Resources

Digital tools from Ciencia & Vida and its research teams for exploring biological data and supporting your research.

Scientific applications

Explore data, analyze experiments and organize your laboratory records with our digital platforms.

Introduction · 55 sSource ↗

Video transcript

Text shown in the video, in order. The clip has no audio. Interface examples accompany each explanation.

0:00–0:04
Centro Ciencia y Vida. Electronic laboratory notebook. Your team's experimental record. Access with a Ciencia & Vida account. elab.cienciavida.org
Institutional title card · identical to the closing
0:04–0:11
The experimental record underpins traceability and reproducibility in research.
Example interface · official eLabFTW documentation
0:11–0:18
Ciencia & Vida provides an electronic laboratory notebook built on the open-source eLabFTW.
Example interface · official eLabFTW documentation
0:18–0:26
Each experiment is documented as an entry with its procedure, results and attached files.
Example interface · official eLabFTW documentation
0:26–0:31
Templates standardise the team's recurring protocols.
Example interface · official eLabFTW documentation
0:31–0:37
Entries can be linked to equipment, reagents and other laboratory resources.
Example interface · official eLabFTW documentation
0:37–0:42
Tags and search make any record retrievable.
Example interface · official eLabFTW documentation
0:42–0:48
Access permissions define who can view or edit each entry.
Example interface · official eLabFTW documentation

Electronic laboratory notebook
Your team's experimental record
Access with a Ciencia & Vida account
elab.cienciavida.org

Electronic laboratory notebook

Bring experiments, protocols and research records together in one place. Ciencia & Vida’s electronic laboratory notebook helps research teams organize their work and document each step of the experimental process.

Introduction · 65 sSource ↗

Video transcript

Text shown in the video, in order. The clip has no audio. Interface examples accompany each explanation.

0:00–0:04
Centro Ciencia y Vida. scExplorer. Single-cell RNA-seq analysis. apps.cienciavida.org/scexplorer
Institutional title card · identical to the closing
0:04–0:11
Single-cell RNA sequencing measures gene expression in thousands of individual cells.
Public scExplorer guide
0:11–0:19
Its analysis involves several stages: quality control, dimensionality reduction, clustering and annotation.
Public scExplorer guide
0:19–0:25
scExplorer brings this workflow together in a web platform from Ciencia & Vida.
0:25–0:32
It accepts user data or a public reference dataset, starting with quality control.
Public example
0:32–0:40
Cells are projected into UMAP space and clustered by their expression profiles.
Public example
0:40–0:46
Differential expression analysis identifies the marker genes of each cluster.
Public example
0:46–0:53
Additional modules infer trajectories, regulatory networks and cell–cell communication.
0:53–0:59
Each analysis keeps its parameters and downloads as a reproducible package.

scExplorer
Single-cell RNA-seq analysis
apps.cienciavida.org/scexplorer

scExplorer

Explore single-cell RNA sequencing data through quality control, clustering, cell annotation and gene-expression analysis.

Open application

Introduction · 65 sSource ↗

Video transcript

Text shown in the video, in order. The clip has no audio. Interface examples accompany each explanation.

0:00–0:04
Centro Ciencia y Vida. Spatial Data Explorer. Spatial transcriptomics. apps.cienciavida.org/spatial_explorer
Institutional title card · identical to the closing
0:04–0:12
Spatial transcriptomics measures gene expression while preserving each cell's position in the tissue.
0:12–0:18
Spatial Data Explorer brings together real Xenium, Visium and Visium HD datasets.
0:18–0:25
The catalogue can be filtered by tissue, measurement technology and sample preparation.
0:25–0:32
The viewer overlays the tissue image, cell segmentation and the expression of each gene.
0:32–0:39
Expression is inspected per cell, spot or bin, depending on the technology.
Public example
0:39–0:46
Expression clusters and spatial domains (BANKSY) are projected onto the tissue.
Public example
0:46–0:52
Molecular signatures outline inferred structures, such as epithelial compartments.
Public example
0:52–1:00
Signed-in users can define regions of interest, run analyses and save their work.

Spatial Data Explorer
Spatial transcriptomics
apps.cienciavida.org/spatial_explorer

Spatial Data Explorer

Explore spatial gene expression alongside tissue images, with interactive views of Xenium, Visium and Visium HD datasets.

Open application

Introduction · 65 sSource ↗

Video transcript

Text shown in the video, in order. The clip has no audio. Interface examples accompany each explanation.

