Biology subjects

Sharifi-Zarchi, A.

Publications and source records attributed to Sharifi-Zarchi, A..

11 recordsLinked to original sources

bioRxiv2024

ARYANA-BS: Context-Aware Alignment of Bisulfite-Sequencing Reads

MotivationDNA methylation plays a crucial role in biological processes, including imprinting, development, inflammation, and several disorders, such as cancer. Bisulfite sequencing (BS) is the gold standard for single-base resolution in measuring DNA methylation. This process involves treating genomic DNA with sodium bisulfite, followed by polymerase chain reaction (PCR), converting unmethylated cytosines to thymines (C to T) and guanines to adenines (G to A). However, aligning reads obtained through next-generation sequencing (NGS) of the converted DNA is challenging due to the high number of mismatches caused by these conversions. Various BS aligners aim to simplify BS read alignment to conventional DNA alignment by modifying the reference genome and/or reads. Methods include three-letter alignment and wild-card alignment, each with its limitations. This work introduces a novel BS aligner, ARYANA-BS, which departs from conventional DNA aligners by considering base alterations in BS reads within its alignment engine. Leveraging well-established DNA methylation patterns in different genomic contexts, ARYANA-BS generates five indexes from the reference, aligns each read to all indexes, and selects the hit with the minimum penalty. To further enhance alignment accuracy, an optional EM step has been developed, incorporating methylation probability information in the decision-making process for the index with the minimum penalty for each read. The presented approach seeks to improve the accuracy of BS read alignment by accounting for the intricacies of DNA methylation patterns in diverse genomic contexts. ResultsOur experimental results, based on both simulated and real data, demonstrate that ARYANA-BS achieves state-of-the-art accuracy while maintaining competitive speed and memory usage. AvailabilityThe source code of ARYANA and ARYANA-BS, the read simulator for both normal and bisulfite-treated reads, SAM file analyzer which is used for post processing of the alignment penalties, and test procedures for benchmarking different aligners using simulated and real data, are publicly available in https://github.com/hnikaein/aryana. Contactsharifi@sharif.edu Supplementary informationSupplementary data are available at Journal Name online.

bioinformatics↗

bioRxiv2023

InterOpt: Improved gene expression quantification in qPCR experiments using weighted aggregation of reference genes

Quantification of gene expression is a crucial task in biomedical studies. Although high-throughput methods enable rapid and simultaneous expression quantification of coding or non-coding regions, qPCR is still vastly used due to its high availability, sensitivity, specificity, reproducibility, low cost, and ease of use. A limitation of qPCR has been the need of internal controls or reference genes (RGs) with stable expression in different conditions, to normalize the expression level of the other target genes. So far, several stability criteria and numerous methods for selecting a group of RGs have been proposed, however, important challenges must be addressed. Here we introduce a mathematical basis for precise modeling of qPCR expression normalization and justify widely used stability measures of RGs. We then propose a family of scale-invariant functions, as an alternative to the geometric mean, to optimize aggregated expression of RGs. We provide closed-form optimizations for several scale-invariant aggregation functions. Among them, we show the superiority of weighted geometric mean, whose parameters optimize standard deviation as the stability measure of aggregated RGs expression. We provide experimental support for this finding using real data of solid tumors and liquid biopsies of different sample sizes. The proposed methods can be easily integrated in the existing qPCR expression normalization pipelines of genes and non-coding RNAs. We also provide an implementation of the proposed methods as an R package, with GPU acceleration. Availability and implementationhttps://github.com/asalimih/InterOpt Contactasalimih@gmail.com

bioinformatics↗

bioRxiv2023

Revealing the Grammar of Small RNA Secretion Using Interpretable Machine Learning

