Biology subjects

Samadi, M.

Publications and source records attributed to Samadi, M..

3 recordsLinked to original sources

bioRxiv2025

Parabricks: GPU Accelerated Universal Pan-Instrument Genomics Analysis Software Suite

Next-generation sequencing (NGS) has transformed genomics, enabling breakthroughs in biotechnology, healthcare, and pharmaceuticals. However, exponential data growth-outpacing Moores Law-presents critical computational challenges for secondary analysis. We introduce Parabricks, a freely accessible, GPU-accelerated software suite supporting diverse workflows, including wholegenome, exome, transcriptome, and methylation analysis. Compatible across major short read and long read sequencing platforms, it reduces processing time from hours to minutes while maintaining concordance with established standards, this acceleration translates to significant cost savings. Deployable on-premises or through cloud platforms, Parabricks ensures scalable, genomic processing. Integrated AI-driven workflows and deep learning models further enhance precision, offering a universal solution for high-throughput genomic research and clinical diagnostics.

bioinformatics↗

bioRxiv2025

DeepSAP: Improved RNA-Seq Alignment by Integrating Transcriptome Guidance with Transformer-Based Splice Junction Scoring

Advancements in high-throughput sequencing have revolutionized the field of transcriptomics, pro-viding unprecedented insights into gene expression, splicing events, and fusions. Despite these advancements, the analysis of RNA-seq data remains challenging due to the presence of complex splice junctions, multi-mapped reads, and chimeric events. In this study, we present DeepSAP, an innovative approach that improves the accuracy of RNA-seq alignment by integrating Transcriptome-Guided Genomic Alignment, as implemented in GSNAP, with improved splice junction scoring, predicted by a transformer-based deep learning model. Our work demonstrates synergy between these methods, resulting in enhanced detection of splice junctions, identification of indels, and resolution of complex splicing patterns. On a standard benchmark of human simulated datasets, DeepSAP achieves the highest mean F1 score (0.971) for splice junction detection, outperforming DRAGEN (0.933), novoSplice (0.914), STAR (0.821), HISAT2 (0.662), and Subjunc (0.770). By integrating the unique capabilities of transcriptome-guided alignment and large language models, our splice junction scoring approach captures intricate sequence patterns surrounding splice donor and acceptor sites, providing significant advancement in RNA-seq data analysis.

bioinformatics↗

bioRxiv2021

de novo variant calling identifies cancer mutation profiles in the 1000 Genomes Project

Detection of de novo variants (DNVs) is critical for studies of disease-related variation and mutation rates. We developed a GPU-based workflow to rapidly call DNVs (HAT) and demonstrated its effectiveness by applying it to 4,216 Simons Simplex Collection (SSC) whole-genome sequenced parent-child trios from DNA derived from blood. In our SSC DNV data, we identified 78 {+/-} 15 DNVs per individual, 18% {+/-} 5% at CpG sites, 75% {+/-} 9% phased to the paternal chromosome of origin, and an average allele balance of 0.49. These calculations are all in line with DNV expectations. We sought to build a control DNV dataset by running HAT on 602 whole-genome sequenced parent-child trios from DNA derived from lymphoblastoid cell lines (LCLs) from the publicly available 1000 Genomes Project (1000G). In our 1000G DNV data, we identified 740 {+/-} 967 DNVs per individual, 14% {+/-} 4% at CpG sites, 61% {+/-} 11% phased to the paternal chromosome of origin, and an average allele balance of 0.41. Of the 602 trios, 80% had > 100 DNVs and we hypothesized the excess DNVs were cell line artifacts. Several lines of evidence in our data suggest that this is true and that 1000G does not appear to be a static reference. By mutation profile analysis, we tested whether these cell line artifacts were random and found that 40% of individuals in 1000G did not have random DNV profiles; rather they had DNV profiles matching B-cell lymphoma. Furthermore, we saw significant excess of protein-coding DNVs in 1000G in the gene IGLL5 that has already been implicated in this cancer. As a result of cell line artifacts, 1000G has variants present in DNA repair genes and at Clinvar pathogenic or likely-pathogenic sites. Our study elucidates important implications of the use of sequencing data from LCLs for both reference building projects as well as disease-related projects whereby these data are used in variant filtering steps.

genomics↗