Bioinformatics

Transforming Data into Discovery with Expert Bioinformatics

Nucleome Informatics is a leading bioinformatics company headquartered in Hyderabad, India, dedicated to delivering innovative solutions in genomics, transcriptomics, metagenomics, and epigenomics. Our expert team of bioinformaticians and biologists leverages cutting-edge software pipelines and high-performance computing infrastructure to generate precise and actionable biological insights.

As a company, we develop and provide customized bioinformatics platforms and workflows optimized for both short-read and long-read sequencing technologies. Our expertise spans variant detection, structural variation analysis, gene expression profiling, and epigenetic modification characterization, addressing diverse applications such as clinical genomics, precision medicine, pharmacogenomics, targeted sequencing panels, and advanced research.

Committed to scientific collaboration, Nucleome Informatics partners with researchers and clinicians to co-create tailored analytical approaches that align with specific research questions and clinical objectives. Whether integrating genomic data with AI/ML models or delivering comprehensive analytical solutions for clinical decision support, Nucleome Informatics stands at the forefront of bioinformatics innovation, powering transformative discoveries and improved healthcare outcomes.

Clinical Data Analysis

Nucleome Informatics integrates cutting-edge artificial intelligence (AI) and machine learning (ML) technologies into genomics data analysis to enhance precision, scalability, and insight discovery. Our AI/ML-powered bioinformatics pipelines process whole genome sequencing (WGS) data from both short-read and long-read platforms, enabling advanced clinical variant detection, structural variant identification, and comprehensive annotation of exome sequencing variants in VCF files. These intelligent algorithms facilitate efficient data interpretation, refined pathogenicity predictions, and actionable clinical reporting. By combining genomics expertise with AI-driven data analysis, Nucleome delivers robust, reproducible, and clinically relevant insights that accelerate diagnosis, personalized treatment, and research breakthroughs in oncology, rare diseases, agriculture, and beyond. This synergy of AI, ML, and genomics positions Nucleome at the forefront of precision medicine and data-driven genomics innovation.

De novo Genome Assembly

Nucleome Informatics offers a cutting-edge genome assembly pipeline designed to deliver accurate, high-quality genome reconstructions tailored to research and clinical needs. Our comprehensive pipeline focuses on de novo assembly, leveraging the latest sequencing technologies and computational tools. Starting with raw sequencing data, our pipeline processes and filters reads through rigorous quality control steps to ensure optimal input for assembly. For novel genomes or samples without reference sequences, our de novo assembly reconstructs complete genomes by carefully piecing together sequencing reads, revealing previously uncharacterized regions. Our advanced pipeline also supports phased assemblies for maternal and paternal haplotypes, improving accuracy in diploid and polyploid genomes. Genomic outputs include high-quality contigs, scaffolds, and detailed variant calls, facilitating downstream comparative genomics, functional analysis, and clinical interpretation. With customizable workflows, scalable computational infrastructure, and expert bioinformatics support, Nucleome’s genome assembly service empowers researchers and clinicians to unlock the full potential of their genomic data.

Genome Assembly Curation

The next step in the large-scale sequencing process is often referred to as ``finishing.`` In this step, contiguous segments of sequence are ordered and linked to one another and any ambiguities or discrepancies among the individual reads are resolved. Once this is concluded, a relatively rigorous quality check and verification is performed. At this stage any suspicious assemblies are analysed and either verified or disassembled

Genome Annotation

Genome annotation is the process of attaching biological information to sequences. It consists of three main steps:
1. Identifying portions of the genome that do not code for proteins
2. Identifying elements on the genome, a process called gene prediction, and attaching biological information to these elements Structural annotation consists of the identification of genomic elements.
1. ORFs and their localisation 2. gene structure 3. coding regions 4. location of regulatory motifs
Functional annotation consists of attaching biological information to genomic elements.
1. biochemical function 2. biological function 3. involved regulation and interactions 4. expression
We provide both automated and manual annotation of prokaryotic and eukaryotic genome sequences.

Epigenomic Analysis (ChIP-Seq and BS-Seq)

Data from ChIP-Seq experiments are aligned to a reference genome and analysed for peak enrichment to identify DNA-protein binding sites. Differential peak analysis between experiments can be used to identify binding sites specific to certain conditions or proteins. DNA methylation patterns are detected by aligning sequence data derived from bisulfite-treated DNA (BS-Seq) to both a reference genome and a version of the reference genome that has been in silico bisulfite converted. This dual alignment analysis enables more accurate identification of methylated sites and their boundaries.

Transcriptome Analysis

criptome (RNA-Seq) data can be analysed to determine gene or isoform level expression profiles, sequence variation, and differential expression between multiple conditions and/or time points.De novo transcriptome assembly is the method of creating a transcriptome without the aid of a reference genome. Reference based transcriptome analysis can also be done for samples with available annotated genome sequence in public domain. We offer following RNA seq analysis services:
1. Alignment to a reference genome and detection of expressed sequences.
2. Identification and quantitation of exons and genes, splice junctions, single nucleotide variants, small indels, and novel transcripts.
3. Annotated and uniform output including links to databases to make it easier to browse information on specific genes of interest and
4. Study-level analysis for discovery of significant differential expression patterns and associations with molecular pathways.

SNP, Indel, and Structural Variant Detection

Using analysis pipelines developed specifically for variant detection, sequence data is aligned to available reference sequences. SNPs, indels (insertions and deletions), and structural variants are detected, quality-filtered, and annotated (coding, non-coding, synonymous, non-synonymous, etc.). In order to identify novel or rare variants, the variants are compared to a public database of known variants that includes the latest dbSNP and 1000Genomes data as well as other known variants from multiple organisms. Data visualization tools are available to browse the results. Comparative analysis of variants calls is also available.

Pathway & Network Analysis

Given a set of variant positions, genes, or other loci associated with a particular phenotype, we use software packages, including Ingenuity Pathway Analysis (IPA), developed specifically to analyse gene pathways and networks to find associations with functional profiles, tissue or disease specific biomarkers, and other genes in the same pathways and networks. We also use open-source network and visualization tools DAVID, Cytoscape, and Reactome to complement network/pathway analysis to increase the accuracy and sensitivity of network/biomarker identification.

Custom Data Analysis

Our computational infrastructure and expertise enables cutting-edge custom analysis for a broad range of genomics applications. We will customize an analysis plan for each project in close consultation with each investigator.

Metagenomic Sequence Assembly

The field of metagenomics is concerned with the analysis of communities by sampling the DNA of all species in a given microbial community. The assembly of metagenomes poses greater and more complex challenges than single-genome assembly as the relative abundances of the species in a microbiome are not uniform. Voluminous parallel sequencing datasets, especially metagenomic experiments, require distributed computing for de novo assembly and taxonomic profiling.
Using a range of sequence assembly tools tuned to the appropriate genome size and characteristics, we produce an optimized assembly and deliver contigs, scaffolds, and metrics in a variety of standard formats.