Knowledge-based analysis of microarray gene expression data by using support vector machines

We introduce a method of functionally classifying genes by using gene expression data from DNA microarray hybridization experiments. The method is based on the theory of support vector machines (SVMs). SVMs are considered a supervised computer learning method because they exploit prior knowledge of gene function to identify unknown genes of similar function from expression data. SVMs avoid several problems associated with unsupervised clustering methods, such as hierarchical clustering and self-organizing maps. SVMs have many mathematical features that make them attractive for gene expression analysis, including their flexibility in choosing a similarity function, sparseness of solution when dealing with large data sets, the ability to handle large feature spaces, and the ability to identify outliers. We test several SVMs that use different similarity metrics, as well as some other supervised learning methods, and find that the SVMs best identify sets of genes with a common function using expression data. Finally, we use SVMs to predict functional roles for uncharacterized yeast ORFs based on their expression data.

Importance of replication in microarray gene expression studies: Statistical methods and evidence from repetitive cDNA hybridizations

We present statistical methods for analyzing replicated cDNA microarray expression data and report the results of a controlled experiment. The study was conducted to investigate inherent variability in gene expression data and the extent to which replication in an experiment produces more consistent and reliable findings. We introduce a statistical model to describe the probability that mRNA is contained in the target sample tissue, converted to probe, and ultimately detected on the slide. We also introduce a method to analyze the combined data from all replicates. Of the 288 genes considered in this controlled experiment, 32 would be expected to produce strong hybridization signals because of the known presence of repetitive sequences within them. Results based on individual replicates, however, show that there are 55, 36, and 58 highly expressed genes in replicates 1, 2, and 3, respectively. On the other hand, an analysis by using the combined data from all 3 replicates reveals that only 2 of the 288 genes are incorrectly classified as expressed. Our experiment shows that any single microarray output is subject to substantial variability. By pooling data from replicates, we can provide a more reliable analysis of gene expression data. Therefore, we conclude that designing experiments with replications will greatly reduce misclassification rates. We recommend that at least three replicates be used in designing experiments by using cDNA microarrays, particularly when gene expression data from single specimens are being analyzed.

The Stanford Microarray Database

The Stanford Microarray Database (SMD) stores raw and normalized data from microarray experiments, and provides web interfaces for researchers to retrieve, analyze and visualize their data. The two immediate goals for SMD are to serve as a storage site for microarray data from ongoing research at Stanford University, and to facilitate the public dissemination of that data once published, or released by the researcher. Of paramount importance is the connection of microarray data with the biological data that pertains to the DNA deposited on the microarray (genes, clones etc.). SMD makes use of many public resources to connect expression information to the relevant biology, including SGD [Ball,C.A., Dolinski,K., Dwight,S.S., Harris,M.A., Issel-Tarver,L., Kasarskis,A., Scafe,C.R., Sherlock,G., Binkley,G., Jin,H. et al. (2000) Nucleic Acids Res., 28, 77–80], YPD and WormPD [Costanzo,M.C., Hogan,J.D., Cusick,M.E., Davis,B.P., Fancher,A.M., Hodges,P.E., Kondu,P., Lengieza,C., Lew-Smith,J.E., Lingner,C. et al. (2000) Nucleic Acids Res., 28, 73–76], Unigene [Wheeler,D.L., Chappey,C., Lash,A.E., Leipe,D.D., Madden,T.L., Schuler,G.D., Tatusova,T.A. and Rapp,B.A. (2000) Nucleic Acids Res., 28, 10–14], dbEST [Boguski,M.S., Lowe,T.M. and Tolstoshev,C.M. (1993) Nature Genet., 4, 332–333] and SWISS-PROT [Bairoch,A. and Apweiler,R. (2000) Nucleic Acids Res., 28, 45–48] and can be accessed at http://genome-www.stanford.edu/microarray.

