Sharif Digital Repository

Nowadays, many progresses in biology and medicine such as diagnosis of diseases and drug discoveries depend heavily on analyzing biological datasets collected from advanced machines. DNA Microarrays are amongst such machines applicable in measuring the expressions levels of thousand of genes and genotyping of a set of single nucleotide polymorphic sites to name a few. Compared to the more advanced Next Generation Sequencing (NGS) technology, the microarray platform produces lower quality of datasets. However, there has been tones of efforts to produce, process, and curate datasets from microarrays based on well designed protocols for sample preparation, hybridization, image processing, and... 

We construct and analyse a computational model that predicts the outcome of alternative splicing by recognizing features in RNA sequences. The computational model can be viewed as a “splicing simulator” for a range of healthy human tissues. It takes as input a pre-mRNA sequence surrounding a possibly alternatively spliced exon and estimates the inclusion level of that exon in mature RNA, after splicing occurs. The model is trained using a supervised machine learning framework where the training examples are the alternatively spliced exons, the feature vectors are derived RNA sequences near these exons, and the targets are their corresponding splicing outcomes in healthy individuals. The... 

Gene expression microarrays include thousands of probes spotted on their surface to measure the expression level of a set of genes. Identifying the amount of a transcript level by hybridization, each probe is complementary to a fragment of a specific gene transcripts. Although probes are designed to avoid crosshybridization to non-specific transcripts, occurrences of cross-hybridizations is inevitable due to massive probes that are spotted on microarrays. The main question is whether these non-specific cross-hybridization have significant effect on the downstream analysis of gene expression microarray datasets. This thesis aims at answering to this question by considering datasets from... 

Bulk tissue RNA-seq data has been widely used for investigating the transcriptome and analyzing it for different purposes. A single bulk sample of a heterogeneous population includes different cell-types each in different proportions. Bulk tissue RNA-seq measures the average expression level of genes across these cell types and does not account for cross-subject variation in cell-type compositions. Furthermore, biological signals might be masked by taking the average of gene expressions. Because of these reasons, bulk-RNA-seq is not suffcient for studying complex tissues. Knowing these cell-type compositions are important, because studying cell-specific changes in the transcriptome might be... 

Single Cell RNA sequencing (scRNA-seq) data provides more information about gene expression at cellular level. However, because of noise and sparsity that exist in scRNA-seq data, analysis of this data has faced to obstacles. Global normalization approach can not resolve correctly missing data that come from technical variability. So this approach cause emerging incorrect bias and dishonest conclusion about cell type. In this study we review some models for scRNA-seq data imputation,explain a new method for filtering genes and clustering data and use matrix completion algorithm for imputation data  

Single-cell RNA-sequencing has opened opportunities to unlock the cell transcriptome at cellular-level resolution and investigate cell population structures and cell-type-specific gene expression patterns. Previously, we were only able to perform bulk RNA sequencing regardless of the cell type composition of cell populations. Now, with the advancements in sequencing technologies, it is possible to extract cell-level and cell-type-specific sequences, related to each type of cell separately and in a scalable and high-throughput manner. For such large studies, logistical constraints inevitably dictate that data be generated separately i.e., at different times and with different operators.... 

Single-cell RNA sequencing (scRNA-seq) technologies have empowered us to study gene expressions at the single-cell resolution. These technologies are developed based on barcoding of single cells and sequencing of transcriptome using next-generation sequencing technologies. Achieving this single-cell resolution is specially important when the target population is complex or heterogeneous, which is the case for most biological samples, including tissue samples and tumor biopsies.Single-cell technologies suffer from high amounts of noise and missing values, generally known as dropouts. This complexity can affect a number of key downstream analyses such as differential expression analysis,... 

DNA methylation in one of the most important epigenetic variations, which causes significant variations in gene expressions of mammalians. Our current knowledge about DNA methylation is based on measurments from samples of bulk data which cause ambiguity in intracellular differences and analysis of rare cell samples. For this reason, the ability to measure DNA methylation in single-cells has the potential to play an important role in understanding many biological processes including embryonic developement, disease progression including cancer, aging, chromosome instability, X chromosome inactivation, cell differentiation and genes regulation. Recent technological advances have enabled... 

Based on the importance of DNA/RNA binding proteins in different cellular processes, finding binding sites of them play crucial role in many applications, like designing drug/vaccine, designing protein, and cancer control. Many studies target this issue and try to improve the prediction accuracy with three strategies: complex neural-network structures, various types of inputs, and ML methods to extract input features. But due to the growing volume of sequences, these methods face serious processing challenges. So, this paper presents KDeep, based on CNN-LSTM and the primary form of DNA/RNA sequences as input. As the key feature improving the prediction accuracy, we propose a new encoding... 

As sequencing technologies have advanced in the field of single cells, it has become possible to investigate complex and rare cell populations and discover regulatory relationships between genes. The detection of rare cells has been greatly facilitated by this technology. However, due to the large volume of data and the complex and uncertain distribution of data, as well as the high rate of technical zeros, the analysis of single cell data clusters remains a computational and statistical challenge. Dimensionality reduction is a significant component of big data analysis. Machine learning methods provide the possibility of better analysis by reducing the non-linear dimensions of data. A graph...