Command Line Parameters Research Articles

MetaBakery: a Singularity implementation of bioBakery tools as a skeleton application for efficient HPC deconvolution of microbiome metagenomic sequencing data to machine learning ready information.

In this study, we present MetaBakery (http://metabakery.fe.uni-lj.si), an integrated application designed as a framework for synergistically executing the bioBakery workflow and associated utilities. MetaBakery streamlines the processing of any number of paired or unpaired fastq files, or a mixture of both, with optional compression (gzip, zip, bzip2, xz, or mixed) within a single run. MetaBakery uses programs such as KneadData (https://github.com/bioBakery/kneaddata), MetaPhlAn, HUMAnN and StrainPhlAn as well as integrated utilities and extends the original functionality of bioBakery. In particular, it includes MelonnPan for the prediction of metabolites and Mothur for calculation of microbial alpha diversity. Written in Python 3 and C++ the whole pipeline was encapsulated as Singularity container for efficient execution on various computing infrastructures, including large High-Performance Computing clusters. MetaBakery facilitates crash recovery, efficient re-execution upon parameter changes, and processing of large data sets through subset handling and is offered in three editions with bioBakery ingredients versions 4, 3 and 2 as versatile, transparent and well documented within the MetaBakery Users' Manual (http://metabakery.fe.uni-lj.si/metabakery_manual.pdf). It provides automatic handling of command line parameters, file formats and comprehensive hierarchical storage of output to simplify navigation and debugging. MetaBakery filters out potential human contamination and excludes samples with low read counts. It calculates estimates of alpha diversity and represents a comprehensive and augmented re-implementation of the bioBakery workflow. The robustness and flexibility of the system enables efficient exploration of changing parameters and input datasets, increasing its utility for microbiome analysis. Furthermore, we have shown that the MetaBakery tool can be used in modern biostatistical and machine learning approaches including large-scale microbiome studies.

An automated interface for sedimentation velocity analysis in SEDFIT.

Sedimentation velocity analytical ultracentrifugation (SV-AUC) is an indispensable tool for the study of particle size distributions in biopharmaceutical industry, for example, to characterize protein therapeutics and vaccine products. In particular, the diffusion-deconvoluted sedimentation coefficient distribution analysis, in the software SEDFIT, has found widespread applications due to its relatively high resolution and sensitivity. However, a lack of suitable software compatible with Good Manufacturing Practices (GMP) has hampered the use of SV-AUC in this regulatory environment. To address this, we have created an interface for SEDFIT so that it can serve as an automatically spawned module with controlled data input through command line parameters and output of key results in files. The interface can be integrated in custom GMP compatible software, and in scripts that provide documentation and meta-analyses for replicate or related samples, for example, to streamline analysis of large families of experimental data, such as binding isotherm analyses in the study of protein interactions. To test and demonstrate this approach we provide a MATLAB script mlSEDFIT.

Genotyping Microbial Communities with MIDAS2: From Metagenomic Reads to Allele Tables.

The Metagenomic Intra-Species Diversity Analysis System 2 (MIDAS2) is a scalable pipeline that identifies single nucleotide variants and gene copy number variants in metagenomes using comprehensive reference databases built from public microbial genome collections (metagenotyping). MIDAS2 is the first metagenotyping tool with functionality to control metagenomic read mapping filters and to customize the reference database to the microbial community, features that improve the precision and recall of detected variants. In this article we present four basic protocols for the most common use cases of MIDAS2, along with supporting protocols for installation and use. In addition, we provide in-depth guidance on adjusting command line parameters, editing the reference database, optimizing hardware utilization, and understanding the metagenotyping results. All the steps of metagenotyping, from raw sequencing reads to population genetic analysis, are demonstrated with example data in two downloadable sequencing libraries of single-end metagenomic reads representing a mixture of multiple bacterial species. This set of protocols empowers users to accurately genotype hundreds of species in thousands of samples, providing rich genetic data for studying the evolution and strain-level ecology of microbial communities. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Species prescreening Basic Protocol 2: Download MIDAS reference database Basic Protocol 3: Population single nucleotide variant calling Basic Protocol 4: Pan-genome copy number variant calling Support Protocol 1: Installing MIDAS2 Support Protocol 2: Command line inputs Support Protocol 3: Metagenotyping with a custom collection of genomes Support Protocol 4: Metagenotyping with advanced parameters.

