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Crispin J Miller

Publications and source records attributed to Crispin J Miller.

7 recordsLinked to original sources

Hybridization interactions between probesets in short oligo microarrays lead to spurious correlations.

BACKGROUND: Microarrays measure the binding of nucleotide sequences to a set of sequence specific probes. This information is combined with annotation specifying the relationship between probes and targets and used to make inferences about transcript- and, ultimately, gene expression. In some situations, a probe is capable of hybridizing to more than one transcript, in others, multiple probes can target a single sequence. These 'multiply targeted' probes can result in non-independence between measured expression levels. RESULTS: An analysis of these relationships for Affymetrix arrays considered both the extent and influence of exact matches between probe and transcript sequences. For the popular HGU133A array, approximately half of the probesets were found to interact in this way. Both real and simulated expression datasets were used to examine how these effects influenced the expression signal. It was found not only to lead to increased signal strength for the affected probesets, but the major effect is to significantly increase their correlation, even in situations when only a single probe from a probeset was involved. By building a network of probe-probeset-transcript relationships, it is possible to identify families of interacting probesets. More than 10% of the families contain members annotated to different genes or even different Unigene clusters. Within a family, a mixture of genuine biological and artefactual correlations can occur. CONCLUSION: Multiple targeting is not only prevalent, but also significant. The ability of probesets to hybridize to more than one gene product can lead to false positives when analysing gene expression. Comprehensive annotation describing multiple targeting is required when interpreting array data.

Artifacts↗

Quantitative proteomics reveals posttranslational control as a regulatory factor in primary hematopoietic stem cells.

The proteome is determined by rates of transcription, translation, and protein turnover. Definition of stem cell populations therefore requires a stem cell proteome signature. However, the limit to the number of primary cells available has restricted extensive proteomic analysis. We present a mass spectrometric method using an isobaric covalent modification of peptides for relative quantification (iTRAQ), which was employed to compare the proteomes of approximately 1 million long-term reconstituting hematopoietic stem cells (Lin(-)Sca(+)Kit(+); LSK(+)) and non-long-term reconstituting progenitor cells (Lin(-)Sca(+)Kit(-); LSK(-)), respectively. Extensive 2-dimensional liquid chromatography (LC) peptide separation prior to mass spectrometry (MS) enabled enhanced proteome coverage with relative quantification of 948 proteins. Of the 145 changes in the proteome, 54% were not seen in the transcriptome. Hypoxia-related changes in proteins controlling metabolism and oxidative protection were observed, indicating that LSK(+) cells are adapted for anaerobic environments. This approach can define proteomic changes in primary samples, thereby characterizing the molecular signature of stem cells and their progeny.

Animals↗

Simpleaffy: a BioConductor package for Affymetrix Quality Control and data analysis.

UNLABELLED: Quality Control is a fundamental aspect of successful microarray data analysis. Simpleaffy is a BioConductor package that provides access to a variety of QC metrics for assessing the quality of RNA samples and of the intermediate stages of sample preparation and hybridization. Simpleaffy also offers fast implementations of popular algorithms for generating expression summaries and detection calls. AVAILABILITY: Simpleaffy can be downloaded from http://www.bioconductor.org. SUPPLEMENTARY INFORMATION: Additional information can be found on the supplementary website located at http://bioinformatics.picr.man.ac.uk.

Computational Biology↗

ADAPT: a database of affymetrix probesets and transcripts.

UNLABELLED: ADAPT is an online database providing comprehensive mappings between Affymetrix probes and RefSeq and Ensembl transcripts. ADAPT was designed to help interpret microarray experiments by providing a means to explore the many-to-many relationships that exist between probes, probesets, transcripts and genes. AVAILABILITY: ADAPT can be queried via the web at http://bioinformatics.picr.man.ac.uk/adapt

DNA Probes↗

Amplification protocols introduce systematic but reproducible errors into gene expression studies.

The desire to perform microarray experiments with small starting amounts of RNA has led to the development of a variety of protocols for preparing and amplifying mRNA. This has consequences not only for the standardization of experimental design, but also for reproducibility and comparability between experiments. Here we investigate the differences between the Affymetrix standard and small sample protocols and address the data analysis issues that arise when comparing samples and experiments that have been processed in different ways. We show that data generated on the same platform using different protocols are not directly comparable. Further, protocols introduce systematic biases that can be largely accounted for by using the correct data analysis techniques.

Data Interpretation, Statistical↗

Bioinformatics goes back to the future.

The need to turn raw data into knowledge has led the bioinformatics field to focus increasingly on the manipulation of information. By drawing parallels with both cryptography and artificial intelligence, we can develop an understanding of the changes that are occurring in bioinformatics, and how these changes are likely to influence the bioinformatics job market.

Artificial Intelligence↗