AQME: A forensic mitochondrial DNA analysis tool for next-generation sequencing data.

The feasibility of generating mitochondrial DNA (mtDNA) data has expanded considerably with the advent of next-generation sequencing (NGS), specifically in the generation of entire mtDNA genome (mitogenome) sequences. However, the analysis of these data has emerged as the greatest challenge to implementation in forensics. To address this need, a custom toolkit for use in the CLC Genomics Workbench (QIAGEN, Hilden, Germany) was developed through a collaborative effort between the Armed Forces Medical Examiner System - Armed Forces DNA Identification Laboratory (AFMES-AFDIL) and QIAGEN Bioinformatics. The AFDIL-QIAGEN mtDNA Expert, or AQME, generates an editable mtDNA profile that employs forensic conventions and includes the interpretation range required for mtDNA data reporting. AQME also integrates an mtDNA haplogroup estimate into the analysis workflow, which provides the analyst with phylogenetic nomenclature guidance and a profile quality check without the use of an external tool. Supplemental AQME outputs such as nucleotide-per-position metrics, configurable export files, and an audit trail are produced to assist the analyst during review. AQME is applied to standard CLC outputs and thus can be incorporated into any mtDNA bioinformatics pipeline within CLC regardless of sample type, library preparation or NGS platform. An evaluation of AQME was performed to demonstrate its functionality and reliability for the analysis of mitogenome NGS data. The study analyzed Illumina mitogenome data from 21 samples (including associated controls) of varying quality and sample preparations with the AQME toolkit. A total of 211 tool edits were automatically applied to 130 of the 698 total variants reported in an effort to adhere to forensic nomenclature. Although additional manual edits were required for three samples, supplemental tools such as mtDNA haplogroup estimation assisted in identifying and guiding these necessary modifications to the AQME-generated profile. Along with profile generation, AQME reported accurate haplogroups for 18 of the 19 samples analyzed. The single errant haplogroup assignment, although phylogenetically close, identified a bug that only affects partial mitogenome data. Future adjustments to AQME's haplogrouping tool will address this bug as well as enhance the overall scoring strategy to better refine and automate haplogroup assignments. As NGS enables broader use of the mtDNA locus in forensics, the availability of AQME and other forensic-focused mtDNA analysis tools will ease the transition and further support mitogenome analysis within routine casework. Toward this end, the AFMES-AFDIL has utilized the AQME toolbox in conjunction with the CLC Genomics Workbench to successfully validate and implement two NGS mitogenome methods.

Effects of selenomethionine on the gene expression profile of cloned human prostate cancer cells representing a phenotypic continuum of cancer progression.

We previously characterized three cell clones that were derived by limiting dilution from a human prostate cancer cell line (LNCaP) representing a phenotypic continuum of cancer progression (1). The present study was undertaken to examine the effects of L-selenomethionine (SeM), a potential cancer chemopreventive agent, on the gene expression profile of the cultured cell clones. Following a three-day incubation period with SeM, total RNA was extracted, and the gene expression profile was evaluated using Affymetrix human HG U133A microarrays and analyzed by ViaLogy's (Altadena, CA) VMAxS platform deploying quantum resonance interferometry (QRI) processing. The differentially expressed genes and corresponding biological processes were compared across the different treatments and cell types. Whereas SeM significantly affected RNA-DNA metabolism and protein transport and metabolism in all of the cell types evaluated, significant effects of SeM on genes mainly involved in the pathways of cell cycle, growth, differentiation, and apoptosis were observed only in the cell clone with a more malignant phenotype.

The balance of reproducibility, sensitivity, and specificity of lists of differentially expressed genes in microarray studies.

BACKGROUND: Reproducibility is a fundamental requirement in scientific experiments. Some recent publications have claimed that microarrays are unreliable because lists of differentially expressed genes (DEGs) are not reproducible in similar experiments. Meanwhile, new statistical methods for identifying DEGs continue to appear in the scientific literature. The resultant variety of existing and emerging methods exacerbates confusion and continuing debate in the microarray community on the appropriate choice of methods for identifying reliable DEG lists. RESULTS: Using the data sets generated by the MicroArray Quality Control (MAQC) project, we investigated the impact on the reproducibility of DEG lists of a few widely used gene selection procedures. We present comprehensive results from inter-site comparisons using the same microarray platform, cross-platform comparisons using multiple microarray platforms, and comparisons between microarray results and those from TaqMan - the widely regarded "standard" gene expression platform. Our results demonstrate that (1) previously reported discordance between DEG lists could simply result from ranking and selecting DEGs solely by statistical significance (P) derived from widely used simple t-tests; (2) when fold change (FC) is used as the ranking criterion with a non-stringent P-value cutoff filtering, the DEG lists become much more reproducible, especially when fewer genes are selected as differentially expressed, as is the case in most microarray studies; and (3) the instability of short DEG lists solely based on P-value ranking is an expected mathematical consequence of the high variability of the t-values; the more stringent the P-value threshold, the less reproducible the DEG list is. These observations are also consistent with results from extensive simulation calculations. CONCLUSION: We recommend the use of FC-ranking plus a non-stringent P cutoff as a straightforward and baseline practice in order to generate more reproducible DEG lists. Specifically, the P-value cutoff should not be stringent (too small) and FC should be as large as possible. Our results provide practical guidance to choose the appropriate FC and P-value cutoffs when selecting a given number of DEGs. The FC criterion enhances reproducibility, whereas the P criterion balances sensitivity and specificity.

