Publications by authors named "Mohammadamin Edrisi"
Copy number aberrations (CNAs) are ubiquitous in many types of cancer. Inferring CNAs from cancer genomic data could help shed light on the initiation, progression, and potential treatment of cancer. While such data have traditionally been available via "bulk sequencing," the more recently introduced techniques for single-cell DNA sequencing (scDNAseq) provide the type of data that makes CNA inference possible at the single-cell resolution.
View Article and Find Full Text PDFCancers develop and progress as mutations accumulate, and with the advent of single-cell DNA and RNA sequencing, researchers can observe these mutations and their transcriptomic effects and predict proteomic changes with remarkable temporal and spatial precision. However, to connect genomic mutations with their transcriptomic and proteomic consequences, cells with either only DNA data or only RNA data must be mapped to a common domain. For this purpose, we present MaCroDNA, a method that uses maximum weighted bipartite matching of per-gene read counts from single-cell DNA and RNA-seq data.
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- Single-nucleotide variants (SNVs) are common genetic variations, and new methods for detecting them from single-cell DNA sequencing, like SCIΦ and scVILP, face scalability issues despite their accuracy.
- A new method called Phylovar improves upon existing techniques by efficiently handling large genomic datasets while outperforming SCIΦ in speed and providing more accurate results than non-phylogeny-aware methods like Monovar.
- Phylovar has been successfully applied to real biological datasets, detecting significant SNVs in both triple-negative breast cancer and neuron cells related to neurodegenerative diseases, and is available for public use on GitHub.
Article Synopsis
- Deep Learning (DL) is a technology that helps scientists predict how proteins are structured, solving a problem that has been around for 50 years.
- The paper talks about how DL is being used in different areas like predicting protein functions, changing genes, and connecting data together, while also pointing out some challenges it faces.
- Finally, the authors summarize the specific and general issues that DL has in studying living things (biosciences).
Copy number aberrations (CNAs), which are pathogenic copy number variations (CNVs), play an important role in the initiation and progression of cancer. Single-cell DNA-sequencing (scDNAseq) technologies produce data that is ideal for inferring CNAs. In this review, we review eight methods that have been developed for detecting CNAs in scDNAseq data, and categorize them according to the steps of a seven-step pipeline that they employ.
View Article and Find Full Text PDFSingle-cell DNA sequencing technologies are enabling the study of mutations and their evolutionary trajectories in cancer. Somatic copy number aberrations (CNAs) have been implicated in the development and progression of various types of cancer. A wide array of methods for CNA detection has been either developed specifically for or adapted to single-cell DNA sequencing data.
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