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Abstract The focus of this review is software for the genotyping of microarray single nucleotide polymorphisms, in particular software for Affymetrix and Illumina arrays. Different statistical principles and ideas have been applied to the construction of genotyping algorithms -- for example, likelihood versus Bayesian modelling, and whether to genotype one or all arrays at a time. The release of new arrays is generally followed by new, or updated, algorithms.

Abstract The CATH database provides hierarchical classification of protein domains based on their folding patterns. Domains are obtained from protein structures deposited in the Protein Data Bank and both domain identification and subsequent classification use manual as well as automated procedures. The accompanying website http://www.cathdb.info provides an easy-to-use entry to the classification, allowing for both browsing and downloading of data. Here, we give a brief review of the database, its corresponding website and some related tools.

Abstract Background Approximately half of all human genes use alternative transcription start sites (TSSs) to control mRNA levels and broaden the transcriptional output in healthy tissues. Aberrant expression patterns promoting carcinogenesis, however, may arise from alternative promoter usage. Results By profiling 108 colorectal samples using exon arrays, we identified nine genes (TCF12, OSBPL1A, TRAK1, ANK3, CHEK1, UGP2, LMO7, ACSL5, and SCIN) showing tumor-specific alternative TSS usage in both adenoma and cancer samples relative to normal mucosa. Analysis of independent exon array data sets corroborated these findings. Additionally, we confirmed the observed patterns for selected mRNAs using quantitative real-time reverse-transcription PCR. Interestingly, for some of the genes, the tumor-specific TSS usage was not restricted to colorectal cancer. A comprehensive survey of the nine genes in lung, bladder, liver, prostate, gastric, and brain cancer revealed significantly altered mRNA isoform ratios for CHEK1, OSBPL1A, and TCF12 in a subset of these cancer types. To identify the mechanism responsible for the shift in alternative TSS usage, we antagonized the Wnt-signaling pathway in DLD1 and Ls174T colorectal cancer cell lines, which remarkably led to a shift in the preferred TSS for both OSBPL1A and TRAK1. This indicated a regulatory role of the Wnt pathway in selecting TSS, possibly also involving TP53 and SOX9, as their transcription binding sites were enriched in the promoters of the tumor preferred isoforms together with their mRNA levels being increased in tumor samples. Finally, to evaluate the prognostic impact of the altered TSS usage, immunohistochemistry was used to show deregulation of the total protein levels of both TCF12 and OSBPL1A, corresponding to the mRNA levels observed. Furthermore, the level of nuclear TCF12 had a significant correlation to progression free survival in a cohort of 248 stage II colorectal cancer samples. Conclusions Alternative TSS usage in colorectal adenoma and cancer samples has been shown for nine genes, and OSBPL1A and TRAK1 were found to be regulated in vitro by Wnt signaling. TCF12 protein expression was upregulated in cancer samples and correlated with progression free survival.

Abstract Background Patterns of genome-wide methylation vary between tissue types. For example, cancer tissue shows markedly different patterns from those of normal tissue. In this paper we propose a beta-mixture model to describe genome-wide methylation patterns based on probe data from methylation microarrays. The model takes dependencies between neighbour probe pairs into account and assumes three broad categories of methylation, low, medium and high. The model is described by 37 parameters, which reduces the dimensionality of a typical methylation microarray significantly. We used methylation microarray data from 42 colon cancer samples to assess the model. Results Based on data from colon cancer samples we show that our model captures genome-wide characteristics of methylation patterns. We estimate the parameters of the model and show that they vary between different tissue types. Further, for each methylation probe the posterior probability of a methylation state (low, medium or high) is calculated and the probability that the state is correctly predicted is assessed. We demonstrate that the model can be applied to classify cancer tissue types accurately and that the model provides accessible and easily interpretable data summaries. Conclusions We have developed a beta-mixture model for methylation microarray data. The model substantially reduces the dimensionality of the data. It can be used for further analysis, such as sample classification or to detect changes in methylation status between different samples and tissues.

