions could be compared to suitable published experimental evidence, we found very good agreement. We further accumulated statistics from multiple substitutions across various binding sites in an attempt to deduce general properties that characterize nucleotide substitutions that are more likely to alter expression. We found that substitutions involving Adenine are more likely to retain the expression pattern and that substitutions involving Guanine are more likely to alter expression compared to the rest of the substitutions. Our results should facilitate the prediction of the expression outcomes of binding site variations. One typical important implication i

Data files and codes used in the reanalysis of the mouse encode comparative gene expression data

<p>We provide supplementary files of the python codes used to process and prepare the data for analysis with R, and the data files for the python codes. We also provide the R codes we used to perform the different analyses as supplementary files, as well as the input for the R codes. Please see supplementary text files for more details.</p

Taxonomic Classification of Bacterial 16S rRNA Genes Using Short Sequencing Reads: Evaluation of Effective Study Designs

<div><p>Massively parallel high throughput sequencing technologies allow us to interrogate the microbial composition of biological samples at unprecedented resolution. The typical approach is to perform high-throughout sequencing of 16S rRNA genes, which are then taxonomically classified based on similarity to known sequences in existing databases. Current technologies cause a predicament though, because although they enable deep coverage of samples, they are limited in the length of sequence they can produce. As a result, high-throughout studies of microbial communities often do not sequence the entire 16S rRNA gene. The challenge is to obtain reliable representation of bacterial communities through taxonomic classification of short 16S rRNA gene sequences. In this study we explored properties of different study designs and developed specific recommendations for effective use of short-read sequencing technologies for the purpose of interrogating bacterial communities, with a focus on classification using naïve Bayesian classifiers. To assess precision and coverage of each design, we used a collection of ∼8,500 manually curated 16S rRNA gene sequences from cultured bacteria and a set of over one million bacterial 16S rRNA gene sequences retrieved from environmental samples, respectively. We also tested different configurations of taxonomic classification approaches using short read sequencing data, and provide recommendations for optimal choice of the relevant parameters. We conclude that with a judicious selection of the sequenced region and the corresponding choice of a suitable training set for taxonomic classification, it is possible to explore bacterial communities at great depth using current technologies, with only a minimal loss of taxonomic resolution.</p> </div

Classification performance of combined 100 nt single-read predictions, as compared to the best performing paired-end configurations.

<p>We combined predictions made for different 100 nt fragments of the same sequence, by selecting the prediction with the highest confidence score at the genus level (or the lowest common level available). We evaluated the performance, at ranks genus and family (left and right panels, respectively), of combinations of fragments from the V3 and V4 regions (top and bottom panels, respectively) with fragments from each of the other regions examined, and compared it to the performance of the V3 and V4 100 nt paired-end configurations (pointed to by arrows). We used the results of leave-k-out tests classifying the LTP sequences to determine confidence score thresholds for a set of desired false prediction rate (FPR) values (x axis), so that the FPR would be at most the desired value. We then used these thresholds to calculate the classification coverage of sequences from environmental (uncultured) bacteria that corresponds to the desired FPR (y axis). <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0053608#pone.0053608.s006" target="_blank">Figure S6</a> compares the performance of the combinations for the ranks order, class, and phylum.</p

Performance of different training sets in the classification of 100 nt reads from the V4 amplicon.

<p>Each panel compares the performance of the training sets (described in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0053608#pone-0053608-t001" target="_blank">Table 1</a>) for a different rank. We used the results of leave-k-out tests classifying the LTP sequences to determine confidence score thresholds for a set of desired false prediction rate (FPR) values (x axis), so that the FPR would be at most the desired value. We then used these thresholds to calculate the classification coverage of sequences from environmental (uncultured) bacteria that corresponds to the desired FPR (y axis).</p

Classification performance of different experimental designs.

<p>Each panel compares performance of different regions for a different combination of rank (genus or family) and sequencing strategy (100/120 nt single/paired-end reads). We used the results of leave-k-out tests classifying the LTP sequences to determine confidence score thresholds for a set of desired false prediction rate (FPR) values (x axis), so that the FPR would be at most the desired value (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0053608#pone.0053608.s010" target="_blank">Tables S4</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0053608#pone.0053608.s011" target="_blank">S5</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0053608#pone.0053608.s012" target="_blank">S6</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0053608#pone.0053608.s013" target="_blank">S7</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0053608#pone.0053608.s014" target="_blank">S8</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0053608#pone.0053608.s015" target="_blank">S9</a>, and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0053608#pone.0053608.s016" target="_blank">S10</a>). We then used these thresholds to calculate the classification coverage of sequences from environmental (uncultured) bacteria that corresponds to the desired FPR (y axis). <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0053608#pone.0053608.s005" target="_blank">Figure S5</a> compares the performance of different regions across the same sequencing configurations for the ranks order, class, and phylum.</p

Training sets used for the naïve Bayesian classification of bacterial 16S rRNA gene sequences.

a<p>Except for the ‘RDP TS6’ training set, which always trains on the full sequence, numbers are only for the testing of 100 nt single-reads from the V4 region. For the three other training sets, which train only on the region to be classified, the number of sequences reflects both the number of sequences covering this region (all three training sets) and its degree of redundancy (‘unfiltered RDP’ and ‘filtered NCBI’).</p>b<p>The numbers are for the ‘original non-redundant training set’ (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0053608#s3" target="_blank">Methods</a> section ‘Leave k out classification testing’); numbers for each leave-k-out iteration may vary slightly.</p