PanDrugs provides a platform to guide the selection of therapies from the results of genome-wide studies in cancer disease.

Using 4 alternative inputs (e.g. standard VCF files, RNK files, gene list and drug query), PanDrugs identify actionable molecular alterations and prioritize drugs by calculating gene-drug scores which takes into account: i) genomic feature evidence by mutation impact score; ii) target pathway context; iii) drug approval status (FDA, clinical trial or experimental small molecule inhibitors) and iv) manually-curated pharmacological information retrieved from the literature.

PanDrugs scores combines biological and clinical relevance of the genes and their susceptibility to be targeted reflecting the strength or evidence level of the gene-drug association in order to assist the clinical decision making.


DEWE (Differential Expression Workflow Executor) is an open-source software to perform genetic differential expression analysis of RNA-Seq data.

DEWE offers built-in, easy-to-configure workflows that facilitate the execution of DE analyses. Currently, DEWE provides two differential expression analysis workflows: HISAT2, StringTie and Ballgown and Bowtie2, StringTie and R libraries (Ballgown and edgeR).

A key feature of DEWE is the interactive results management and visualisation. The main results of the execution of a built-in workflow are the outputs provided by Ballgown or edgeR. These outputs are automatically visualised in DEWE after the workflow execution and they can be reopened at any later time.


SEDA (SEquence DAtaset builder) is an open source application for processing FASTA files containing DNA and protein sequences.

Among other functions, SEDA allows you to:

  • Filter sequences based on different criteria (including text patterns).
  • Translate nucleic acid sequences into amino acid sequences.
  • Execute Blast analyses.
  • Remove duplicated sequences.
  • Sort, merge, split or reformat files.

EvoPPI is an open-source web application to compare protein-protein interactions (PPIs) from different databases and species.

EvoPPI has some unique features such as the use of a versatile BLAST approach for defining orthologous/paralogous genes (that allows, for instance, to predict PPI networks in species where such data does not exist using a comparative approach), and the use of colour codes for an easy visualization of the differences between datasets .

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