Abstract Background Different cell subpopulations in a single tumor may show diverse capacities for growth, differentiation, metastasis formation, and sensitivity to treatments. Thus, heterogeneity is an important feature of tumors. However, due to limitations in experimental and analytical techniques, tumor heterogeneity has rarely been studied in detail. Presentation of the hypothesis Different tumor types have different heterogeneity patterns, thus heterogeneity could be a characteristic feature of a particular tumor type. Testing the hypothesis We applied our previously published mathematical heterogeneity model to decipher tumor heterogeneity through the analysis of genetic copy number aberrations revealed by array CGH data for tumors of three different tissues: breast, colon, and skin. The model estimates the number of subpopulations present in each tumor. The analysis confirms that different tumor types have different heterogeneity patterns. Computationally derived genomic copy number profiles from each subpopulation have also been analyzed and discussed with reference to the multiple hypothetical relationships between subpopulations in origin-related samples. Implications of the hypothesis Our observations imply that tumor heterogeneity could be seen as an independent parameter for determining the characteristics of tumors. In the context of more comprehensive usage of array CGH or genome sequencing in a clinical setting our study provides a new way to realize the full potential of tumor genetic analysis.

Abstract Background The High Resolution Melting (HRM) technology has recently been introduced as a rapid and robust analysis tool for the detection of DNA methylation. The methylation status of multiple tumor suppressor genes may serve as biomarkers for early cancer diagnostics, for prediction of prognosis and for prediction of response to treatment. Therefore, it is important that methodologies for detection of DNA methylation continue to evolve. Sensitive Melting Analysis after Real Time - Methylation Specific PCR (SMART-MSP) and Methylation Sensitive - High Resolution Melting (MS-HRM) are two methods for single locus DNA methylation detection based on HRM. Methods Here, we have assessed the quality of DNA extracted from up to 30 years old Formalin Fixed Paraffin Embedded (FFPE) tissue for DNA methylation analysis using SMART-MSP and MS-HRM. The quality assessment was performed on DNA extracted from 54 Non-Small Cell Lung Cancer (NSCLC) samples derived from FFPE tissue, collected over 30 years and grouped into five years intervals. For each sample, the methylation levels of the CDKN2A (p16) and RARB promoters were estimated using SMART-MSP and MS-HRM assays designed to assess the methylation status of the same CpG positions. This allowed for a direct comparison of the methylation levels estimated by the two methods for each sample. Results CDKN2A promoter methylation levels were successfully determined by SMART-MSP and MS-HRM in all 54 samples. Identical methylation estimates were obtained by the two methods in 46 of the samples. The methylation levels of the RARB promoter were successfully determined by SMART-MSP in all samples. When using MS-HRM to assess RARB methylation five samples failed to amplify and 15 samples showed a melting profile characteristic for heterogeneous methylation. Twenty-seven of the remaining 34 samples, for which the methylation level could be estimated, gave the same result as observed when using SMART-MSP. Conclusion MS-HRM and SMART-MSP can be successfully used for single locus methylation studies using DNA derived from up to 30 years old FFPE tissue. Furthermore, it can be expected that MS-HRM and SMART-MSP will provide similar methylation estimates when assays are designed to analyze the same CpG positions.

Abstract Background The temporal dynamics and formation of plant-pollinator networks are difficult to study as it requires detailed observations of how the networks change over time. Understanding the temporal dynamics might provide insight into sustainability and robustness of the networks and how they react to environmental changes, such as global warming. Here we study an Arctic plant-pollinator network in two consecutive years using a simple mathematical model and describe the temporal dynamics (daily assembly and disassembly of links) by random mechanisms. Results We develop a mathematical model with parameters governed by the probabilities for entering, leaving and making connections in the network and demonstrate that A. The dynamics is described by very similar parameters in both years despite a strong turnover in the composition of the pollinator community and different climate conditions, B. There is a drastic change in the temporal behaviour a few days before the end of the season in both years. This change leads to the collapse of the network and does not correlate with weather parameters, C. We estimate that the number of available pollinator species is about 80 species of which 75-80% are observed in each year, D. The network does not reach an equilibrium state (as defined by our model) before the collapse set in and the season is over. Conclusion We have shown that the temporal dynamics of an Arctic plant-pollinator network can be described by a simple mathematical model and that the model allows us to draw biologically interesting conclusions. Our model makes it possible to investigate how the network topology changes with changes in parameter values and might provide means to study the effect of climate on plant-pollinator networks.

