Biology subjects

Balazs, Z.

Publications and source records attributed to Balazs, Z..

9 recordsLinked to original sources

bioRxiv2026

LoRTIA Plus: a chemistry-agnostic, feature-first software package for long-read transcriptome annotation

Long-read RNA sequencing (lrRNA-seq) enables direct reconstruction of full-length transcripts, yet existing annotation tools show variable performance across genomes and library chemistries, particularly for novel isoforms. We present LoRTIA Plus, a chemistry-agnostic suite for transcriptome annotation and reconstruction from lrRNA-seq data. LoRTIA Plus first detects and filters transcription start sites (TSSs), transcription end sites (TESs), and introns using adapter-aware and quality-based criteria, and evaluates read support before assembling high-confidence transcript models. We benchmarked LoRTIA Plus against bambu, FLAIR, IsoQuant, and NAGATA on KSHV transcriptomes with dense overlap, using a validated literature-supported boundary set, and on transcriptomes from three human cell lines from the Long Read RNA-seq Genome Annotation Assessment Project (LRGASP) sequenced with five long-read chemistries. On KSHV, LoRTIA Plus achieved the highest F1 scores for TSSs, TESs, and transcripts in both direct-cDNA and direct-RNA datasets by improving recall without sacrificing precision. Across human datasets, LoRTIA Plus consistently ranked among the top boundary annotators across all chemistries and was the best-performing tool in PCR-based libraries, while remaining highly competitive on native RNA. Junction- and isoform-level analyses show that LoRTIA Plus yields a rich, reproducible repertoire of novel isoforms and transcript boundaries from viral to human transcriptomes.

genomics↗

bioRxiv2025

Uncovering Cas9 PAM diversity through metagenomic mining and machine learning

Recognition of protospacer adjacent motifs (PAMs) is crucial for target site recognition by CRISPR-Cas systems. In genome editing applications, the requirement for specific PAM sequences at the target locus imposes substantial constraints, driving efforts to search for novel Cas9 orthologs with extended or alternative PAM compatibilities. Here, we present CRISPR-PAMdb, a comprehensive and publicly accessible database compiling Cas9 protein sequences from 3.8 million bacterial and archaeal genomes and PAM profiles from 7.4 million phage and plasmid sequences. Through spacer-protospacer alignment, we inferred consensus PAM preferences for 8,003 unique Cas9 clusters. To extend PAM discovery beyond traditional alignment-based approaches, we developed CICERO, a machine learning model predicting PAM preferences directly from Cas9 protein sequences. Built on the ESM2 protein language model and trained on the CRISPR-PAMdb database, CICERO achieved an average accuracy of 0.68 on test data and 0.75 on experimentally validated Cas9 orthologs. For Cas9 clusters where alignment-based predictions were infeasible, CICERO generated PAM profiles for an additional 50,308 Cas9 proteins, including 17,453 high-confidence predictions with accuracies above 0.86. CRISPR-PAMdb, alongside CICERO models, enables large-scale exploration of PAM diversity across Cas9 proteins, accelerating design of next-generation CRISPR-Cas9 tools for precise genome engineering.

synthetic biology↗

bioRxiv2025

SAUNA - Simulated Annealing for Unique Nucleosome Arrangements from Cell-Free DNA-Sequencing Data

WithdrawalThis preprint has been withdrawn by the authors. While the preprint cites the work by Schoepflin et al. (2013) (reference [20] in the preprint), it has been brought to the authors attention that the preprint may not appropriately acknowledge the extent to which their work depends on the work by Schoepflin et al. (2013). The authors apologize for this oversight and want to emphasize that they did not intend to misattribute the work of Schoepflin et al. (2013) and want to thank them for the code of NucPosSimulator which is a core component of SAUNAs algorithm. Therefore, the authors do not wish this work to be cited as reference for the project. For any questions, please contact the corresponding author.

bioinformatics↗

bioRxiv2023

Predicting prime editing efficiency across diverse edit types and chromatin contexts with machine learning

