Glenn Stone

Fast, accurate error-correction of amplicon pyrosequences using Acacia

To the Editor: Microbial diversity metrics based on high-throughput amplicon sequencing are compromised by read errors. Roche 454 GS FLX Titanium pyrosequencing is currently the most widely used technology for amplicon-based microbial community studies, despite high homopolymer-associated insertion-deletion error rates1,2. Currently, there are two software packages, AmpliconNoise3 and Denoiser4, that are commonly used to correct amplicon pyrosequencing errors. AmpliconNoise applies an approximate likelihood using empirically derived error distributions to remove pyrosequencing noise from reads. AmpliconNoise is highly effective at noise removal but is computationally intensive3. Denoiser is a faster algorithm that uses frequency-based heuristics rather than statistical modeling to cluster reads. Neither tool modifies individual reads; instead both select an ‘error-free’ read to represent reads in a given cluster. We developed a tool for homopolymer error-correction that has greater scalability than existing tools. We explored whether there was sufficient information in the FASTA files alone to achieve the sensitivity and specificity of AmpliconNoise and Denoiser, which both use raw flowgrams. Our error-correction tool, Acacia, meets these objectives. First, Acacia reduces the number and complexity of alignments. Rather than performing all-against-all alignments in a cluster, each read in the cluster is aligned to a dynamically updated cluster consensus; the alignment algorithm is made more efficient using heuristics that only consider homopolymer overand under-calls. Secondly, Acacia uses a quicker but less sensitive statistical approach to distinguish between error and genuine sequence differences (Supplementary Methods and Supplementary Notes 1–3). We measured the performance of Acacia relative to AmpliconNoise and Denoiser using three synthetic small subunit ribosomal RNA (SSU rRNA) gene amplicon data sets (‘artificial’, ‘divergent’ and ‘titanium’) previously used to benchmark the latter tools3,4. For each data set, we recorded the peak memory usage and CPU run time (Supplementary Table 1). We benchmarked AmpliconNoise using only the smaller artificial data set, which was sufficient to indicate that this software was impractical for analyzing larger data sets. The peak memory used by Acacia was 1–4× higher than that used by Denoiser and ~14× lower than by AmpliconNoise. Acacia ran on all data sets in under 1 minute, was up to ~500× faster than Denoiser for the titanium data set, and more than 2,000× faster than AmpliconNoise for the artificial data set. Acacia processed larger contemporary data sets (200,000 GS FLX Titanium reads) in under 80 CPU minutes. We next benchmarked the error-correction sensitivity and specificity of Acacia. For convenience, we refer to correction of individual reads although corrections are derived from either a cluster consensus (Acacia) or representative read (AmpliconNoise and Denoiser). Despite working with the less precise rounded flow values, Acacia corrected the majority of GS FLX Titanium homopolymer errors corrected by AmpliconNoise and Denoiser (Fig. 1a). As expected, Acacia has less sensitivity than AmpliconNoise and Denoiser for correcting substitution errors because it only attempts to correct homopolymer errors. Acacia did, however, correct ~40% of the AmpliconNoiseand Denoiser-corrected substitutions in the titanium data set (Fig. 1b) because these errors were the consequence of consecutive over-under calls or vice versa. We found that AmpliconNoise and Denoiser introduced a substantial number of errors, most of them non-homopolymer substitutions, during error-correction (Fig. 1c). Notably, Acacia introduced 2× and 12× fewer errors than AmpliconNoise and Denoiser, respectively. Errors

Shining a Light on Dark Sequencing: Characterising Errors in Ion Torrent PGM Data

The Ion Torrent Personal Genome Machine (PGM) is a new sequencing platform that substantially differs from other sequencing technologies by measuring pH rather than light to detect polymerisation events. Using re-sequencing datasets, we comprehensively characterise the biases and errors introduced by the PGM at both the base and flow level, across a combination of factors, including chip density, sequencing kit, template species and machine. We found two distinct insertion/deletion (indel) error types that accounted for the majority of errors introduced by the PGM. The main error source was inaccurate flow-calls, which introduced indels at a raw rate of 2.84% (1.38% after quality clipping) using the OneTouch 200 bp kit. Inaccurate flow-calls typically resulted in over-called short-homopolymers and under-called long-homopolymers. Flow-call accuracy decreased with consecutive flow cycles, but we also found significant periodic fluctuations in the flow error-rate, corresponding to specific positions within the flow-cycle pattern. Another less common PGM error, high frequency indel (HFI) errors, are indels that occur at very high frequency in the reads relative to a given base position in the reference genome, but in the majority of instances were not replicated consistently across separate runs. HFI errors occur approximately once every thousand bases in the reference, and correspond to 0.06% of bases in reads. Currently, the PGM does not achieve the accuracy of competing light-based technologies. However, flow-call inaccuracy is systematic and the statistical models of flow-values developed here will enable PGM-specific bioinformatics approaches to be developed, which will account for these errors. HFI errors may prove more challenging to address, especially for polymorphism and amplicon applications, but may be overcome by sequencing the same DNA template across multiple chips.

