Biology subjects

Lassmann, T.

Publications and source records attributed to Lassmann, T..

10 recordsLinked to original sources

bioRxiv2026

Interaction-finder: automated literature-based discovery of biological entity associations with quote-level provenance

Identifying interactions between biological entities is a cornerstone of molecular research, but assembling such lists from the literature is slow and tedious. For many research questions, no curated database exists, leaving researchers to survey the relevant literature themselves. We present interaction-finder, a tool that automates this process: given a topic string and user-defined entity types, it discovers relevant literature through O_SCPLOWLLMC_SCPLOW-guided iterative search, extracts candidate associations from full-text articles, and produces a ranked list where every association is backed by quoted passages verified against the source text. A self-contained interactive O_SCPLOWHTMLC_SCPLOW report enables rapid triage of the results. Evaluated across 60 topics in three domains (celltype-cellmarker, disease-gene, and ligand-receptor), interaction-finder recalls 1.2-4.3x as many known associations as single-shot prompting and an off-the-shelf deep-research framework, with all extracted quotes verified against source text. To assess candidates unrecognised from the gold-standard databases, we scored each candidate using an independent O_SCPLOWLLMC_SCPLOW judge blind to the tools reasoning. Across the three domains, unverified candidates score similarly to gold-standard associations. We find the gold-standard associations are enriched at the top of our ranked candidates, with an overall recall@20 of 0.61. Interaction-finder is freely available at https://github.com/tecosaur/interaction_finder under an O_SCPLOWMITC_SCPLOW licence.

bioinformatics↗

bioRxiv2025

Uncovering biological patterns across studies through automated large-scale reanalyses of public transcriptomic data

Large amounts of transcriptomic data have been made available in public repositories. Systematic reanalyses of these data offer the potential to identifying conserved biological patterns or context-specific signatures. However, this is a labour intensive process requiring bioinformatic expertise and a long chain of manual decision making. Use of LLMs and agentic systems holds promise for automating these otherwise time-consuming tasks. Here, we present UORCA (Unified -Omics Reference Corpus of Analyses), a tool to systematically identify and analyse public transcriptomic datasets. UORCA uses an LLM-assisted framework to search for datasets relevant to a research question. These datasets are analysed through a multi-agent system that performs a standardised bioinformatic analyses to identify differentially expressed genes. Results of each analysis are then displayed in an interactive visual interface. We found that UORCA recapitulated findings reported from a manual comparison of datasets, but also found biological signatures that were not initially described. We find that UORCA generates targeted hypotheses relevant for drug design, and facilitates evaluation of experimental results where they differ from past literature. Together, these findings demonstrate how UORCA accelerates biomedical discovery by enabling scientists to extract actionable findings from diverse public datasets.

bioinformatics↗

bioRxiv2025

Age-dependent tumor-immune interactions underlie immunotherapy response in pediatric cancer

Pediatric cancers originate in rapidly growing tissues within the context of a developing host. However, the interactions between cancer cells and the developing immune system are incompletely understood. Here, we established a suite of pediatric syngeneic mouse cancer models across diverse anatomical sites and compared their tumor immune microenvironment with that in adult mice. Tumors in pediatric mice exhibited significantly accelerated growth and diminished leukocyte infiltration, dominated by naive-like PD-1low/CD8+ T cells, and proliferative MHCIIlow/PD-L1hi/CD86low macrophages. Tumor-infiltrating leukocytes in pediatric mice were enriched for MYC targets, which was also observed in pediatric patient samples. Furthermore, pediatric mice displayed poor responses to anti-PD-1/PD-L1 or bispecific T cell engager antibodies, which could be reversed by inducing a proinflammatory microenvironment via MYC inhibition or inducing macrophage polarization to an MHCIIhi phenotype. These findings underscore the significant influence of young age on cancer immune responses and reveal potential new therapeutic opportunities for pediatric cancers. HIGHLIGHTSO_LIAllograft tumors exhibit markedly accelerated growth in pediatric hosts compared to adults. C_LIO_LITumors growing in pediatric mice have reduced leukocyte infiltration, dominated by naive-like PD-1low/CD8+ T cells, and MHCIIlow/M2-like macrophages. C_LIO_LIEnrichment of MYC target genes is observed in pediatric mouse tumors and confirmed in primary patient tumor samples. C_LIO_LIPediatric mice display reduced response to anti-PD-1/PD-L1 and BiTE immunotherapy, which can be reversed by remodeling the TIME, using either MYC inhibition or macrophage polarization. C_LI

cancer biology↗

bioRxiv2025

GeneInsight: Condensing Gene Set Knowledge via Language Models

Interpreting gene sets is often complicated by the overwhelming number of annotations associated with individual genes, making it difficult to extract meaningful biological insights. To address this issue, we developed GeneInsight, an AI-powered tool that combines advanced topic modelling with large language models to automatically synthesise diverse biological annotations from literature, gene ontologies, and databases such as STRING. GeneInsight consolidates extensive annotations into coherent thematic summaries that render such data readily interpretable, thereby enabling the rapid extraction of biologically significant insights that conventional enrichment analyses often overlook.

bioinformatics↗

bioRxiv2023

Spectral detection of condition-specific biological pathways in single-cell gene expression data

