Results: This paper presents ScAsAT (single-cell ATAC-seq analysis tool), a complete pipeline to process scATAC-seq data with simple steps. The pipeline is developed in a Jupyter notebook environment that holds the executable code along with the necessary description and results. For the initial sequence processing steps, the pipeline uses a number of well-known tools which it executes from a python environment for each of the fastq files. While functions for the data analysis part are mostly written in R, it is robust, flexible, interactive and easy to extend. The pipeline was applied to a single-cell ATAC-seq dataset in order to identify different cell-types from a complex cell mixture. The results from Scasat showed that open chromatin location corresponding to potential regulatory elements can account for cellular heterogeneity and can identify regulatory regions that separates cells from a complex population.Availability: The jupyter notebook with the complete pipeline applied to the dataset published with this paper are publicly available on the Github (https://github.com/ManchesterBioinference/Scasat).An additional notebook is also provided for analysis of a publicly available dataset. The fastq files are submitted at ArrayExpress database at EMBL-EBI (www.ebi.ac.uk/arrayexpress) under accession number E-MTAB-6116.Contact: syed.murtuzabaker@manchester.ac.uk and magnus.rattray@manchester.ac.uk Supplementary information: Supplementary data are available at bioRxiv online.
The goal ofthis work is to design a new adaptive routing technique for ad hoc wireless networks. This paper proposed the basic deign of the algorithm that works based on the principle of Ant Colony Optimization (A CO). This is a probabilistic adaptive technique that changes its routes with the change of nenvork topology over the period oftime by learning its environment. It identifies appropriate paths with the feedback of previously travelled packets and maintains routing table accordingly. A selfmade simulator implemented on C++ is used to evaluate performance of this algorithm on the basis of diverse adaptive issues such as Change of Probability, Growth of Pheromone Intensity, Randomness of the Selection and Packet sending rate through different paths.
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