SVLearn: a dual-reference machine learning approach enables accurate cross-species genotyping of structural variants
Prelude SVlearn is a computational tool for structural variation (SV) genotyping, originally developed using machine learning methods such as Random Forest. While it demonstrated strong predictive capabilities, during the development process we also further explored whether deep learning techniques could enhance its performance. In this post, I would like to briefly present this investigation from a deep learning perspective.
Mar 11, 2025
PyPropel: a Python-based tool for efficiently processing and characterising protein data
Mar 1, 2025
UMIche: A platform for robust UMI-centric simulation and analysis in bulk and single-cell sequencing
Jan 1, 2025
Tresor: An integrated platform for simulating transcriptomic reads with realistic PCR error representation across various RNA sequencing technologies
Jan 1, 2025
mclUMI: Markov clustering of unique molecular identifiers enables dynamic removal of PCR duplicates
Jan 1, 2025
Enhancing single-cell transcriptomics using interposed anchor oligonucleotide sequences
Video An anchor-interposed bead is designed to enhance single-cell long-read transcriptomics by simply placing an anchor sequence between cell barcode and a UMI. This anchor provides a clear demarcation point, enhancing UMI recognition and minimising synthesis errors.
Jan 1, 2025
Evaluating Performance of Different RNA Secondary Structure Prediction Programs Using Self-cleaving Ribozymes
Jun 1, 2024
Transient Mild Photothermia Improves Therapeutic Performance of Oral Nanomedicines with Enhanced Accumulation in the Colitis Mucosa
Jan 1, 2024
PLMC: Language Model of Protein Sequences Enhances Protein Crystallization Prediction
Jan 1, 2024
Correcting PCR amplification errors in unique molecular identifiers to generate accurate numbers of sequencing molecules
intro coming soon…
Jan 1, 2024