Amygdala-enriched genes identified by microarray technology are restricted to specific amygdaloid subnuclei

Microarray technology represents a potentially powerful method for identifying cell type- and regionally restricted genes expressed in the brain. Here we have combined a microarray analysis of differential gene expression among five selected brain regions, including the amygdala, cerebellum, hippocampus, olfactory bulb, and periaqueductal gray, with in situ hybridization. On average, 0.3% of the 34,000 genes interrogated were highly enriched in each of the five regions, relative to the others. In situ hybridization performed on a subset of amygdala-enriched genes confirmed in most cases the overall region-specificity predicted by the microarray data and identified additional sites of brain expression not examined on the microarrays. Strikingly, the majority of these genes exhibited boundaries of expression within the amygdala corresponding to cytoarchitectonically defined subnuclei. These results define a unique set of molecular markers for amygdaloid subnuclei and provide tools to genetically dissect their functional roles in different emotional behaviors.

Statistical modeling of large microarray data sets to identify stimulus-response profiles

A statistical modeling approach is proposed for use in searching large microarray data sets for genes that have a transcriptional response to a stimulus. The approach is unrestricted with respect to the timing, magnitude or duration of the response, or the overall abundance of the transcript. The statistical model makes an accommodation for systematic heterogeneity in expression levels. Corresponding data analyses provide gene-specific information, and the approach provides a means for evaluating the statistical significance of such information. To illustrate this strategy we have derived a model to depict the profile expected for a periodically transcribed gene and used it to look for budding yeast transcripts that adhere to this profile. Using objective criteria, this method identifies 81% of the known periodic transcripts and 1,088 genes, which show significant periodicity in at least one of the three data sets analyzed. However, only one-quarter of these genes show significant oscillations in at least two data sets and can be classified as periodic with high confidence. The method provides estimates of the mean activation and deactivation times, induced and basal expression levels, and statistical measures of the precision of these estimates for each periodic transcript.

Host microarray analysis reveals a role for the Salmonella response regulator phoP in human macrophage cell death

Bacterial pathogens manipulate host cells to promote pathogen survival and dissemination. We used a 22,571 human cDNA microarray to identify host pathways that are affected by the Salmonella enterica subspecies typhimurium phoP gene, a transcription factor required for virulence, by comparing the expression profiles of human monocytic tissue culture cells infected with either the wild-type bacteria or a phoP∷Tn10 mutant strain. Both wild-type and phoP∷Tn10 bacteria induced a common set of genes, many of which are proinflammatory. Differentially expressed genes included those that affect host cell death, suggesting that the phoP regulatory system controls bacterial genes that alter macrophage survival. Subsequent experiments showed that the phoP∷Tn10 mutant strain is defective for killing both cultured and primary human macrophages but is able to replicate intracellularly. These experiments indicate that phoP plays a role in Salmonella-induced human macrophage cell death.

Recursive partitioning for tumor classification with gene expression microarray data

Precise classification of tumors is critically important for cancer diagnosis and treatment. It is also a scientifically challenging task. Recently, efforts have been made to use gene expression profiles to improve the precision of classification, with limited success. Using a published data set for purposes of comparison, we introduce a methodology based on classification trees and demonstrate that it is significantly more accurate for discriminating among distinct colon cancer tissues than other statistical approaches used heretofore. In addition, competing classification trees are displayed, which suggest that different genes may coregulate colon cancers.

Parallel human genome analysis: microarray-based expression monitoring of 1000 genes.

Microarrays containing 1046 human cDNAs of unknown sequence were printed on glass with high-speed robotics. These 1.0-cm2 DNA "chips" were used to quantitatively monitor differential expression of the cognate human genes using a highly sensitive two-color hybridization assay. Array elements that displayed differential expression patterns under given experimental conditions were characterized by sequencing. The identification of known and novel heat shock and phorbol ester-regulated genes in human T cells demonstrates the sensitivity of the assay. Parallel gene analysis with microarrays provides a rapid and efficient method for large-scale human gene discovery.

Party Elites and the Search for Credibility : Plaid Cymru and the SNP as New Parties of Government

Peer reviewed

Investigação de mecanismos genéticos e epigenéticos em distúrbios do crescimento humano

