Stitchprofiles.uio.no: analysis of partly melted DNA conformations using stitch profiles

In this study, we describe a web server that performs computations on DNA melting, thus predicting the localized separation of the two strands for sequences provided by the users. The output types are stitch profiles, melting curves, probability profiles, etc. Stitch profile diagrams visualize the ensemble of alternative conformations that DNA can adopt with different probabilities. For example, a stitch profile shows the possible loop openings in terms of their locations, sizes, probabilities and fluctuations at a given temperature. Sequences with lengths up to several tens or hundreds of kilobase pairs can be analysed. The tools are freely available at

Options available for profiling small samples: a review of sample amplification technology when combined with microarray profiling

The possibility of performing microarray analysis on limited material has been demonstrated in a number of publications. In this review we approach the technical aspects of mRNA amplification and several important implicit consequences, for both linear and exponential procedures. Amplification efficiencies clearly allow profiling of extremely small samples. The conservation of transcript abundance is the most important issue regarding the use of sample amplification in combination with microarray analysis, and this aspect has generally been found to be acceptable, although demonstrated to decrease in highly diluted samples. The fact that variability and discrepancies in microarray profiles increase with minute sample sizes has been clearly documented, but for many studies this does appear to have affected the biological conclusions. We suggest that this is due to the data analysis approach applied, and the consequence is the chance of presenting misleading results. We discuss the issue of amplification sensitivity limits in the light of reports on fidelity, published data from reviewed articles and data analysis approaches. These are important considerations to be reflected in the design of future studies and when evaluating biological conclusions from published microarray studies based on extremely low input RNA quantities

The mathematics of tanning

<p>Abstract</p> <p>Background</p> <p>The pigment melanin is produced by specialized cells, called melanocytes. In healthy skin, melanocytes are sparsely spread among the other cell types in the basal layer of the epidermis. Sun tanning results from an UV-induced increase in the release of melanin to neighbouring keratinocytes, the major cell type component of the epidermis as well as redistribution of melanin among these cells. Here we provide a mathematical conceptualization of our current knowledge of the tanning response, in terms of a dynamic model. The resolution level of the model is tuned to available data, and its primary focus is to describe the tanning response following UV exposure.</p> <p>Results</p> <p>The model appears capable of accounting for available experimental data on the tanning response in different skin and photo types. It predicts that the thickness of the epidermal layer and how far the melanocyte dendrites grow out in the epidermal layers after UV exposure influence the tanning response substantially.</p> <p>Conclusion</p> <p>Despite the paucity of experimental validation data the model is constrained enough to serve as a foundation for the establishment of a theoretical-experimental research programme aimed at elucidating the more fine-grained regulatory anatomy underlying the tanning response.</p

Large-scale inference of the point mutational spectrum in human segmental duplications

<p>Abstract</p> <p>Background</p> <p>Recent segmental duplications are relatively large (≥ 1 kb) genomic regions of high sequence identity (≥ 90%). They cover approximately 4–5% of the human genome and play important roles in gene evolution and genomic disease. The DNA sequence differences between copies of a segmental duplication represent the result of various mutational events over time, since any two duplication copies originated from the same ancestral DNA sequence. Based on this fact, we have developed a computational scheme for inference of point mutational events in human segmental duplications, which we collectively term duplication-inferred mutations (DIMs). We have characterized these nucleotide substitutions by comparing them with high-quality SNPs from dbSNP, both in terms of sequence context and frequency of substitution types.</p> <p>Results</p> <p>Overall, DIMs show a lower ratio of transitions relative to transversions than SNPs, although this ratio approaches that of SNPs when considering DIMs within most recent duplications. Our findings indicate that DIMs and SNPs in general are caused by similar mutational mechanisms, with some deviances at the CpG dinucleotide. Furthermore, we discover a large number of reference SNPs that coincide with computationally inferred DIMs. The latter reflects how sequence variation in duplicated sequences can be misinterpreted as ordinary allelic variation.</p> <p>Conclusion</p> <p>In summary, we show how DNA sequence analysis of segmental duplications can provide a genome-wide mutational spectrum that mirrors recent genome evolution. The inferred set of nucleotide substitutions represents a valuable complement to SNPs for the analysis of genetic variation and point mutagenesis.</p

Segmentation of DNA sequences into twostate regions and melting fork regions

The accurate prediction and characterization of DNA melting domains by computational tools could facilitate a broad range of biological applications. However, no algorithm for melting domain prediction has been available until now. The main challenges include the difficulty of mathematically mapping a qualitative description of DNA melting domains to quantitative statistical mechanics models, as well as the absence of 'gold standards' and a need for generality. In this paper, we introduce a new approach to identify the twostate regions and melting fork regions along a given DNA sequence. Compared with an ad hoc segmentation used in one of our previous studies, the new algorithm is based on boundary probability profiles, rather than standard melting maps. We demonstrate that a more detailed characterization of the DNA melting domain map can be obtained using our new method, and this approach is independent of the choice of DNA melting model. We expect this work to drive our understanding of DNA melting domains one step further.Comment: 17 pages, 8 figures; new introduction, added refs, minor change

Tumor classification and marker gene prediction by feature selection and fuzzy c-means clustering using microarray data