Evaluation of DNA microarray results with quantitative gene expression platforms.

We have evaluated the performance characteristics of three quantitative gene expression technologies and correlated their expression measurements to those of five commercial microarray platforms, based on the MicroArray Quality Control (MAQC) data set. The limit of detection, assay range, precision, accuracy and fold-change correlations were assessed for 997 TaqMan Gene Expression Assays, 205 Standardized RT (Sta)RT-PCR assays and 244 QuantiGene assays. TaqMan is a registered trademark of Roche Molecular Systems, Inc. We observed high correlation between quantitative gene expression values and microarray platform results and found few discordant measurements among all platforms. The main cause of variability was differences in probe sequence and thus target location. A second source of variability was the limited and variable sensitivity of the different microarray platforms for detecting weakly expressed genes, which affected interplatform and intersite reproducibility of differentially expressed genes. From this analysis, we conclude that the MAQC microarray data set has been validated by alternative quantitative gene expression platforms thus supporting the use of microarray platforms for the quantitative characterization of gene expression.

Using RNA sample titrations to assess microarray platform performance and normalization techniques.

We have assessed the utility of RNA titration samples for evaluating microarray platform performance and the impact of different normalization methods on the results obtained. As part of the MicroArray Quality Control project, we investigated the performance of five commercial microarray platforms using two independent RNA samples and two titration mixtures of these samples. Focusing on 12,091 genes common across all platforms, we determined the ability of each platform to detect the correct titration response across the samples. Global deviations from the response predicted by the titration ratios were observed. These differences could be explained by variations in relative amounts of messenger RNA as a fraction of total RNA between the two independent samples. Overall, both the qualitative and quantitative correspondence across platforms was high. In summary, titration samples may be regarded as a valuable tool, not only for assessing microarray platform performance and different analysis methods, but also for determining some underlying biological features of the samples.

The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements.

Over the last decade, the introduction of microarray technology has had a profound impact on gene expression research. The publication of studies with dissimilar or altogether contradictory results, obtained using different microarray platforms to analyze identical RNA samples, has raised concerns about the reliability of this technology. The MicroArray Quality Control (MAQC) project was initiated to address these concerns, as well as other performance and data analysis issues. Expression data on four titration pools from two distinct reference RNA samples were generated at multiple test sites using a variety of microarray-based and alternative technology platforms. Here we describe the experimental design and probe mapping efforts behind the MAQC project. We show intraplatform consistency across test sites as well as a high level of interplatform concordance in terms of genes identified as differentially expressed. This study provides a resource that represents an important first step toward establishing a framework for the use of microarrays in clinical and regulatory settings.

Interchromosomal segmental duplications explain the unusual structure of PRSS3, the gene for an inhibitor-resistant trypsinogen.

Homo sapiens possess several trypsinogen or trypsinogen-like genes of which three (PRSS1, PRSS2, and PRSS3) produce functional trypsins in the digestive tract. PRSS1 and PRSS2 are located on chromosome 7q35, while PRSS3 is found on chromosome 9p13. Here, we report a variation of the theme of new gene creation by duplication: the PRSS3 gene was formed by segmental duplications originating from chromosomes 7q35 and 11q24. As a result, PRSS3 transcripts display two variants of exon 1. The PRSS3 transcript whose gene organization most resembles PRSS1 and PRSS2 encodes a functional protein originally named mesotrypsinogen. The other variant is a fusion transcript, called trypsinogen IV. We show that the first exon of trypsinogen IV is derived from the noncoding first exon of LOC120224, a chromosome 11 gene. LOC120224 codes for a widely conserved transmembrane protein of unknown function. Comparative analyses suggest that these interchromosomal duplications occurred after the divergence of Old World monkeys and hominids. PRSS3 transcripts consist of a mixed population of mRNAs, some expressed in the pancreas and encoding an apparently functional trypsinogen and others of unknown function expressed in brain and a variety of other tissues. Analysis of the selection pressures acting on the trypsinogen gene family shows that, while the apparently functional genes are under mild to strong purifying selection overall, a few residues appear under positive selection. These residues could be involved in interactions with inhibitors.

Genomic analysis of the olfactory receptor region of the mouse and human T-cell receptor alpha/delta loci.

We have conducted a comparative genomic analysis of several olfactory receptor (OR) genes that lie immediately 5' to the V-alpha gene segments at the mouse and human T-cell receptor (TCR) alpha/delta loci. Five OR genes are identified in the human cluster. The murine cluster has at least six OR genes; the first five are orthologous to the human genes. The sixth mouse gene has arisen since mouse-human divergence by a duplication of a approximately 10-kb block. One pair of OR paralogs found at the mouse and human loci are more similar to each other than to their corresponding orthologs. This paralogous "twinning" appears to be under selection, perhaps to increase sensitivity to particular odorants or to resolve structurally-similar odorants. The promoter regions of the mouse OR genes were identified by RACE-PCR. Orthologs share extensive 5' UTR homology, but we find no significant similarity among paralogs. These findings extend previous observations that suggest that OR genes do not share local significant regulatory homology despite having a common regulatory agenda. We also identified a diverged TCR-alpha gene segment that uses a divergent recombination signal sequence (RSS) to initiate recombination in T-cells from within the OR region. We explored the hypothesis that OR genes may use DNA recombination in expressing neurons, e.g., to recombine ORs into a transcriptionally active locus. We searched the mouse sequence for OR-flanking RSS motifs, but did not find evidence to suggest that these OR genes use TCR-like recombination target sequences.