Human genome sequencing using unchained base reads on self-assembling DNA nanoarrays.

Genome sequencing of large numbers of individuals promises to advance the understanding, treatment, and prevention of human diseases, among other applications. We describe a genome sequencing platform that achieves efficient imaging and low reagent consumption with combinatorial probe anchor ligation chemistry to independently assay each base from patterned nanoarrays of self-assembling DNA nanoballs. We sequenced three human genomes with this platform, generating an average of 45- to 87-fold coverage per genome and identifying 3.2 to 4.5 million sequence variants per genome. Validation of one genome data set demonstrates a sequence accuracy of about 1 false variant per 100 kilobases. The high accuracy, affordable cost of $4400 for sequencing consumables, and scalability of this platform enable complete human genome sequencing for the detection of rare variants in large-scale genetic studies.

In vivo transcriptional profile analysis reveals RNA splicing and chromatin remodeling as prominent processes for adult neurogenesis.

Neural stem cells and neurogenesis persist in the adult mammalian brain subventricular zone (SVZ). Cells born in the rodent SVZ migrate to the olfactory bulb (Ob) where they differentiate into interneurons. To determine the gene expression and functional profile of SVZ neurogenesis, we performed three complementary sets of transcriptional analysis experiments using Affymetrix GeneChips: (1) comparison of adult mouse SVZ and Ob gene expression profiles with those of the striatum, cerebral cortex, and hippocampus; (2) profiling of SVZ stem cells and ependyma isolated by fluorescent-activated cell sorting (FACS); and (3) analysis of gene expression changes during in vivo SVZ regeneration after anti-mitotic treatment. Gene Ontology (GO) analysis of data from these three separate approaches showed that in adult SVZ neurogenesis, RNA splicing and chromatin remodeling are biological processes as statistically significant as cell proliferation, transcription, and neurogenesis. In non-neurogenic brain regions, RNA splicing and chromatin remodeling were not prominent processes. Fourteen mRNA splicing factors including Sf3b1, Sfrs2, Lsm4, and Khdrbs1/Sam68 were detected along with 9 chromatin remodeling genes including Mll, Bmi1, Smarcad1, Baf53a, and Hat1. We validated the transcriptional profile data with Northern blot analysis and in situ hybridization. The data greatly expand the catalogue of cell cycle components, transcription factors, and migration genes for adult SVZ neurogenesis and reveal RNA splicing and chromatin remodeling as prominent biological processes for these germinal cells.

Empirical characterization of the expression ratio noise structure in high-density oligonucleotide arrays.

BACKGROUND: High-density oligonucleotide arrays (HDONAs) are a powerful tool for assessing differential mRNA expression levels. To establish the statistical significance of an observed change in expression, one must take into account the noise introduced by the enzymatic and hybridization steps, called type I noise. We undertake an empirical characterization of the experimental repeatability of results by carrying out statistical analysis of a large number of duplicate HDONA experiments. RESULTS: We assign scoring functions for expression ratios and associated quality measures. Both the perfect-match (PM) probes and the differentials between PM and single-mismatch (MM) probes are considered as raw intensities. We then calculate the log-ratio of the noise structure using robust estimates of their intensity-dependent variance. The noise structure in the log-ratios follows a local log-normal distribution in both the PM and PM-MM cases. Significance relative to the type I noise can therefore be quantified reliably using the local standard deviation (SD). We discuss the intensity dependence of the SD and show that ratio scores greater than 1.25 are significant in the mid- to high-intensity range. CONCLUSIONS: The noise inherent in HDONAs is characteristically dependent on intensity and can be well described in terms of local normalization of log-ratio distributions. Therefore, robust estimates of the local SD of these distributions provide a simple and powerful way to assess significance (relative to type I noise) in differential gene expression, and will be helpful in practice for improving the reliability of predictions from hybridization experiments.

Characterization of the expression ratio noise structure in high-density oligonucleotide arrays.

BACKGROUND: High-density oligonucleotide microarrays provide a powerful tool for assessing differential mRNA expression levels. Characterizing the noise resulting from the enzymatic and hybridization steps, called type I noise, is essential for attributing significance measures to the differential expression scores. We introduce scoring functions for expression ratios, and associated quality measures. Both the PM (Perfect Match) probes and PM-MM differentials (MM is the single MisMatch) are considered as raw intensities. We then characterize the log-ratio noise structure using robust estimates of their intensity dependent variance. RESULTS: We show the relationships between the obtained ratios and their quality measures. The complementarity of PM and PM-MM methods is emphasized by the probe sets signal to noise measures. Using a large set of replicate experiments, we demonstrate that the noise structure in the log-ratios very closely follows a local log-normal distribution for both the PM and PM-MM cases. Therefore, significance relative to the type I noise can be quantified reliably using the local STD. We discuss the intensity dependence of the STD and show that ratio scores >1.25 are significant in the mid- to high-intensity range. CONCLUSIONS: The ratio noise structure inherent to high-density oligonucleotide arrays can be well described in terms of local log-normal ratio distributions with characteristic intensity dependence. Therefore, robust estimates of the local STD of these distributions provide a simple and powerful way for assessing significance (relative to type I noise) in differential gene expression. This approach will be helpful for improving the reliability of predictions from hybridization experiments in general.