Small non-coding RNAs can be secreted through a variety of mechanisms, including exosomal sorting, in small extracellular vesicles, and within lipoprotein complexes 1,2. However, the mechanisms that govern their sorting and secretion are still not well understood. In this study, we present ExoGRU, a machine learning model that predicts small RNA secretion probabilities from primary RNA sequence. We experimentally validated the performance of this model through ExoGRU-guided mutagenesis and synthetic RNA sequence analysis, and confirmed that primary RNA sequence is a major determinant in small RNA secretion. Additionally, we used ExoGRU to reveal cis and trans factors that underlie small RNA secretion, including known and novel RNA-binding proteins, e.g., YBX1, HNRNPA2B1, and RBM24. We also developed a novel technique called exoCLIP, which reveals the RNA interactome of RBPs within the cell-free space. We used exoCLIP to reveal the RNA interactome of HNRNPA2B1 and RBM24 in extracellular vesicles. Together, our results demonstrate the power of machine learning in revealing novel biological mechanisms. In addition to providing deeper insight into complex processes such as small RNA secretion, this knowledge can be leveraged in therapeutic and synthetic biology applications.

systems biology↗

bioRxiv2022

Alignment of Single-Molecule Sequencing Reads by Enhancing the Accuracy and Efficiency of Locality-Sensitive Hashing

Aligning single-molecule sequencing (SMS) reads to a reference genome has been computationally challenging due to the high sequencing error rates in this technology. Short distances between consecutive errors in SMS reads confront finding seeds, subsequences of the reads with exact matches to the reference, that specifically target a unique genomic position. To overcome this issue, one can look for similarities, rather than exact matches. MinHash, a locality-sensitive hashing (LSH) scheme, measures the similarity of two sequences by listing all k-mers of each one and approximating the fraction of common k-mers between them using a family of hash functions, which usually includes hundreds to thousands of different hash functions in order to increase the measurement accuracy. MinHash is used to address various bioinformatics problems, including the assembly of SMS reads. Here, we enhance both the efficiency and accuracy of the MinHash scheme by algorithmic techniques. We use a single hash function, rather than hundreds or thousands of different hash functions as used in the other MinHash-based algorithms, without losing the accuracy. We also double the size of the seed sequences by allowing one sequencing error of any form inside a pair of k-mers, which has a significant impact on the accuracy. We show algorithm, called Aryana-LoR, outperforms the accuracy of the other existing SMS aligners in both E-coli and Human genomes. AvailabilityAryana-LoR is freely available at https://gitlab.com/hnikaein/aryana-LoR

bioinformatics↗

bioRxiv2021

Identification of hub genes and pathways in nonalcoholic steatohepatitis by integrated bioinformatics analysis

BackgroundCurrently, non-alcoholic fatty liver disease (NAFLD) is one of the most common chronic liver diseases in the world. Forecasting the short-term, up to 2025, NASH due to fibrosis is one of the leading causes of liver transplantation. Cohort studies revealed that non-alcoholic steatohepatitis (NASH) has a higher risk of fibrosis progression among NAFLD patients. Identifying differentially expressed genes helps to determine NASH pathogenic pathways, make more accurate diagnoses, and prescribe appropriate treatment. Methods and ResultsIn this study, we found 11 NASH datasets by searching in the Gene Expression Omnibus (GEO) database. Subsequently, NASH datasets with low-quality control scores were excluded. Four datasets were analyzed with packages of R/Bioconductor. Then, all integrated genes were Imported into Cytoscape to illustrate the protein-protein interactions network. All hubs and nodes degree has been calculated to determine the hub genes with critical roles in networks.Possible correlations between expression profiles of mutual DEGs were identified employing Principal Component Analysis (PCA). Primary analyzed data were filtered based on gene expression (logFC > 1, logFC < -1) and adj-P-value (<0.05). Ultimately, among 379 DEGs, we selected the top 10 genes (MYC, JUN, EGR1, FOS, CCL2, IL1B, CXCL8, PTGS2, IL6, SERPINE1) as candidates among up and down regulated genes, and critical pathways such as IL-6, IL-17, TGF {beta}, and TNF were identified. ConclusionThe present study suggests an important DEGs, biological processes, and critical pathways involved in the pathogenesis of NASH disease. Further investigations are needed to clarify the exact mechanisms underlying the development and progression of NASH disease.

