We have the SOLUTIONS to meet your BIOINFORMATICS needs

When it comes to developing custom pipelines, implementing the most cutting edge analyses & profiling techniques, we work with our clients to ensure their informatics needs are met. Whether implementing a sophisticated analysis method, designing a custom analysis pipeline, utilizing cutting-edge machine learning techniques for biomarker classification, or simply producing publication-ready figures, our staff combines years of experience with highly qualified personnel to meet your every need.

Below, we have provided a listing of some of our most commonly used tools, software packages, etc. when performing advanced analysis. However, the below tools represent the “tip of the iceberg”. When it comes to our customized solutions, our staff is highly familiar with a variety of statistical programing languages (RPASWSAS), general programming languages (Perl, Python, GO, etc.), shell languages (bash, zshell, etc.), and operating system (Windows, MacOs, Linux, etc.).

GeneMaths XT 2.1

GeneMaths XT 2.1 is perhaps the most complete and professional software for microarray analysis available. Its advanced concept of layers and subsets makes it is possible to work with different layers for different data outputs, for example Cy3 and Cy5, standard deviations, error values, etc., as well as different subsets of genes and/or arrays. Also unique is the full error handling through all analysis, mining, and statistics functions. Further features include: * Active History concept: record and repeat all steps of the analysis through history files and templates * Reliable data normalization for all types of arrays * Interactive querying and pattern matching * Easy plot wizard for all types of plots and graphs * Unsupervised learning through transversal clustering, hierarchical k-means partitioning, PCA, Discriminant Analysis, Self-organizing maps * Supervised learning using neural networks, support-vector machines, k-nearest neighbor * Analysis of variance, multivariate analysis, and a large number of statistical tests * Special analysis tools for time-course experiments * Seamless interaction between all analysis applications * Interactivity with databases on the Internet * Automation and customization through powerful script language


Gene Set Enrichment Analysis (GSEA)

With the explosion of microarray solutions and high-throughput sequencing technologies, RNA expression analysis has become a standard tool in biomedical research. Despite the advancements in genomics technologies, extracting the biological relevance from these applications presents a challenge. The Broad Institute’s GSEA software provides an open-source interface for implementing a computational method that determines whether an a priori defined set of genes shows statistically significant, concordant differences between two biological states. Using this method, GSEA provides an integrated solution to perform pathway enrichment analysis across a multitude of databases (GO, KEGG, Pathway Commons, etc.), allowing researchers to glean the biological and functional relevance from a priori sets of genes.


Bioconductor Software

The Genome Explorations team always seeks to push the limits of bioinformatics and provide cutting-edge applications and analysis solutions. Oftentimes, this means implementing the latest non-standardized scientific algorithms for data normalization and analysis while exploring the most advanced visualization tools available. Based primarily on the statistical R programming language, Bioconductor is an open source and open development software project for the analysis and comprehension of genomic data. Bioconductor provides access to the most powerful statistical and graphical methods for the analysis of genomic data. Combined with the proficiency of our bioinformatics team, Genome Explorations can provide and implement virtually any analysis or data visualization package for: pre-processing of Affymetrix and cDNA array data, identification of differentially expressed genes, linear and non-linear modeling, prediction, resampling, cluster analysis, time series analysis, supervised classification via support vector machines.