Model-Centric Fulfillment Operations and Maintenance Automation

As networks have evolved, there has been an evolution in how they are managed as well. This evolution has seen a move from manual configuration via command line interface (CLI) to script-based automation and eventually to a template-based approach with workflow to coordinate multiple templates and scripts. The next step in this evolution is the introduction of models to provide a more dynamic capability than is in place today. This chapter will discuss three major layers of modelling that should be considered during implementation of this approach: device models focused on the configuration of the hardware itself; service models focused on the customer or network facing services that leverage the hardware level configuration; and operational models focused on people, processes, and tools involved in application of device and service models. This includes the orchestration of activities with other tools, such as operational support systems (OSS) and business support systems (BSS).

A guide to phylotranscriptomic analysis for phycologists

Phylotranscriptomics is the study of phylogenetic relationships among taxa based on their DNA sequences derived from transcriptomes. Because of the relatively low cost of transcriptome sequencing compared with genome sequencing and the fact that phylotranscriptomics is almost as reliable as phylogenomics, the phylotranscriptomic analysis has recently emerged as the preferred method for studying evolutionary biology. However, it is challenging to perform transcriptomic and phylogenetic analyses together without programming expertise. This study presents a protocol for phylotranscriptomic analysis to aid marine biologists unfamiliar with UNIX command-line interface and bioinformatics tools. Here, we used transcriptomes to reconstruct a molecular phylogeny of dinoflagellate protists, a diverse and globally abundant group of marine plankton organisms whose large and complex genomic sequences have impeded conventional phylogenic analysis based on genomic data. We hope that our proposed protocol may serve as practical and helpful information for the training and education of novice phycologists.

UCEasy: A software package for automating and simplifying the analysis of ultraconserved elements (UCEs)

The use of Ultraconserved Elements (UCEs) as genetic markers in phylogenomics has become popular and has provided promising results. Although UCE data can be easily obtained from targeted enriched sequencing, the protocol for in silico analysis of UCEs consist of the execution of heterogeneous and complex tools, a challenge for scientists without training in bioinformatics. Developing tools with the adoption of best practices in research software can lessen this problem by improving the execution of computational experiments, thus promoting better reproducibility. We present UCEasy, an easy-to-install and easy-to-use software package with a simple command line interface that facilitates the computational analysis of UCEs from sequencing samples, following the best practices of research software. UCEasy is a wrapper that standardises, automates and simplifies the quality control of raw reads, assembly and extraction and alignment of UCEs, generating at the end a data matrix with different levels of completeness that can be used to infer phylogenetic trees. We demonstrate the functionalities of UCEasy by reproducing the published results of phylogenomic studies of the bird genus Turdus (Aves) and of Adephaga families (Coleoptera) containing genomic datasets to efficiently extract UCEs.

miR2Trait: an integrated resource for investigating miRNA-disease associations

ABSTRACTMicroRNAs are key components of cellular regulatory networks, and breakdown in miRNA function could lead to cascading effects culminating in pathophenotypes. A better understanding of the role of miRNAs in diseases would aid human health. Here, we have devised a method for comprehensively mapping the associations between miRNAs and diseases by merging on a common key between two curated omics databases. The resulting bidirectional resource, miR2Trait, is more detailed than earlier catalogues, uncovers new relationships, and includes analytical utilities to interrogate and extract knowledge from these datasets. The resource could aid in identifying the disease enrichment of a user-given set of miRNAs and analyzing the miRNA profile of a specified diseasome. miR2Trait is available as both a web-server (https://sas.sastra.edu/pymir18) and an open-source command-line interface (https://github.com/miR2Trait) under MIT license for both commercial and non-commercial use. The datasets are available for download at: https://doi.org/10.6084/m9.figshare.8288825.