Heterologous microarray experiments used to identify the early gene response to heat stress in a coral reef fish

Coral reef fishes are expected to experience rising sea surface temperatures due to climate change. How well tropical reef fishes will respond to these increased temperatures and which genes are important in the response to elevated temperatures is not known. Microarray technology provides a powerful tool for gene discovery studies, but the development of microarrays for individual species can be expensive and time‐consuming. In this study, we tested the suitability of a Danio rerio oligonucleotide microarray for application in a species with few genomic resources, the coral reef fish Pomacentrus moluccensis. Results from a comparative genomic hybridization experiment and direct sequence comparisons indicate that for most genes there is considerable sequence similarity between the two species, suggesting that the D. rerio array is useful for genomic studies of P. moluccensis. We employed this heterologous microarray approach to characterize the early transcriptional response to heat stress in P. moluccensis. A total of 111 gene loci, many of which are involved in protein processing, transcription, and cell growth, showed significant changes in transcript abundance following exposure to elevated temperatures. Changes in transcript abundance were validated for a selection of candidate genes using quantitative real‐time polymerase chain reaction. This study demonstrates that heterologous microarrays can be successfully employed to study species for which specific microarrays have not yet been developed, and so have the potential to greatly enhance the utility of microarray technology to the field of environmental and functional genomics.

Development and validation of a novel molecular biomarker diagnostic test for the early detection of sepsis

IntroductionSepsis is a complex immunological response to infection characterized by early hyper-inflammation followed by severe and protracted immunosuppression, suggesting that a multi-marker approach has the greatest clinical utility for early detection, within a clinical environment focused on Systemic Inflammatory Response Syndrome (SIRS) differentiation. Pre-clinical research using an equine sepsis model identified a panel of gene expression biomarkers that define the early aberrant immune activation. Thus, the primary objective was to apply these gene expression biomarkers to distinguish patients with sepsis from those who had undergone major open surgery and had clinical outcomes consistent with systemic inflammation due to physical trauma and wound healing.MethodsThis was a multi-centre, prospective clinical trial conducted across four tertiary critical care settings in Australia. Sepsis patients were recruited if they met the 1992 Consensus Statement criteria and had clinical evidence of systemic infection based on microbiology diagnoses (n = 27). Participants in the post-surgical (PS) group were recruited pre-operatively and blood samples collected within 24 hours following surgery (n = 38). Healthy controls (HC) included hospital staff with no known concurrent illnesses (n = 20). Each participant had minimally 5 ml of PAXgene blood collected for leucocyte RNA isolation and gene expression analyses. Affymetrix array and multiplex tandem (MT)-PCR studies were conducted to evaluate transcriptional profiles in circulating white blood cells applying a set of 42 molecular markers that had been identified a priori. A LogitBoost algorithm was used to create a machine learning diagnostic rule to predict sepsis outcomes.ResultsBased on preliminary microarray analyses comparing HC and sepsis groups, a panel of 42-gene expression markers were identified that represented key innate and adaptive immune function, cell cycling, WBC differentiation, extracellular remodelling and immune modulation pathways. Comparisons against GEO data confirmed the definitive separation of the sepsis cohort. Quantitative PCR results suggest the capacity for this test to differentiate severe systemic inflammation from HC is 92%. The area under the curve (AUC) receiver operator characteristics (ROC) curve findings demonstrated sepsis prediction within a mixed inflammatory population, was between 86 and 92%.ConclusionsThis novel molecular biomarker test has a clinically relevant sensitivity and specificity profile, and has the capacity for early detection of sepsis via the monitoring of critical care patients.

