A variety of software tools and statistical code for biostatistical and bioinformatical analyses have been developed by members of the Division of Quantitative Sciences. For more information, please contact the tool’s developer.

Common Statistical Analyses

SAS Macros

  • A collection of SAS macros to automate common analyses.
  • DemographTable: Creates a demographic table for data with two treatment groups.
  • Frequency_t: Creates a frequency table for multiple variables.
  • TTest_FDR:  Performs two samples independent T-test and use FDR to adjust for multiple tests.
  • SAS Code file: UFHCCmacros
  • Documentation: SAS Macro Guide
  • Note: All macros here assumed that your data is cleaned properly. If you are not sure how to clean your data, please refer to the Data cleaning Tips.

Functional annotation of sequence data


  • Java desktop application.
  • Tool for predicting the function of genes starting from their nucleotide sequences. The tool also preforms gene prediction, analysis of RNA-seq data and interpretation based on the predicted functions.
  • Blast2GO: a universal tool for annotation, visualization and analysis in functional genomics research. Conesa A, Götz S, García-Gómez JM, Terol J, Talón M, Robles M. Bioinformatics. 2005 Sep 15;21(18):3674-6. Epub 2005 Aug 4.
  • High-throughput functional annotation and data mining with the Blast2GO suite. Götz S, García-Gómez JM, Terol J, Williams TD, Nagaraj SH, Nueda MJ, Robles M, Talón M, Dopazo J, Conesa A. Nucleic Acids Res. 2008 Jun;36(10):3420-35. doi: 10.1093/nar/gkn176
  • Developed by: Ana Conesa Laboratory


  • Java desktop application.
  • Annotation of function at isoform level. Statistical analysis of altenrative splicing and functional interpretation of results.
  • Developed by: Ana Conesa Laboratory


  • Webserver.
  • Tool for the identification of long-non coding RNAs acting as sponges of microRNAs.
  • spongeScan: A web for detecting microRNA binding elements in lncRNA sequences. Furió-Tarí P, Tarazona S, Gabaldón T, Enirght AJ, Conesa A. Nucleic Acids Res. 2016 Jul 8;44(W1):W176-80. doi: 10.1093/nar/gkw443. Epub 2016 May 19.
  • Developed by: Ana Conesa Laboratory

Multomics data integration


  • Web application.
  • Application of the analysis and visualization of multomics data on the template of KEGG pathways.
  • PaintOmics 3: a web resource for the pathway analysis and visualization of multi-omics data.Hernandez-de-Diego R, Tarazona S, Martinez-Mira C, Balzano-Nogueira L, Furió-Tarí P, Pappas  G, Conesa A. Nucleic Acid Research, May 25th 2018.
  • Developed by: Ana Conesa Laboratory



  • Tool for the annotation and storage and annotation of multiomics datasets.
  • STATegra EMS: An Experiment Management System for complex next-generation omics  experiments. Hernández R, Boix-Chova N, Gómez-Cabrero D, Tegner J, Abugessaisa I, Conesa A.  BMC Systems Biology 2014, 8(Suppl 2):S9
  • Developed by: Ana Conesa Laboratory


  • Python package.
  • Tool for linking NGS region-based data to gene annotations for data integration.
  • RGmatch: Matching genomic regions to proximal genes in omics data integration Furió-Tarí P, Conesa A, Tarazona S. BMC Bioinformatics 2016 Nov 22 17:1293 doi: 10.1186/s12859-016-1293-1
  • Developed by: Ana Conesa Laboratory

Quality Control of NGS data


  • Java desktop application
  • Quality control of mapped reads from multiple omics technologies.
    Qualimap 2: advanced multi-sample quality control for high-throughput sequencing data.Okonechnikov K, Conesa A, Garcia-Alcalde, F. Bioinformatics. 2016 Jan 15;32(2):292-4
    Qualimap: evaluating next generation sequencing alignment data.
    García-Alcalde F, Okonechnikov K, Carbonell J, Ruiz LM, Götz S, Tarazona S, Meyer TF, Conesa A.Bioinformatics 2012;28(20):2678-9.
  • Developed by: Ana Conesa Laboratory


  • Python package.
  • Quality control and annotation of Pacbio Iso-seq data
    SQANTI: extensive characterization of long read transcript sequences for quality control in full-length transcriptome identification and quantification.
    Tardaguila M, de la Fuente L, Marti C, Pereira C, del Risco H, Ferrell M, Mellado M, Macchietto M, Verheggen K, Edelmann M, Ezkurdia I, Vazquez J, Tress M, Mortazavi A, Martens L, Rodriguez-Navarro S, Moreno-Manzano V and Conesa A.  Genome Research, 2018. February 9, 2018, doi:10.1101/gr.222976.117.
  • Developed by: Ana Conesa Laboratory

Statistical Analysis of gene expression data


  • R package.
  • Analysis of gene expression time series data. Includes analysis of microarrays, RNA-seq and differential isoform expression
    Next-maSigPro: updating maSigPro Bioconductor package for RNA-seq time series. Nueda MJ, Tarazona S and Conesa A. Bioinformatics. 2014 Sep 15;30(18):2598-602.
    Next-maSigPro: updating maSigPro Bioconductor package for RNA-seq time series. Nueda MJ, Tarazona S and Conesa A. Bioinformatics. 2014 Sep 15;30(18):2598-602
    maSigPro: a method to identify significantly differential expression profiles in time-course microarray experiments.  Conesa A, Nueda MJ, Ferrer A, Talon M.  Bioinformatics 2006, May 1;22(9):1096-102
  • Developed by: Ana Conesa Laboratory


  • R package.
  • Quality Control of RNA-seq quantification data, and differential expression analysis using non-parametric statistics.
  • Data quality aware analysis of differential expression in RNA-seq with NOISeq R/Bioc  package. Tarazona S, Furio-Tari P, Turra D, Di Prieto A, Nueda Mª J, Ferrer A, Conesa A. Nucleic Acids Res. 2015 Dec 2;43(21):e140.
  • Developed by: Ana Conesa Laboratory


  • R package.
  • Removal of batch effect in quantitative NGS data.
  • ARSyN: a method for the identification and removal of systematic noise in multifactorial time course microarray experiments. Nueda MJ, Ferrer A, Conesa A. Biostatistics 2012 (3):553-66
  • Developed by: Ana Conesa Laboratory
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