Parcela considerável de pacientes com distúrbios de crescimento não têm a causa de seus quadros clínicos estabelecida, incluindo aproximadamente 50% dos pacientes com diagnóstico clínico de síndrome de Silver−Russell (SRS) e 10-20% dos pacientes com síndrome de Beckwith-Wiedemann (BWS). O objetivo deste estudo foi investigar as causas genéticas e epigenéticas de distúrbios de crescimento, de etiologia desconhecida, numa contribuição para o entendimento de mecanismos que regulam o crescimento. O estudo compreendeu: (1) a investigação de microdesequilíbrios cromossômicos, por aCGH; (2) a análise do perfil de expressão alelo-específica de genes sujeitos a imprinting (IG), por pirossequenciamento (PSQ) ou sequenciamento de Sanger; (3) a investigação do padrão de metilação global em pacientes com restrição de crescimento, utilizando microarray de metilação. A casuística constituiu-se de 41 pacientes não aparentados, com distúrbios de crescimento, de etiologia desconhecida: (1) 25, com hipótese diagnóstica de SRS; (2) seis, com restrição de crescimento intrauterino e peso ao nascimento abaixo do 10º percentil, associados a outros sinais clínicos; (3) sete, com hipótese diagnóstica de BWS; e (4) três, com macrossomia pré-natal ou pós-natal, associada a outros sinais. A investigação de microdesequilíbrios cromossômicos foi realizada em 40 pacientes. Foram detectadas 58 variantes raras em 30/40 pacientes (75%): 40 foram consideradas provavelmente benignas (18 pacientes, 45%), 12, com efeito patogênico desconhecido (11 pacientes, 27,5%), duas, provavelmente patogênicas (um paciente, 2,5%) e quatro, patogênicas (três pacientes, 7,5%). Essas frequências são comparáveis àquelas descritas em estudos que investigaram CNV em grupos de pacientes com distúrbios de crescimento e outras alterações congênitas, incluindo SRS, e mostram a importância da investigação de microdesequilíbrios cromossômicos nesses pacientes. A diversidade dos microdesequilíbrios cromossômicos identificados é reflexo da heterogeneidade clínica das casuísticas. Neste estudo, muitos dos pacientes com hipótese diagnóstica de SRS e BWS apresentavam sinais clínicos atípicos, explicando a ausência neles das alterações (epi)genéticas que causam essas síndromes. A identificação de CNV características de outras síndromes reflete a sobreposição de sinais clínicos com BWS e SRS. A análise do perfil de expressão alelo-específica de IG foi realizada em um subgrupo de 18 pacientes com restrição de crescimento. Trinta IG com função em proliferação celular, crescimento fetal ou neurodesenvolvimento foram inicialmente selecionados. Após seleção de SNP transcritos com alta frequência na população, genotipagem de pacientes, genitores e indivíduos controle, determinação da expressão dos IG em sangue periférico e seu padrão de expressão (mono ou bialélico), 13 IG, expressos no sangue, tiveram a expressão alelo-específica avaliada, sete deles por PSQ e seis por sequenciamento de Sanger. Alterações no perfil de expressão de dois genes, de expressão normalmente paterna, foram detectadas em 4/18 pacientes (22%). Este estudo é o primeiro a utilizar pirossequenciamento e sequenciamento de Sanger na avaliação do perfil de expressão alelo-específica de IG, em pacientes com restrição de crescimento. Apesar de terem limitações, ambas as técnicas mostraram-se robustas e revelaram alterações de expressão alélica interessantes; entretanto, a relação dessas alterações com o quadro clínico dos pacientes permanece por esclarecer. A investigação da metilação global do DNA foi realizada em subgrupo de 21 pacientes com restrição de crescimento e em 24 indivíduos controle. Dois tipos de análise foram realizados: (1) análise diferencial de grupo e (2) análise diferencial individual. Na primeira análise, em que foi comparado o padrão de metilação do grupo de pacientes com quadro clínico sugestivo de SRS (n=16) com o do grupo controle (n=24), não houve indicação de hipo ou hipermetilação global no grupo SRS. Na segunda análise, foi comparado o padrão de metilação de cada um dos 21 pacientes com restrição de crescimento e dos 24 indivíduos controle, com o padrão de metilação do grupo controle. O número médio de CpG hipermetilados e de segmentos diferencialmente metilados (SDM) foi significativamente maior nos pacientes. Foram identificados 82 SDM hipermetilados, estando 57 associados a gene(s) (69,5%), em 16 pacientes, e 51 SDM hipometilados, 41 deles associados a gene(s) (80,4%), em 10 pacientes. A análise de ontologia genética dos 61 genes associados aos SDM hipo ou hipermetilados nos pacientes destacou genes que atuam no desenvolvimento e na morfogênese do sistema esquelético e de órgãos fetais, e na regulação da transcrição gênica e de processos metabólicos. Alterações de metilação em genes que atuam em processos de proliferação e diferenciação celulares e crescimento foram identificadas em 9/20 dos pacientes (45%), sugerindo implicação clínica. Não foi detectada alteração epigenética comum aos pacientes com diagnóstico clínico de SRS, explicável provavelmente pela heterogeneidade clínica. A investigação de metilação global, utilizando microarray, produziu novos dados que podem contribuir para a compreensão de mecanismos moleculares que influenciam o crescimento pré- e pós-natal. Na translocação aparentemente equilibrada - t(5;6)(q35.2;p22.3)dn, detectada em paciente com suspeita clínica de SRS, a interrupção de um gene, pela quebra no cromossomo 6, pode ser a causa do quadro clínico; alternativamente, a translocação pode ter impactado a regulação de genes de desenvolvimento localizados próximos aos pontos de quebra. A análise de expressão em sangue periférico mostrou que os níveis de cDNA do gene, interrompido pelo ponto de quebra da translocação, estavam reduzidos à metade. Além de sinais típicos da SRS, a paciente apresentava algumas características clínicas sugestivas de displasia cleidocraniana. Assim, a translocação t(5;6) pode ter alterado a interação de genes de desenvolvimento e seus elementos reguladores, levando à desregulação de sua expressão espaço-temporal, e resultando num fenótipo atípico, com características sobrepostas de mais de uma síndrome genética

Tissue-specific gene expression in soybean (Glycine max) detected by cDNA microarray analysis

We have constructed cDNA microarrays for soybean (Glycine max L. Merrill), containing approximately 4,100 Unigene ESTs derived from axenic roots, to evaluate their application and utility for functional genomics of organ differentiation in legumes. We assessed microarray technology by conducting studies to evaluate the accuracy of microarray data and have found them to be both reliable and reproducible in repeat hybridisations. Several ESTs showed high levels (>50 fold) of differential expression in either root or shoot tissue of soybean. A small number of physiologically interesting, and differentially expressed sequences found by microarray analysis were verified by both quantitative real-time RT-PCR and Northern blot analysis. There was a linear correlation (r(2) = 0.99, over 5 orders of magnitude) between microarray and quantitative real-time RT-PCR data. Microarray analysis of soybean has enormous potential not only for the discovery of new genes involved in tissue differentiation and function, but also to study the expression of previously characterised genes, gene networks and gene interactions in wild-type, mutant or transgenic; plants.

Microarray expression profiling in melanoma reveals a BRAF mutation signature

We have used microarray gene expression pro. ling and machine learning to predict the presence of BRAF mutations in a panel of 61 melanoma cell lines. The BRAF gene was found to be mutated in 42 samples (69%) and intragenic mutations of the NRAS gene were detected in seven samples (11%). No cell line carried mutations of both genes. Using support vector machines, we have built a classifier that differentiates between melanoma cell lines based on BRAF mutation status. As few as 83 genes are able to discriminate between BRAF mutant and BRAF wild-type samples with clear separation observed using hierarchical clustering. Multidimensional scaling was used to visualize the relationship between a BRAF mutation signature and that of a generalized mitogen-activated protein kinase ( MAPK) activation ( either BRAF or NRAS mutation) in the context of the discriminating gene list. We observed that samples carrying NRAS mutations lie somewhere between those with or without BRAF mutations. These observations suggest that there are gene-specific mutation signals in addition to a common MAPK activation that result from the pleiotropic effects of either BRAF or NRAS on other signaling pathways, leading to measurably different transcriptional changes.

SeqDoC: rapid SNP and mutation detection by direct comparison of DNA sequence chromatograms

Background: This paper describes SeqDoC, a simple, web-based tool to carry out direct comparison of ABI sequence chromatograms. This allows the rapid identification of single nucleotide polymorphisms (SNPs) and point mutations without the need to install or learn more complicated analysis software. Results: SeqDoC produces a subtracted trace showing differences between a reference and test chromatogram, and is optimised to emphasise those characteristic of single base changes. It automatically aligns sequences, and produces straightforward graphical output. The use of direct comparison of the sequence chromatograms means that artefacts introduced by automatic base-calling software are avoided. Homozygous and heterozygous substitutions and insertion/deletion events are all readily identified. SeqDoC successfully highlights nucleotide changes missed by the Staden package 'tracediff' program. Conclusion: SeqDoC is ideal for small-scale SNP identification, for identification of changes in random mutagenesis screens, and for verification of PCR amplification fidelity. Differences are highlighted, not interpreted, allowing the investigator to make the ultimate decision on the nature of the change.