CalcOPP: a program for the calculation of one-particle potentials (OPPs)

In recent years, one-particle potentials (OPPs) derived from neutron-diffraction data have become a popular means to estimate activation energies of ion migration in solids. Computer programs for their calculation, however, have mostly been private in-house solutions. The software CalcOPP presented herein permits calculating two- or three-dimensional OPPs either from probability density functions put out by the crystallographic suite Jana2006/ Jana2020 (including error maps) or from scattering-density maps reconstructed using the maximum entropy method (MEM) implementation Dysnomia. The title program is open-source, written in modern free-form Fortran and Python 3, and available free of charge under the permissive MIT License. Executables are published for 64-bit Microsoft Windows and Linux platforms and can be controlled via an intuitive graphical user interface or via command-line interface. Depending on the kind of input, CalcOPP’s output is readily visualized with standard crystallographic software or plotting applications. The release of the program not only makes the rather powerful OPP method more transparent, but it also opens it up to a broader, less programming-oriented public.

Efficient Transfer of data from RDBMS to HDFS and conversion to JSON format

Apache Sqoop is mainly used to efficiently transfer large volumes of data between Apache Hadoop and relational databases. It helps to certain tasks, such as ETL (Extract transform load) processing, from an enterprise data warehouse to Hadoop, for efficient execution at a much less cost. Here first we import the table which presents in MYSQL Database with the help of command-line interface application called Sqoop and there is a chance of addition of new rows and updating new rows then we have to execute the query again. So, with the help of our project there is no need of executing queries again for that we are using Sqoop job, which consists of total commands for import and next after import we retrieve the data from hive using Java JDBC and we convert the data to JSON Format, which consists of data in an organized way and easy to access manner by using GSON Library. Keywords: Sqoop, Json, Gson, Maven and JDBC

CurD: A Tool for Diffusion Analyses on Curved Membranes

Curved membranes are abundant and functionally relevant in living matter, yet they have eluded computational studies due to methodological limitations. With multiple approaches available for setting up simulations on such curved membranes, there is a growing need for efficient and versatile tools to analyze their outcomes. Here, we present CurD, a freely available tool for analyzing the diffusion of membrane constituents along curved surfaces. The tool efficiently uses the Vertex-oriented Triangle Propagation algorithm to compute geodesic distances significantly faster than conventional implementations of path-finding algorithms, while providing a friendly command-line interface. With CurD, we resolve the effects of curvature and lipid packing densities on the diffusion of lipids on two curved membranes---one with only mean curvature and another with both Mean and Gaussian curvature. We find that Gaussian curvature plays a surprisingly small role, whereas mean curvature or packing of lipid headgroups largely dictates their mobility.

OLOGRAM-MODL: mining enriched n-wise combinations of genomic features with Monte Carlo and dictionary learning

Most epigenetic marks, such as Transcriptional Regulators or histone marks, are biological objects known to work together in n-wise complexes. A suitable way to infer such functional associations between them is to study the overlaps of the corresponding genomic regions. However, the problem of the statistical significance of n-wise overlaps of genomic features is seldom tackled, which prevent rigorous studies of n-wise interactions. We introduce OLOGRAM-MODL, which considers overlaps between n ≥ 2 sets of genomic regions, and computes their statistical mutual enrichment by Monte Carlo fitting of a Negative Binomial distribution, resulting in more resolutive P-values. An optional machine learning method is proposed to find complexes of interest, using a new itemset mining algorithm based on dictionary learning which is resistant to noise inherent to biological assays. The overall approach is implemented through an easy-to-use CLI interface for workflow integration, and a visual tree-based representation of the results suited for explicability. The viability of the method is experimentally studied using both artificial and biological data. This approach is accessible through the command line interface of the pygtftk toolkit, available on Bioconda and from https://github.com/dputhier/pygtftk

MoloVol: an easy-to-use program to calculate various volumes and surface areas of chemical structures and identify cavities

Cavities are a ubiquitous feature of chemical structures encountered in various fields ranging from supramolecular chemistry to molecular biology. They are involved in the encapsulation, transport, and transformation of guest molecules, thus necessitating a precise and accessible tool for estimating and visualizing their size and shape. MoloVol, a free, user-parametrizable, open-source software, developed for calculating a range of geometric features for both unit cell and isolated structures is presented here. MoloVol utilizes up to two spherical probes to define cavities, surfaces, and volumes. The program was optimized by combining an octree data structure with voxel-partitioned space allowing for even high-resolution protein structure calculations at reasonable timescales. MoloVol comes with a user-friendly graphic interface along with a command line interface for high throughput calculations. It was written in C++ and is available on Windows, macOS, and Linux distributions.