Bioinformatics Analysis Services

Machine learning is a branch of artificial intelligence (AI) based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention. The major contribution of AI in bioinformatics analyses depends on pattern matching and knowledge based learning systems to solve the biological problems.

Before machine learning emerged, bioinformatics and other biological fields faced the problem of extracting valuable insights from large biological datasets. But as of today, ML techniques such as deep learning can learn the features of complex datasets and present them in a manner that is easy to understand.

Machine learning has multiple applications in diverse fields, ranging from natural language processing to healthcare. Machine learning in bioinformatics is the application of machine learning algorithms in bioinformatics; genomics, proteomics, microarrays, systems biology, evolution, and text mining. It serves as an advanced tool in the bioinformatics area which deals with molecular phenotypes, drug discovery, and aids in determining unfamiliar diseases etc.

Molecular Docking is a bioinformatics modeling technique which involves the interaction of two or more molecules to give the stable adduct. Molecular docking is an important approach for designing new drugs and vaccines and other bioinformatics analysis as well. It predicts the three-dimensional structure of any complex depending upon binding properties of ligand and target. Molecular docking generates different possible adduct structures that are ranked and grouped together using a scoring function in the software.

Molecular interactions including protein-protein, enzyme-substrate, protein-nucleic acid, drug-protein, and drug-nucleic acid play important roles in many essential biological processes, such as signal transduction, transport, cell regulation, gene expression control, enzyme inhibition, antibody–antigen recognition and even the assembly of multi-domain proteins. These interactions very often lead to the formation of stable protein–protein or protein-ligand complexes that are essential to perform their biological functions.

Molecular docking classifies biomolecules into three categories: Proteins, Ligands and Peptides. The most important types of docking include protein-protein docking, protein-ligand docking and protein-peptide docking.

RNA-Seq is an exciting and in-demand next-generation sequencing (NGS) method used for identifying genes and pathways underlying certain diseases or conditions. Over the past decade, RNA-seq has become an indispensable tool for transcriptome-wide analysis of differential gene expression and differential splicing of mRNAs. RNA-seq offers many advantages over microarray technology.

RNA sequencing data analysis emphasizes the complicated mechanisms of gene regulation. It analyzes the transcriptome, indicating which of the genes encoded in our DNA are turned on or off and to what extent. RNA-seq data allows for a wide range of analyses to address countless research questions across the fields of biology and biomedicine.

Newly emerging and reemerging infectious viral diseases have threatened humanity throughout history. The unprecedented scale and rapidity of dissemination of recent emerging infectious diseases pose new challenges for vaccine developers, regulators, health authorities and political constituencies. Vaccines are biological substances that are utilized to stimulate antibody production within the body of an organism to provide active immunity against foreign organisms, mostly viruses and bacteria. Vaccines not only arrest the beginning of different diseases but also assign a gateway for its elimination and reduce toxicity. Vaccines are the most cost-effective public health interventions.

Bioinformatics is also involved in medication development aimed at bio-productive and pharmaceutical/vaccine development. Computational approach in drug discovery helps in identifying safe and novel vaccines. In silico (computational) analysis saves time, cost, and labor for developing the vaccine and drugs. Chimeric vaccines are types of recombinant vaccines, produced by substituting genes from the target pathogen in a closely related organism, for similar genes. Chimeric vaccines are useful in studying infectious diseases, including many neglected diseases.

Computational Drug Designing has become the go-to requirement for the researchers, scientists and the pharmaceuticals who fight against the fatal disease. Computational drug discovery is an effective strategy for accelerating and economizing drug discovery and development processes. Because of the dramatic increase in the availability of biological macromolecule and small molecule information, the applicability of computational drug discovery has been extended and broadly applied to nearly every stage in the drug discovery and development workflow, including target identification and validation, lead discovery and optimization and preclinical tests.

Bioinformatics analysis can not only accelerate drug target identification and drug candidate screening and refinement, but also facilitate characterization of side effects and predict drug resistance. High-throughput data such as genomic, epigenetic, genome architecture, cistromic, transcriptomic, proteomic, and ribosome profiling data have all made significant contributions to mechanism-based drug discovery and drug repurposing. Accumulation of protein and RNA structures, as well as development of homology modeling and protein structure simulation, coupled with large structure databases of small molecules and metabolites, paved the way for more realistic protein-ligand docking experiments and more informative virtual screening.

Molecular dynamics (MD) is a computer simulation method for analyzing the physical movements of atoms and molecules. The atoms and molecules are allowed to interact for a fixed period of time, giving a view of the dynamic “evolution” of the system. In the most common version, the trajectories of atoms and molecules are determined by numerically solving Newton’s equations of motion for a system of interacting particles, where forces between the particles and their potential energies are often calculated using interatomic potentials or molecular mechanics force fields. MD simulations are nowadays routinely applied to macromolecular systems of biological and pharmaceutical interest. MD simulation analysis is one of the essential steps while designing novel drugs using computational approaches.

Atomistic computer simulations of macromolecular (for example, protein) receptors and their associated small-molecule ligands play an essential role in drug discovery. The static models produced by NMR, X-ray crystallography, and 3D structure prediction provide valuable insights into macromolecular structure, but molecular recognition and drug binding are very dynamic processes. When a small molecule like a drug (for example, a ligand) approaches its target (for example, a receptor) in solution, it encounters not a single, frozen structure, but rather a macromolecule in constant motion.

ChIP is an antibody-based technique that is used to enrich specific DNA-binding proteins in addition to their DNA targets. This is used to investigate a specific protein-DNA interaction or multiple protein-DNA interactions across a subset of genes or the whole genome. Chromatin immunoprecipitation (ChIP) assay when combined with sequencing resulted in a powerful high-throughput technique known as the ChIP-Seq.

