At Biotechvana, we are pleased to offer our users advanced transcriptomic and epigenomic data analysis services aimed at comprehensive characterization of gene expression and epigenetic regulation in model, non-model, and complex samples.
In the field of Transcriptomics, we offer different types of analyses tailored to the experimental needs of each project, including:
- Bulk RNA-seq (mRNA / long RNA): global quantification of genes and transcripts.
- Small RNA / microRNA-seq: identification and quantification of small regulatory RNAs and their potential target genes.
- Single-cell RNA-seq: analysis of gene expression at the single-cell level, including clustering and cell type annotation.
- Spatial transcriptomics: study of gene expression while preserving spatial information within the tissue.
In the field of Epigenomics, our services focus on the study of epigenetic modifications that regulate gene expression:
- Methylseq/MeDIPseq: detection and quantification of methylation levels across the genome, identifying differentially methylated regions.
Each project is approached in a personalized way, adapting the workflow to the specific characteristics of the experimental design.
This analysis enables global quantification of gene expression and transcripts from cell populations or whole tissues. It is a robust approach to identify differentially expressed genes, characterize transcriptomic profiles across conditions, and support functional and comparative studies.
- Quality assessment of sequencing reads (sff, fastq, sam, fasta, etc.).
- Read preprocessing, including demultiplexing and removal of low-quality sequences, primer/adapter remnants, and artifacts.
- Read curation when appropriate.
- Reference indexing.
- Mapping of processed fastq libraries against the reference genome/transcriptome.
- Inference of mapping performance metrics.
- Creation of junction libraries.
- Transcriptome assembly and reporting of genes and/or isoforms.
- Normalization.
- Differential expression testing among genes, exons, isoforms, or promoters across samples.
- Differential enrichment analysis of Gene Ontology (GO-seq) categories.
- Differential enrichment analysis of metabolic pathways.
- Data integration and interrogation.
- Protein–protein interaction and other network analyses.
- Correlation analysis.
- Other statistical analyses.
This approach is aimed at the identification and quantification of small regulatory RNAs, including microRNAs and other non-coding RNAs. The analysis enables the study of post-transcriptional regulatory mechanisms, as well as the exploration of potential interactions between microRNAs and their target genes.
- Quality assessment of sequencing reads (sff, fastq, sam, fasta, etc.).
- Read preprocessing, including demultiplexing and removal of low-quality sequences, primer/adapter remnants, and artifacts.
- Read curation when appropriate.
- Mapping of processed fastq libraries against the reference genome/transcriptome.
- Inference of mapping performance metrics.
- Establishment of polarity and size of sRNAs.
- Determination of loci and target genes.
- Determination of loci and target gene features: intergenic or exonic regions.
- Within- and between-sample normalization.
- Differential expression analysis of sRNAs.
- Functional characterization and analysis of loci and target genes.
- Differential GO-seq enrichment analysis of loci and target genes.
- Differential pathway enrichment analysis of loci and target genes.
- Identification of phased sRNAs.
- Identification and prediction of secondary structure.
- Target gene prediction.
- Data integration and functional analysis of loci.
- Characterization of miRNA polymorphisms.
- Correlation analysis.
- Other statistical analyses.
Single-cell transcriptomics analysis enables characterization of cellular heterogeneity within tissues or complex populations. This approach facilitates the identification of cell types and states, the study of cellular trajectories, and the comparison of expression profiles across conditions.
- Quality assessment of sequencing reads (sff, fastq, sam, fasta, etc.).
- Read preprocessing, including demultiplexing and removal of low-quality sequences, primer/adapter remnants, and artifacts.
- Read curation when appropriate.
- Reference indexing.
- Alignment or pseudoalignment and UMI quantification.
- Inference of mapping performance metrics.
- Filtering of low-quality cells, artifacts, and genes expressed in a small number of cells.
- Normalization.
- Selection of highly variable genes and dimensionality reduction.
- Integration and batch correction across samples/conditions.
- Clustering algorithm to group cells.
- Cell type annotation.
- Detection of marker genes per cluster/cell type.
- Differential expression analysis between cell populations, conditions, or states.
- Cell–to-cell communication analysis.
- Trajectory and pseudotime inference.
- RNA velocity.
- Functional enrichment analysis.
Spatial transcriptomics enables analysis of gene expression while preserving spatial information within the tissue. This approach provides an integrated view of tissue organization, the distribution of cell types, and expression patterns associated with the spatial context.
- Quality assessment of sequencing reads (sff, fastq, sam, fasta, etc.).
- Read preprocessing, including demultiplexing and removal of low-quality sequences, primer/adapter remnants, and artifacts.
- Read curation when appropriate.
- Read alignment against the reference transcriptome.
- Tissue detection in histological images.
- Read quantification.
- Quality control and filtering of low-quality spots and genes expressed in a small number of spots.
- Normalization.
- Selection of highly variable genes and dimensionality reduction.
- Clustering of spots within the tissue.
- Detection of marker genes per cluster/cell type.
- Deconvolution using a scRNA-seq or snRNA-seq dataset as reference.
- Detection of spatially variable genes.
- Detection of spatial domains.
- Differential expression analysis between cell populations, conditions, or states.
- Functional enrichment analysis.
- Cell–to-cell communication analysis.
These analyses focus on DNA methylation as a key mechanism of epigenetic regulation. They enable identification and comparison of differentially methylated regions across the genome, supporting studies on gene regulation, development, adaptation, and disease.
- Quality assessment of sequencing reads (sff, fastq, sam, fasta, etc.).
- Read preprocessing, including demultiplexing and removal of low-quality sequences, primer/adapter remnants, and artifacts.
- Read curation when appropriate.
- Methylation reference reconstruction.
- Mapping of processed fastq libraries onto the reference genome.
- Inference of mapping performance metrics.
- Testing of differential methylation across genomic regions between samples.
- Annotation of regulatory elements and genes (CpG islands, promoters, introns, exons, etc.) containing or overlapping differentially methylated regions.
- Differential enrichment analysis of Gene Ontology (GO-seq) categories.
- Differential enrichment analysis of metabolic pathways.
- Data integration and interrogation.
- Protein–protein interaction and other network analyses.
- Correlation analysis.
- Other statistical analyses.