Unsupervised read-level differential analysis recovers established lncRNA biomarkers; uncovers new prognostic transposable-element reads in adrenocortical carcinoma and sarcomas; and extracts signals even from reads that fail to align.
Malva is presented, a computational platform that enables ultrafast, species-agnostic and reference-free interrogation of the raw sequence space, enabling searching for any sequence, mutation, splice junction or pathogen, or spatial location of arbitrary transcripts.
D. León-Periñán, Nikos Karaiskos, N. Rajewsky· Nature· 1 citation
GEOMeta provides a scalable resource and reproducible framework for metadata curation in the Gene Expression Omnibus, and benchmarked transcriptome representation models for predicting sex, age, tissue and disease from transcriptome embeddings.
Xiaodan Zhang, S. Paithankar, Jing Pu et al.· bioRxiv· 0 citations
This study provides a systematic assessment of four commercially available workflows for performing LR snRNA-seq and highlights key methodological trade-offs related to distinct library preparation strategies, thus providing practical guidance for future isoform-resolved transcriptome studies at the single-nucleus leve...
F. Köhler, Anna Delgado-Tejedor, Maik Zehnsdorf et al.· bioRxiv· 0 citations
Long-read RNA sequencing directly resolves the full structures of RNA transcripts. Advances in throughput now enable the generation of deeply sequenced cohorts of hundreds of samples, making joint transcript discovery across large datasets possible. However, existing transcript identification methods were designed for...
E. Dolzhenko, Megan D. Schertzer, Ryan Gossart et al.· bioRxiv· 1 citation
Summary High-throughput transcriptomics has made gene signatures central to interpreting gene expression data, with applications in diagnosis, prognosis, and prediction. Quantifying signature activity and assessing its robustness remain challenging because scoring methods primarily rely on various assumptions, and no s...
Tommaso Giacomello, S. Mazzara, Gennaro Abbruzzese et al.· bioRxiv· 0 citations
A cell-by-gene count matrix is the artifact of a single-cell RNA-seq experiment that is most often stored, shared, and reanalyzed. It is the output of a computation whose inputs are the sequenced molecules and a gene annotation, and while the molecules never change, the annotation is revised continually. Once the matri...
Rob Patro· bioRxiv· 0 citations
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