Prime editing is a powerful genome editing technology, but its efficiency varies depending on the pegRNA design and target locus. Existing computational models for predicting prime editing rates are limited by their focus on specific edit types and by omitting the local chromatin environment. In our study, we developed machine learning models that predict prime editing efficiencies across a wide range of edit types up to 15 bp (PRIDICT2.0) and in different chromatin contexts (ePRIDICT). Both models can be accessed at www.pridict.it.

molecular biology↗

bioRxiv2023

Memory Th1 cells modulate heterologous diseases through innate function

Through immune memory, infections have a lasting effect on the host. While memory cells enable accelerated and enhanced responses upon re-challenge with the same pathogen, their impact on susceptibility to unrelated diseases is unclear. We identify a subset of memory T helper 1 (Th1) cells termed innate acting memory T (TIA) cells that originate from a viral infection and produce IFN-{gamma} with innate kinetics upon heterologous challenge in vivo. Activation of memory TIA cells is induced in response to IL-12 in combination with IL-18 or IL-33 but is TCR-independent. Rapid IFN-{gamma} production by memory TIA cells is protective in subsequent heterologous challenge with the bacterial pathogen Legionella pneumophila. In contrast, antigen-independent re-activation of CD4+ memory TIA cells accelerates disease onset in an autoimmune model of multiple sclerosis. Our findings demonstrate that memory Th1 cells can acquire additional TCR-independent functionality to mount rapid, innate-like responses that modulate susceptibility to heterologous challenges.

immunology↗

bioRxiv2022

ROS induction as a strategy to target persister cancer cells with low metabolic activity in NRAS mutated melanoma

Metabolic reprogramming is an emerging hallmark of resistance to cancer therapy but may generate vulnerabilities that can be targeted with small molecules. Multi-omics analysis revealed that NRAS-mutated melanoma cells with a mesenchymal transcriptional profile adopt a quiescent metabolic program to resist cellular stress response induced by MEK-inhibitor resistance. However, as a result of elevated baseline ROS levels, these cells become highly sensitive to ROS induction. In vivo xenograft experiments and single-cell RNA sequencing demonstrated that intra-tumor heterogeneity requires the combination of a ROS-inducer and a MEK-inhibitor to target both tumor growth and metastasis. By ex vivo pharmacoscopy of 62 human metastatic melanomas, we found that MEK-inhibitor resistant tumors significantly benefitted from the combination therapy. Finally, we profiled 486 cancer cell lines and revealed that oxidative stress responses and translational suppression are biomarkers of ROS-inducer sensitivity, independent of cancer indication. These findings link transcriptional plasticity to a metabolic phenotype that can be inhibited by ROS-inducers in melanoma and other cancers. Statement of SignificanceTargeted-therapy resistance in cancer arises from genetic selection and both transcriptional and metabolic adaptation. We show that metabolic reprogramming sensitizes resistant cells to ROS-induction in combination with pathway inhibitors. Predictive biomarkers of metabolic sensitivity to ROS-inducing agents were identified in many cancer entities, highlighting the generalizability of this treatment approach. Graphical summary O_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY C_FIG_DISPLAY

cancer biology↗

bioRxiv2021

Combined Nanopore and Single-Molecule Real-Time Sequencing Survey of Human Betaherpesvirus 5 Transcriptome

Long-read sequencing (LRS), a powerful novel approach, is able to read full-length transcripts and confers a major advantage over the earlier gold standard short-read sequencing in the efficiency of identifying for example polycistronic transcripts and transcript isoforms, including transcript length- and splice variants. In this work, we profile the human cytomegalovirus transcriptome using two third-generation LRS platforms: the Sequel from Pacific BioSciences, and MinION from Oxford Nanopore Technologies. We carried out both cDNA and direct RNA sequencing, and applied the LoRTIA software, developed in our laboratory, for the transcript annotations. This study identified a large number of novel transcript variants, including splice isoforms and transcript start and end site isoforms, as well as putative mRNAs with truncated in-frame ORFs (located within the larger ORFs of the canonical mRNAs), which potentially encode N-terminally truncated polypeptides. Our work also disclosed a highly complex meshwork of transcriptional read-throughs and overlaps.

