STRIDE: a command-line HMM-based identifier and sub-classifier of Plasmodium falciparum RIFIN and STEVOR variant surface antigen families

Background RIFINs and STEVORs are variant surface antigens expressed by P. falciparum that play roles in severe malaria pathogenesis and immune evasion. These two highly diverse multigene families feature multiple paralogs, making their classification challenging using traditional bioinformatic methods. Results STRIDE (STevor and RIfin iDEntifier) is an HMM-based, command-line program that automates the identification and classification of RIFIN and STEVOR protein sequences in the malaria parasite Plasmodium falciparum. STRIDE is more sensitive in detecting RIFINs and STEVORs than available PFAM and TIGRFAM tools and reports RIFIN subtypes and the number of sequences with a FHEYDER amino acid motif, which has been associated with severe malaria pathogenesis. Conclusions STRIDE will be beneficial to malaria research groups analyzing genome sequences and transcripts of clinical field isolates, providing insight into parasite biology and virulence.

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).

GenOtoScope: Towards automating ACMG classification of variants associated with congenital hearing loss

Since next-generation sequencing (NGS) has become widely available, large gene panels containing up to several hundred genes can be sequenced cost-efficiently. However, the interpretation of the often large numbers of sequence variants detected when using NGS is laborious, prone to errors and often not comparable across laboratories. To overcome this challenge, the American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG/AMP) introduced standards and guidelines for the interpretation of sequencing variants. Further gene- and disease-specific refinements regarding hereditary hearing loss have been developed since then. With more than 200 genes associated with hearing disorders, the manual inspection of possible causative variants is especially difficult and time consuming. We developed an open-source bioinformatics tool GenOtoScope, which automates all ACMG/AMP criteria that can be assessed without further individual patient information or human curator investigation, including the refined loss of function criterion (“PVS1”). Two types of interfaces are provided: (i) a command line application to classify sequence variants in batches for a set of patients and (ii) a user-friendly website to classify single variants. We compared the performance of our tool with two other variant classification tools using two hearing loss data sets, which were manually annotated either by the ClinGen Hearing Loss Gene Curation Expert Panel or the diagnostics unit of our human genetics department. GenOtoScope achieved the best average accuracy and precision for both data sets. Compared to the second-best tool, GenOtoScope improved accuracy metric by 25.75% and 4.57% and precision metric by 52.11% and 12.13% on the two data sets respectively. The web interface is freely accessible. The command line application along with all source code, documentation and example outputs can be found via the project GitHub page.

Interactive visualization of climate model data via Python or GUI with psyplot

<div> <p><span data-contrast="auto">psyplot (</span><span data-contrast="none">https://psyplot.github.io</span><span data-contrast="auto">) is an open-source data visualization framework that integrates rich computational and mathematical software packages (such as xarray and matplotlib) into a flexible framework for visualization. It differs from most of the visual analytic software such that it focuses on extensibility in order to flexibly tackle the different types of analysis questions that arise in pioneering research. The design of the high-level API of the framework enables a simple and standardized usage from the command-line, python scripts or Jupyter notebooks. A modular plugin framework enables a flexible development of the framework that can potentially go into many different directions. The additional enhancement with a graphical user interface (GUI) makes it the only visualization framework that can be handled from the convenient command-line or scripts, as well as via point-click handling. It additionally allows to build further desktop applications on top of the existing framework.</span><span data-ccp-props="{"201341983":0,"335559739":160,"335559740":259}"> </span></p> </div> <div> <p><span data-contrast="auto">In this presentation, I will show the main functionalities of psyplot, with a special focus on the visualization of unstructured grids (such as the ICON model by the German Weather Service (DWD)), and the usage of psyplot on the HPC facilities of the DKRZ (mistral, jupyterhub, remote desktop, etc.). My demonstration will cover the basic structure of the psyplot framework and how to use psyplot in python scripts (and Jupyter notebooks). I will demonstrate a quick demo of to the psyplot GUI and psy-view, a ncview-like interface built upon psyplot, and talk about different features such as reusing plot configurations and exporting figures.</span></p> </div>

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.

Automatic lexical collocate extraction for corpus-based ontology building and refinement

Traditional corpus-based methods rely on manual inspection and extraction of lexical collocates in the study of selection preferences, which is a very costly, labor-intensive, and time-consuming task. Devising automatic methods for lexical collocate extraction becomes necessary to handle this task and the immensity of corpora available. With a view to leveraging the Sketch Engine platform and in-built corpora, we propose a working prototype of a Lexical Collocate Extractor (LeCoExt) command-line tool that mines lexical collocates from all types of verbs according to their syntactic constituents and Collocate Frequency Score (CFS). This might be the first tool that performs comprehensive corpus-based studies of the selection preferences of individual or groups of verbs exploiting the capabilities offered by Sketch Engine. This tool might facilitate the task of extracting rich lexico-semantic knowledge from diverse corpora in a few seconds and at a click away. We test its performance for ontology building and refinement departing from a previous detailed analysis of stealing verbs carried out by Fernández-Martínez & Faber (2020). We show how the proposed tool is used to extract conceptual-cognitive knowledge from the THEFT scenario and implement it into FunGramKB Core Ontology through the creation and modification of theft-related conceptual units.

An empirical investigation of command-line customization

The interactive command line, also known as the shell, is a prominent mechanism used extensively by a wide range of software professionals (engineers, system administrators, data scientists, etc.). Shell customizations can therefore provide insight into the tasks they repeatedly perform, how well the standard environment supports those tasks, and ways in which the environment could be productively extended or modified. To characterize the patterns and complexities of command-line customization, we mined the collective knowledge of command-line users by analyzing more than 2.2 million shell alias definitions found on GitHub. Shell aliases allow command-line users to customize their environment by defining arbitrarily complex command substitutions. Using inductive coding methods, we found three types of aliases that each enable a number of customization practices: Shortcuts (for nicknaming commands, abbreviating subcommands, and bookmarking locations), Modifications (for substituting commands, overriding defaults, colorizing output, and elevating privilege), and Scripts (for transforming data and chaining subcommands). We conjecture that identifying common customization practices can point to particular usability issues within command-line programs, and that a deeper understanding of these practices can support researchers and tool developers in designing better user experiences. In addition to our analysis, we provide an extensive reproducibility package in the form of a curated dataset together with well-documented computational notebooks enabling further knowledge discovery and a basis for learning approaches to improve command-line workflows.

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.