With recent explosion in the diversity and volume of genetic data generated, an increasing number of genetic simulation programs have been developed to aid the development of statistical methods for the analysis of such data in all genetic-related disciplines (Escalona et al., 2016; Hoban et al., 2012; Peng et al., 2015; Ritchie and Bush, 2010). Despite the recognized importance of genetic simulation tools, it is often difficult to discover and select the right simulation tool for a particular study due to differences in the type of genetic data of interest, simulation methods, features, terminologies and assumptions (Mechanic et al., 2012), and lack of external evaluations of the usability and maintenance status of published simulators. To address these issues, we want to encourage use of the Genetic Simulation Resources (GSR) online catalog and search tool (https://popmodels.cancercontrol.cancer.gov/gsr/) and participation in the GSR Certification Program. GSR was created in 2013 to help researchers sort through and compare a large number of genetic simulation programs to identify the best tool for their research topic (Peng et al., 2013). The GSR catalog currently features more than 100 simulators that use various simulation techniques (coalescent and forward-time simulations are the most common methods), and with application areas such as linkage analysis, genome wide association and sequencing analyses. GSR records basic information, features of the simulation programs and lists typical applications of the software. After reviewing the software listed in GSR, we realized that the quality and usefulness of published simulation tools varied greatly due to inaccessible source code, lack of or incomplete documentation, difficulties in installation and execution, lack of support from authors and lack of programs maintenance (Peng et al., 2015). An approach to address questions about the quality and utility of tools was suggested in a workshop in 2013 (Chen et al., 2015), to evaluate genetic simulation tools based on a defined checklist of features that may benefit end users. Therefore, the GSR Certification Program was developed and implemented to appraise tools based on criteria in the categories of accessibility, documentation, application and support (https://popmodels.cancercontrol.cancer.gov/gsr/certification/). It is hoped that evaluating software based on these criteria will encourage improvements in reporting and documentation of simulation software tools. Moreover, the GSR certification criteria ensure that these programs are more readily findable and usable by the research community and consistent with the FAIR (Findability, Accessibility, Interoperability and Reusability) principles for scientific data management and stewardship (Wilkinson et al., 2016). The GSR Certification Program is overseen by the GSR Certification Committee, which currently consists of NIH staff and extramural researchers. The manuscript authors invite volunteers who wish to participate in the evaluation process to contact the corresponding author of this letter. The GSR Certification Committee is in process of reviewing all packages cataloged in GSR, beginning with newer software packages and packages that are considered actively maintained and broadly used in the scientific community. The review process consists of two stages; an initial review where a GSR moderator completes a questionnaire based on publicly available information (e.g. publication, web site, documentation, Pubmed citation, issue tracker), and a panel review during which three members of the GSR Certification Committee decide about award of any certificates (Accessibility, Documentation, Application and Support). Simulators with one or more GSR certificates are prominently listed on the GSR web site. Simulators which obtain all 4 GSR certificates are invited to place a GSR Certified tile on the homepages for their software. The GSR Certification Program provides a service to the community by evaluating genetic simulation software, with the ultimate goal of promoting the development and application of genetic simulation software. With Bioinformatics being the target journal for many simulation programs, it would be beneficial to authors, the journal and the scientific community if package authors are encouraged to register their simulation programs at the GSR where they are evaluated to conform to basic guidelines for accessibility, documentation, application and support for GSR certifications. We believe this policy could facilitate dissemination of software and ensure the quality and usability of genetic simulation software. The development and maintenance of GSR has been supported by contracts from the National Cancer Institute, National Institutes of Health [HHSN261201100558P] and [HHSN261201300002I]. Conflict of Interest: none declared.

BACKGROUND: Multilayered hierarchical gene regulatory networks (ML-hGRNs) are very important for understanding genetics regulation of biological pathways. However, there are currently no computational algorithms available for directly building ML-hGRNs that regulate biological pathways. RESULTS: A bottom-up graphic Gaussian model (GGM) algorithm was developed for constructing ML-hGRN operating above a biological pathway using small- to medium-sized microarray or RNA-seq data sets. The algorithm first placed genes of a pathway at the bottom layer and began to construct a ML-hGRN by evaluating all combined triple genes: two pathway genes and one regulatory gene. The algorithm retained all triple genes where a regulatory gene significantly interfered two paired pathway genes. The regulatory genes with highest interference frequency were kept as the second layer and the number kept is based on an optimization function. Thereafter, the algorithm was used recursively to build a ML-hGRN in layer-by-layer fashion until the defined number of layers was obtained or terminated automatically. CONCLUSIONS: We validated the algorithm and demonstrated its high efficiency in constructing ML-hGRNs governing biological pathways. The algorithm is instrumental for biologists to learn the hierarchical regulators associated with a given biological pathway from even small-sized microarray or RNA-seq data sets.