IQGAP1 and IGFBP2: Valuable Biomarkers for Determining Prognosis in Glioma Patients

Abstract Clinical treatment decisions and the survival outcomes of patients with gliomas are directly impacted by accurate tumor classification. New and more reliable prognostic markers are needed to better identify the variable duration of survival among histologically defined glioma grades. Microarray expression analysis and immunohistochemistry were used to identify biomarkers associated with gliomas with more aggressive biologic behaviors. The protein expression of IQGAP1 and IGFBP2, when used in conjunction with the World Health Organization grading system, readily identified and defined a subgroup of patients with grade III gliomas whose prognosis was poor. In addition, in patients with glioblastoma multiforme, in whom IQGAP1 and IGFBP2 were absent, long-term survival of more than 3 years was observed. The use of these markers confirmed a nonuniform distribution of survival in those with World Health Organization grade III and IV tumors. Thus, IQGAP1 and IGFBP2 immunostaining supplements current histologic grading by offering additional prognostic and predictive information.

From transcriptome to biological function: environmental stress in an ectothermic vertebrate, the coral reef fish Pomacentrus moluccensis

BackgroundOur understanding of the importance of transcriptional regulation for biological function is continuously improving. We still know, however, comparatively little about how environmentally induced stress affects gene expression in vertebrates, and the consistency of transcriptional stress responses to different types of environmental stress. In this study, we used a multi-stressor approach to identify components of a common stress response as well as components unique to different types of environmental stress. We exposed individuals of the coral reef fish Pomacentrus moluccensis to hypoxic, hyposmotic, cold and heat shock and measured the responses of approximately 16,000 genes in liver. We also compared winter and summer responses to heat shock to examine the capacity for such responses to vary with acclimation to different ambient temperatures.ResultsWe identified a series of gene functions that were involved in all stress responses examined here, suggesting some common effects of stress on biological function. These common responses were achieved by the regulation of largely independent sets of genes; the responses of individual genes varied greatly across different stress types. In response to heat exposure over five days, a total of 324 gene loci were differentially expressed. Many heat-responsive genes had functions associated with protein turnover, metabolism, and the response to oxidative stress. We were also able to identify groups of co-regulated genes, the genes within which shared similar functions.ConclusionThis is the first environmental genomic study to measure gene regulation in response to different environmental stressors in a natural population of a warm-adapted ectothermic vertebrate. We have shown that different types of environmental stress induce expression changes in genes with similar gene functions, but that the responses of individual genes vary between stress types. The functions of heat-responsive genes suggest that prolonged heat exposure leads to oxidative stress and protein damage, a challenge of the immune system, and the re-allocation of energy sources. This study hence offers insight into the effects of environmental stress on biological function and sheds light on the expected sensitivity of coral reef fishes to elevated temperatures in the future.

Pharmacodynamics of growth hormone abuse biomarkers and the influence of gender and testosterone: a randomized double-blind placebo-controlled study in young recreational athletes.

CONTEXT IGF axis proteins and collagen peptides are promising markers of GH abuse. OBJECTIVE Our objective was to investigate whether responses of serum IGF axis and collagen markers to GH differ between men and women, and are influenced by testosterone (T). DESIGN This was a randomized, double-blind, placebo-controlled study of 8-wk treatment followed by 6-wk washout. SETTING The study was performed at a clinical research facility. PARTICIPANTS A total of 96 recreationally trained healthy athletes (63 men, 33 women), aged 18-40 yr, were studied. INTERVENTION All subjects received GH (2 mg/d sc) or placebo for 8 wk; men also received T (250 mg/wk im) or placebo for 5 wk. MAIN OUTCOME MEASURES Serum IGF axis proteins (IGF-I, IGF binding protein-3, and acid labile subunit) and collagen peptides (N-terminal propeptide of type I procollagen, C-terminal telopeptide of type I collagen, and N-terminal propeptide of type III procollagen) were measured. RESULTS GH induced significant increases in IGF axis and collagen markers that were greater in men than women (P < 0.001). Of the IGF axis markers, IGF-I showed the greatest increase. The relative incremental responses of the collagen markers in general were greater than the IGF markers, especially for PIIINP. The collagen markers increased and decreased more slowly with most remaining elevated (P < 0.01) after 6 wk, in comparison to IGF markers, which returned to baseline within 1 wk. Addition of T to GH amplified the response of PIIINP by more than 1.5-fold but did not affect any other marker. T alone did not affect IGF axis markers but modestly increased collagen markers. CONCLUSIONS These markers of GH abuse are less responsive in women. The increases in collagen markers have a different time course to the IGF markers and extend the window of detection in both sexes. The response of PIIINP is increased by coadministration of T.

