widgetcon: A website and program for quick conversion among common population genetic data formats

One of the most tedious steps in genetic data analyses is the reformatting data generated with one program for use with other applications. This conversion is necessary because comprehensive evaluation of the data may be based on different algorithms included in diverse software, each requiring a distinct input format. A platform‐independent and freely available program or a web‐based tool dedicated to such reformatting can save time and efforts in data processing. Here, we report widgetcon , a website and a program which has been developed to quickly and easily convert among various molecular data formats commonly used in phylogenetic analysis, population genetics, and other fields. The web‐based service is available at https://www.widgetcon.net . The program and the website convert the major data formats in four basic steps in less than a minute. The resource will be a useful tool for the research community and can be updated to include more formats and features in the future.     

formatomatic: a program for converting diploid allelic data between common formats for population genetic analysis

There has been a great increase in both the number of population genetic analysis programs and the size of data sets being studied with them. Since the file formats required by the most popular and useful programs are variable, automated reformatting or conversion between them is desirable. formatomatic is an easy to use program that can read allelic data files in genepop , raw (csv ) or convert formats and create data files in nine formats: raw (csv ), arlequin , genepop , immanc /bayesass +, migrate , newhybrids , msvar , baps and structure . Use of formatomatic should greatly reduce time spent reformatting data sets and avoid unnecessary errors.     

Enabling high-throughput data management for systems biology: the Bioinformatics Resource Manager

The Bioinformatics Resource Manager (BRM) is a software environment that provides the user with data management, retrieval and integration capabilities. Designed in collaboration with biologists, BRM simplifies mundane analysis tasks of merging microarray and proteomic data across platforms, facilitates integration of users' data with functional annotation and interaction data from public sources and provides connectivity to visual analytic tools through reformatting of the data for easy import or dynamic launching capability. BRM is developed using Java and other open-source technologies for free distribution. AVAILABILITY: BRM, sample data sets and a user manual can be downloaded from http://www.sysbio.org/dataresources/brm.stm.     

BACA: a mitochondrial genome retriever,organizer and visualizer

Retrieving and organizing data from complete genomes is a time‐consuming task, even more so if the interest lies only in part of the genome (for nongenomic analysis). Furthermore, when comparing several genomes or genes, data retrieval has to be repeated multiple times. We present baca , a software for retrieving, organizing and visualizing multiple mitochondrial genomes. baca takes a GenBank query, retrieves all related genomes and generates multiple fasta files organized both by genomes and genes. A web‐based user interface and an interactive graphical map of all genomes with all genes are also provided. The program is available from http://cibio.up.pt/software/baca .     

TREEasy: An automated workflow to infer gene trees,species trees,and phylogenetic networks from multilocus data

Multilocus genomic data sets can be used to infer a rich set of information about the evolutionary history of a lineage, including gene trees, species trees, and phylogenetic networks. However, user‐friendly tools to run such integrated analyses are lacking, and workflows often require tedious reformatting and handling time to shepherd data through a series of individual programs. Here, we present a tool written in Python—TREEasy—that performs automated sequence alignment (with MAFFT), gene tree inference (with IQ‐Tree), species inference from concatenated data (with IQ‐Tree and RaxML‐NG), species tree inference from gene trees (with ASTRAL, MP‐EST, and STELLS2), and phylogenetic network inference (with SNaQ and PhyloNet). The tool only requires FASTA files and nine parameters as inputs. The tool can be run as command line or through a Graphical User Interface (GUI). As examples, we reproduced a recent analysis of staghorn coral evolution, and performed a new analysis on the evolution of the “WGD clade” of yeast. The latter revealed novel patterns that were not identified by previous analyses. TREEasy represents a reliable and simple tool to accelerate research in systematic biology ( https://github.com/MaoYafei/TREEasy ).     

SwissPIT: An workflow‐based platform for analyzing tandem‐MS spectra using the Grid

The identification and characterization of peptides from MS/MS data represents a critical aspect of proteomics. It has been the subject of extensive research in bioinformatics resulting in the generation of a fair number of identification software tools. Most often, only one program with a specific and unvarying set of parameters is selected for identifying proteins. Hence, a significant proportion of the experimental spectra do not match the peptide sequences in the screened database due to inappropriate parameters or scoring schemes. The Swiss protein identification toolbox (swissPIT) project provides the scientific community with an expandable multitool platform for automated in‐depth analysis of MS data also able to handle data from high‐throughput experiments. With swissPIT many problems have been solved: The missing standards for input and output formats (A), creation of analysis workflows (B), unified result visualization (C), and simplicity of the user interface (D). Currently, swissPIT supports four different programs implementing two different search strategies to identify MS/MS spectra. Conceived to handle the calculation‐intensive needs of each of the programs, swissPIT uses the distributed resources of a Swiss‐wide computer Grid (http://www.swing‐grid.ch).     

