Biology subjects

Faridani, O. R.

Publications and source records attributed to Faridani, O. R..

5 recordsLinked to original sources

bioRxiv2025

Medlib: A Feature-Rich C/C++ Library for Exact Alignment of Nanopore Sequences Using Multiple Edit Distance

Pairwise sequence alignment is a cornerstone of bioinformatics, driving applications from variant calling to barcode demultiplexing. However, the advent of long-read, high-error sequencing platforms--such as Oxford Nanopore Technologies--demands alignment tools that not only deliver exact results but also accommodate the elevated noise inherent in these data. Here, we introduce Medlib (Multiple Edit Distance Library) that offers both high performance and novel, biologically motivated features. Medlibs threshold mode allows users to retrieve every alignment with edit distance k [≤] kth and optionally alignment score s [≥] sth, ensuring comprehensive discovery of all plausible matches. Counterintuitively, this exhaustive retrieval outpaces traditional minimum-error searches. Medlib further provides the first exact implementations of overlap-aware reporting and motif boundary treatment. Medlib supports multiple input formats, predefined and custom alphabets, affine-gap scoring applied retrospectively, built-in dedicated multithreading schemes, and user-defined function callbacks for immediate post-processing. Benchmarking against Edlib demonstrates a 1.6x speedup in minimum mode and even greater gains (2.5x) in threshold mode--while delivering up to twice as many alignments. Medlib thus offers a robust, feature-rich engine for exact pairwise alignment in modern, high-throughput sequencing workflows. Availability and ImplementationSource code, installation instructions and test data are freely available for download at https://github.com/Mehdi-Rafeie/Medlib, under the MIT license.

bioinformatics↗

bioRxiv2025

Blastocoel fluid RNA predicts pregnancy outcome in assisted reproduction

Nearly one in eight couples are affected by infertility, with many relying on assisted reproductive technologies (ART) to conceive. However, selecting the highest-quality embryo in ART remains a major challenge, as current assessment methods are often subjective, or invasive, and lack precision. Here, we introduce a novel strategy that analyses embryo-derived polyadenylated RNA in blastocoel fluid to more accurately predict pregnancy outcomes. Elevated RNA levels were strongly associated with implantation failure, particularly in embryos from women over the age of 34. Our predictive model developed using our sample cohort, incorporating both RNA and maternal age, demonstrated exceptional performance, achieving 76% accuracy in the training set and 73% in independent validation in predicting implantation outcome-- highlighting a promising advancement in embryo selection and ART success.

molecular biology↗

bioRxiv2024

Landscape of microRNA and target expression variation and covariation in single mouse embryonic stem cells

MicroRNAs are small RNA molecules that can repress the expression of protein coding genes post-transcriptionally. Previous studies have shown that microRNAs can also have alternative functions including target noise buffering and co-expression, but these observations have been limited to a few microRNAs. Here we systematically study microRNA alternative functions in mouse embryonic stem cells, by genetically deleting Drosha - leading to global loss of microRNAs. We apply complementary single-cell RNA-seq methods to study the variation of the targets and the microRNAs themselves, and transcriptional inhibition to measure target half-lives. We find that microRNAs form four distinct co-expression groups across single cells. In particular the mir-290 and the mir-182 clusters are abundantly, variably and inversely expressed. Intriguingly, some cells have global biases towards specific miRNAs originating from either end of the hairpin precursor, suggesting the presence of unknown regulatory cofactors. We find that miRNAs generally increase variation and covariation of their targets at the RNA level, but we also find miRNAs such as miR-182 that appear to have opposite functions. In particular, miRNAs that are themselves variable in expression, such as miR-291a, are more likely to induce covariations. In summary, we apply genetic perturbation and multi-omics to give the first global picture of microRNA dynamics at the single cell level.

genomics↗

bioRxiv2020

Profiling of small non-coding RNAs across cellular and biofluid compartments: implications for multiple sclerosis immunopathology

Multiple sclerosis (MS), a chronic inflammatory disease of the central nervous system (CNS), is associated with dysregulation of microRNAs (miRNA). We here analyzed all classes of small non-coding RNAs (sncRNAs) in matching peripheral blood mononuclear cells (PBMCs), plasma, cerebrospinal fluid (CSF) cells and cell-free CSF from relapsing-remitting (RRMS, n=12 in relapse, n=11 in remission), secondary progressive (SPMS, n=6) MS patients and non-inflammatory and inflammatory neurological disease controls (NINDC, n=11; INDC, n=5). We show widespread changes in small nuclear, nucleolar, transfer RNAs and miRNAs. In CSF cells, 133/133 and 115/117 differentially expressed sncRNAs are increased in RRMS relapse compared to remission and RRMS compared to NINDC, respectively. In contrast, 65/67 differentially expressed PBMC sncRNAs are decreased in RRMS compared to NINDC. The striking contrast between periphery and CNS suggests that sncRNA-mediated mechanisms, including alternative splicing, RNA degradation and mRNA translation, regulate the transcriptome of pathogenic cells primarily in the target organ.

molecular biology↗

bioRxiv2019

Single-cell RNA counting at allele- and isoform-resolution using Smart-seq3

Large-scale sequencing of RNAs from individual cells can reveal patterns of gene, isoform and allelic expression across cell types and states1. However, current single-cell RNA-sequencing (scRNA-seq) methods have limited ability to count RNAs at allele- and isoform resolution, and long-read sequencing techniques lack the depth required for large-scale applications across cells2,3. Here, we introduce Smart-seq3 that combines full-length transcriptome coverage with a 5 unique molecular identifier (UMI) RNA counting strategy that enabled in silico reconstruction of thousands of RNA molecules per cell. Importantly, a large portion of counted and reconstructed RNA molecules could be directly assigned to specific isoforms and allelic origin, and we identified significant transcript isoform regulation in mouse strains and human cell types. Moreover, Smart-seq3 showed a dramatic increase in sensitivity and typically detected thousands more genes per cell than Smart-seq2. Altogether, we developed a short-read sequencing strategy for single-cell RNA counting at isoform and allele-resolution applicable to large-scale characterization of cell types and states across tissues and organisms.

genomics↗