Biology subjects

Monther Alhamdoosh

Publications and source records attributed to Monther Alhamdoosh.

3 recordsLinked to original sources

bioRxiv2016

Modelling the transcription factor DNA-binding affinity using genome-wide ChIP-based data

Understanding protein-DNA binding affinity is still a mystery for many transcription factors (TFs). Although several approaches have been proposed in the literature to model the DNA-binding specificity of TFs, they still have some limitations. Most of the methods require a cut-off threshold in order to classify a K-mer as a binding site (BS) and finding such a threshold is usually done by handcraft rather than a science. Some other approaches use a prior knowledge on the biological context of regulatory elements in the genome along with machine learning algorithms to build classifier models for TFBSs. Noticeably, these methods deliberately select the training and testing datasets so that they are very separable. Hence, the current methods do not actually capture the TF-DNA binding relationship. In this paper, we present a threshold-free framework based on a novel ensemble learning algorithm in order to locate TFBSs in DNA sequences. Our proposed approach creates TF-specific classifier models using genome-wide DNA-binding experiments and a prior biological knowledge on DNA sequences and TF binding preferences. Systematic background filtering algorithms are utilized to remove non-functional K-mers from training and testing datasets. To reduce the complexity of classifier models, a fast feature selection algorithm is employed. Finally, the created classifier models are used to scan new DNA sequences and identify potential binding sites. The analysis results show that our proposed approach is able to identify novel binding sites in the Saccharomyces cerevisiae genome.\n\nContactmonther.alhamdoosh@unimelb.edu.au, dh.wang@latrobe.edu.au\n\nAvailabilityhttp://homepage.cs.latrobe.edu.au/dwang/DNNESCANweb

Bioinformatics↗

bioRxiv2016

ANFIS-based Fuzzy Systems for Searching DNA-Protein Binding Sites

Transcriptional regulation mainly controls how genes are expressed and how cells behave based on the transcription factor (TF) proteins that bind upstream of the transcription start sites (TSSs) of genes. These TF DNA binding sites (TFBSs) are usually short (5-15 base pairs) and degenerate (some positions can have multiple possible alternatives). Traditionally, computational methods scan DNA sequences using the position weight matrix (PWM) of a given TF, calculate binding scores for each K-mer against the PWM, and finally classify a K-mer as to whether it is a putative TFBS or a background sequence based on a cut-off threshold. The FSCAN system, which is proposed in this paper, employs machine learning techniques to build a learner model that is able to identify TFBSs in a set of bound sequences without the need for a cut-off threshold. Our proposed method utilizes fuzzy inference techniques along with a distribution-based filtering algorithm to predict the binding sites of a TF given its PWM model and phastCons scores for the input DNA sequences. Data imbalance reduction techniques are also used to ease the learning of the adaptive-neuro fuzzy inference system (ANFIS) algorithm. The proposed system is tested on 22 ChIP-chip sequence-sets from the Saccharomyces Cerevisiae genome. Our results show that FSCAN outperforms other approaches like MatInspector and MATCH and is quite robust. As more transcriptional data becomes available, our proposed framework encourages the use of fuzzy logic techniques in the prediction of TFBSs.

Bioinformatics↗

bioRxiv2016

Combining multiple tools outperforms individual methods in gene set enrichment analyses

Gene set enrichment (GSE) analysis allows researchers to efficiently extract biological insight from long lists of differentially expressed genes by interrogating them at a systems level. In recent years, there has been a proliferation of GSE analysis methods and hence it has become increasingly difficult for researchers to select an optimal GSE tool based on their particular data set. Moreover, the majority of GSE analysis methods do not allow researchers to simultaneously compare gene set level results between multiple experimental conditions.\n\nResults: The ensemble of genes set enrichment analyses (EGSEA) is a method developed for RNA-sequencing data that combines results from twelve algorithms and calculates collective gene set scores to improve the biological relevance of the highest ranked gene sets. redEGSEAs gene set database contains around 25,000 gene sets from sixteen collections. It has multiple visualization capabilities that allow researchers to view gene sets at various levels of granularity. EGSEA has been tested on simulated data and on a number of human and mouse data sets and, based on biologists' feedback, consistently outperforms the individual tools that have been combined. Our evaluation demonstrates the superiority of the ensemble approach for GSE analysis, and its utility to effectively and efficiently extrapolate biological functions and potential involvement in disease processes from lists of differentially regulated genes.\n\nAvailability and Implementation: EGSEA is available as an R package at http://www.bioconductor.org/packages/EGSEA/. The gene sets collections are available in the R package EGSEAdata from http://www.bioconductor.org/packages/EGSEA/.

Bioinformatics↗