0:00–0:04
Centro Ciencia y Vida. TRACE Explorer. Gene expression and survival in cancer. apps.cienciavida.org/tcga_explorer
Institutional title card · identical to the closing
0:04–0:11
Public cancer cohorts such as TCGA combine gene-expression profiles with clinical data.
0:11–0:19
TRACE Explorer analyses them in the browser, alongside curated cohorts and de-identified user data.
0:19–0:25
An analysis starts from a cohort and a gene or expression signature.
0:25–0:33
Patients are grouped by expression level and their survival curves are compared.
Example
0:33–0:39
Each result includes the statistical model, its diagnostics and warnings.
Example
0:39–0:47
It also offers expression comparisons, gene-set enrichment analysis (GSEA) and pan-cancer analyses.
0:47–0:53
Results describe exploratory associations, not clinical predictions.
Example
0:53–0:59
Figures, data, methods and an R script to rerun the analysis can be exported.
Example

TRACE Explorer
Gene expression and survival in cancer
apps.cienciavida.org/tcga_explorer

TRACE Explorer

Investigate gene expression and its association with survival across cancer cohorts, including TCGA, using genes or expression signatures.

Open application

Introduction · 55 sSource ↗

Video transcript

Text shown in the video, in order. The clip has no audio. Interface examples accompany each explanation.

0:00–0:04
Centro Ciencia y Vida. FlowCV. Flow cytometry analysis. apps.cienciavida.org/flowcv
Institutional title card · identical to the closing
0:04–0:11
Flow cytometry measures multiple markers in thousands of individual cells per sample.
Public example
0:11–0:19
FlowCV, an open-source application, organises analysis as a visual workflow, from FCS file to figure.
Public example
0:19–0:26
Each stage is a node in the workflow: quality control, population gating and clustering.
Public example
0:26–0:34
Clustering algorithms identify cell populations without relying solely on manual gating.
Public example
0:34–0:39
Populations are reviewed on interactive UMAP maps.
Public example
0:39–0:44
The expression of each marker can be compared across populations.
Official FlowCV screenshot
0:44–0:52
Figure Studio combines plots from different steps and exports them as PNG, SVG or PDF.
Official FlowCV screenshot

FlowCV
Flow cytometry analysis
apps.cienciavida.org/flowcv

FlowCV

Analyze flow cytometry data with quality control, population gating, clustering and interactive visualizations.

Open application

Introduction · 1 minSource ↗

Video transcript

Text shown in the video, in order. The clip has no audio. Interface examples accompany each explanation.

0:00–0:04
Centro Ciencia y Vida. GRNContext. Gene regulatory networks in cancer. Bioinformatics, 2026. apps.cienciavida.org/grncontext
Institutional title card · identical to the closing
0:04–0:11
Reference networks describe possible interactions between transcription factors and target genes.
0:11–0:18
They do not indicate which are active in a given biological context, such as a tumour type.
0:18–0:26
GRNContext contextualises these networks across 33 TCGA cancer types using their transcriptomic profiles.
0:26–0:34
A consensus score from statistical and machine-learning methods prioritises candidate regulators.
0:34–0:41
A search starts from a gene and shows its top-ranked regulators in each cohort.
0:41–0:49
Comparing cohorts highlights regulatory differences between tissues.
0:49–0:56
Subnetworks can be exported as a starting point for hypotheses and experimental validation.

GRNContext
Gene regulatory networks in cancer
Bioinformatics, 2026
apps.cienciavida.org/grncontext

GRNContext

Explore gene regulatory networks across TCGA cancer cohorts, identify transcription factors and compare gene programs between tissues.

Open application

Introduction · 1 minSource ↗

Video transcript

Text shown in the video, in order. The clip has no audio. Interface examples accompany each explanation.

0:00–0:04
Centro Ciencia y Vida. MobHunter. Mobile genetic elements in microbial genomes. Nucleic Acids Research, 2025. informatica.utem.cl/mobhunter
Institutional title card · identical to the closing
0:04–0:11
Horizontal gene transfer introduces mobile genetic elements into microbial genomes.
Precomputed example
0:11–0:19
These elements can confer traits such as antibiotic resistance or new metabolic capabilities.
Precomputed example
0:19–0:25
Available tools usually target only some types of elements.
0:25–0:32
MobHunter integrates standalone tools and in-house algorithms into a single analysis workflow.
0:32–0:40
It produces consensus identifications and score-supported classifications by family.
Precomputed example
0:40–0:47
A circular genome map shows the location of each identified region.
Precomputed example
0:47–0:53
Results can be downloaded as an annotated GenBank file for further analysis.
Precomputed example

MobHunter
Mobile genetic elements in microbial genomes
Nucleic Acids Research, 2025
informatica.utem.cl/mobhunter

MobHunter

Predict and classify mobile genetic elements in prokaryotic genomes. Produces predictions and annotated GenBank files.

Contact the laboratory for access.

Tool information

MutantElec

Simulate protein mutations and compare their electrostatic effects using three-dimensional structures.

Open application

AFAL

Explore amino acid frequencies around ligands in protein structures from the PDB.

Open application