Abstract Background Identification of disease susceptible genes requires access to DNA from numerous well-characterised subjects. Archived residual dried blood spot samples from national newborn screening programs may provide DNA from entire populations and medical registries the corresponding clinical information. The amount of DNA available in these samples is however rarely sufficient for reliable genome-wide scans, and whole-genome amplification may thus be necessary. This study assess the quality of DNA obtained from different amplification protocols by evaluating fidelity and robustness of the genotyping of 610,000 single nucleotide polymorphisms, using the Illumina Infinium HD Human610-Quad BeadChip. Whole-genome amplified DNA from 24 neonatal dried blood spot samples stored between 15 to 25 years was tested, and high-quality genomic DNA from 8 of the same individuals was used as reference. Results Using 3.2 mm disks from dried blood spot samples the optimal DNA-extraction and amplification protocol resulted in call-rates between 99.15% – 99.73% (mean 99.56%, N = 16), and conflicts with reference DNA in only three per 10,000 genotype calls. Conclusion Whole-genome amplified DNA from archived neonatal dried blood spot samples can be used for reliable genome-wide scans and is a cost-efficient alternative to collecting new samples.

Abstract Background Non-muscle invasive bladder neoplasms with invasion of the lamina propria (stage T1) or high grade of dysplasia are at "high risk" of progression to life-threatening cancer. However, the individual course is difficult to predict. Chromosomal instability (CI) is associated with high tumor stage and grade, and possibly with the risk of progression. Methods To investigate the relationship between CI and subsequent disease progression, we performed a case-control-study of 125 patients with "high-risk" non-muscle invasive bladder neoplasms, 67 with later disease progression, and 58 with no progression. Selection criteria were conservative (non-radical) resections and full prospective clinical follow-up (> 5 years). We investigated primary lesions in 59, and recurrent lesions in 66 cases. We used Affymetrix GeneChip® Mapping 10 K and 50 K SNP microarrays to evaluate genome wide chromosomal imbalance (loss-of-heterozygosity and DNA copy number changes) in 48 representative tumors. DNA copy number changes of 15 key instability regions were further investigated using QPCR in 101 tumors (including 25 tumors also analysed on 50 K SNP microarrays). Results Chromosomal instability did not predict any higher risk of subsequent progression. Stage T1 and high-grade tumors had generally more unstable genomes than tumors of lower stage and grade (mostly non-primary tumors following a "high-risk" tumor). However, about 25% of the "high-risk" tumors had very few alterations. This was independent of subsequent progression. Recurrent lesions represent underlying field disease. A separate analysis of these lesions did neither reflect any difference in the risk of progression. Of specific chromosomal alterations, a possible association between loss of chromosome 8p11 and the risk of progression was found. However, the predictive value was limited by the heterogeneity of the changes. Conclusion Chromosomal instability (CI) was associated with "high risk" tumors (stage T1 or high-grade), but did not predict subsequent progression. Recurrences after "high-risk" tumors had fewer chromosomal alterations, but there was no association with the risk of progression in this group either. Thus, the prediction of progression of "high risk" non-muscle invasive bladder tumors using chromosomal changes is difficult. Loss of chromosome 8p11 may play a role in the progression process. About 25% of the "high risk" tumors were chromosomal stable.

Abstract Background Array-based comparative genomic hybridization (CGH) is a commonly-used approach to detect DNA copy number variation in whole genome-wide screens. Several statistical methods have been proposed to define genomic segments with different copy numbers in cancer tumors. However, most tumors are heterogeneous and show variation in DNA copy numbers across tumor cells. The challenge is to reveal the copy number profiles of the subpopulations in a tumor and to estimate the percentage of each subpopulation. Results We describe a relation between experimental data and exact DNA copy number and develop a statistical method to reveal the heterogeneity of tumors containing a mixture of different-stage cells. Furthermore, we validate the method on simulated data and apply the method to 29 pairs of breast primary tumors and their matched lymph node metastases. Conclusion We demonstrate a new method for CGH array analysis that allows a tumor sample to be classified according to its heterogeneity. The method gives an interpretable series of copy number profiles, one for each major subpopulation in a tumor. The profiles facilitate identification of copy number alterations in cancer development.

