Import On Bionano Access For Mac
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NxClinical software allows us to comprehensively analyze CNV,LOH and sequence variants together. The local database functionality has provided a lot of valuable experience for us. We can directly access up-to-date integrated database within NxClinical software and add tracks to the genome browser for clinical analysis requirements.
Structural variants (SV) have been linked to important bovine disease phenotypes, but due to the difficulty of their accurate detection with standard sequencing approaches, their role in shaping important traits across cattle breeds is largely unexplored. Optical mapping is an alternative approach for mapping SVs that has been shown to have higher sensitivity than DNA sequencing approaches. The aim of this project was to use optical mapping to develop a high-quality database of structural variation across cattle breeds from different geographical regions, to enable further study of SVs in cattle. To do this we generated 100X Bionano optical mapping data for 18 cattle of nine different ancestries, three continents and both cattle sub-species. In total we identified 13,457 SVs, of which 1,200 putatively overlap coding regions. This resource provides a high-quality set of optical mapping-based SV calls that can be used across studies, from validating DNA sequencing-based SV calls to prioritising candidate functional variants in genetic association studies and expanding our understanding of the role of SVs in cattle evolution.
Ultimately, we expect the database to enable further insights into SVs, an understudied class of genetic variation in cattle, giving access to a catalogue of thousands of variants present across multiple breeds worldwide.
Yes, multiple people can access the same data concurrently as all data is stored in a single central repository. Users can view the same sample at the same time from different locations, and when a user wants to make changes, the sample goes into edit mode and locks, preventing other users from making changes at the same time. Other users can continue viewing the data but cannot make changes. Once the first user has finished making changes, the changes are immediately visible to other users and another user can now edit the sample.
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Abstract:The localization of Bcl-2 family members at the mitochondrial outer membrane (MOM) is a crucial step in the implementation of apoptosis. We review evidence showing the role of the components of the mitochondrial import machineries (translocase of the outer membrane (TOM) and the sorting and assembly machinery (SAM)) in the mitochondrial localization of Bcl-2 family members and how these machineries regulate the function of pro- and anti-apoptotic proteins in resting cells and in cells committed into apoptosis.Keywords: bcl-2 family; mitochondrial import machineries; apoptosis
CellBench Thispackage contains infrastructure for benchmarking analysis methodsand access to single cell mixture benchmarking data. It provides aframework for organising analysis methods and testing combinationsof methods in a pipeline without explicitly laying out eachcombination. It also provides utilities for sampling and filteringSingleCellExperiment objects, constructing lists of functions withvarying parameters, and multithreaded evaluation of analysismethods.
enrichTF Astranscription factors (TFs) play a crucial role in regulating thetranscription process through binding on the genome alone or in acombinatorial manner, TF enrichment analysis is an efficient andimportant procedure to locate the candidate functional TFs from aset of experimentally defined regulatory regions. While it iscommonly accepted that structurally related TFs may have similarbinding preference to sequences (i.e. motifs) and one TF may havemultiple motifs, TF enrichment analysis is much more challengingthan motif enrichment analysis. Here we present a R package for TFenrichment analysis which combine motif enrichment with the PECAmodel.
pipeFrame pipeFrameis an R package for building a componentized bioinformaticspipeline. Each step in this pipeline is wrapped in the framework,so the connection among steps is created seamlessly andautomatically. Users could focus more on fine-tuning argumentsrather than spending a lot of time on transforming file format,passing task outputs to task inputs or installing the dependencies.Componentized step elements can be customized into other newpipelines flexibly as well. This pipeline can be split into severalimportant functional steps, so it is much easier for users tounderstand the complex arguments from each step rather thanparameter combination from the whole pipeline. At the same time,componentized pipeline can restart at the breakpoint and avoidrerunning the whole pipeline, which may save a lot of time forusers on pipeline tuning or such issues as power off or processother interrupts.
HCAData This packageallows a direct access to the dataset generated by the Human CellAtlas project for further processing in R and Bioconductor, in thecomfortable format of SingleCellExperiment objects (available inother formats here: ).
Copyright: 2016 Ekmekci 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.
Exercise 10: The standard FASTA file-format, used to represent protein and nucleic acid sequences, consists of two parts: (i) The first line is a description of the biomolecule, starting with a greater-than sign () in the first column; this sign is immediately followed by a non-whitespace character and any arbitrary text that describes the sequence name and other information (e.g., database accession identifiers). (ii) The subsequent lines specify the biomolecular sequence as single-letter codes, with no blank lines allowed. A protein example follows:>trQ8ZYG5Q8ZYG5_PYRAE (Sm-like) OS = P aerophilum GN = PAE0790 MASDISKCFATLGATLQDSIGKQVLVKLRDSHEIRGILRSFDQHVNLLLEDAEEIIDGNVYKRGTMVVRGENVLFISPVP
Many third-party Python libraries are now well-established. In general, these mature projects are (i) well-documented, (ii) freely available as stable (production) releases, (iii) undergoing continual development to add new features, and (iv) characterized by large user-bases and active communities (mailing lists, etc.). A useful collection of such tools can be found at the SciPy resource [97,98], which is a platform for the maintenance and distribution of several popular packages: (i) , which is invaluable for matrix-related calculations ; (ii) , which provides routines from linear algebra, signal processing, statistics, and a wealth of other numerical tools; (iii) , which facilitates data import, management, and organization ; and (iv) , a premiere codebase for plotting and general-purpose visualization . The package extends SciPy with machine learning and statistical analysis functionalities . Other statistical tools are available in the standard library, in [97,98], and in ; finally, many more-specialized packages also exist, such as  and . Properly interacting with Python modules, such as those mentioned above, is detailed in Supplemental Chapter 4 (S1 Text).
The Molecular Design Toolkit is an open source environment that aims to seamlessly integrated molecular simulation, visualization and cloud computing. It offers access to a large and still-growing set of computational modelling methods with a science-focused Python API, that can be easily installed using PIP. It is ideal for building into a Jupyter notebook.The API is designed to handle both small molecules and large bimolecular structures, molecular mechanics and QM calculations.
I've recently published a Vortex script to access the information, I've now published an iPython notebook that also shows how to import the data. Why not give it a try and then contribute your findings and suggestions to the Open Source Malaria project.
Polymers with the ability to respond to external physical or chemical stimulus are defined as smart or stimuli-responsive polymers. Half century ago, smart polymers have been utilized to control the release of biologically active cargos, which played a very important role in the research and development of nanomedicines. As introduced in previous section, various external stimuli have been reported in smart polymeric systems, including US, pH, and magnetic fields [1, 7, 16]. The physical/ chemical properties of polymers can smartly transform in responding to those stimuli. The drug release rate can be controlled by the intensity of applied stimuli to the fabricated carriers. 153554b96e