Steps:

• Quality filtering
• Adapter trimming
• Discarding rRNA-mapped reads or contaminants (per request)

Output files include:

• quality report (HTML or PDF formats)
• filtered reads in FASTQ format (per request)

Steps:

• Estimating gene and transcript expression values
• Differential expression analysis
• Enrichment analysis of KEGG pathways and Gene Ontology terms

Output files include:

• Tab-separated files with calculated expression values for genes and transcripts; these files might be open in a spreadsheet, such as MS Excel (the expression values are provided in FPKM/RPKM and TPM units)
• A list of differentially expressed genes, with fold change values and adjusted P-values
• Raw expression values for each gene in each condition as a single table (per request)
• A heatmap of differentially expressed genes in PDF format
• MA and volcano plots - essential diagnostic plots for differential expression analysis, in PDF format

Steps:

• Read mapping against a reference genome sequence
• Quality control of mapping results and post-processing them
• Ab initio assembly of the transcriptome (detection of novel genes and splicing isoforms, including antisense and intronic transcripts)
• Quality filtering of the transcriptome (if required)
• Comparison with a reference transcriptome/annotations (if available)
• Possible further steps: Expression analysis; Annotation of transcriptomes

Output files include:

• Quality report from read mapping
• A file in BAM format containing raw results of read mapping (per request)
• The transcriptome in GFF/GTF and FASTA formats; the GFF/GTF files might be used for direct visualization of the transcriptome in genome browsers, such as IGV
• Sashimi plots for genes of interest (per request)

Steps:

• De novo assembly of the transcriptome
• Calculation of quality measures
• Possible further steps: Expression analysis; Annotation of transcriptomes

Output files include:

• A transcriptome in FASTA format
• Quality report for the transcriptome assembly

Steps:

• Identification of protein-coding genes
• Annotation of proteins: finding structural and functional domains; similarity search against databases of annotated proteins in other organisms
• Assignment of KEGG pathways and Gene Ontology (GO) terms
• Identification of tRNAs, rRNAs, snoRNAs, snRNAs
• Identification of long non-coding RNAs (lncRNAs)
• Identification of circRNAs
• Possible further steps: identification of microRNAs

Output files include:

• A single tab-delimited file (to be open in a spreadsheet program, such as MS Excel) with most of annotation results, including assigned KEGG and GO terms and found protein domains
• FASTA and GTF/GFF/BED files with identified open reading frames and different classes of non-coding RNAs

• Identification of miRNAs and phased siRNAs
• Quantification and differential expression
• IsomiR and miRNA editing analysis
• Degradome-Seq data processing
• Identification of sRNA targets
• Data upload to miRBase

Support in the preparation, measurement and analysis of proteomics and peptidomics data.

Colaboration in research planning and sample preparation.

Assistance with biological interpretation of data and preliminary validation of results.

Services in the field of mass spectrometry data analysis:

• Quality control.
• Identification of proteins and peptides using both sequence database searches and de novo approaches.
• Data preparation and processing for quantitative LC/MS experiments.
• DIA data analysis (SWATH).
• Statistical analysis and visualisation.
• Identification of sample preparation and measurement problems.

• SNPs, small INDELs, structural variants
• Germline and somatic alterations (e.g. tumor vs normal tissue)
• Whole Genome Sequencing (WGS), Exome-Seq, targeted sequencing

• Automated annotation of prokaryotic and eukaryotic genomes
• De novo genome assembly and annotation
• Genome annotation with RNA-Seq data
• Identification of non-coding RNAs (long non-coding RNAs, microRNAs and other ncRNAs)
• Identification of repetitive elements
• Setting up stand-alone (IGV) and online (JBrowse) genome browsers
• Building online databases with genomic and transcriptomic data

• ChIP-Seq: Pol II, histone modifications
• Finding RNA editing events
• Finding targets for microRNAs
• Analysis of splicing (different variants)
• Data upload to NGS databases (Sequence Read Archive, Gene Expression Omnibus)

• PCR/qPCR primers design
• Custom qPCR probes design