bioinformatics↗

bioRxiv2020

StrongestPath: a Cytoscape application for protein-protein interaction analysis

BackgroundStrongestPath is a Cytoscape 3 application that enables to look for one or more cascades of interactions connecting two single or groups of proteins in a collection of protein-protein interaction (PPI) network or signaling network databases. When there are different levels of confidence over the interactions, it is able to process them and identify the cascade of interactions having the highest total confidence score. Given a set of proteins, StrongestPath can extract and show the network of interactions among them from the given databases, and expand the network by adding new proteins having the most interactions with highest total confidence to the current proteins. The application can also identify any activation or inhibition regulatory paths between two distinct sets of transcription factors and target genes. This application can be either used with a set of built-in human and mouse PPI or signaling databases, or any user-provided database for some organism. ResultsOur results on 12 signaling pathways from the NetPath database demonstrate that the application can be used for indicating proteins which may play significant roles in the middle of the pathway by finding the strongest path(s) in the PPI or signaling network. ConclusionEasy access to multiple public large databases, generating output in a short time, addressing some key challenges in one platform and providing a user-friendly graphical interface make the StrongestPath easy to use.

systems biology↗

bioRxiv2020

DNA-RNA hybrid (R-loop): from a unified picture of the mammalian telomere to the genome-wide

Local three-stranded DNA-RNA hybrid regions of the genomes (R-loops) have been detected either by binding of a monoclonal antibody (DRIP assay) or by enzymatic recognition by RNaseH. Such a structure has been postulated for the mouse and human telomeres, clearly suggested by the identification of the complementary RNA TERRA. It was, however, evidenced by DRIP exclusively in human cancer and not in normal human nor in any mouse cell. Based on the observation that in several fractionation procedures, DNA-RNA hybrids copurify with double-stranded DNA, we developed a preparative approach that allows detection of stable DNA-RNA triplexes in a complex genome, their physical isolation and their RNA nucleotide sequence. We then define in the normal mouse and human genomes the notion of terminal R-loop complexes including TERRA molecules synthesized from local promoters of every chromosome. In addition to the telomeric structures since, the finding of the RNA peak, applied in addition to the whole murine sperm genomes, has highlighted a reproducible profile of the R-loop complexes from entire genome suggesting a defined profile for a future generation.

genetics↗

bioRxiv2020

BNrich: A Bayesian network approach to the pathway enrichment analysis

MotivationOne of the most popular techniques in biological studies for analyzing high throughput data is pathway enrichment analysis (PEA). Many researchers apply the existing methods without considering the topology of pathways or at least they have overlooked a significant part of the structure, which may reduce the accuracy and generalizability of the results. Developing a new approach while considering gene expression data and topological features like causal relations regarding edge directions will help the investigators to achieve more accurate results. ResultsWe proposed a new pathway enrichment analysis based on Bayesian network (BNrich) as an approach in PEA. To this end, the cycles were eliminated in 187 KEGG human signaling pathways concerning intuitive biological rules and the Bayesian network structures were constructed. The constructed networks were simplified by the Least Absolute Shrinkage Selector Operator (LASSO), and their parameters were estimated using the gene expression data. We finally prioritize the impacted pathways by Fishers Exact Test on significant parameters. Our method integrates both edge and node related parameters to enrich modules in the affected signaling pathway network. In order to evaluate the proposed method, consistency, discrimination, false positive rate and empirical P-value criteria were calculated, and the results are compared to well-known enrichment methods such as signaling pathway impact analysis (SPIA), bi-level meta-analysis (BLMA) and topology-based pathway enrichment analysis (TPEA). AvailabilityThe R package is available on carn.

bioinformatics↗

bioRxiv2018

A Novel Workflow for Helth Survey Analysis: Results on the Yazd Health Study (YaHS)

