AI Tools Used for NGS Data Analysis

Next-Generation Sequencing (NGS) has transformed genomics by enabling rapid, high-throughput sequencing of DNA and RNA. However, the massive volume and complexity of NGS data pose significant analytical challenges. Enter Artificial Intelligence (AI): a game-changer for processing, analyzing, and interpreting genomic data. This blog explores some of the most impactful AI tools currently used in NGS data analysis.

1. Variant Calling with AI

Identifying genetic variants such as SNPs (single nucleotide polymorphisms) and indels (insertions and deletions) is fundamental in genomics. Traditional variant callers rely on statistical and probabilistic models, but AI-powered tools leverage deep learning to recognize complex sequencing error patterns and biological signals, resulting in superior accuracy and reproducibility.

• DeepVariant: Developed by Google Brain, DeepVariant transforms raw sequencing reads into high-fidelity variant calls using convolutional neural networks (CNNs). It excels at reducing false positives in whole-genome and exome sequencing.
• Clair3: Designed for long-read data such as Oxford Nanopore and PacBio, Clair3 integrates pileup and full-alignment information through deep learning models, enhancing germline variant calling speed and accuracy.
• NeuSomatic: Employs CNN architectures specifically for somatic mutation detection in cancer samples, which are often heterogeneous and have low variant allele frequencies.
• PEPPER-Margin-DeepVariant: A comprehensive pipeline combining AI-powered basecalling, haplotype phasing, and variant calling optimized for long-read data, addressing challenges in structural variant detection.

These tools not only improve clinical diagnostic accuracy but also facilitate large-scale population genomics and precision medicine applications.

2. AI in Base Calling and Read Alignment

Accurate base calling — converting raw electrical or optical signals into nucleotide sequences — is crucial, especially for noisy long-read technologies. AI enhances these foundational steps to improve downstream analysis quality.

• Bonito & Dorado: AI-based basecallers developed by Oxford Nanopore Technologies (ONT) that use recurrent neural networks (RNNs) and transformer architectures to improve signal-to-base translation accuracy.

• DeepAlign: A machine learning-driven aligner that optimizes read placement by learning from large datasets, leading to more accurate mapping of reads in repetitive or complex genomic regions.

• AI models also refine quality scores and detect sequencing errors, ultimately increasing confidence in variant detection and structural analysis.

3. Single-Cell and Bulk Transcriptomics

Transcriptomics measures gene expression across cells or tissues, generating high-dimensional data. AI models aid in data denoising, batch correction, clustering, and cell-type classification, especially critical for single-cell RNA sequencing (scRNA-seq).

• scVI (single-cell Variational Inference) and scANVI: Variational autoencoder-based probabilistic models that correct for technical noise and identify distinct cell populations.

• DeepImpute: Employs deep neural networks to impute missing or dropout gene expression values, improving downstream analyses like differential expression.

• Tangram: Uses deep learning to integrate spatial transcriptomics data with scRNA-seq, enabling spatial localization of cell types within tissue architecture.

These approaches empower researchers to explore cellular heterogeneity, developmental trajectories, and disease-associated cell states with unprecedented resolution.

4. Cancer Genomics and Somatic Mutation Detection

Cancer genomes harbor somatic mutations that can be rare and difficult to distinguish from sequencing errors. AI excels at detecting these variants in the complex background of tumor heterogeneity.

• NeuSomatic: A CNN-based somatic variant caller trained on simulated and real tumor data, demonstrating improved sensitivity in detecting low-frequency mutations.

• SomaticSeq: Integrates outputs from multiple traditional variant callers and applies machine learning ensemble methods to enhance somatic variant detection reliability.

• AI-based models are crucial for early cancer detection, therapeutic target identification, and monitoring tumor evolution and resistance mechanisms.

5. Metagenomics

Metagenomics studies microbial communities by sequencing environmental DNA. AI models improve classification and functional annotation of mixed microbial populations.

• DeepMicrobes: Uses deep learning to classify metagenomic reads by learning k-mer patterns, surpassing traditional alignment-based methods.

• Kraken + ML filters: Combines fast k-mer-based taxonomic classification with machine learning filters to minimize false positives and improve species-level resolution.

• AI aids in discovering novel microbial species, predicting microbial gene functions, and analyzing microbiome interactions in ecosystems like the gut, soil, and oceans.

6. Epigenomic and Methylation Analysis

Epigenomics focuses on chemical modifications such as DNA methylation and histone modifications that regulate gene expression without altering the DNA sequence. AI models predict and interpret these modifications from sequencing data.

• DeepCpG: A hybrid CNN-RNN architecture that predicts CpG methylation states by combining DNA sequence features and neighboring methylation patterns.

• Basset: Utilizes CNNs to model chromatin accessibility from DNA sequences, identifying regulatory elements like enhancers and promoters.

• Such AI tools help unravel mechanisms of gene regulation, developmental biology, and epigenetic changes involved in diseases such as cancer.

7. AI in Multi-Omics Integration

Modern biological research often combines multiple omics datasets — genomics, transcriptomics, proteomics, metabolomics — to capture comprehensive biological insights. AI frameworks integrate these heterogeneous data types to identify shared pathways and interactions.

• MOFA+ (Multi-Omics Factor Analysis): Employs matrix factorization and Bayesian inference to discover latent factors explaining variability across datasets.

• MAUI: An autoencoder-based deep learning tool that extracts integrated latent features, improving clustering, classification, and biomarker discovery.

• AI-driven multi-omics integration provides holistic understanding of cellular states, disease mechanisms, and potential therapeutic targets.

Role of IBRI Noida in NGS Data Analysis Training

The Indian Biological Sciences and Research Institute (IBRI) in Noida is playing a vital role in building genomic data science expertise in India. Through its structured training programs and workshops, IBRI provides hands-on training in NGS data analysis with a focus on dry lab techniques. Participants are exposed to real datasets and learn to use state-of-the-art bioinformatics tools, including AI-driven platforms for variant calling, expression analysis, and genome annotation. The training emphasizes practical applications in clinical genomics, molecular diagnostics, and research, thereby empowering scientists and students to confidently handle complex sequencing data and contribute to cutting-edge discoveries.

Conclusion

AI is reshaping the landscape of NGS data analysis by offering faster, more accurate, and scalable solutions across genomics, transcriptomics, epigenomics, and metagenomics. As these tools continue to evolve, they promise to unlock new biological insights and accelerate precision medicine.

Whether you're working on cancer genomics, single-cell analysis, or environmental microbiomes, integrating AI into your NGS pipeline can significantly enhance your research outcomes. Furthermore, with advancements in cloud computing and open-source software, these powerful tools are now more accessible to scientists and clinicians than ever before.