genodive version 3.0: Easy‐to‐use software for the analysis of genetic data of diploids and polyploids

genodive version 3.0 is a user‐friendly program for the analysis of population genetic data. This version presents a major update from the previous version and now offers a wide spectrum of different types of analyses. genodive has an intuitive graphical user interface that allows direct manipulation of the data through transformation, imputation of missing data, and exclusion and inclusion of individuals, population and/or loci. Furthermore, genodive seamlessly supports 15 different file formats for importing or exporting data from or to other programs. One major feature of genodive is that it supports both diploid and polyploid data, up to octaploidy (2n = 8x) for some analyses, but up to hexadecaploidy (2n = 16x) for other analyses. The different types of analyses offered by genodive include multiple statistics for estimating population differentiation (φ_ST, F_ST, F?_ST, G_ST, G?_ST, G??_ST, D_est, R_ST, ρ), analysis of molecular variance‐based K‐means clustering, Hardy–Weinberg equilibrium, hybrid index, population assignment, clone assignment, Mantel test, Spatial Autocorrelation, 23 ways of calculating genetic distances, and both principal components and principal coordinates analyses. A unique feature of genodive is that it can also open data sets with nongenetic variables, for example environmental data or geographical coordinates that can be included in the analysis. In addition, genodive makes it possible to run several external programs (lfmm , structure , instruct and vegan ) directly from its own user interface, avoiding the need for data reformatting and use of the command line. genodive is available for computers running Mac OS X 10.7 or higher and can be downloaded freely from: http://www.patrickmeirmans.com/software .     

Metabolomics spectral formatting, alignment and conversion tools (MSFACTs)

MOTIVATION: The amplified interest in metabolic profiling has generated the need for additional tools to assist in the rapid analysis of complex data sets. RESULTS: A new program; metabolomics spectral formatting, alignment and conversion tools, (MSFACTs) is described here for the automated import, reformatting, alignment, and export of large chromatographic data sets to allow more rapid visualization and interrogation of metabolomic data. MSFACTs incorporates two tools: one for the alignment of integrated chromatographic peak lists and another for extracting information from raw chromatographic ASCII formatted data files. MSFACTs is illustrated in the processing of GC/MS metabolomic data from different tissues of the model legume plant, Medicago truncatula. The results document that various tissues such as roots, stems, and leaves from the same plant can be easily differentiated based on metabolite profiles. Further, similar types of tissues within the same plant, such as the first to eleventh internodes of stems, could also be differentiated based on metabolite profiles. AVAILABILITY: Freely available upon request for academic and non-commercial use. Commercial use is available through licensing agreement http://www.noble.org/PlantBio/MS/MSFACTs/MSFACTs.html.     

Databases and software for the analysis of mutations in the human p53 gene, the human hprt gene and both the lacI and lacZ gene in transgenic rodents.

We have created databases and software applications for the analysis of DNA mutations at the humanp53gene, the humanhprtgene and both the rodent transgeniclacIandlacZlocus. The databases themselves are stand-alone dBASE files and the software for analysis of the databases runs on IBM-compatible computers. Each database has a separate software analysis program. The software created for these databases permit the filtering, ordering, report generation and display of information in the database. In addition, a significant number of routines have been developed for the analysis of single base substitutions. One method of obtaining the databases and software is via the World Wide Web (WWW). Open the following home page with a Web Browser: http://sunsite.unc.edu/dnam/mainpage.ht ml . Alternatively, the databases and programs are available via public FTP from: anonymous@sunsite.unc.edu . There is no password required to enter the system. The databases and software are found beneath the subdirectory: pub/academic/biology/dna-mutations. Two other programs are available at the site-a program for comparison of mutational spectra and a program for entry of mutational data into a relational database.     

Diving in uncharted waters: An updated genetics toolkit highlights the challenges of polyploidy in landscape genomics analyses

As molecular ecologists, we have by necessity become adept at working across computational platforms. A diverse community of scientists has developed a broad array of analytical resources spanning command line to graphical user interface across Linux, Mac, and Windows environments and a dizzying array of program‐specific input formats. In light of this, we often explore our data like free divers – filling our lungs with air and descending for a short period of time into one part of our data set before resurfacing, reformatting, and preparing for our next analysis. In this issue of Molecular Ecology Resources, Meirmans (2020) presents an updated version of GenoDive, a program with a toolkit that provides users with the opportunity to stay a while and delve deeper into the diverse portfolio of information provided by a genomic data set. The comprehensive nature of GenoDive coupled with its unique capability to handle both diploid and polyploid data also provides an opportunity to reflect on the unevenness of resources available for the analysis of polyploid versus diploid data. Since new updates include the addition of plug‐ins for genotype‐environment association analyses, we limit the observations presented here to the common tools used for landscape genomics analyses.     