A Roadmap to Sequence Assembly Evaluation Tools

The assembly evaluation process is the starting step towards meaningful downstream data analysis. We need to know how much accurate information is included in an assembled sequence before going further to any data analysis stage. Four basic metrics are targeted by different assembly evaluation tools: contiguity, accuracy, completeness, and contamination. Some tools evaluate these metrics based on comparing the assembly results to a closely related reference. Others utilize different types of heuristics to overcome the missing guiding reference, such as the consistency between assembly results and sequencing reads. In this paper, we discuss the assembly evaluation process as a core stage in any sequence assembly pipeline and present a roadmap that is followed by most assembly evaluation tools to assess different metrics. We highlight the challenges that currently exist in the assembly evaluation tools and summarize their technical and practical details to help the end-users choose the best tool according to their working scenarios. To address the similarities/differences among different assembly assessment tools, including their evaluation approaches, metrics, comprehensive nature, limitations, usability and how the evaluated results are presented to the end-user, we provide a practical example for evaluating Velvet assembly results for S. aureus dataset from GAGE competition. A Github repository (https://github.com/SaraEl-Metwally/Assembly-Evaluation-Tools) is created for evaluation result details along with their generated command line parameters.

Vargas: heuristic-free alignment for assessing linear and graph read aligners.

MotivationRead alignment is central to many aspects of modern genomics. Most aligners use heuristics to accelerate processing, but these heuristics can fail to find the optimal alignments of reads. Alignment accuracy is typically measured through simulated reads; however, the simulated location may not be the (only) location with the optimal alignment score.ResultsVargas implements a heuristic-free algorithm guaranteed to find the highest-scoring alignment for real sequencing reads to a linear or graph genome. With semiglobal and local alignment modes and affine gap and quality-scaled mismatch penalties, it can implement the scoring functions of commonly used aligners to calculate optimal alignments. While this is computationally intensive, Vargas uses multi-core parallelization and vectorized (SIMD) instructions to make it practical to optimally align large numbers of reads, achieving a maximum speed of 456 billion cell updates per second. We demonstrate how these ‘gold standard’ Vargas alignments can be used to improve heuristic alignment accuracy by optimizing command-line parameters in Bowtie 2, BWA-maximal exact match and vg to align more reads correctly.Availability and implementationSource code implemented in C++ and compiled binary releases are available at https://github.com/langmead-lab/vargas under the MIT license.Supplementary information Supplementary data are available at Bioinformatics online.

Cryptographic key protection against FROST for mobile devices

With the flourish of applications based on the internet of things and cloud computing, privacy issues have been attracting a lot of attentions. Although the increasing use of full disk encryption (FDE) significantly hamper privacy leakage and digital forensics, cold boot attacks have thwarted FDE since forensic recovery of scrambled telephones (FROST), a forensic tool, is proposed. The cryptographic keys which are stored in the mobile devices are inclined to be obtained by FROST. Recent research results have shown CPU-bound encryption methods to resist FROST. However, these methods performs AES encryption solely on CPU registers, whose advantage comes at the cost of encryption speed. This paper, therefore, presents a cryptographic key protection scheme for android devices which prevents FROST from acquiring the key of AES by changing storage location of the key in memory. The storage location of the key is switched to the fixed position where command line parameters will be stored when android boots. Therefore, the key will be covered by command line parameters while the system reboots, which negates FROST from obtaining the key. Compared with the popular CPU-bound encryption methods, our method has less impact on encryption efficiency and employs no additional storage resources.

AMI-diagram: Mining Facts from Images

AMI-Diagram is a tool for mining facts from diagrams, and converting the graphics primitives into XML. Targets include X-Y plots, barcharts, chemical structure diagrams and phylogenetic trees. Part of the ContentMine framework for automatically extracting science from the published literature, AMI can ingest born-digital diagrams either as latent vectors, pixel diagrams or scanned documents. For high-quality/resolution diagrams the process is automatic; command line parameters can be used for noisy or complex diagrams. This article provides on overview of the tool which is currently being deployed to alpha-testers, especially in chemistry and phylogenetics.