Single cell RNA sequencing is an ubiquitous method for studying changes in cellular states within and across conditions. Differential expression (DE) analysis may miss subtle differences, especially where transcriptional variability is not unique to a specific condition, but shared across multiple conditions or phenotypes. Here, we present CDR-g (Concatenate-Decompose-Rotate genomics), a fast and scalable strategy based on spectral factorisation of gene coexpression matrices. CDR-g detects subtle changes in gene coexpression across a continuum of biological states in multi-condition single cell data. CDR-g collates these changes and builds a detailed profile of differential cell states. Applying CDR-g, we show that it identifies biological pathways not detected using conventional DE analysis and delineates novel, condition-specific subpopulations in single-cell datasets.

bioinformatics↗

bioRxiv2019

Systematic bias assessment in solid tissue 10x scRNA-seq workflows

Single-cell and single-nucleus RNA sequencing have been widely adopted in studies of heterogeneous tissues to estimate their cellular composition and obtain transcriptional profiles of individual cells. However, the current fragmentary understanding of artefacts introduced by sample preparation protocols impedes the selection of optimal workflows and compromises data interpretation. To bridge this gap, we compared performance of several workflows applied to adult mouse kidneys. Our study encompasses two tissue dissociation protocols, two cell preservation methods, bulk tissue RNA sequencing, single-cell and three single-nucleus RNA sequencing workflows for the 10x Genomics Chromium platform. These experiments enable a systematic comparison of recovered cell types and their transcriptional profiles across the workflows and highlight protocol-specific biases important for the experimental design and data interpretation.

genomics↗

bioRxiv2018

C1 CAGE detects transcription start sites and enhancer activity at single-cell resolution

Single-cell transcriptomic profiling is a powerful tool to explore cellular heterogeneity. However, most of these methods focus on the 3-end of polyadenylated transcripts and provide only a partial view of the transcriptome. We introduce C1 CAGE, a method for the detection of transcript 5-ends with an original sample multiplexing strategy in the C1 microfluidic system. We first quantified the performance of C1 CAGE and found it as accurate and sensitive as other methods in C1 system. We then used it to profile promoter and enhancer activities in the cellular response to TGF-{beta} of lung cancer cells and discovered subpopulations of cells differing in their response. We also describe enhancer RNA dynamics revealing transcriptional bursts in subsets of cells with transcripts arising from either strand within a single-cell in a mutually exclusive manner, which was validated using single molecule fluorescence in-situ hybridization.

genomics↗

bioRxiv2017

Linking FANTOM5 CAGE Peaks To Annotations With CAGEscan

The FANTOM5 expression atlas is a quantitative measurement of the activity of nearly 200,000 promoter regions across nearly 2,000 different human primary cells, tissue types and cell lines. Generation of this atlas was made possible by the use of CAGE, an experimental approach to localise transcription start sites at single-nucleotide resolution by sequencing the 5' ends of capped RNAs after their conversion to cDNAs. While 50% of CAGE-defined promoter regions could be confidently associated to adjacent transcriptional units, nearly 100,000 promoter regions remained gene-orphan. To address this, we used the CAGEscan method, in which random-primed 5'-cDNAs are paired-end sequenced. Pairs starting in the same region are assembled in transcript models called CAGEscan clusters. Here, we present the production and quality control of CAGEscan libraries from 56 FANTOM5 RNA sources, which enhances the FANTOM5 expression atlas by providing experimental evidence associating core promoter regions with their cognate transcripts.

genomics↗

bioRxiv2016

Shared activity patterns arising at genetic susceptibility loci reveal underlying genomic and cellular architecture of human disease.

Genetic variants underlying complex traits, including disease susceptibility, are enriched within the transcriptional regulatory elements, promoters and enhancers. There is emerging evidence that regulatory elements associated with particular traits or diseases share patterns of transcriptional regulation. Accordingly, shared transcriptional regulation (coexpression) may help prioritise loci associated with a given trait, and help to identify the biological processes underlying it. Using cap analysis of gene expression (CAGE) profiles of promoter and enhancer-derived RNAs across 1824 human samples, we have quantified coexpression of RNAs originating from trait-associated regulatory regions using a novel analytical method (network density analysis; NDA). For most traits studied, sequence variants in regulatory regions were linked to tightly coexpressed networks that are likely to share important functional characteristics. These networks implicate particular cell types and tissues in disease pathogenesis; for example, variants associated with ulcerative colitis are linked to expression in gut tissue, whereas Crohns disease variants are restricted to immune cells. We show that this coexpression signal provides additional independent information for fine mapping likely causative variants. This approach identifies additional genetic variants associated with specific traits, including an association between the regulation of the OCT1 cation transporter and genetic variants underlying circulating cholesterol levels. This approach enables a deeper biological understanding of the causal basis of complex traits.\n\nONE SENTENCE SUMMARYWe discover that variants associated with a specific disease share expression profiles across tissues and cell types, enabling fine mapping and identification of new disease-associated variants, illuminating key cell types involved in disease pathogenesis.

genomics↗

bioRxiv2016

Single-cell transcriptomes of fluorescent, ubiquitination-based cell cycle indicator cells

We used a transgenic HeLa cell line that reports cell cycle phases through fluorescent, ubiquitination-based cell cycle indicators (Fucci), to produce a reference dataset of more than 270 curated single cells. Microscopic images were taken from each cell followed by RNA-sequencing, so that single-cell expression data is associated to the fluorescence intensity of the Fucci probes in the same cell. We developed an open data management and quality control workflow that enables users to replicate the processing of the sequence and microscopic image data that we deposited in public repositories. The workflow outputs a table with metadata, that is the starting point for further studies on these data. Beyond its use for cell cycle studies, We also expect that our workflow can be adapted to other single-cell projects using a similar combination of sequencing data and fluorescence measurements.

genomics↗