2026· Methods in molecular biology· Vol 3037, pp.
215-242
· 0 citations
Medicine
TL;DR
This chapter introduces a practical protocol that contrasts a conventional variant-calling approach with a modern AI/ML-based method using single-cell RNA sequencing (scRNA-seq) data, and demonstrates that AI-based methods, such as DeepVariant, provide enhanced accuracy and confidence in genotype prediction compared to rule-based tools such as RNA-Mutect2.
Whole-exome sequencing (WES) enables the identification of rare germline variants contributing to pediatric diseases. Trio-based sequencing, comparing affected children with their parents, is particularly effective for rare disease genetics. However, WES data analysis requires bioinformatics expertise, varies across in...
Sara-Luisa Reh, C. Walter, J. Lohse et al.· Scientific Reports· 0 citations
A workflow to minimize the effect of WES capture inconsistencies in single-nucleotide variation (SNV) data is proposed, which leads to a considerable decrease in the batch effect signal, potentially increasing the likelihood of finding true biological signals.
Laura Jarosz, M. Ochocki, Julia Merta et al.· Methods and Protocols· 0 citations
Widespread genetic testing has expanded variant identification, yet functional characterization remains a bottleneck in genome guided medicine. Here, we present a modified Variant Abundance by Massively Parallel Sequencing (VAMP-seq) platform integrating experimental and computational approaches for high-resolution abu...
Xiao-Li Cai, Dao-Yi Wang, Jia-Meng Hu et al.· bioRxiv· 0 citations
Background Whole-exome sequencing is a widely used technology to identify pathogenic variants in cancer. Although sequencing itself has become increasingly accessible, downstream analysis remains computationally complex, presenting a challenge for many researchers. Existing pipelines lack integrated support for somatic...
Nicholas E. Bambach, J. Ricarte-Filho, E. R. Reichenberger et al.· Cancer Informatics· 0 citations
This chapter covers quality control, normalization, sparse data handling, and transcript quantification for both bulk and snRNA-seq, along with strategies to address challenges such as multi-mapped reads and batch effects, and examines how artificial intelligence and machine learning techniques can improve data process...
S. P. Dharshini, Y.-H. Taguchi, M. Gromiha· Methods in molecular biology· 0 citations
A machine learning model is trained, DBP-CanPred, to identify driver mutations in DBPs using the sequence-derived evolutionary features, as well as structure-based features such as mutation-perturbed structural descriptors, which contributes to understanding mutation patterns in DNA-binding proteins and supports varian...
A. Phogat, Sowmya Ramaswamy Krishnan, Medha Pandey et al.· Frontiers in Bioinformatics· 0 citations
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