A SVM-based IDS Alarms Filtering Method

In view of the existing IDS are widespread the problem of high false alarm rate, this paper proposes a kind of alarm information filtering method of IDS based on support vector machine (SVM). The method consists of two parts, training, and data prediction. Model training including parsing command line parameters, read the training sample, select the appropriate penalty coefficient, kernel function and kernel parameter, statistical types and the number of each type of sample, sample training data grouping, using the minimum sequence optimization algorithm C - SVM classifier model. Training data to predict including read alarm data and based on the model of C - SVM classifier model calculation values of decision alarm data. Theoretical analysis and experimental data show that the rational selection of kernel function and kernel parameters and the training data set, this method can effectively reduce the intrusion detection system false alarm rate.

Design of Executing ELF Files for Multi-Process Micro-Kernel Embedded OS on ARM

This paper describes the design and implementation of executing ELF files for ARM-based multi-process micro-kernel embedded operating system, including overview of ELF files, key techniques of executing ELF files, such as installing command-line parameters and environment variables, loading ELF file, setting related registers and PCB fields, and starting the executable ELF file, etc. Based on the functionality of executing ELF files described in this paper, the corresponding shell interpreter and shell commands can be designed and executed. At the final, the paper provides some demo examples of shell commands to show the effect.

ANVAYA: A WORKFLOWS ENVIRONMENT FOR AUTOMATED GENOME ANALYSIS

Anvaya is a workflow environment for automated genome analysis that provides an interface for several bioinformatics tools and databases, loosely coupled together in a coordinated system, enabling the execution of a set of analyses tools in series or in parallel. It is a client-server workflow environment that has an advantage over existing software as it enables extensive pre & post processing of biological data in an efficient manner. "Anvaya" offers the user, novel functionalities to carry out exhaustive comparative analysis via "custom tools," which are tools with new functionality not available in standard tools, and "built-in PERL parsers," which automate data-flow between tools that hitherto, required manual intervention. It also provides a set of 11 pre-defined workflows for frequently used pipelines in genome annotation and comparative genomics ranging from EST assembly and annotation to phylogenetic reconstruction and microarray analysis. It provides a platform that serves as a single-stop solution for biologists to carry out hassle-free and comprehensive analysis, without being bothered about the nuances involved in tool installation, command line parameters, format conversions required to connect tools and manage/process multiple data sets at a single instance.

Key2Ann: a tool to process sequence sets by replacing database identifiers with a human-readable annotation.

Deducing common properties or degrees of phylogenetic relationship by analyzing a grouping or clustering of sequence sets is a frequently used technique in computational biology. If interpreted by means of visual inspection, the conclusions depend for many of these applications on meaningful names for the input data. In accordance with the aim of the analysis, the sequences should be provided with names indicating the function of the genes or gene-products, the phylogenetic position or other properties characterizing the contributing species. However, sequences extracted from databases are most often annotated with identifiers which only implicitly contain the desired information. To solve this problem, we have designed and implemented a tool named Key2Ann, which replaces in multiple fasta files the database keys with short terms indicating the taxonomic position or other features like the gene name or the EC-number. In addition, properties like habitat, growth temperature or the degree of pathogenicity can be coded for microbial species. To allow for highest flexibility, the user can control the composition of the names by means of command line parameters. Key2Ann is written in Java and can be downloaded via http://www-bioinf.uni-regensburg.de/downl/Key2Ann.zip. We demonstrate the usage of Key2Ann by discussing three typical examples of phylogenetic analysis.

Key2Ann: a tool to process sequence sets by replacing database identifiers with a human-readable annotation

Summary Deducing common properties or degrees of phylogenetic relationship by analyzing a grouping or clustering of sequence sets is a frequently used technique in computational biology. If interpreted by means of visual inspection, the conclusions depend for many of these applications on meaningful names for the input data. In accordance with the aim of the analysis, the sequences should be provided with names indicating the function of the genes or gene-products, the phylogenetic position or other properties characterizing the contributing species. However, sequences extracted from databases are most often annotated with identifiers which only implicitly contain the desired information. To solve this problem, we have designed and implemented a tool named Key2Ann, which replaces in multiple fasta files the database keys with short terms indicating the taxonomic position or other features like the gene name or the EC-number. In addition, properties like habitat, growth temperature or the degree of pathogenicity can be coded for microbial species. To allow for highest flexibility, the user can control the composition of the names by means of command line parameters. Key2Ann is written in Java and can be downloaded via http://www-bioinf.uni-regensburg.de/downl/Key2Ann.zip. We demonstrate the usage of Key2Ann by discussing three typical examples of phylogenetic analysis.