Health surveys are one of the most prominent sources of information in medical sciences. Analysis of a health survey not only gives information about the inherent features of the population under study but also sheds light on the interactions between different health factors. Even though there has been a multitude of methodologies proposed for survey analysis, many of them are biased towards the previous hypotheses, the fact which may lead to spurious results by neglecting the whole structure of relations among the factors. Here, we proposed our novel workflow on survey analysis. By gathering the previously less-utilized graphical models on survey analysis, newly-proposed methods, and commonly-used tools of prediction and hypothesis testing our workflow provides the medical community with an end-to-end pipeline for comprehensive statistical analysis of health surveys. To demonstrate the functionality of our workflow, we validate it on the Yazd Health Study (YaHS) dataset, in which a questionnaire of 300 questions from 10000 participants associated with 40 laboratory measurements from a subset of 4010 individuals is acquired from the residents of the Yazd greater area of Iran. Many of our findings are aligned with previous medical knowledge. Most of our novel findings are surprisingly observed in the clinics, each of which is potential to raise a promising hypothesis.

bioinformatics↗

bioRxiv2018

Pin1 Regulatory miRNAs as Novel Candidates for Alzheimer’s Disease Treatment

Alzheimers disease (AD) is the sixth leading cause of death in elderly people whose pathological hallmarks include senile plaques and neurofibrillary tangles (NFTs). The tangles are composed of hyperphosphorylated tau, which is a microtubule-associated protein and its hyperphosphorylation would result in its aggregation and neural cell death. Recently, it has been shown that phosphorylated tau at Thr231 exists in two distinct cis & trans conformations, whose conversion is being mediated by Pin1 isomerase and that the cis, but not the trans, is extremely neurotoxic and drives tau hyperphosphorylation. It has been demonstrated that Pin1 inhibition reflects cis pT231-tau accumulation in neurons but its overactivation is observed in cancer stem cells. Hence, a precise Pin1 regulation is required to keep cells in healthy conditions. As miRNAs play a crucial role in fine-tuning of the gene-expression level, we hypothesized that they might regulate the Pin1 dosage. Nonetheless, the possible regulatory roles of miRNAs in progression of AD by regulating PIN1 is not well studied. We aimed to identify potential miRNAs that down-regulate PIN1 in AD. This can uncover new regulatory mechanisms that result in AD. Thus, we performed a comprehensive study of miRNAs, capable in regulating Pin1, through whole-genome meta-analysis by integrating miRNA expression profiles of 846 different biological samples, along with a systematic literature review and data mining of multiple experimental and predicted miRNA-target databases. We created a list of 56 candidates, which was then short-listed to 10 miRNAs with vigorous experimental evidence. We examined the expression patterns of these miRNAs in the AD and healthy controls and integrated mRNA and miRNA expression profiles to study possible interactions between miRNAs and Pin1. Moreover, we performed an in-silico functional analysis by integrating data of knock-in and knock-down experiments of the candidate miRNAs, and highlighted miR296-5p, miR200b, miR200c, miR140-5p, and miR874 as strong candidate Pin1 regulators. These findings would have profound implications in developing novel therapeutic strategies for AD by blocking expression of highlighted miRNAs using antagomirs.

neuroscience↗

bioRxiv2018

Enhanced Waddington Landscape Model with Cell-CellCommunication Can Explain Molecular Mechanisms ofSelf-Organization

The molecular mechanisms of self-organization that orchestrate embryonic cells to create astonishing patterns have been among major questions of developmental biology. It is recently shown that embryonic stem cells (ESCs), when cultured on particular micropatterns, can self-organize and mimic early steps of pre-implantation embryogenesis. A systems-biology model to address this observation from a dynamical systems perspective is essential. Here, we propose a multicellular mathematical model for pattern formation during in vitro gastrulation of human ESCs. This model enhances the basic principles of Waddington epigenetic landscape with cell-cell communication, in order to enable pattern and tissue formation. To prevent overfitting of the model, there is a minimal number of parameters in the model, which are sufficient to address different experimental observations such as the formation of three germ layers and trophectoderm, responses to altered culture conditions and micropattern diameters, and unexpected spotted forms of the germ layers under certain conditions. This model provides a basis for in-silico modeling of self-organization.

systems biology↗