Tools

Here you will find the computational tools developed on the Dalmolin Systems Biology Group. We are committed to the best practices on software developing and documentation. However if you need any help on the usage, feel free to contact us.

KEGG Pathway Viewer
KEGG Pathway Viewer The online tool to visualize and interact with KEGG metabolic pathways

The network elaborated in our study contemplates protein classifications according to the AP detection algorithm,facilitating the visual identification of the most important proteins of a network.

Transcriptogramer
Transcriptogramer R/Bioconductor package for transcriptional analysis based on transcriptograms

Transcriptograms are genome wide gene expression profiles that provide a global view for the cellular metabolism, while indicating gene sets whose expressions are altered.

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GenePlast
GenePlast Evolutionary and plasticity analysis of orthologous groups

Geneplast is designed for evolutionary and plasticity analysis based on orthologous groups distribution in a given species tree.

MEDUSA/EURYALE
MEDUSA/EURYALE Analysis pipelines for Shotgun metagenomics data

A pipeline for sensitive taxonomic classification and flexible functional annotation of shotgun metagenomic data.

BulkRNA
BulkRNA An analysis pipeline for pre-processing, alignment and quantification of bulk RNA-Seq data.

Dalmolin Group’s workflow for pre-processing, alignment and quantification of bulk RNA-seq data.

geneplast.data
geneplast.data Supporting data for geneplast evolutionary analyses

Provides datasets from different sources via AnnotationHub to use in geneplast pipelines. The datasets have species, phylogenetic trees, and orthology relationships among eukaryotes from different orthologs databases.

annotate
annotate Annotate each query using the best alignment for which a mapping is known.

A Python tool that annotates each query from a BLAST/DIAMOND tabular output using the best alignment for which a mapping is known.

Seriation
Seriation C code to find a suitable linear order for a set of proteins.

Solves the Seriation problem finding a suitable linear order for a set of proteins. The result is a list of proteins ordered in one dimension such that functionally associated proteins are closer.