Abstract Background In recent years it has been demonstrated that structural variations, such as indels (insertions and deletions), are common throughout the genome, but the implications of structural variations are still not clearly understood. Long tandem repeats (e.g. microsatellites or simple repeats) are known to be hypermutable (indel-rich), but are rare in exons and only occasionally associated with diseases. Here we focus on short (imperfect) tandem repeats (STRs) which fall below the radar of conventional tandem repeat detection, and investigate whether STRs are targets for disease-related mutations in human exons. In particular, we test whether they share the hypermutability of the longer tandem repeats and whether disease-related genes have a higher STR content than non-disease-related genes. Results We show that validated human indels are extremely common in STR regions compared to non-STR regions. In contrast to longer tandem repeats, our definition of STRs found them to be present in exons of most known human genes (92%), 99% of all STR sequences in exons are shorter than 33 base pairs and 62% of all STR sequences are imperfect repeats. We also demonstrate that STRs are significantly overrepresented in disease-related genes in both human and mouse. These results are preserved when we limit the analysis to STRs outside known longer tandem repeats. Conclusion Based on our findings we conclude that STRs represent hypermutable regions in the human genome that are linked to human disease. In addition, STRs constitute an obvious target when screening for rare mutations, because of the relatively low amount of STRs in exons (1,973,844 bp) and the limited length of STR regions.

Abstract Background Mechanisms underlying the malignant development in bladder cancer are still not well understood. Lipolysis stimulated lipoprotein receptor (LSR) has previously been found to be upregulated by P53. Furthermore, we have previously found LSR to be differentially expressed in bladder cancer. Here we investigated the role of LSR in bladder cancer. Methods A time course siRNA knock down experiment was performed to investigate the functional role of LSR in SW780 bladder cancer cells. Since LSR was previously shown to be regulated by P53, siRNA against TP53 was included in the experimental setup. We used Affymetrix GeneChips for measuring gene expression changes and we used Ingenuity Pathway Analysis to investigate the relationship among differentially expressed genes upon siRNA knockdown. Results By Ingenuity Pathway analysis of the microarray data from the different timepoints we identified six gene networks containing genes mainly related to the functional categories "cancer", "cell death", and "cellular movement". We determined that genes annotated to the functional category "cellular movement" including "invasion" and "cell motility" were highly significantly overrepresented. A matrigel assay showed that 24 h after transfection the invasion capacity was significantly increased 3-fold (p < 0.02) in LSR-siRNA transfected cells, and 2.7-fold (p < 0.02) in TP53-siRNA transfected cells compared to controls. After 48 h the motility capacity was significantly increased 3.5-fold (p < 0.004) in LSR-siRNA transfected cells, and 4.7-fold (p < 0.002) in TP53-siRNA transfected cells compared to controls. Conclusion We conclude that LSR may impair bladder cancer cells from gaining invasive properties.

