Free Downloads


tranSMART 1.2 Training Manual, October 2015 – Download

tranSMART GWAS ETL Loader with documentation, April 2015 – Overview and Github for GWAS ETL package


Rancho BioSciences curates public and private datasets and imports these datasets into a variety of databases.

As an example of this utility, the following public studies, taken from NCBI’s Gene Expression Omnibus related to oncology, inflammation and asthma have been manually curated by Rancho BioSciences and deposited into a publically available tranSMART instance for the tranSMART Foundation. Our goal is to work with partners to provide more manually curated data sets and to put them into the public domain. To get the ball rolling we are donating the following GEO datasets that have been manually curated, organized and are available to download FREE of charge.

If you are interested in collaborating on other datasets, public or internal, please contact


GSE1456: Tissue material was collected from all breast cancer patients receiving surgery at Karolinska Hospital from 1994-1996. This series contains expression data for 159 tumors from which RNA could be collected in sufficient amounts and quality for analysis – View GSE1456 Files

GSE4271: In this study authors investigated 77 primary high-grade astrocytomas and 23 matched recurrences so that changes in gene expression related to both survival and disease progression can be identified. Samples in the study include WHO grade III and IV astrocytomas with a wide range of survival times – View GSE4271 Files

GSE4698: Gene expression profiling was performed on 60 prospectively collected samples of children with first relapse of acute lymphoblastic leukemia enrolled on the relapse trial ALL-REZ BFM 2002 of the Berlin-Frankfurt-Muenster study group – View GSE4698 Files

GSE4922: Authors of this study investigated the expression profiles of 347 primary invasive breast tumors on Affymetrix microarrays. Three separate breast cancer cohorts were analyzed: 1) Uppsala (n=249), 2) Stockholm (n=58), 3) Singapore (n=40). The Uppsala and Singapore data can be accessed in GSE4922. The Stockholm cohort data can be accessed at GEO Series GSE1456, available above – View GSE4922 Files

GSE20194: GGene expression data from 230 stage I-III breast cancers were generated from fine needle aspiration specimens of newly diagnosed breast cancers before any therapy. The biopsy specimens were collected sequentially during a prospective pharmacogenomic marker discovery study between 2000 and 2008. These specimens represent 70-90% pure neoplastic cells with minimal stromal contamination. In the study, patients received 6 months of preoperative (neoadjuvant) chemotherapy including paclitaxel, 5-fluorouracil, cyclophosphamide and doxorubicin followed by surgical resection of the cancer – View GSE20194 Files

GSE27831: Gene expression profiles of 29 unique samples from uveal melanoma patients were measured on Affymetrix microarray. In addition, expression of syntenin-1 was measured by RT-PCR and this data is also available in the study – View GSE27831 Files


GSE8650: Authors analyzed gene expression profiles in 19 pediatric patients with SoJIA during the systemic phase of the disease (fever and/or arthritis), 25 SoJIA patients with no systemic symptoms (arthritis only or no symptoms), 39 healthy controls, 94 pediatric patients with acute viral and bacterial infections (available under GSE6269), 38 pediatric patients with Systemic Lupus Erythematosus (SLE), and 6 patients with a second IL-1 mediated disease known as PAPA syndrome – View GSE8650 Files

GSE13732: In this study authors developed biomarkers that may predict development of CIS into a full multiple sclerosis. Expression data was taken from 37 CIS patients and 28 healthy controls at baseline. 34 CIS patients and 10 healthy controls were resampled at a second time point, approximately one year later. Patients were followed clinically for up to two years to determine the TTC (time to conversion to MS) – View GSE13732 Files

GSE24060: Gene expression profiling from monozygotic twin pairs with various systemic autoimmune diseases were investigated in this study. RNA microarray analyses (Agilent Human 1A(V2) 20K oligo arrays) quantified differential gene expression in blood from 20 monozygotic (MZ) twin pairs, to minimize polymorphic gene effects, discordant for SAID (six with systemic lupus erythematosus (SLE), six rheumatoid arthritis (RA), eight idiopathic inflammatory myopathies (IIM)) and 40 unrelated – matched controls – View GSE24060 Files

GSE17755: This study contains a gene expression profile of peripheral blood cells. These PBMCs were isolated from patients (248 in total) with rheumatoid arthritis (RA)/ systemic lupus erythematosus (SLE)/ polyarticular type juvenile idiopathic arthritis (polyJIA)/ systemic-onset JIA (sJIA) vs healthy children (HC) and healthy individual (HI) – View GSE17755 Files


GSE13168: Authors assessed the effects of epidermal growth factor and interleukin 1-beta stimulation, and the modulatory effects of glucocorticoids treatment and protein kinase A inhibition, on the airway smooth muscle transcriptome by microarray analysis. The samples from 4 donors were subjected to different stimulations by Il-1b and EGF (or both) with or without pre-treatment with fluticasone, and data was collected at different timepoints – View GSE13168 Files


Lupus proto array analysis.  Michael Turewicz (Medizinisches Proteom-Center, Ruhr-University Bochum, Germany), maintainer of the PAA Bioconductor package, welcomed the new features: “Rancho’s customizations regarding a better results visualization and improved batch filtering provide valuable new features to the PAA package for the analysis of protein array experiments. Contributions from the user community are the strength of open source software development.” The complete Rancho-modified PAA package, example R script, and test data are available in GitHub ( The new batch correction and plotting features will also be added to future releases of the PAA package in Bioconductor. Bioconductor’s next release will be in April 2016.

Read More

Biosensor data in tranSMART with Matlab integration. SQL scripts and instructions can be found in the github repository: