HiCBricks: building blocks for efficient handling of large Hi-C datasets
نویسندگان
چکیده
منابع مشابه
Hi-Corrector: a fast, scalable and memory-efficient package for normalizing large-scale Hi-C data
UNLABELLED Genome-wide proximity ligation assays, e.g. Hi-C and its variant TCC, have recently become important tools to study spatial genome organization. Removing biases from chromatin contact matrices generated by such techniques is a critical preprocessing step of subsequent analyses. The continuing decline of sequencing costs has led to an ever-improving resolution of the Hi-C data, result...
متن کاملGrapHi-C: Graph-based visualization of Hi-C Datasets
Background: Hi-C is a proximity-based ligation reaction used to detect regions of the genome that are close in 3D space (or “interacting”). Typically, results from Hi-C experiments (whole-genome contact maps) are visualized as heatmaps or Circos plots. While informative, these visualizations do not intuitively represent the complex organization and folding of the genome in 3D space, making the ...
متن کاملUvf - Unified Volume Format: A General System for Efficient Handling of Large Volumetric Datasets.
With the continual increase in computing power, volumetric datasets with sizes ranging from only a few megabytes to petascale are generated thousands of times per day. Such data may come from an ordinary source such as simple everyday medical imaging procedures, while larger datasets may be generated from cluster-based scientific simulations or measurements of large scale experiments. In comput...
متن کاملEfficient Support Vector Learning for Large Datasets
In recent years, we have witnessed significant increase in the amount of data in digital format, due to the widespread use of computers and advances in storage systems. As the volume of digital information increases, there arises the need for more effective tools to better find, filter and manage these resources. Therefore, developing fast and highly accurate algorithms to automatically classif...
متن کاملEfficient Gaussian process regression for large datasets.
Gaussian processes are widely used in nonparametric regression, classification and spatiotemporal modelling, facilitated in part by a rich literature on their theoretical properties. However, one of their practical limitations is expensive computation, typically on the order of n3 where n is the number of data points, in performing the necessary matrix inversions. For large datasets, storage an...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Bioinformatics
سال: 2019
ISSN: 1367-4803,1460-2059
DOI: 10.1093/bioinformatics/btz808