The missing graphical user interface for genomics

The Galaxy package empowers regular users to perform rich DNA sequence analysis through a much-needed and user-friendly graphical web interface.See research article http://genomebiology.com/2010/11/8/R86Research highlightWith the advent of affordable and high-throughput DNA sequencing, sequencing is becoming an essential component in nearly every genetics lab. These data are being generated to probe sequence variations, to understand transcribed, regulated or methylated DNA elements, and to explore a host of other biological features across the tree of life and across a range of environments and conditions. Given this deluge of data, novices and experts alike are facing the daunting challenge of trying to analyze the raw sequence data computationally. With so many tools available and so many assays to analyze, how can one be expected to stay current with the state of the art? How can one be expected to learn to use each tool and construct robust end-to-end analysis pipelines, all while ensuring that input formats, command-line options, sequence databases and program libraries are set correctly? Finally, once the analysis is complete, how does one ensure the results are reproducible and transparent for others to scrutinize and study?In an article published in Genome Biology, Jeremy Goecks, Anton Nekrutenko, James Taylor and the rest of the Galaxy Team (Goecks et al. [1]) make a great advance towards resolving these critical questions with the latest update to their Galaxy Project. The ambitious goal of Galaxy is to empower regular users to carry out their own computational analysis without having to be an expert in computational biology or computer science. Galaxy adds a desperately needed graphical user interface to genomics research, making data analysis universally accessible in a web browser, and freeing users from the minutiae of archaic command-line parameters, data formats and scripting languages. Data inputs and computational steps are selected from dynamic graphical menus, and the results are displayed in intuitive plots and summaries that encourage interactive workflows and the exploration of hypotheses. The underlying data analysis tools can be almost any piece of software, written in any language, but all their complexity is neatly hidden inside of Galaxy, allowing users to focus on scientific rather than technical questions.

46 THE INFLUENCE OF LED IRRADIATION AT DIFFERENT WAVELENGTHS AND EXPOSURES ON PLATELET AGGREGATION

A new version of the “MinFinder” program is presented that offers an augmented linking procedure for Fortran-77 subprograms, two additional stopping rules and a new start-point rejection mechanism that saves a significant portion of gradient and function evaluations. The method is applied on a set of standard test functions and the results are reported.Program title: MinFinder v2.0Catalogue identifier: ADWU_v2_0Program summary URL: http://cpc.cs.qub.ac.uk/summaries/ADWU_v2_0.htmlProgram obtainable from: CPC Program Library, Queen's University, Belfast, N. IrelandLicensing provisions: Standard CPC Licence, http://cpc.cs.qub.ac.uk/licence/licence.htmlNo. of lines in distributed program, including test data, etc.: 14 150No. of bytes in distributed program, including test data, etc.: 218 144Distribution format: tar.gzProgramming language used: GNU C++, GNU FORTRAN, GNU CComputer: The program is designed to be portable in all systems running the GNU C++ compilerOperating system: Linux, Solaris, FreeBSDRAM: 200 000 bytesClassification: 4.9Catalogue identifier of previous version: ADWU_v1_0Journal reference of previous version: Computer Physics Communications 174 (2006) 166–179Does the new version supersede the previous version?: YesNature of problem: A multitude of problems in science and engineering are often reduced to minimizing a function of many variables. There are instances that a local optimum does not correspond to the desired physical solution and hence the search for a better solution is required. Local optimization techniques can be trapped in any local minimum. Global optimization is then the appropriate tool. For example, solving a non-linear system of equations via optimization, one may encounter many local minima that do not correspond to solutions, i.e. they are far from zero.Solution method: Using a uniform pdf, points are sampled from a rectangular domain. A clustering technique, based on a typical distance and a gradient criterion, is used to decide from which points a local search should be started. Further searching is terminated when all the local minima inside the search domain are thought to be found. This is accomplished via three stopping rules: the “double-box” stopping rule, the “observables” stopping rule and the “expected minimizers” stopping rule.Reasons for the new version: The link procedure for source code in Fortran 77 is enhanced, two additional stopping rules are implemented and a new criterion for accepting-start points, that economizes on function and gradient calls, is introduced.Summary of revisions:1.Addition of command line parameters to the utility program make_program.2.Augmentation of the link process for Fortran 77 subprograms, by linking the final executable with the g2c library.3.Addition of two probabilistic stopping rules.4.Introduction of a rejection mechanism to the Checking step of the original method, that reduces the number of gradient evaluations.Additional comments: A technical report describing the revisions, experiments and test runs is packaged with the source code.Running time: Depending on the objective function.

