Enabling precision medicine via standard communication of HTS provenance, analysis, and results.

A personalized approach based on a patient's or pathogen's unique genomic sequence is the foundation of precision medicine. Genomic findings must be robust and reproducible, and experimental data capture should adhere to findable, accessible, interoperable, and reusable (FAIR) guiding principles. Moreover, effective precision medicine requires standardized reporting that extends beyond wet-lab procedures to computational methods. The BioCompute framework (https://w3id.org/biocompute/1.3.0) enables standardized reporting of genomic sequence data provenance, including provenance domain, usability domain, execution domain, verification kit, and error domain. This framework facilitates communication and promotes interoperability. Bioinformatics computation instances that employ the BioCompute framework are easily relayed, repeated if needed, and compared by scientists, regulators, test developers, and clinicians. Easing the burden of performing the aforementioned tasks greatly extends the range of practical application. Large clinical trials, precision medicine, and regulatory submissions require a set of agreed upon standards that ensures efficient communication and documentation of genomic analyses. The BioCompute paradigm and the resulting BioCompute Objects (BCOs) offer that standard and are freely accessible as a GitHub organization (https://github.com/biocompute-objects) following the "Open-Stand.org principles for collaborative open standards development." With high-throughput sequencing (HTS) studies communicated using a BCO, regulatory agencies (e.g., Food and Drug Administration [FDA]), diagnostic test developers, researchers, and clinicians can expand collaboration to drive innovation in precision medicine, potentially decreasing the time and cost associated with next-generation sequencing workflow exchange, reporting, and regulatory reviews.

Characterization of serum estradiol level and tissue estrogen receptor immunostaining with clinical response and reproductive factor changes in Chinese female patients with esophageal squamous cell carcinoma.

Pre-menopausal female patients have a prolonged survival than post-menopausal patients, indicating that estrogen and/or estrogen receptor (ER) may have some biological effects on prognosis. ER expression in cancer tissue has been reported to be a significant prognostic marker in multiple human cancers. However, the prognostic value of estrogen and/or ER on female patients with esophageal squamous cell carcinoma (ESCC) is rarely reported. The present study was undertaken to elucidate the associations of serum estradiol level, tissue estrogen receptor alpha (ERα) and estrogen receptor beta (ERß) expression with clinical response and reproductive factor changes in 387 female ESCC patients. Radioimmunoassay revealed that serum estradiol level was higher in pre-menopausal than those in peri-menopausal and post-menopausal patients. Furthermore, patients with higher serum estradiol level appeared to have a better survival. Immunostaining results suggested that ERα positive (+) expression was mainly located in cytoplasm of tumor cells with a positive rate of 69.9% and ERß (+) was mainly located in nucleus of tumor cells with a positive rate of 64.9%. We did not find the relations of ER expression with tumor invasion (P>0.05), lymph node metastasis (P>0.05), TNM staging (P>0.05) and treatment method (P>0.05). Surprisingly, ERα (+) expression was higher in post-menopausal patients than those in pre-menopausal patients (P<0.05). Patients with number of pregnancy≥4 have a higher ERß (+) expression than those patients with≤3 (P<0.05). Univariate and multivariate survival analysis showed that ERß (+) expression in addition to ERα (-) expression are favorable prognostic markers in female ESCC patients (P<0.05). Further related study is needed to in-depth explore the potential mechanisms of ERα and ERß in survival of female patients with ESCC.

Apyrase suppression raises extracellular ATP levels and induces gene expression and cell wall changes characteristic of stress responses.

Plant cells release ATP into their extracellular matrix as they grow, and extracellular ATP (eATP) can modulate the rate of cell growth in diverse tissues. Two closely related apyrases (APYs) in Arabidopsis (Arabidopsis thaliana), APY1 and APY2, function, in part, to control the concentration of eATP. The expression of APY1/APY2 can be inhibited by RNA interference, and this suppression leads to an increase in the concentration of eATP in the extracellular medium and severely reduces growth. To clarify how the suppression of APY1 and APY2 is linked to growth inhibition, the gene expression changes that occur in seedlings when apyrase expression is suppressed were assayed by microarray and quantitative real-time-PCR analyses. The most significant gene expression changes induced by APY suppression were in genes involved in biotic stress responses, which include those genes regulating wall composition and extensibility. These expression changes predicted specific chemical changes in the walls of mutant seedlings, and two of these changes, wall lignification and decreased methyl ester bonds, were verified by direct analyses. Taken together, the results are consistent with the hypothesis that APY1, APY2, and eATP play important roles in the signaling steps that link biotic stresses to plant defense responses and growth changes.