Strong and Consistently Synergistic Inactivation of Spores of Spoilage- Associated Bacillus and Geobacillus spp. by High Pressure and Heat Compared with Inactivation by Heat Alone

ABSTRACT The inactivation of spores of four low-acid food spoilage organisms by high pressure thermal (HPT) and thermal-only processing was compared on the basis of equivalent thermal lethality calculated at a reference temperature of 121.1°C (F z 121.1 ° C, 0.1 MPa or 600 MPa) and characterized as synergistic, not different or protective. In addition, the relative resistances of spores of the different spoilage microorganisms to HPT processing were compared. Processing was performed and inactivation was compared in both laboratory and pilot scale systems and in model (diluted) and actual food products. Where statistical comparisons could be made, at least 4 times and up to around 190 times more inactivation (log10 reduction/minute at F T z 121.1 ° C) of spores of Bacillus amyloliquefaciens, Bacillus sporothermodurans, and Geobacillus stearothermophilus was achieved using HPT, indicating a strong synergistic effect of high pressure and heat. Bacillus coagulans spores were also synergistically inactivated in diluted and undiluted Bolognese sauce but were protected by pressure against thermal inactivation in undiluted cream sauce. Irrespective of the response characterization, B. coagulans and B. sporothermodurans were identified as the most HPT-resistant isolates in the pilot scale and laboratory scale studies, respectively, and G. stearothermophilus as the least in both studies and all products. This is the first study to comprehensively quantitatively characterize the responses of a range of spores of spoilage microorganisms as synergistic (or otherwise) using an integrated thermal-lethality approach (F T z ). The use of the F T z approach is ultimately important for the translation of commercial minimum microbiologically safe and stable thermal processes to HPT processes.

Detection of growth hormone doping by gene expression profiling of peripheral blood.

CONTEXT GH abuse is a significant problem in many sports, and there is currently no robust test that allows detection of doping beyond a short window after administration. OBJECTIVE Our objective was to evaluate gene expression profiling in peripheral blood leukocytes in-vivo as a test for GH doping in humans. DESIGN Seven men and thirteen women were administered GH, 2 mg/d sc for 8 wk. Blood was collected at baseline and at 8 wk. RNA was extracted from the white cell fraction. Microarray analysis was undertaken using Agilent 44K G4112F arrays using a two-color design. Quantitative RT-PCR using TaqMan gene expression assays was performed for validation of selected differentially expressed genes. RESULTS GH induced an approximately 2-fold increase in circulating IGF-I that was maintained throughout the 8 wk of the study. GH induced significant changes in gene expression with 353 in women and 41 in men detected with a false discovery rate of less than 5%. None of the differentially expressed genes were common between men and women. The maximal changes were a doubling for up-regulated or halving for down-regulated genes, similar in magnitude to the variation between individuals. Quantitative RT-PCR for seven target genes showed good concordance between microarray and quantitative PCR data in women but not in men. CONCLUSION Gene expression analysis of peripheral blood leukocytes is unlikely to be a viable approach for the detection of GH doping.

Parameter estimation for robust HMM analysis of ChIP-chip data

BackgroundTiling arrays are an important tool for the study of transcriptional activity, protein-DNA interactions and chromatin structure on a genome-wide scale at high resolution. Although hidden Markov models have been used successfully to analyse tiling array data, parameter estimation for these models is typically ad hoc. Especially in the context of ChIP-chip experiments, no standard procedures exist to obtain parameter estimates from the data. Common methods for the calculation of maximum likelihood estimates such as the Baum-Welch algorithm or Viterbi training are rarely applied in the context of tiling array analysis.ResultsHere we develop a hidden Markov model for the analysis of chromatin structure ChIP-chip tiling array data, using t emission distributions to increase robustness towards outliers. Maximum likelihood estimates are used for all model parameters. Two different approaches to parameter estimation are investigated and combined into an efficient procedure.ConclusionWe illustrate an efficient parameter estimation procedure that can be used for HMM based methods in general and leads to a clear increase in performance when compared to the use of ad hoc estimates. The resulting hidden Markov model outperforms established methods like TileMap in the context of histone modification studies.