Abstract Background Mutations in the mismatch repair genes hMLH1 and hMSH2 predispose to hereditary non-polyposis colorectal cancer (HNPCC). Genetic screening of more than 350 Danish patients with colorectal cancer (CRC) has led to the identification of several new genetic variants (e.g. missense, silent and non-coding) in hMLH1 and hMSH2. The aim of the present study was to investigate the frequency of these variants in hMLH1 and hMSH2 in Danish patients with sporadic colorectal cancer and in the healthy background population. The purpose was to reveal if any of the common variants lead to increased susceptibility to colorectal cancer. Methods Associations between genetic variants in hMLH1 and hMSH2 and sporadic colorectal cancer were evaluated using a case-cohort design. The genotyping was performed on DNA isolated from blood from the 380 cases with sporadic colorectal cancer and a sub-cohort of 770 individuals. The DNA samples were analyzed using Single Base Extension (SBE) Tag-arrays. A Bonferroni corrected Fisher exact test was used to test for association between the genotypes of each variant and colorectal cancer. Linkage disequilibrium (LD) was investigated using HaploView (v3.31). Results Heterozygous and homozygous changes were detected in 13 of 35 analyzed variants. Two variants showed a borderline association with colorectal cancer, whereas the remaining variants demonstrated no association. Furthermore, the genomic regions covering hMLH1 and hMSH2 displayed high linkage disequilibrium in the Danish population. Twenty-two variants were neither detected in the cases with sporadic colorectal cancer nor in the sub-cohort. Some of these rare variants have been classified either as pathogenic mutations or as neutral variants in other populations and some are unclassified Danish variants. Conclusion None of the variants in hMLH1 and hMSH2 analyzed in the present study were highly associated with colorectal cancer in the Danish population. High linkage disequilibrium in the genomic regions covering hMLH1 and hMSH2, indicate that common genetic variants in the two genes in general are not involved in the development of sporadic colorectal cancer. Nevertheless, some of the rare unclassified variants in hMLH1 and hMSH2 might be involved in the development of colorectal cancer in the families where they were originally identified.

Abstract Background Carcinoma in situ (CIS) is believed to be a precursor of invasive bladder cancer. Identification of CIS is a valuable prognostic factor since radical treatment strategies can be offered these patients before the disease becomes invasive. Methods We developed a pathway based classifier approach to predict presence or absence of CIS in patients suffering from non muscle invasive bladder cancer. From Ingenuity Pathway Analysis we considered four canonical signalling pathways (p38 MAPK, FGF, Calcium, and cAMP pathways) with most coherent expression of transcription factors (TFs) across samples in a set of twenty-eight non muscle invasive bladder carcinomas. These pathways contained twelve TFs in total. We used the expression of the TFs to predict presence or absence of CIS in a Leave-One-Out Cross Validation classification. Results We showed that TF expression levels in three pathways (FGF, p38 MAPK, and calcium signalling) or the expression of the twelve TFs together could be used to predict presence or absence of concomitant CIS. A cluster analysis based on expression of the twelve TFs separated the samples in two main clusters: one branch contained 11 of the 15 patients without concomitant CIS and with the majority of the genes being down regulated; the other branch contained 10 of 13 patients with concomitant CIS, and here genes were mostly up regulated. The expression in the CIS group was comparable to the expression of twenty-three patients suffering from muscle-invasive bladder carcinoma. Finally, we validated our results in an independent test set and found that prediction of CIS status was possible using TF expression of the p38 MAPK pathway. Conclusion We conclude that it is possible to use pathway analysis for molecular classification of bladder tumors.

Abstract Background MicroRNAs (miRNAs) are short, non-coding RNA molecules that are directly involved in post-transcriptional regulation of gene expression. The mature miRNA sequence binds to more or less specific target sites on the mRNA. Both their small size and sequence specificity make the detection of completely new miRNAs a challenging task. This cannot be based on sequence information alone, but requires structure information about the miRNA precursor. Unlike comparative genomics approaches, ab initio approaches are able to discover species-specific miRNAs without known sequence homology. Results MiRPred is a novel method for ab initio prediction of miRNAs by genome scanning that only relies on (predicted) secondary structure to distinguish miRNA precursors from other similar-sized segments of the human genome. We apply a machine learning technique, called linear genetic programming, to develop special classifier programs which include multiple regular expressions (motifs) matched against the secondary structure sequence. Special attention is paid to scanning issues. The classifiers are trained on fixed-length sequences as these occur when shifting a window in regular steps over a genome region. Various statistical and empirical evidence is collected to validate the correctness of and increase confidence in the predicted structures. Among other things, we propose a new criterion to select miRNA candidates with a higher stability of folding that is based on the number of matching windows around their genome location. An ensemble of 16 motif-based classifiers achieves 99.9 percent specificity with sensitivity remaining on an acceptable high level when requiring all classifiers to agree on a positive decision. A low false positive rate is considered more important than a low false negative rate, when searching larger genome regions for unknown miRNAs. 117 new miRNAs have been predicted close to known miRNAs on human chromosome 19. All candidate structures match the free energy distribution of miRNA precursors which is significantly shifted towards lower free energies. We employed a human EST library and found that around 75 percent of the candidate sequences are likely to be transcribed, with around 35 percent located in introns. Conclusion Our motif finding method is at least competitive to state-of-the-art feature-based methods for ab initio miRNA discovery. In doing so, it requires less previous knowledge about miRNA precursor structures while programs and motifs allow a more straightforward interpretation and extraction of the acquired knowledge.

