Whether you want to:
- Plan a new experiment
- Leverage cloud computing to accelerate your data analysis.
- Utilise advanced machine learning methods to tease out meaningful patterns in your data.
- Get clarity on experimental output
Our biostatistics and bioinformatics techniques can help you:
- Make sense of generated big omics data,
- Validate and identify biomarkers in the field of molecular life science
- Perform upstream analysis to assure the quality of next generation sequencing data
- Tease out meaningful pattern through appropriate biostatistical methods.
- Leverage advanced multi-objective ensemble dimensionality reduction techniques
- Combine your results with information about the interactions of molecules (genes, RNA, proteins, microbiome).
- identify a minimum number of biomarkers required to predict the phenotype under study with the highest possible accuracy
Bioinformatics expertise at any stage of your project – from the design stage to final analysis. We tailor the latest analytics methodologies to best serve your research goals.
- GENOMICS
- PROTEOMICS
- TRANSCRIPTOMICS
- Full genome sequence data analysis:
- Complete sequences of any number of genomes enable comparisons between species in terms of the amount and type of genetic variation. Alleles can be analysed to assess the speed at which change occurred and determine a rate of evolutionary divergence between species.
- Partial gene sequence data analysis:
- The function of single genes or gene fragments can be investigated through partial gene sequencing. This allows the relationship between species to be explored. Genetic, morphological and ecological characteristics of an organism can be studied against other data to determine evolutionary relationships.
- Genome assembly (de novo genome assembly, reference guided assembly)
- Genome annotation (insilico by gene prediction software, or combined with transcriptome data in R-Seq)
- Read filtering and cleaning
- Genome mapping
- Variant calling (Single nucleotide polymorphisms (SNP) called against a reference genome, or against a combination of genomes, combine variants lists to find de novo mutations via comparison and filtering).
- Variant annotation ( variants can be annotated with location on the genome, variant type homo/heterozygous, function classification of exon variants, amino acid changes, database identifiers, using annotations for variant ranking and filtering, predicting pathogenicity of exonic variants )
- Copy number analysis (copy number generation for chromosome segments, genes and exons)
Metagenomics involves extracting DNA directly from non lab samples, such as biomes. This form of sampling can then be used for full gene sequencing and to catalogue full gene products and infer impacts of the microenvironment. metagenomics can be used for the discovery of new organisms, genes, and gene products.
- gene expression patterns clustering
- class prediction
- biological mechanism prediction
- microbiome analysis
Linkage mapping in twin studies to link specific genes to behavioural phenotypes.
Candidate gene approach can be used to to evaluate the association between specific alleles and particular traits or behaviours. Causal links in candidate genes can be determined by analysing data from knock-out/knock-in approaches such as in transgenic mice models.
Often candidate gene are first determined through larger-scale approaches such as genome sequencing of thousands of genes at once, and microarray comparison techniques.
Multi-gene analysis or whole-genome single nucleotide polymorphism profiles (SNP) can be used to identify genetic loci associated with known drug responses. This can be relevant in studies of pharmacokinetics, pharmacodynamics and evaluations of drug safety. Phenotypic groups can be assigned to individuals by allele detection using DND chip microarrays. This can aid in the prediction of adverse drug reactions (ADRs) and drug hypermetabolisation.
Heritable changes not encoded in the DNA sequence such as methylation changes have been shown to be associated with the disease. Epigenomic analysis can involve mapping heritable changes such as cytosine methylation of DNA at CpG dinucleotides. This requires a comparison of methylation levels across numerous samples. Methylation-based predictive models can then be derived in order to inform further research. Techniques such as MeDiP-Seq can aid in the mapping, annotation and comparison of methylated DNA and unmethylated cytosines. Other sequencing techniques such as BBSeq, RRBS-seq can help to investigate methylated individual cytosines.
Non-methylation mechanisms such as covalent histone modifications, micro-RND interactions and chromatin remodelling complexes can also be mapped. The ChIP-Seq technique allows whole genome histone modifications to be investigated by analysing protein interactions with DNA.
Proteomics is the study of the proteome, the set of proteins in the cell or tissue, including protein quantity and diversity. Proteomics offers a snapshot of the proteome, which is in constant flux, and does not pick up on dynamic protein changes and interactions such as post translational modifications.
As a single gene can produce multiple versions of a protein vis alternate RNA splicing, proteomics can enable a more complex investigation of evolutionary processes than genomics.
Changes in the proteome can reveal changes in the environment or correspondingly the health of cells as a method to assess disease states.
Functional annotation and enrichment analysis
- GO, COG, KOG, KEGG and Domain annotation analysis
- GO, KEGG and Domain enrichment analysis
- Directed Acyclic Graphs (DAG)s
Clustering analysis
- Heirachical clustering
- K means clustering
Network analysis
- protein-protein interaction analysis
- IPA analysis
- co-expression network analysis
Proteomic analysis of PTMs
- PTM prediction analysis
- PTM cross-talk analysis
- Conserved sequence analysis
Protein sequence, structure and evolution analysis
Different cells of an organism have the same genes but tend to show different patterns of gene expression. Transcriptomes allow the study of gene activity and therefore expression. The transcriptome, which accounts for less than 5% of the human genome, can be compared between cell or tissue types, and the constituents of specific cell types can be analysed to give an indication of transcription factors contributing to health or disease processes.
Transcriptomics can tell us when and where genes are turned on or off, this can lead to possible targets for gene therapy. The characteristics of these targets can be revealed by further analysis and this can inform therapeutic approach to these targets. As such it may be an informative addition to any genomics, clinical or life sciences research enquiry.
Identifying specific genes expressed in a cell at specific times can be achieved through the following techniques:
Expressed sequences analyses:
- Total RNA-Seq (Whole Transcriptome) Sequencing Data Analysis
- RNA-Seq (mRNA) Data Analysis
- Small RNA Resequencing Data Analysis
- Transcriptome mapping
- Transcriptome de novo assembly
- Microarrays analysis:
- Comparing mRNA levels for thousands of genes at once. This enables the compilation of transcription profiles which allow for the molecular classification of disease and the study of mechanisms behind its progression.
- SAGE:
- Serial analysis of gene expression, massively parallel signature sequencing, pyrosequencing, and expressed sequence tags — the data sets produced by these techniques facilitate and complement microarray analyses.
establish functional linkages between fully sequenced genomes and their expressed RNA products.
Other key techniques in transcriptomics include:
