Single-cell RNA sequencing (scRNA-seq) enables high-resolution exploration of cellular diversity and gene regulation, yet analyzing such data remains challenging due to technical and methodological limitations. Existing task-specific deep generative models like Variational Auto-Encoder (VAE) and its variants struggle to incorporate external biological knowledge, while transformer-based foundational large Language Models (LLMs or large LaMs) face limitations in computational cost and applicability to tabular gene expression data. Here, we introduce sciLaMA (single-cell interpretable Language Model Adapter), a novel representation learning framework that bridges these gaps by integrating static gene embeddings from multimodal LaMs with scRNA-seq tabular data through a paired-VAE architecture. Our approach generates context-aware representations for both cells and genes and outperforms state-of-the-art methods in key single-cell downstream tasks, including batch effect correction, cell clustering, and cell-state-specific gene marker and module identification, while maintaining computational efficiency. sciLaMA offers a computationally efficient, unified framework for comprehensive single-cell data analysis and biologically interpretable gene module discovery.

MOTIVATION: With the vast improvements in sequencing technologies and increased number of protocols, sequencing is being used to answer complex biological problems. Subsequently, analysis pipelines have become more time consuming and complicated, usually requiring highly extensive prevalidation steps. Here, we present SeqWho, a program designed to assess heuristically the quality of sequencing files and reliably classify the organism and protocol type by using Random Forest classifiers trained on biases native in k-mer frequencies and repeat sequence identities. RESULTS: Using one of our primary models, we show that our method accurately and rapidly classifies human and mouse sequences from nine different sequencing libraries by species, library and both together, 98.32%, 97.86% and 96.38% of the time, respectively. Ultimately, we demonstrate that SeqWho is a powerful method for reliably validating the quality and identity of the sequencing files used in any pipeline. AVAILABILITY AND IMPLEMENTATION: https://github.com/DaehwanKimLab/seqwho. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Open, collaborative research is a powerful paradigm that can immensely strengthen the scientific process by integrating broad and diverse expertise. However, traditional research and multi-author writing processes break down at scale. We present new software named Manubot, available at https://manubot.org, to address the challenges of open scholarly writing. Manubot adopts the contribution workflow used by many large-scale open source software projects to enable collaborative authoring of scholarly manuscripts. With Manubot, manuscripts are written in Markdown and stored in a Git repository to precisely track changes over time. By hosting manuscript repositories publicly, such as on GitHub, multiple authors can simultaneously propose and review changes. A cloud service automatically evaluates proposed changes to catch errors. Publication with Manubot is continuous: When a manuscript's source changes, the rendered outputs are rebuilt and republished to a web page. Manubot automates bibliographic tasks by implementing citation by identifier, where users cite persistent identifiers (e.g. DOIs, PubMed IDs, ISBNs, URLs), whose metadata is then retrieved and converted to a user-specified style. Manubot modernizes publishing to align with the ideals of open science by making it transparent, reproducible, immediate, versioned, collaborative, and free of charge.