Abstract Background Affymetrix SNP arrays can interrogate thousands of SNPs at the same time. This allows us to look at the genomic content of cancer cells and to investigate the underlying events leading to cancer. Genomic copy-numbers are today routinely derived from SNP array data, but the proposed algorithms for this task most often disregard the genotype information available from germline cells in paired germline-tumour samples. Including this information may deepen our understanding of the "true" biological situation e.g. by enabling analysis of allele specific copy-numbers. Here we rely on matched germline-tumour samples and have developed a Hidden Markov Model (HMM) to estimate allelic copy-number changes in tumour cells. Further with this approach we are able to estimate the proportion of normal cells in the tumour (mixture proportion). Results We show that our method is able to recover the underlying copy-number changes in simulated data sets with high accuracy (above 97.71%). Moreover, although the known copy-numbers could be well recovered in simulated cancer samples with more than 70% cancer cells (and less than 30% normal cells), we demonstrate that including the mixture proportion in the HMM increases the accuracy of the method. Finally, the method is tested on HapMap samples and on bladder and prostate cancer samples. Conclusion The HMM method developed here uses the genotype calls of germline DNA and the allelic SNP intensities from the tumour DNA to estimate allelic copy-numbers (including changes) in the tumour. It differentiates between different events like uniparental disomy and allelic imbalances. Moreover, the HMM can estimate the mixture proportion, and thus inform about the purity of the tumour sample.

Abstract Background Present protein interaction network data sets include only interactions among subsets of the proteins in an organism. Previously this has been ignored, but in principle any global network analysis that only looks at partial data may be biased. Here we demonstrate the need to consider network sampling properties explicitly and from the outset in any analysis. Results Here we study how properties of the yeast protein interaction network are affected by random and non-random sampling schemes using a range of different network statistics. Effects are shown to be independent of the inherent noise in protein interaction data. The effects of the incomplete nature of network data become very noticeable, especially for so-called network motifs. We also consider the effect of incomplete network data on functional and evolutionary inferences. Conclusion Crucially, when only small, partial network data sets are considered, bias is virtually inevitable. Given the scope of effects considered here, previous analyses may have to be carefully reassessed: ignoring the fact that present network data are incomplete will severely affect our ability to understand biological systems.

Abstract Background Human endogenous retroviruses (HERVs) comprise a large class of repetitive retroelements. Most HERVs are ancient and invaded our genome at least 25 million years ago, except for the evolutionary young HERV-K group. The far majority of the encoded genes are degenerate due to mutational decay and only a few non-HERV-K loci are known to retain intact reading frames. Additional intact HERV genes may exist, since retroviral reading frames have not been systematically annotated on a genome-wide scale. Results By clustering of hits from multiple BLAST searches using known retroviral sequences we have mapped 1.1% of the human genome as retrovirus related. The coding potential of all identified HERV regions were analyzed by annotating viral open reading frames (vORFs) and we report 7836 loci as verified by protein homology criteria. Among 59 intact or almost-intact viral polyproteins scattered around the human genome we have found 29 envelope genes including two novel gammaretroviral types. One encodes a protein similar to a recently discovered zebrafish retrovirus (ZFERV) while another shows partial, C-terminal, homology to Syncytin (HERV-W/FRD). Conclusions This compilation of HERV sequences and their coding potential provide a useful tool for pursuing functional analysis such as RNA expression profiling and effects of viral proteins, which may, in turn, reveal a role for HERVs in human health and disease. All data are publicly available through a database at http://www.retrosearch.dk.