SUMMARY: Many simulation methods and programs have been developed to simulate genetic data of the human genome. These data have been widely used, for example, to predict properties of populations retrospectively or prospectively according to mathematically intractable genetic models, and to assist the validation, statistical inference and power analysis of a variety of statistical models. However, owing to the differences in type of genetic data of interest, simulation methods, evolutionary features, input and output formats, terminologies and assumptions for different applications, choosing the right tool for a particular study can be a resource-intensive process that usually involves searching, downloading and testing many different simulation programs. Genetic Simulation Resources (GSR) is a website provided by the National Cancer Institute (NCI) that aims to help researchers compare and choose the appropriate simulation tools for their studies. This website allows authors of simulation software to register their applications and describe them with well-defined attributes, thus allowing site users to search and compare simulators according to specified features. AVAILABILITY: http://popmodels.cancercontrol.cancer.gov/gsr.

BACKGROUND: Identification of transcription factors (TFs) involved in a biological process is the first step towards a better understanding of the underlying regulatory mechanisms. However, due to the involvement of a large number of genes and complicated interactions in a gene regulatory network (GRN), identification of the TFs involved in a biology process remains to be very challenging. In reality, the recognition of TFs for a given a biological process can be further complicated by the fact that most eukaryotic genomes encode thousands of TFs, which are organized in gene families of various sizes and in many cases with poor sequence conservation except for small conserved domains. This poses a significant challenge for identification of the exact TFs involved or ranking the importance of a set of TFs to a process of interest. Therefore, new methods for recognizing novel TFs are desperately needed. Although a plethora of methods have been developed to infer regulatory genes using microarray data, it is still rare to find the methods that use existing knowledge base in particular the validated genes known to be involved in a process to bait/guide discovery of novel TFs. Such methods can replace the sometimes-arbitrary process of selection of candidate genes for experimental validation and significantly advance our knowledge and understanding of the regulation of a process. RESULTS: We developed an automated software package called TF-finder for recognizing TFs involved in a biological process using microarray data and existing knowledge base. TF-finder contains two components, adaptive sparse canonical correlation analysis (ASCCA) and enrichment test, for TF recognition. ASCCA uses positive target genes to bait TFS from gene expression data while enrichment test examines the presence of positive TFs in the outcomes from ASCCA. Using microarray data from salt and water stress experiments, we showed TF-finder is very efficient in recognizing many important TFs involved in salt and drought tolerance as evidenced by the rediscovery of those TFs that have been experimentally validated. The efficiency of TF-finder in recognizing novel TFs was further confirmed by a thorough comparison with a method called Intersection of Coexpression (ICE). CONCLUSIONS: TF-finder can be successfully used to infer novel TFs involved a biological process of interest using publicly available gene expression data and known positive genes from existing knowledge bases. The package for TF-finder includes an R script for ASCCA, a Perl controller, and several Perl scripts for parsing intermediate outputs. The package is available upon request ([email protected]). The R code for standalone ASCCA is also available.

BACKGROUND: Due to the high cost and low reproducibility of many microarray experiments, it is not surprising to find a limited number of patient samples in each study, and very few common identified marker genes among different studies involving patients with the same disease. Therefore, it is of great interest and challenge to merge data sets from multiple studies to increase the sample size, which may in turn increase the power of statistical inferences. In this study, we combined two lung cancer studies using microarray GeneChip, employed two gene shaving methods and a two-step survival test to identify genes with expression patterns that can distinguish diseased from normal samples, and to indicate patient survival, respectively. RESULTS: In addition to common data transformation and normalization procedures, we applied a distribution transformation method to integrate the two data sets. Gene shaving (GS) methods based on Random Forests (RF) and Fisher's Linear Discrimination (FLD) were then applied separately to the joint data set for cancer gene selection. The two methods discovered 13 and 10 marker genes (5 in common), respectively, with expression patterns differentiating diseased from normal samples. Among these marker genes, 8 and 7 were found to be cancer-related in other published reports. Furthermore, based on these marker genes, the classifiers we built from one data set predicted the other data set with more than 98% accuracy. Using the univariate Cox proportional hazard regression model, the expression patterns of 36 genes were found to be significantly correlated with patient survival (p < 0.05). Twenty-six of these 36 genes were reported as survival-related genes from the literature, including 7 known tumor-suppressor genes and 9 oncogenes. Additional principal component regression analysis further reduced the gene list from 36 to 16. CONCLUSION: This study provided a valuable method of integrating microarray data sets with different origins, and new methods of selecting a minimum number of marker genes to aid in cancer diagnosis. After careful data integration, the classification method developed from one data set can be applied to the other with high prediction accuracy.