MOTIVATION: High-throughput RNA sequencing has significantly advanced transcriptomic profiling in oncology. Millions of RNA-seq datasets have accumulated in public databases such as the Sequence Read Archive (SRA). However, fragmented, ambiguous, or missing metadata can severely limit accurate cohort selection, introduce bias, and delay discoveries. RESULTS: To address these issues, we introduce 'Metappuccino', a hybrid metadata enrichment tool built on Mistral-7B-Instruct and specialized via low-rank adaptation (LoRA). Metappuccino reconstructs 19 metadata classes (e.g. organ, disease, cell type) by combining deterministic extraction/normalization with model-based completion: 4 submission-mandatory fields are read directly from SRA/API records, while the remaining 15 classes are obtained through validated rule-based extraction when explicitly supported by the context and otherwise predicted by the LoRA-specialized model when information is missing or ambiguous. To promote robust, context-aware inference rather than memorization, we designed training and data partitioning to minimize leakage and preserve generalization. When applicable, predicted values are mapped to standardized ontologies to ensure consistent, interoperable annotations. Across our benchmarks, Metappuccino substantially improves accuracy over the base model, matches or exceeds recent larger open-source LLMs, and reduces inference time by up to two-fold relative to these baselines. By enriching under-annotated public RNA-seq records, Metappuccino increases the usability of SRA datasets for large-scale reuse, with applications that extend beyond oncology transcriptomics. AVAILABILITY AND IMPLEMENTATION: Metappuccino source code is available on: github.com/chumphati/Metappuccino. The fine-tuned LLM, MetappuccinoLLModel, is available on: huggingface.co/chumphati/MetappuccinoLLModel. Both repositories are released under Apache-2.0 license.

MOTIVATION: KaMRaT is designed for processing large k-mer count tables derived from multi-sample, RNA-seq data. Its primary objective is to identify condition-specific or differentially expressed sequences, regardless of gene or transcript annotation. RESULTS: KaMRaT is implemented in C++. Major functions include scoring k-mers based on count statistics, merging overlapping k-mers into contigs and selecting k-mers based on their occurrence across specific samples. AVAILABILITY AND IMPLEMENTATION: Source code and documentation are available via https://github.com/Transipedia/KaMRaT.

MOTIVATION: RNA sequencing is now widely performed to study differential expression among experimental conditions. As tests are performed on a large number of genes, stringent false-discovery rate control is required at the expense of detection power. Ad hoc filtering techniques are regularly used to moderate this correction by removing genes with low signal, with little attention paid to their impact on downstream analyses. RESULTS: We propose a data-driven method based on the Jaccard similarity index to calculate a filtering threshold for replicated RNA sequencing data. In comparisons with alternative data filters regularly used in practice, we demonstrate the effectiveness of our proposed method to correctly filter lowly expressed genes, leading to increased detection power for moderately to highly expressed genes. Interestingly, this data-driven threshold varies among experiments, highlighting the interest of the method proposed here. AVAILABILITY: The proposed filtering method is implemented in the R package HTSFilter available on Bioconductor.