BACKGROUND: Using DNA microarrays, we have developed two novel models for tumor classification and target gene prediction. First, gene expression profiles are summarized by optimally selected Self-Organizing Maps (SOMs), followed by tumor sample classification by Fuzzy C-means clustering. Then, the prediction of marker genes is accomplished by either manual feature selection (visualizing the weighted/mean SOM component plane) or automatic feature selection (by pair-wise Fisher's linear discriminant). RESULTS: The proposed models were tested on four published datasets: (1) Leukemia (2) Colon cancer (3) Brain tumors and (4) NCI cancer cell lines. The models gave class prediction with markedly reduced error rates compared to other class prediction approaches, and the importance of feature selection on microarray data analysis was also emphasized. CONCLUSIONS: Our models identify marker genes with predictive potential, often better than other available methods in the literature. The models are potentially useful for medical diagnostics and may reveal some insights into cancer classification. Additionally, we illustrated two limitations in tumor classification from microarray data related to the biology underlying the data, in terms of (1) the class size of data, and (2) the internal structure of classes. These limitations are not specific for the classification models used

Cancer Predisposition Sequencing Reporter (CPSR): A flexible variant report engine for high-throughput germline screening in cancer

The value of high-throughput germline genetic testing is increasingly recognized inclinical cancer care. Disease-associated germline variants in cancer patients areimportant for risk management and surveillance, surgical decisions and can also havemajor implications for treatment strategies since many are in DNA repair genes. Withthe increasing availability of high-throughput DNA sequencing in cancer clinics andresearch, there is thus a need to provide clinically oriented sequencing reports forgermline variants and their potential therapeutic relevance on a per-patient basis. Tomeet this need, we have developed the Cancer Predisposition Sequencing Reporter(CPSR), an open-source computational workflow that generates a structured reportof germline variants identified in known cancer predisposition genes, highlightingmarkers of therapeutic, prognostic and diagnostic relevance. A fully automated vari-ant classification procedure based on more than 30 refined American College ofMedical Genetics and Genomics (ACMG) criteria represents an integral part of theworkflow. Importantly, the set of cancer predisposition genes profiled in the reportcan be flexibly chosen from more than 40 virtual gene panels established by scientificexperts, enabling customization of the report for different screening purposes andclinical contexts. The report can be configured to also list actionable secondary vari-ant findings, as recommended by ACMG. CPSR demonstrates comparable sensitivityand specificity for the detection of pathogenic variants when compared to otheralgorithms in the field. Technically, the tool is implemented in Python/R, and is freelyavailable through Docker technology. Source code, documentation, example reportsand installation instructions are accessible via the project GitHub page: https://github.com/sigven/cpsr.publishedVersio

Limitations of mRNA amplification from small-size cell samples

BACKGROUND: Global mRNA amplification has become a widely used approach to obtain gene expression profiles from limited material. An important concern is the reliable reflection of the starting material in the results obtained. This is especially important with extremely low quantities of input RNA where stochastic effects due to template dilution may be present. This aspect remains under-documented in the literature, as quantitative measures of data reliability are most often lacking. To address this issue, we examined the sensitivity levels of each transcript in 3 different cell sample sizes. ANOVA analysis was used to estimate the overall effects of reduced input RNA in our experimental design. In order to estimate the validity of decreasing sample sizes, we examined the sensitivity levels of each transcript by applying a novel model-based method, TransCount. RESULTS: From expression data, TransCount provided estimates of absolute transcript concentrations in each examined sample. The results from TransCount were used to calculate the Pearson correlation coefficient between transcript concentrations for different sample sizes. The correlations were clearly transcript copy number dependent. A critical level was observed where stochastic fluctuations became significant. The analysis allowed us to pinpoint the gene specific number of transcript templates that defined the limit of reliability with respect to number of cells from that particular source. In the sample amplifying from 1000 cells, transcripts expressed with at least 121 transcripts/cell were statistically reliable and for 250 cells, the limit was 1806 transcripts/cell. Above these thresholds, correlation between our data sets was at acceptable values for reliable interpretation. CONCLUSION: These results imply that the reliability of any amplification experiment must be validated empirically to justify that any gene exists in sufficient quantity in the input material. This finding has important implications for any experiment where only extremely small samples such as single cell analyses or laser captured microdissected cells are available

MUTYH Mutations Do Not Cause HNPCC or Late Onset Familial Colorectal Cancer

Recently, carriers of biallelic mutations in the base excision repair gene MUTYH, have been demonstrated to have a predisposition for multiple adenomas and colorectal cancer. Still, many questions remain unanswered concerning MUTYH. We have addressed the following: Do biallelic MUTYH mutation carriers invariably demonstrate FAP, and may MUTYH be a gene causing HNPCC, HNPCC-like or dominantly inherited late onset colorectal cancer? We examined affecteds from our total series of HNPCC, HNPCC-like and dominantly inherited late onset colorectal cancer kindreds not demonstrated to have any MMR mutations. Bloodsamples from 96 patients were subjected to sequencing of exon 7 and exon 13 in the MUTYH gene. Two heterozygotes and one homozygote for the European founder mutations were found. The homozygous carrier did not meet criteria for FAP/AFAP. We conclude that MUTYH, when mutated, causes a rare recessively inherited disorder including colorectal- and duodenal cancers. It is not verified that heterozygous carriers of MUTYH mutations have an increased risk of cancer, and they do not explain the occurrence of familial colorectal cancer in the population