Development of an oligonucleotide-based SNP detection method on lateral flow strips using hexapet tages

Detection of point mutations or single nucleotide polymorphisms (SNPs) is important in relation to disease susceptibility or detection in pathogens of mutations determining drug resistance or host range. There is an emergent need for rapid detection methods amenable to point-of-care applications. The purpose of this study was to reduce to practice a novel method for SNP detection and to demonstrate that this technology can be used downstream of nucleic acid amplification. The authors used a model system to develop an oligonucleotide-based SNP detection system on nitrocellulose lateral flow strips. To optimize the assay they used cloned sequences of the herpes simplex virus-1 (HSV-1) DNA polymerase gene into which they introduced a point mutation. The assay system uses chimeric polymerase chain reaction (PCR) primers that incorporate hexameric repeat tags ("hexapet tags"). The chimeric sequences allow capture of amplified products to predefined positions on a lateral flow strip. These "hexapet" sequences have minimal cross-reactivity and allow specific hybridization-based capture of the PCR products at room temperature onto lateral flow strips that have been striped with complementary hexapet tags. The allele-specific amplification was carried out with both mutant and wild-type primer sets present in the PCR mix ("competitive" format). The resulting PCR products carried a hexapet tag that corresponded with either a wild-type or mutant sequence. The lateral flow strips are dropped into the PCR reaction tube, and mutant sequence and wild-type sequences diffuse along the strip and are captured at the corresponding position on the strip. A red line indicative of a positive reaction is visible after 1 minute. Unlike other systems that require separate reactions and strips for each target sequence, this system allows multiplex PCR reactions and multiplex detection on a single strip or other suitable substrates. Unambiguous visual discrimination of a point mutation under room temperature hybridization conditions was achieved with this model system in 10 minutes after PCR. The authors have developed a capture-based hybridization method for the detection and discrimination of HSV-1 DNA polymerase genes that contain a single nucleotide change. It has been demonstrated that the hexapet oligonucleotides can be adapted for hybridization on the lateral flow strip platform for discrimination of SNPs. This is the first step in demonstrating SNP detection on lateral flow using the hexapet oligonucleotide capture system. It is anticipated that this novel system can be widely used in point-of-care settings.

A microarray study of post-mortem mRNA degradation in mouse brain tissue

Background: Although there is evidence that post-mortem interval (PMI) is not a major contributor to reduced overall RNA integrity, it may differentially affect a subgroup of gene transcripts that are susceptible to PMI-related degradation. This would particularly have ramifications for microarray studies that include a broad spectrum of genes. Method: Brain tissue was removed from adult mice at 0, 6, 12, 18, 24,36 and 48 h post-mortem. RNA transcript abundance was measured by hybridising RNA from the zero time point with test RNA from each PMI time point, and differential gene expression was assessed using cDNA microarrays. Sequence and ontological analyses were performed on the group of RNA transcripts showing greater than two-fold reduction. Results: Increasing PMI was associated with decreased tissue pH and increased RNA degradation as indexed by 28S/18S ribosomal RNA ratio. Approximately 12% of mRNAs detected on the arrays displayed more than a two-fold decrease in abundance by 48 It post-mortem. An analysis of nucleotide composition provided evidence that transcripts with the AUUUA motif in the 3' untranslated region (3'UTR) were more susceptible to PMI-related RNA degradation, compared to transcripts not carrying the 3'UTR AUUUA motif. Consistent with this finding, ontological analysis showed transcription factors and elements to be over-represented in the group of transcripts susceptible to degradation. Conclusion: A subgroup of mammalian mRNA transcripts are particularly susceptible to PMI-related degradation, and as a group, they are more likely to carry the YUTR AUUUA motif. PMI should be controlled for in human and animal model post-mortem brain studies, particularly those including a broad spectrum of mRNA transcripts. (c) 2005 Elsevier B.V. All rights reserved.