Methods for Generating Complex Networks with Selected Structural Properties for Simulations: A Review and Tutorial for Neuroscientists

Many simulations of networks in computational neuroscience assume completely homogenous random networks of the Erdös–Rényi type, or regular networks, despite it being recognized for some time that anatomical brain networks are more complex in their connectivity and can, for example, exhibit the “scale-free” and “small-world” properties. We review the most well known algorithms for constructing networks with given non-homogeneous statistical properties and provide simple pseudo-code for reproducing such networks in software simulations. We also review some useful mathematical results and approximations associated with the statistics that describe these network models, including degree distribution, average path length, and clustering coefficient. We demonstrate how such results can be used as partial verification and validation of implementations. Finally, we discuss a sometimes overlooked modeling choice that can be crucially important for the properties of simulated networks: that of network directedness. The most well known network algorithms produce undirected networks, and we emphasize this point by highlighting how simple adaptations can instead produce directed networks

Assessing long-distance RNA sequence connectivity via RNA-templated DNA-DNA ligation

Many RNAs, including pre-mRNAs and long non-coding RNAs, can be thousands of nucleotides long and undergo complex post-transcriptional processing. Multiple sites of alternative splicing within a single gene exponentially increase the number of possible spliced isoforms, with most human genes currently estimated to express at least ten. To understand the mechanisms underlying these complex isoform expression patterns, methods are needed that faithfully maintain long-range exon connectivity information in individual RNA molecules. In this study, we describe SeqZip, a methodology that uses RNA-templated DNA-DNA ligation to retain and compress connectivity between distant sequences within single RNA molecules. Using this assay, we test proposed coordination between distant sites of alternative exon utilization in mouse Fn1, and we characterize the extraordinary exon diversity of Drosophila melanogaster Dscam1

Assassins and apples: the environmental constraints of two snails that threaten Australian aquatic systems

Context: Alien freshwater snails pose a substantial risk to Australian native aquatic biota.Aims: This study aims to determine the thermal and salinity ranges of two introduced species within Australia, Pomacea sp. and Anentome sp., to facilitate predictions of their potential geographic range should they become widely established.Methods: Laboratory tests were conducted to assess behavioural responses of snails to altered temperature or salinity after different acclimation regimes.Key results: After acclimation at 25°C, Pomacea sp. had a median activity range of 13.5–38°C and Anentome sp. of 12–38.5°C. Higher acclimation temperatures produced observable effects, whereas lower acclimation temperatures did not. Salinity tolerances differed, with Pomacea sp. remaining active at up to 8 parts per thousand (ppt) (after acclimation at 25°C), with acclimation at 20°C resulting in a lower salinity tolerance. By contrast, Anentome sp. snails were active at up to 5 ppt after low salinity acclimation, demonstrating enhanced salinity tolerance compared with non-salinity acclimations.Conclusions: These results showed that both snails are capable of surviving temperatures and salinities that would allow invasion into subtropical and warm-temperate Australian aquatic systems.Implications: Free from the constraints of natural predators, competitors, and parasites, these snails should be of great concern to biosecurity agencies in Australia

A metadata-aware application for remote scoring and exchange of tissue microarray images.

BACKGROUND: The use of tissue microarrays (TMA) and advances in digital scanning microscopy has enabled the collection of thousands of tissue images. There is a need for software tools to annotate, query and share this data amongst researchers in different physical locations. RESULTS: We have developed an open source web-based application for remote scoring of TMA images, which exploits the value of Microsoft Silverlight Deep Zoom to provide a intuitive interface for zooming and panning around digital images. We use and extend existing XML-based standards to ensure that the data collected can be archived and that our system is interoperable with other standards-compliant systems. CONCLUSION: The application has been used for multi-centre scoring of TMA slides composed of tissues from several Phase III breast cancer trials and ten different studies participating in the International Breast Cancer Association Consortium (BCAC). The system has enabled researchers to simultaneously score large collections of TMA and export the standardised data to integrate with pathological and clinical outcome data, thereby facilitating biomarker discovery.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

High resolution melting for mutation scanning of TP53 exons 5-8.

