Biology subjects

Ratul, M. R. Z.

Publications and source records attributed to Ratul, M. R. Z..

3 recordsLinked to original sources

bioRxiv2024

Reference-free Analysis of scRNA-seq Data Reveals Elevated rRNA and mtRNA Transcription during Neurogenesis in Axolotl

Analysis of single-cell RNA-seq data is typically performed on a gene expression matrix estimated by aligning reads to a reference transcriptome. However, this approach is difficult to apply to organisms with no or incomplete reference transcriptomes. In addition, events deviating from the reference remain undetected. Here we present a reference-free method to analyze single-cell RNA-seq data based on k-mers. We assess the performance of our method on a metastatic renal cell carcinoma dataset and find that it is largely able to capture differentially expressed genes. We then analyze a recently generated dataset to study neurogenesis in Axolotl and observe increased levels of transcription of rRNA and mtRNA during neurogenesis as well as a miRNA with previously predicted links to neuronal development. We also detect lncRNAs and intron retention in heart disease-related genes in diseased cardiomyocytes in an analysis of a congenital heart disease dataset.

bioinformatics↗

bioRxiv2024

RNA-DCGen: Dual Constrained RNA Sequence Generation with LLM-Attack

Designing RNA sequences with specific properties is critical for developing personalized medications and therapeutics. While recent diffusion and flow-matching-based generative models have made strides in conditional sequence design, they face two key limitations: specialization for fixed constraint types, such as tertiary structures, and lack of flexibility in imposing additional conditions beyond the primary property of interest. To address these challenges, we introduce RNA-DCGen, a generalized framework for RNA sequence generation that is adaptable to any structural or functional properties through straightforward finetuning with an RNA language model (RNA-LM). Additionally, RNA-DCGen can enforce conditions on the generated sequences by fixing specific conserved regions. On RNA generation conditioned on RNA distance maps, RNA-DCGen generates sequences with an average R2 score of 0.625 compared to random sequences that score only 0.118 over 250 generations as judged by a separate more capable RNA-LM. When conditioned on RNA secondary structures, RNA-DCGen achieves an average F1 score of 0.4 against a random baseline of 0.006.

bioinformatics↗

bioRxiv2023

wQFM-DISCO: DISCO-enabled wQFM improves phylogenomic analyses despite the presence of paralogs

Gene trees often differ from the species trees that contain them due to various factors, including incomplete lineage sorting (ILS), gene duplication and loss (GDL), and horizontal gene transfer (HGT). Several highly accurate species tree estimation methods have been introduced to explicitly address ILS, including AS-TRAL, a widely used statistically consistent method, and wQFM, a quartet amalgamation approach that is experimentally shown to be more accurate than ASTRAL. Two recent advancements, ASTRAL-Pro and DISCO, have emerged in the field of phylogenomics to consider gene duplication and loss (GDL) events. ASTRAL-Pro introduces a refined measure of quartet similarity, accounting for both orthology and paralogy. DISCO, on the other hand, offers a general strategy to decompose multicopy gene family trees into a collection of single-copy trees, allowing the utilization of methods previously designed for species tree inference in the context of single-copy gene trees. In this study, we first introduce some variants of DISCO to examine its underlying hypotheses and present analytical results on the statistical guarantees of DISCO. In particular, we introduce DISCO-R, a variant of DISCO with a refined and improved pruning strategy that provides more accurate and robust results. We then propose wQFM-DISCO (wQFM paired with DISCO) as an adaptation of wQFM to handle multicopy gene trees resulting from GDL events. Extensive evaluation studies on a collection of simulated and real data sets demonstrate that wQFM-DISCO is significantly more accurate than ASTRAL-Pro and other competing methods.

bioinformatics↗