NMπ—improved re‐implementation of NM+, a software for estimating gene dispersal and mating patterns

This study introduces the NMπ computer program designed for estimation of plant mating system and seed and pollen dispersal kernels. NMπ is a re‐implementation of the NM+ program and provides new features such as support for multicore processors, explicit treatment of dioecy, the possibility of incorporating uniparentally cytoplasmic markers, the possibility of assessing assortative mating due to phenotypic similarity and inference about offspring genealogies. The probability model of parentage (the neighbourhood model) accounts for missing data and genotyping errors, which can be estimated along with regular parameters of the mating system. The program has virtually no restrictions with respect to a number of individuals, markers or phenotypic characters. A console version of NMπ can be run under a wide variety of operating systems, including Windows, Linux or Mac OS. For Windows users, a graphical user interface is provided to facilitate operating the software. The program, user manual and example data are available on http://www.ukw.edu.pl/pracownicy/plik/igor_chybicki/3694/ .     

Virtual Expert Mass Spectrometrist: iTRAQ tool for database‐dependent search,quantitation and result storage

A frequent goal of MS‐based proteomics experiments nowadays is to quantify changes in the abundance of proteins across several biological samples. The iTRAQ labeling method is a powerful technique; when combined with LC coupled to MS/MS it allows relative quantitation of up to eight different samples simultaneously. Despite the usefulness of iTRAQ current software solutions have limited functionality and require the combined use of several software programs for analysis of the data from different MS vendors. We developed an integrated tool, now available in the virtual expert mass spectrometrist (VEMS) program, for database‐dependent search of MS/MS spectra, quantitation and database storage for iTRAQ‐labeled samples. VEMS also provides useful alternative report types for large‐scale quantitative experiments. The implemented statistical algorithms build on quantitative algorithms previously used in proposed iTRAQ tools as described in detail herein. We propose a new algorithm, which provides more accurate peptide ratios for data that show an intensity‐dependent saturation. The accuracy of the proposed iTRAQ algorithm and the performance of VEMS are demonstrated by comparing results from VEMS, MASCOT and PEAKS Q obtained by analyzing data from a reference mixture of six proteins. Users can download VEMS and test data from “ http://www.portugene.com/software.html ”.     

Meta-analysis of untargeted metabolomic data from multiple profiling experiments

metaXCMS is a software program for the analysis of liquid chromatography/mass spectrometry-based untargeted metabolomic data. It is designed to identify the differences between metabolic profiles across multiple sample groups (e.g., 'healthy' versus 'active disease' versus 'inactive disease'). Although performing pairwise comparisons alone can provide physiologically relevant data, these experiments often result in hundreds of differences, and comparison with additional biologically meaningful sample groups can allow for substantial data reduction. By performing second-order (meta-) analysis, metaXCMS facilitates the prioritization of interesting metabolite features from large untargeted metabolomic data sets before the rate-limiting step of structural identification. Here we provide a detailed step-by-step protocol for going from raw mass spectrometry data to metaXCMS results, visualized as Venn diagrams and exported Microsoft Excel spreadsheets. There is no upper limit to the number of sample groups or individual samples that can be compared with the software, and data from most commercial mass spectrometers are supported. The speed of the analysis depends on computational resources and data volume, but will generally be less than 1 d for most users. metaXCMS is freely available at http://metlin.scripps.edu/metaxcms/.     

DNAssist: the integrated editing and analysis of molecular biology sequences in windows

MOTIVATION: The programs currently available for the analysis of nucleic acid and protein sequences suffer from a variety of problems: Web-based programs often require inconvenient reformatting of sequences when proceeding from one analysis to the next, and commercial-console-based programs are cost prohibitive. Here, we report the development of DNASSIST:, an inexpensive, multiple-document, interface program for the fully integrated editing and analysis of nucleic acid and protein sequences in the familiar environment of Microsoft Windows.     

