Empowering Bioinformatics Through Computational Intelligence

Our Key Expertise Is Into

Analysis of positive/negative batch control

Report generation for Variant Caller assay

Variant Calling Tools We Have Experience In Handling

SAMtools/BCFtools

Bedtools

Major Variants We Are Working On, Which Are Considered For DNA Samples Include

Snps

Indels

Mnvs

Complex variants

Major Variants We Are Working On, Which Are Considered For RNA Samples Include

Fusion

Splice variants

Data Preprocessing and Quality Control

Raw Data Input: Software solutions can import raw sequencing data (e.g., FASTQ files) generated from high-throughput sequencing technologies like Illumina or PacBio.

Quality Control: Tools embedded in the software perform quality checks, trimming low-quality bases, and filtering out poor-quality reads to ensure accurate variant detection.

Alignment: Reads are aligned to a reference genome (e.g., using algorithms like BWA or Bowtie) to identify positions where the sample DNA differs from the reference.

Variant Detection

Single Nucleotide Variants (SNVs): Detects changes in a single nucleotide base (A, T, C, G). Algorithms like HaplotypeCaller or FreeBayes are designed for SNP detection.

Insertions and Deletions (Indels): Identifies small insertions or deletions of bases in the genome, which can cause frameshift mutations.

Structural Variants (SVs): Detects larger-scale changes in the genome, including duplications, inversions, translocations, and copy number variations (CNVs).

Somatic vs. Germline Variants: Software can differentiate between variants present in all cells (germline) and those present in only certain cell types (somatic), important in cancer research.

Variant Filtering and Annotation

Variant Quality Filtering: After variant detection, the software applies quality filters (e.g., based on read depth, allele frequency, or base quality scores) to remove low-confidence variants.

Variant Annotation: Associates detected variants with functional information such as gene location, predicted impact on protein function, disease association, or known variants from public databases (e.g., dbSNP, ClinVar).

Phasing: Identifies whether variants are located on the same or different chromosomes, important for understanding the inheritance pattern of variants.

Data Visualization

Genome Browsers: Tools like IGV (Integrative Genomics Viewer) allow visualization of sequencing data, read alignments, and detected variants in the context of the reference genome.

Variant Effect Visualization: The software can provide graphical interpretations of variant effects on genes and proteins, helping researchers understand potential impacts on biological function.

Scalability and Parallelization

Cloud Integration: Many solutions provide cloud-based architectures that allow processing large datasets efficiently by leveraging distributed computing resources.

Batch Processing: Software solutions often support batch processing of multiple samples, streamlining workflows for large-scale studies such as population genomics or cohort studies.

Speed and Efficiency: Optimized algorithms, such as GATK’s (Genome Analysis Toolkit) best practices, enable efficient and accurate variant calling, minimizing computational resources.

Machine Learning and AI Integration

Variant Prioritization: Machine learning algorithms can help prioritize variants based on their likelihood of being pathogenic or associated with diseases, speeding up downstream analysis.

Error Detection: AI can improve the accuracy of variant calling by reducing false positives and false negatives, especially in challenging regions of the genome (e.g., repetitive sequences).

Interoperability and Standards Compliance

VCF Output (Variant Call Format): The standard format for variant data is VCF, and software solutions support its generation, ensuring compatibility with downstream tools and databases.

Integration with Public Databases: Software solutions often integrate with public genomic databases like ENSEMBL, ClinVar, and the 1000 Genomes Project, enabling easy comparison and annotation of variants.

Open Standards Support: Compliance with widely accepted standards (e.g., GA4GH) ensures that data can be shared and reused across different platforms and research studies.

Reproducibility and Traceability

Version Control: The software provides logging and tracking of analysis versions, enabling reproducibility of results across different experiments.

Audit Trails: Secure logging of all data manipulations ensures traceability for regulatory or clinical research purposes.

Clinical and Research Applications

Personalized Medicine: In clinical settings, variant calling software can identify genetic mutations related to diseases such as cancer or inherited disorders, supporting targeted therapy decisions.

Population Genomics: In research, variant calling supports large-scale studies to understand the genetic basis of traits and diseases in populations.

Gene Editing Research: Variant calling is used in CRISPR/Cas9 experiments to validate successful edits or to check for off-target effects.