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  1. Data visualization: We can provide custom data visualizations to expand on any of the analyses we provide using a wide variety of R (ggplot, etc.) and python (and other) data visualization libraries.
  2. Promoter motif analysis: We have developed in-house an algorithm, SArKS, for de novo discovery of biological sequence motifs and multi-motif domains preferentially present in promoter regions for genes with elevated differential expression scores. We are also happy to apply other standard motif discovery tools (e.g., DREME, HOMER, MEME, MEME-ChIP, etc.) or to help develop custom promoter analysis approaches.
  3. Protein-protein interaction network analysis: Analysis of Affinity-Purification Mass Spec (AP-MS) experiment data to identify high-confidence protein-protein interactions and construct an interaction network. Steps include per-dataset quality control and bait/prey protein interaction quantification, followed by identification of high-confidence experiment-wide bait/prey interactions (e.g. using SAINT or SAINTexpress tools) and visualization of the resulting interaction network using Cytoscape.
  4. Statistical analysis/biostatistics/machine learning problems: Downstream analysis of biological data often requires a number of modern statistical and machine learning methods. We can provide assistance in building, analyzing, and evaluating predictive models (classification, regression, time-to-event, etc.) appropriate for diverse experimental designs (and are also happy to provide advice on experimental design if you are currently planning your next project). We also suggest considering unsupervised multivariate analysis methods (PCA, (N)MDS, t-SNE, clustering, etc.) as particularly useful complements to many of the services we provide.
  5. Benchmarking of tools/pipelines: New bioinformatics tools are introduced everyday and a thorough comparison is required to select the most appropriate tool.  Evaluation of bioinformatics tools for accuracy and performance will be performed using simulated and/or real data.
  6. Application development/Optimization of pipelines to run on HPC environmentsCompute clusters such as those available at the Texas Advanced Computing Center (TACC) offer massive resources for compute-intensive tasks on large data. However, optimizing pipelines to take advantage of the parallel architecture of a compute cluster often requires extra processing steps. We have experience with adapting existing pipelines and software to a massively parallel environment (such as the Trinity transcriptiome assembler and BLAST) and can work with researchers to adapt pipelines for HPC clusters.


Internal customers (payment from a UT Austin account): $88$98/hour

External customers (anyone paying from a non-UT Austin account): $112$124/hour

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