Biology subjects

Zahiri, J.

Publications and source records attributed to Zahiri, J..

5 recordsLinked to original sources

bioRxiv2024

123FASTQ: an intuitive and efficient tool for preprocessing Illumina FASTQ reads

Next-generation sequencing (NGS) has revolutionized genetic research, but preprocessing raw sequencing reads remains a crucial step. Existing tools for quality control (QC) and trimming have limitations, leading to fragmented workflows. In this article, we present 123FASTQ, a comprehensive and user-centric software solution that integrates QC analysis, trimming, and format conversion in a userfriendly interface. 123FASTQ combines the advantages of existing tools while addressing their limitations. It offers improved QC analysis with insightful visualizations, semi-automated trimming recommendations based on QC results, and various trimming options. The software also supports different file formats and provides efficient performance. By simplifying NGS data preprocessing, 123FASTQ enables researchers to utilize high-quality reads for downstream analyses. It is cross-platform and freely available at https://sourceforge.net/projects/project-123ngs/.

bioinformatics↗

bioRxiv2019

RepCOOL: Computational Drug Repositioning Via Integrating Heterogeneous Biological Networks

BackgroundIt often takes more than 10 years and costs more than one billion dollars to develop a new drug for a disease and bring it to the market. Drug repositioning can significantly reduce costs and times in drug development. Recently, computational drug repositioning attracted a considerable amount of attention among researchers, and a plethora of computational drug repositioning methods have been proposed.\n\nMethodsIn this study, we propose a novel network-based method, named RepCOOL, for drug repositioning. RepCOOL integrates various heterogeneous biological networks to suggest new drug candidates for a given disease.\n\nResultsThe proposed method showed a promising performance on benchmark datasets via rigorous cross-validation. Final drug repositioning model has been built based on random forest classifier, after examining various machine learning algorithms. Finally, in a case study, four FDA approved drugs were suggested for breast cancer stage II.\n\nConclusionResults show the strength of the proposed method in detecting true drug-disease relationships. RepCOOL suggested four new drugs for breast cancer stage II namely Doxorubicin, Paclitaxel, Trastuzumab and Tamoxifen.

bioinformatics↗

bioRxiv2019

The First Reconstruction of Intercellular Interaction Network in Mus musculus Immune System

Intercellular interactions play an important role in regulating communications of cells with each other. So far, many studies have been done with both experimental and computational approaches in this field. Therefore, in order to investigate and analyze the intercellular interactions, use of network reconstruction has attracted the attention of many researchers recently. The intercellular interaction network was reconstructed using receptor and ligand interaction dataset and gene expression data of the first phase of the immunological genome project. In the reconstructed network, there are 9271 communications between 162 cells which were created through 460 receptor-ligand interactions. The results indicate that cells of hematopoietic lineages use fewer communication pathways for interacting with each other and the most network communications belong to non-hematopoietic stromal cells and macrophages. The results indicated the importance of the communication of stromal cells with immune cells and also high specificity of genes expression in these cells. The stromal cells have the most autocrine communication, and interactions between the wnt5a with the Ror1/2 and Fzd5a among the stromal lineage cells are abundant.

bioinformatics↗

bioRxiv2017

AntAngioCOOL: An R Package for Computational Detection of Anti-Angiogenic Peptides

Angiogenesis inhibition research is a cutting edge in angiogenesis-dependent disease therapy, and especially in cancer therapy. Recently, studies on anti-angiogenic peptides have provided promising results in the cancer treatment field. In the current study we propose an effective machine learning based R package (AntAngioCOOL) to predict anti-angiogenic peptides. We have examined more than 200 different classifiers to build an efficient predictor. Also, more than 17000 features have been extracted to encode the peptides. However, finally, more than 2000 informative features have been selected to train the classifiers. According to the obtained results AntAngioCOOL can effectively predict anti-angiogenic peptides: this tool achieved sensitivity of 88%, specificity of 77% and accuracy of 75% on independent test set. AntAngioCOOL can be accessed at https://cran.r-project.org/.

bioinformatics↗

bioRxiv2017

afpCOOL: An Accurate Tool for Antifreeze Protein Detection

Various cold-adapted organisms produce antifreeze proteins (AFPs), which prevent to freeze of cell fluids by resisting the growth of the ice crystal. AFPs are currently being recognized in various organisms that are living in extremely low temperatures. AFPs have several important applications in increasing freeze tolerance of plants; maintain the tissue in frozen conditions and producing cold-hardy plants using transgenic technology. Substantial differences in the sequence and structure of the AFPs, pose a challenge for researcher to identify these proteins. In this paper, we proposed a novel method for identifying AFPs using support vector machine (SVM) by incorporating 4 types of features. Results on two benchmark datasets revealed the strength of the proposed method in AFP prediction. Also, according to the results on an independent test set, our method outperformed the current state-of-the-art methods. The further analysis showed the non-satisfactory performance of the BLAST in AFP detection: more than 62% of the BLAST searches have specificity less than 10% and there is no any BLAST search with sensitivity higher than 10%. These results reveal the urgent need for an accurate tool for AFP detection. In addition, the comparison results of the discrimination power of different feature types disclosed that evolutionary features and amino acid composition are the most contributing features in AFP detection. This method has been implemented as a stand-alone tool, namely afpCOOL, for various operating systems to predict AFPs with a user friendly graphical interface.\n\nAvailabilityafpCOOL is freely available at http://bioinf.modares.ac.ir:8080/AFPCOOL/page/afpcool.isp\n\nContactDr Zahiri zahiri@modares.ac.ir

bioinformatics↗