Development and Evaluation of Patient‐Centered Software for a Weight‐Management Clinic

Objective: To describe a weight‐management clinic software system and to report on its preliminary evaluation. Research Methods and Procedures: The software system standardizes the collection of relevant patient information from an initial medical assessment, weekly clinic visits, and laboratory testing protocol of a medically supervised proprietary meal‐replacement program in a university‐based referral clinic. It then generates monthly patient feedback reports with graphs of clinical and laboratory parameters to support a patient‐centered approach to weight management. After patients and clinic physicians review the data to ensure accuracy, the database is used for subsequent patient feedback reports, reports to referring physicians, quality assurance, and research. Clinic physicians and referring physicians were asked to rate their acceptance of the system. In addition, in a retrospective analysis of data generated by the system, outcomes for patients who received system‐generated feedback (n = 620) were compared with those who participated in the program before the introduction of feedback (n = 130). Results: Clinic and referring physicians reported that they had high overall satisfaction with the software and that the system saved them time, and the latter group reported that it decreased laboratory use. Regarding patients, the feedback group had lower dropout rates in the latter half of the program, better rates of attendance, completion of laboratory tests, and weight loss after 8 weeks. Discussion: The software seems to facilitate the effectiveness of the treatment protocol for obesity and generates a high‐quality database for patient care, clinic administration, quality assurance, and research purposes.     

genalex 6: genetic analysis in Excel. Population genetic software for teaching and research

genalex is a user‐friendly cross‐platform package that runs within Microsoft Excel, enabling population genetic analyses of codominant, haploid and binary data. Allele frequency‐based analyses include heterozygosity, F statistics, Nei's genetic distance, population assignment, probabilities of identity and pairwise relatedness. Distance‐based calculations include amova , principal coordinates analysis (PCA), Mantel tests, multivariate and 2D spatial autocorrelation and twogener . More than 20 different graphs summarize data and aid exploration. Sequence and genotype data can be imported from automated sequencers, and exported to other software. Initially designed as tool for teaching, genalex 6 now offers features for researchers as well. Documentation and the program are available at http://www.anu.edu.au/BoZo/GenAlEx/     

Preventing Overestimation of Pixels in Computed Tomography Assessment of Visceral Fat

Objective: Computed tomography (CT) is a common research procedure for measuring abdominal fat distribution, but little is written about the software used to analyze images. Our objective was to compare in‐house and commercially available software for quantitative measurement of abdominal fat distribution. In the process, we encountered some unexpected problems. Research Methods and Procedures: A total of 123 volunteers had single‐slice abdominal CT images taken that were used to evaluate various aspects of the commercial image analysis program. Results: The agreement between the commercial and in‐house programs was excellent (r = 0.996, p < 0.00, 001) for both total and intraabdominal fat, and we were able to reduce between‐observer variability in measured fat areas through the use of statistical handling of region of interest information. We also noted that intracolonic contents sometimes had the same Hounsfield units as adipose tissue. We analyzed single‐slice CT images from 50 volunteers to determine the potential impact of this effect on visceral fat area; the overestimate of visceral fat area was 19 ± 22% (maximum, 112% overestimate). The commercial program could prevent this error, whereas our in‐house program could not. Discussion: We concluded that a readily available commercial image analysis program compares well with a previously validated in‐house program and that it offers some advantages with respect to preventing overestimation of pixels as visceral fat.     

multi‐dice: r package for comparative population genomic inference under hierarchical co‐demographic models of independent single‐population size changes

Population genetic data from multiple taxa can address comparative phylogeographic questions about community‐scale response to environmental shifts, and a useful strategy to this end is to employ hierarchical co‐demographic models that directly test multi‐taxa hypotheses within a single, unified analysis. This approach has been applied to classical phylogeographic data sets such as mitochondrial barcodes as well as reduced‐genome polymorphism data sets that can yield 10,000s of SNPs, produced by emergent technologies such as RAD‐seq and GBS. A strategy for the latter had been accomplished by adapting the site frequency spectrum to a novel summarization of population genomic data across multiple taxa called the aggregate site frequency spectrum (aSFS), which potentially can be deployed under various inferential frameworks including approximate Bayesian computation, random forest and composite likelihood optimization. Here, we introduce the r package multi‐dice , a wrapper program that exploits existing simulation software for flexible execution of hierarchical model‐based inference using the aSFS, which is derived from reduced genome data, as well as mitochondrial data. We validate several novel software features such as applying alternative inferential frameworks, enforcing a minimal threshold of time surrounding co‐demographic pulses and specifying flexible hyperprior distributions. In sum, multi‐dice provides comparative analysis within the familiar R environment while allowing a high degree of user customization, and will thus serve as a tool for comparative phylogeography and population genomics.