BACKGROUND: p53 is commonly inactivated by mutations in the DNA-binding domain in a wide range of cancers. As mutant p53 often influences response to therapy, effective and rapid methods to scan for mutations in TP53 are likely to be of clinical value. We therefore evaluated the use of high resolution melting (HRM) as a rapid mutation scanning tool for TP53 in tumour samples. METHODS: We designed PCR amplicons for HRM mutation scanning of TP53 exons 5 to 8 and tested them with DNA from cell lines hemizygous or homozygous for known mutations. We assessed the sensitivity of each PCR amplicon using dilutions of cell line DNA in normal wild-type DNA. We then performed a blinded assessment on ovarian tumour DNA samples that had been previously sequenced for mutations in TP53 to assess the sensitivity and positive predictive value of the HRM technique. We also performed HRM analysis on breast tumour DNA samples with unknown TP53 mutation status. RESULTS: One cell line mutation was not readily observed when exon 5 was amplified. As exon 5 contained multiple melting domains, we divided the exon into two amplicons for further screening. Sequence changes were also introduced into some of the primers to improve the melting characteristics of the amplicon. Aberrant HRM curves indicative of TP53 mutations were observed for each of the samples in the ovarian tumour DNA panel. Comparison of the HRM results with the sequencing results revealed that each mutation was detected by HRM in the correct exon. For the breast tumour panel, we detected seven aberrant melt profiles by HRM and subsequent sequencing confirmed the presence of these and no other mutations in the predicted exons. CONCLUSION: HRM is an effective technique for simple and rapid scanning of TP53 mutations that can markedly reduce the amount of sequencing required in mutational studies of TP53.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

The pitfalls of platform comparison: DNA copy number array technologies assessed

<p>Abstract</p> <p>Background</p> <p>The accurate and high resolution mapping of DNA copy number aberrations has become an important tool by which to gain insight into the mechanisms of tumourigenesis. There are various commercially available platforms for such studies, but there remains no general consensus as to the optimal platform. There have been several previous platform comparison studies, but they have either described older technologies, used less-complex samples, or have not addressed the issue of the inherent biases in such comparisons. Here we describe a systematic comparison of data from four leading microarray technologies (the Affymetrix Genome-wide SNP 5.0 array, Agilent High-Density CGH Human 244A array, Illumina HumanCNV370-Duo DNA Analysis BeadChip, and the Nimblegen 385 K oligonucleotide array). We compare samples derived from primary breast tumours and their corresponding matched normals, well-established cancer cell lines, and HapMap individuals. By careful consideration and avoidance of potential sources of bias, we aim to provide a fair assessment of platform performance.</p> <p>Results</p> <p>By performing a theoretical assessment of the reproducibility, noise, and sensitivity of each platform, notable differences were revealed. Nimblegen exhibited between-replicate array variances an order of magnitude greater than the other three platforms, with Agilent slightly outperforming the others, and a comparison of self-self hybridizations revealed similar patterns. An assessment of the single probe power revealed that Agilent exhibits the highest sensitivity. Additionally, we performed an in-depth visual assessment of the ability of each platform to detect aberrations of varying sizes. As expected, all platforms were able to identify large aberrations in a robust manner. However, some focal amplifications and deletions were only detected in a subset of the platforms.</p> <p>Conclusion</p> <p>Although there are substantial differences in the design, density, and number of replicate probes, the comparison indicates a generally high level of concordance between platforms, despite differences in the reproducibility, noise, and sensitivity. In general, Agilent tended to be the best aCGH platform and Affymetrix, the superior SNP-CGH platform, but for specific decisions the results described herein provide a guide for platform selection and study design, and the dataset a resource for more tailored comparisons.</p

Statistical and machine learning methods evaluated for incorporating soil and weather into corn nitrogen recommendations

Nitrogen (N) fertilizer recommendation tools could be improved for estimating corn (Zea mays L.) N needs by incorporating site-specific soil and weather information. However, an evaluation of analytical methods is needed to determine the success of incorporating this information. The objectives of this research were to evaluate statistical and machine learning (ML) algorithms for utilizing soil and weather information for improving corn N recommendation tools. Eight algorithms [stepwise, ridge regression, least absolute shrinkage and selection operator (Lasso), elastic net regression, principal component regression (PCR), partial least squares regression (PLSR), decision tree, and random forest] were evaluated using a dataset containing measured soil and weather variables from a regional database. The performance was evaluated based on how well these algorithms predicted corn economically optimal N rates (EONR) from 49 sites in the U.S. Midwest. Multiple algorithm modeling scenarios were examined with and without adjustment for multicollinearity and inclusion of two-way interaction terms to identify the soil and weather variables that could improve three dissimilar N recommendation tools. Results showed the out-of-sample root-mean-square error (RMSE) for the decision tree and some random forest modeling scenarios were better than the stepwise or ridge regression, but not significantly different than any other algorithm. The best ML algorithm for adjusting N recommendation tools was the random forest approach (r2 increased between 0.72 and 0.84 and the RMSE decreased between 41 and 94 kg N ha−1). However, the ML algorithm that best adjusted tools while using a minimal amount of variables was the decision tree. This method was simple, needing only one or two variables (regardless of modeling scenario) and provided moderate improvement as r2 values increased between 0.15 and 0.51 and RMSE decreased between 16 and 66 kg N ha−1. Using ML algorithms to adjust N recommendation tools with soil and weather information shows promising results for better N management in the U.S. Midwest