MinFinder v2.0: An improved version of MinFinder

A new version of the “MinFinder” program is presented that offers an augmented linking procedure for Fortran-77 subprograms, two additional stopping rules and a new start-point rejection mechanism that saves a significant portion of gradient and function evaluations. The method is applied on a set of standard test functions and the results are reported. New version program summary Program title: MinFinder v2.0 Catalogue identifier: ADWU_v2_0 Program summary URL: http://cpc.cs.qub.ac.uk/summaries/ADWU_v2_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: Standard CPC Licence, http://cpc.cs.qub.ac.uk/licence/licence.html No. of lines in distributed program, including test data, etc.: 14 150 No. of bytes in distributed program, including test data, etc.: 218 144 Distribution format: tar.gz Programming language used: GNU C++, GNU FORTRAN, GNU C Computer: The program is designed to be portable in all systems running the GNU C++ compiler Operating system: Linux, Solaris, FreeBSD RAM: 200 000 bytes Classification: 4.9 Catalogue identifier of previous version: ADWU_v1_0 Journal reference of previous version: Computer Physics Communications 174 (2006) 166–179 Does the new version supersede the previous version?: Yes Nature of problem: A multitude of problems in science and engineering are often reduced to minimizing a function of many variables. There are instances that a local optimum does not correspond to the desired physical solution and hence the search for a better solution is required. Local optimization techniques can be trapped in any local minimum. Global optimization is then the appropriate tool. For example, solving a non-linear system of equations via optimization, one may encounter many local minima that do not correspond to solutions, i.e. they are far from zero. Solution method: Using a uniform pdf, points are sampled from a rectangular domain. A clustering technique, based on a typical distance and a gradient criterion, is used to decide from which points a local search should be started. Further searching is terminated when all the local minima inside the search domain are thought to be found. This is accomplished via three stopping rules: the “double-box” stopping rule, the “observables” stopping rule and the “expected minimizers” stopping rule. Reasons for the new version: The link procedure for source code in Fortran 77 is enhanced, two additional stopping rules are implemented and a new criterion for accepting-start points, that economizes on function and gradient calls, is introduced. Summary of revisions: 1. Addition of command line parameters to the utility program make_program. 2. Augmentation of the link process for Fortran 77 subprograms, by linking the final executable with the g2c library. 3. Addition of two probabilistic stopping rules. 4. Introduction of a rejection mechanism to the Checking step of the original method, that reduces the number of gradient evaluations. Additional comments: A technical report describing the revisions, experiments and test runs is packaged with the source code. Running time: Depending on the objective function.

Multivariate curve resolution with non-linear fitting of kinetic profiles

This paper describes the incorporation of a hard modeling step based on a kinetic model, into a soft modeling (multivariate curve resolution) technique. The soft modeling technique allows for the determination of the retention and spectral profiles from overlapped components while the hard modeling step allows for the simultaneous prediction of the rate constants of the various steps in the reaction pathway. The program uses standard MATLAB® functions for determining the solutions of the differential equations as well as for finding the optimal rate constants to describe the kinetic profiles. The kinetic model is entered by a set of command line parameters and can describe any order chemical reaction with multiple reaction pathways. This paper uses simulated first- and second-order reaction data as well as real data to characterize the performance of the program. The algorithm is able to resolve overlapped retention and spectral profiles and predict the rate constants for the reaction.

Software tools for motif and pattern scanning: program descriptions including a universal sequence reading algorithm.

Two programs, MOTIF and PATTERN, that scan sequences for matches to user-defined motifs and patterns of motifs based on identity and set membership are described. The programs use a simple and logical notation to define motifs, and may be used either interactively or by using command line parameters (suitable for batch processing). The two programs described also incorporate a simple, yet reliable, algorithm that automatically detects in which of six possible formats the sequence entry is written.