FamAnn: an automated variant annotation pipeline to facilitate target discovery for family-based sequencing studies.

FamAnn is an automated variant annotation pipeline designed for facilitating target discovery for family-based sequencing studies. It can apply a different inheritance pattern or a de novo mutations discovery model to each family and select single nucleotide variants and small insertions and deletions segregating in each family or shared by multiple families. It also provides a variety of variant annotations and retains and annotates all transcripts hit by a single variant. Excel-compatible outputs including all annotated variants segregating in each family or shared by multiple families will be provided for users to prioritize variants based on their customized thresholds. A list of genes that harbor the segregating variants will be provided as well for possible pathway/network analyses. FamAnn uses the de facto community standard Variant Call Format as the input format and can be applied to whole exome, genome or targeted resequencing data. AVAILABILITY: https://sites.google.com/site/famannotation/home CONTACT: jianchaoyao@gmail.com, kelvinzhang@mednet.ucla.edu, mccombie@cshl.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Genome-scale cluster analysis of replicated microarrays using shrinkage correlation coefficient.

BACKGROUND: Currently, clustering with some form of correlation coefficient as the gene similarity metric has become a popular method for profiling genomic data. The Pearson correlation coefficient and the standard deviation (SD)-weighted correlation coefficient are the two most widely-used correlations as the similarity metrics in clustering microarray data. However, these two correlations are not optimal for analyzing replicated microarray data generated by most laboratories. An effective correlation coefficient is needed to provide statistically sufficient analysis of replicated microarray data. RESULTS: In this study, we describe a novel correlation coefficient, shrinkage correlation coefficient (SCC), that fully exploits the similarity between the replicated microarray experimental samples. The methodology considers both the number of replicates and the variance within each experimental group in clustering expression data, and provides a robust statistical estimation of the error of replicated microarray data. The value of SCC is revealed by its comparison with two other correlation coefficients that are currently the most widely-used (Pearson correlation coefficient and SD-weighted correlation coefficient) using statistical measures on both synthetic expression data as well as real gene expression data from Saccharomyces cerevisiae. Two leading clustering methods, hierarchical and k-means clustering were applied for the comparison. The comparison indicated that using SCC achieves better clustering performance. Applying SCC-based hierarchical clustering to the replicated microarray data obtained from germinating spores of the fern Ceratopteris richardii, we discovered two clusters of genes with shared expression patterns during spore germination. Functional analysis suggested that some of the genetic mechanisms that control germination in such diverse plant lineages as mosses and angiosperms are also conserved among ferns. CONCLUSION: This study shows that SCC is an alternative to the Pearson correlation coefficient and the SD-weighted correlation coefficient, and is particularly useful for clustering replicated microarray data. This computational approach should be generally useful for proteomic data or other high-throughput analysis methodology.

A refined algorithm for multisensor image registration based on pixel migration.

Multimodality image registration via pixel migration is a powerful approach. However, it suffers from a serious problem--the global maximum on the sum of squared gradient magnitude (SSG) surface does not correspond to the correct solution of registration. To solve the problem, we partition the search space into feasible and infeasible regions. The genetic algorithm (global optimizer) is used to obtain a good initial estimate of registration parameters and followed by a fast refining with Powell's approach (local optimizer). The experimental results demonstrate that the use of this modified pixel migration algorithm on multisensor image registration is very effective.

AANT: the Amino Acid-Nucleotide Interaction Database.

We have created an Amino Acid-Nucleotide Interaction Database (AANT; http://aant.icmb.utexas. edu/) that categorizes all amino acid-nucleotide interactions from experimentally determined protein-nucleic acid structures, and provides users with a graphic interface for visualizing these interactions in aggregate. AANT accomplishes this by extracting individual amino acid-nucleotide interactions from structures in the Protein Data Bank, combining and superimposing these interactions into multiple structure files (e.g. 20 amino acids x 5 nucleotides) and grouping structurally similar interactions into more readily identifiable clusters. Using the Chime web browser plug-in, users can view 3D representations of the superimpositions and clusters. The unique collection and representation of data on amino acid-nucleotide interactions facilitates understanding the specificity of protein-nucleic acid interactions at a more fundamental level, and allows comparison of otherwise extremely disparate sets of structures. Moreover, by modularly representing the fundamental interactions that govern binding specificity it may prove possible to better engineer nucleic acid binding proteins.