Browsing by Subject "Data analysis software"

Now showing 1 - 4 of 4

Open Access
BRAPH: A graph theory software for the analysis of brain connectivity
(Public Library of Science, 2017) Mijalkov, M.; Kakaei, E.; Pereira, J. B.; Westman, E.; Volpe, G.
The brain is a large-scale complex network whose workings rely on the interaction between its various regions. In the past few years, the organization of the human brain network has been studied extensively using concepts from graph theory, where the brain is represented as a set of nodes connected by edges. This representation of the brain as a connectome can be used to assess important measures that reflect its topological architecture. We have developed a freeware MatLab-based software (BRAPH–BRain Analysis using graPH theory) for connectivity analysis of brain networks derived from structural magnetic resonance imaging (MRI), functional MRI (fMRI), positron emission tomography (PET) and electroencephalogram (EEG) data. BRAPH allows building connectivity matrices, calculating global and local network measures, performing non-parametric permutations for group comparisons, assessing the modules in the network, and comparing the results to random networks. By contrast to other toolboxes, it allows performing longitudinal comparisons of the same patients across different points in time. Furthermore, even though a user-friendly interface is provided, the architecture of the program is modular (object-oriented) so that it can be easily expanded and customized. To demonstrate the abilities of BRAPH, we performed structural and functional graph theory analyses in two separate studies. In the first study, using MRI data, we assessed the differences in global and nodal network topology in healthy controls, patients with amnestic mild cognitive impairment, and patients with Alzheimer’s disease. In the second study, using resting-state fMRI data, we compared healthy controls and Parkinson’s patients with mild cognitive impairment. © 2017 Mijalkov et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Open Access
Fast and accurate mapping of complete genomics reads
(Academic Press, 2015) Lee, D.; Hormozdiari, F.; Xin, H.; Hach, F.; Mutlu, O.; Alkan C.
Many recent advances in genomics and the expectations of personalized medicine are made possible thanks to power of high throughput sequencing (HTS) in sequencing large collections of human genomes. There are tens of different sequencing technologies currently available, and each HTS platform have different strengths and biases. This diversity both makes it possible to use different technologies to correct for shortcomings; but also requires to develop different algorithms for each platform due to the differences in data types and error models. The first problem to tackle in analyzing HTS data for resequencing applications is the read mapping stage, where many tools have been developed for the most popular HTS methods, but publicly available and open source aligners are still lacking for the Complete Genomics (CG) platform. Unfortunately, Burrows-Wheeler based methods are not practical for CG data due to the gapped nature of the reads generated by this method. Here we provide a sensitive read mapper (sirFAST) for the CG technology based on the seed-and-extend paradigm that can quickly map CG reads to a reference genome. We evaluate the performance and accuracy of sirFAST using both simulated and publicly available real data sets, showing high precision and recall rates.
Open Access
mrsFAST-Ultra: a compact, SNP-aware mapper for high performance sequencing applications
(Oxford University Press, 2014) Hach, F.; Sarrafi, I.; Hormozdiari, F.; Alkan C.; Eichler, E. E.; Sahinalp, S. C.
High throughput sequencing (HTS) platforms generate unprecedented amounts of data that introduce challenges for processing and downstream analysis. While tools that report the 'best' mapping location of each read provide a fast way to process HTS data, they are not suitable for many types of downstream analysis such as structural variation detection, where it is important to report multiple mapping loci for each read. For this purpose we introduce mrsFAST-Ultra, a fast, cache oblivious, SNP-aware aligner that can handle the multi-mapping of HTS reads very efficiently. mrsFAST-Ultra improves mrsFAST, our first cache oblivious read aligner capable of handling multi-mapping reads, through new and compact index structures that reduce not only the overall memory usage but also the number of CPU operations per alignment. In fact the size of the index generated by mrsFAST-Ultra is 10 times smaller than that of mrsFAST. As importantly, mrsFAST-Ultra introduces new features such as being able to (i) obtain the best mapping loci for each read, and (ii) return all reads that have at most n mapping loci (within an error threshold), together with these loci, for any user specified n. Furthermore, mrsFAST-Ultra is SNP-aware, i.e. it can map reads to reference genome while discounting the mismatches that occur at common SNP locations provided by db-SNP; this significantly increases the number of reads that can be mapped to the reference genome. Notice that all of the above features are implemented within the index structure and are not simple post-processing steps and thus are performed highly efficiently. Finally, mrsFAST-Ultra utilizes multiple available cores and processors and can be tuned for various memory settings. Our results show that mrsFAST-Ultra is roughly five times faster than its predecessor mrsFAST. In comparison to newly enhanced popular tools such as Bowtie2, it is more sensitive (it can report 10 times or more mappings per read) and much faster (six times or more) in the multi-mapping mode. Furthermore, mrsFAST-Ultra has an index size of 2GB for the entire human reference genome, which is roughly half of that of Bowtie2. mrsFAST-Ultra is open source and it can be accessed at http://mrsfast.sourceforge.net. © 2014 The Author(s).
Open Access
VISIBIOweb: visualization and layout services for BioPAX pathway models
(Oxford University Press, 2010-06-01) Dilek, Alptuğ; Belviranlı, Mehmet E.; Doğrusöz, Uğur
With recent advancements in techniques for cellular data acquisition, information on cellular processes has been increasing at a dramatic rate. Visualization is critical to analyzing and interpreting complex information; representing cellular processes or pathways is no exception. VISIBIOweb is a free, open-source, web-based pathway visualization and layout service for pathway models in BioPAX format. With VISIBIOweb, one can obtain well-laid-out views of pathway models using the standard notation of the Systems Biology Graphical Notation (SBGN), and can embed such views within onés web pages as desired. Pathway views may be navigated using zoom and scroll tools; pathway object properties, including any external database references available in the data, may be inspected interactively. The automatic layout component of VISIBIOweb may also be accessed programmatically from other tools using Hypertext Transfer Protocol (HTTP). The web site is free and open to all users and there is no login requirement. It is available at: http://visibioweb.patika.org. © The Author(s) 2010. Published by Oxford University Press.