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.

Genomic homeostasis is dysregulated in favour of apoptosis in the colonic epithelium of the azoxymethane treated rat.

BACKGROUND: The acute response to genotoxic carcinogens in rats is an important model for researching cancer initiation events. In this report we define the normal rat colonic epithelium by describing transcriptional events along the anterior-posterior axis and then investigate the acute effects of azoxymethane (AOM) on gene expression, with a particular emphasis on pathways associated with the maintenance of genomic integrity in the proximal and distal compartments using whole genome expression microarrays. RESULTS: There are large transcriptional changes that occur in epithelial gene expression along the anterior-posterior axis of the normal healthy rat colon. AOM administration superimposes substantial changes on these basal gene expression patterns in both the distal and proximal rat colonic epithelium. In particular, the pathways associated with cell cycle and DNA damage and repair processes appear to be disrupted in favour of apoptosis. CONCLUSIONS: The healthy rats' colon exhibits extensive gene expression changes between its proximal and distal ends. The most common changes are associated with metabolism, but more subtle expression changes in genes involved in genomic homeostasis are also evident. These latter changes presumably protect and maintain a healthy colonic epithelium against incidental dietary and environmental insults. AOM induces substantial changes in gene expression, resulting in an early switch in the cell cycle process, involving p53 signalling, towards cell cycle arrest leading to the more effective process of apoptosis to counteract this genotoxic insult.

k-link EST clustering: evaluating error introduced by chimeric sequences under different degrees of linkage.

MOTIVATION: The clustering of expressed sequence tags (ESTs) is a crucial step in many sequence analysis studies that require a high level of redundancy. Chimeric sequences, while uncommon, can make achieving the optimal EST clustering a challenge. Single-linkage algorithms are particularly vulnerable to the effects of chimeras. To avoid chimera-facilitated erroneous merges, researchers using single-linkage algorithms are forced to use stringent sequence-similarity thresholds. Such thresholds reduce the sensitivity of the clustering algorithm. RESULTS: We introduce the concept of k-link clustering for EST data. We evaluate how clustering error rates vary over a range of linkage thresholds. Using k-link, we show that Type II error decreases in response to increasing the number of shared ESTs (ie. links) required. We observe a base level of Type II error likely caused by the presence of unmasked low-complexity or repetitive sequence. We find that Type I error increases gradually with increased linkage. To minimize the Type I error introduced by increased linkage requirements, we propose an extension to k-link which modifies the required number of links with respect to the size of clusters being compared. AVAILABILITY: The implementation of k-link is available under the terms of the GPL from http://www.bioinformatics.csiro.au/products.shtml. k-link is licensed under the GNU General Public License, and can be downloaded from http://www.bioinformatics.csiro.au/products.shtml. k-link is written in C++.