ChIP-seq is a pivotal technology for epigenomic research and study. ChIP is the method of choice for studying epigenomic signatures. ChIP-seq is different from all of the other approaches, which are used for epigenetic research, in the fact that it does not need any prior knowledge as it does not require probes from known sequences. ChIP-seq is a powerful method to identify genome-wide DNA binding sites for a protein of interest. ChIP-Seq is an exciting and in-demand next-generation sequencing (NGS) method used for identifying genes and pathways underlying particular diseases or conditions. Through ChIP-Seq Data Analysis you can find protein-DNA interactions, transcription factor binding sites, histone modifications among other epigenetic signatures.

ATAC-Seq (Assay for Transposase-Accessible Chromatin using sequencing) is a fast and sensitive next-generation sequencing method for mapping genome-wide chromatin accessibility. It uses a hyperactive Tn5 transposase to simultaneously cut and tag open regions of the genome, revealing where the regulatory machinery can engage the DNA. Because it requires very low input material and a simple library preparation, ATAC-Seq has become a method of choice for studying the regulatory landscape of cells and tissues.

Through ATAC-Seq data analysis we identify accessible chromatin regions, call and annotate peaks, profile transcription-factor binding through footprinting, and link open regions to their nearby genes. These insights help researchers understand gene regulation, enhancer activity and how chromatin state changes across conditions, developmental stages and disease.

Metagenomics is the study of genetic material recovered directly from environmental or clinical samples, allowing an entire microbial community to be characterised without the need for culturing. It encompasses both marker-gene approaches, such as 16S, 18S and ITS amplicon sequencing, and whole-genome shotgun sequencing, which together reveal who is present in a community and what they are capable of doing.

Our metagenomics analysis covers quality control, taxonomic profiling, microbial diversity (alpha and beta diversity), functional and pathway annotation, and statistical comparison between groups. These analyses are widely applied in human microbiome research, environmental microbiology, agriculture and clinical diagnostics to connect microbial composition and function with health, disease and ecosystem processes.

Single-cell ATAC-Seq (scATAC-Seq) extends chromatin accessibility profiling to the resolution of individual cells, uncovering the regulatory heterogeneity that bulk assays average away. By measuring open chromatin in thousands of single cells, it reveals distinct regulatory states, cell types and the cis-regulatory elements that define them.

Our scATAC-Seq workflow includes quality control, dimensionality reduction and clustering, peak calling, gene-activity scoring, transcription-factor motif and footprinting analysis, and integration with single-cell RNA-Seq data. This enables reconstruction of gene-regulatory programmes and identification of the enhancers and transcription factors that drive cell identity in development, immunity and disease.

Single-cell RNA sequencing (scRNA-Seq) measures gene expression in individual cells, making it possible to dissect cellular heterogeneity, identify rare cell populations and map the diversity of tissues that traditional bulk RNA-Seq cannot resolve. It has transformed our understanding of development, immunology, cancer and many complex tissues.

Our scRNA-Seq analysis spans quality control and normalisation, dimensionality reduction, unsupervised clustering and cell-type annotation, marker-gene and differential-expression analysis, trajectory and pseudotime inference, and integration across samples and conditions. The result is a clear, biologically interpretable picture of which cell types are present and how their transcriptional programmes change.

Large Language Models (LLMs) are reshaping bioinformatics by learning the language of biology directly from sequence and text. Protein and genomic language models capture the statistical structure of amino-acid and nucleotide sequences, producing powerful embeddings for tasks such as structure and function prediction, variant-effect estimation and protein design, while general and biomedical LLMs accelerate literature mining and knowledge extraction.

We help research teams apply LLMs to their own data, from fine-tuning protein language models and building sequence-embedding pipelines, to retrieval-augmented question answering over biomedical literature and custom assistants for genomic interpretation. These tools turn large, unstructured biological data into actionable predictions and insights.

De novo design uses generative computational methods to create entirely new proteins and small molecules with desired properties, rather than screening existing libraries. Advances in deep generative models and structure prediction now allow researchers to design binders, biologics and novel chemical scaffolds tailored to a specific therapeutic target, dramatically expanding the accessible design space in drug discovery.

Our de novo design services combine generative models, structure-based design and physics-based validation to propose novel proteins and chemical entities, optimise them for potency, selectivity and drug-likeness, and prioritise candidates through docking and molecular-dynamics evaluation. This accelerates early discovery by delivering focused, synthesisable and testable molecules.

De novo enzyme design aims to computationally create new enzymes, or substantially re-engineer existing ones, to catalyse target reactions that may not exist in nature. By combining catalytic-site (theozyme) design, scaffold selection, deep-learning protein design and molecular modelling, it is possible to build biocatalysts with novel or improved activity, specificity and stability.

We support enzyme engineering projects through active-site and scaffold design, mutation prioritisation for activity and thermostability, and computational validation with docking and molecular-dynamics simulations. These approaches power applications in green chemistry, industrial biocatalysis, biosynthesis and therapeutic enzyme development.

Robust, reproducible pipelines are the backbone of modern bioinformatics, turning raw sequencing data into reliable results at scale. We design and build automated analysis workflows using established frameworks such as Nextflow and Snakemake, with containerised environments (Docker and Singularity) that guarantee reproducibility across machines and over time.

Our pipeline development services cover custom workflow design for genomics, transcriptomics, epigenomics and metagenomics, integration with HPC and cloud infrastructure, parallelisation and optimisation, plus version control, documentation and testing. The result is a maintainable, well-documented pipeline your team can run confidently and repeatedly.