genomics↗

bioRxiv2020

Long-read Assays Shed New Light on the Transcriptome Complexity of a Viral Pathogen and on Virus-Host Interaction

Characterization of global transcriptomes using conventional short-read sequencing is challenging because of the insensitivity of these platforms to transcripts isoforms, multigenic RNA molecules, and transcriptional overlaps, etc. Long-read sequencing (LRS) can overcome these limitations by reading full-length transcripts. Employment of these technologies has led to the redefinition of transcriptional complexities in reported organisms. In this study, we applied LRS platforms from Pacific Biosciences and Oxford Nanopore Technologies to profile the dynamic vaccinia virus (VACV) transcriptome and assess the effect of viral infection on host gene expression. We performed cDNA and direct RNA sequencing analyses and revealed an extremely complex transcriptional landscape of this virus. In particular, VACV genes produce large numbers of transcript isoforms that vary in their start and termination sites. A significant fraction of VACV transcripts start or end within coding regions of neighboring genes. We distinguished five classes of host genes according to their temporal responses to viral infection. This study provides novel insights into the transcriptomic profile of a viral pathogen and the effect of the virus on host gene expression. Author SummaryViral transcriptomes that are determined using conventional (first- and second-generation) sequencing techniques are incomplete because these platforms are inefficient or fail to distinguish between types of transcripts and transcript isoforms. In particular, conventional sequencing techniques fail to distinguish between parallel overlapping transcripts, including alternative polycistronic transcripts, transcriptional start site (TSS) and transcriptional end site (TES) isoforms, and splice variants and RNA molecules that are produced by transcriptional read-throughs. Long-read sequencing (LRS) can provide complete sets of RNA molecules, and can therefore be used to assemble complete transcriptome atlases of organisms. Although vaccinia virus (VACV) does not produce spliced RNAs, its transcriptome contains large numbers of TSSs and TESs for individual viral genes and has a high degree of polycistronism, together leading to enormous complexity. In this study, we applied single molecule real-time and nanopore-based cDNA and direct-RNA sequencing methods to investigate transcripts of VACV and the host organism.

genomics↗

bioRxiv2019

Multiple Long-read Sequencing Survey of Herpes Simplex Virus Lytic Transcriptome

Long-read sequencing (LRS) has become increasingly important in RNA research due to its strength in resolving complex transcriptomic architectures. In this regard, currently two LRS platforms have demonstrated adequate performance: the Single Molecule Real-Time Sequencing by Pacific Biosciences (PacBio) and the nanopore sequencing by Oxford Nanopore Technologies (ONT). Even though these techniques produce lower coverage and are more error prone than short-read sequencing, they continue to be more successful in identifying transcript isoforms including polycistronic and multi-spliced RNA molecules, as well as transcript overlaps. Recent reports have successfully applied LRS for the investigation of the transcriptome of viruses belonging to various families. These studies have substantially increased the number of previously known viral RNA molecules. In this work, we used the Sequel and MinION technique from PacBio and ONT, respectively, to characterize the lytic transcriptome of the herpes simplex virus type 1 (HSV-1). In most samples, we analyzed the poly(A) fraction of the transcriptome, but we also performed random oligonucleotide-based sequencing. Besides cDNA sequencing, we also carried out native RNA sequencing. Our investigations identified more than 160 previously undetected transcripts, including coding and non-coding RNAs, multi-splice transcripts, as well as polycistronic and complex transcripts. Furthermore, we determined previously unsubstantiated transcriptional start sites, polyadenylation sites, and splice sites. A large number of novel transcriptional overlaps were also detected. Random-primed sequencing revealed that each convergent gene pair produces non-polyadenylated read-through RNAs overlapping the partner genes. Furthermore, we identified novel replication-associated transcripts overlapping the HSV-1 replication origins, and novel LAT variants with very long 5 regions, which are co-terminal with the LAT-0.7kb transcript. Overall, our results demonstrated that the HSV-1 transcripts form an extremely complex pattern of overlaps, and that entire viral genome is transcriptionally active. In most viral genes, if not in all, both DNA strands are expressed.

genomics↗