The services offered related to Omics Data Analysis are listed below:

1. Advice and experimental design.

Design of experiments and their methodology based on the samples available and the data that can be obtained. Integration with the different omics analysis platforms (Genomics, Transcriptomics, Proteomics and Metabolomics).

2. Computational analysis:

• Genomics/Transcriptomics:

Genome resequencing

⦁ Analysis of DNA-Seq data (exomes, whole genomes and custom sequencing): detection of genomic and structural variants. Identification of SNPs and indels in the whole genome or in regions of interest. Evaluation of the effect of variants. Variant annotation, filtering and association study.

RNA-Seq

⦁ Read alignment and quantification of gene/transcript expression.
Detection of splice sites, transcript assembly and isoform evaluation. Alternative splicing analysis. Quantification of gene expression. Differential gene expression analysis. Detection of fusion transcripts. Functional enrichment analysis.

small RNA-Seq:

⦁ Analysis of non-coding RNAs and small RNAs (small RNA-Seq): identification and classification of non-coding RNAs. Specific database searching (microRNAs, piRNAs, endo-siRNAs). Quantification, annotation of sequences and localisation in the context of the genome.
⦁ miRNA analysis: differential expression, target prediction analysis, target annotation, miRNA precursor prediction, identification of isomiRs, detection of variants in known miRNAs, functional enrichment analysis.

Single-cell

⦁ Single-cell RNA-Seq data analysis: alignment, quantification, cell characterisation, dimensional reduction, clustering, visualisation, differential expression, etc.

De Novo Sequencing

⦁ Genome assembly.
⦁ Identification and functional annotation of genes.

Metagenomics

⦁ Analysis of amplicon sequencing data: quality control, taxonomic assignment, alpha and beta diversity analysis, etc.
⦁ Shot-Gun metagenomics.

Other analyses:

⦁ Refer to the section regarding other specific analyses.

• Proteomics:

Advanced statistical analysis:

⦁ FDR and q-values calculation. Partial least squares (PLS and PLS-DA). LASSO. Elastic Net.

Functional analysis of proteins:

⦁ Over-representation analysis. Enrichment analysis of protein assemblies.

Other analyses:

⦁ Refer to the section regarding other specific analyses.

• Metabolomics:

⦁ Data processing and analysis: Detection. Noise filtering. Normalisation. Multivariate analysis.

Other analyses:

⦁ Refer to the section regarding other specific analyses.