PhyloSuite: An integrated and scalable desktop platform for streamlined molecular sequence data management and evolutionary phylogenetics studies

Multigene and genomic data sets have become commonplace in the field of phylogenetics, but many existing tools are not designed for such data sets, which often makes the analysis time‐consuming and tedious. Here, we present PhyloSuite , a (cross‐platform, open‐source, stand‐alone Python graphical user interface) user‐friendly workflow desktop platform dedicated to streamlining molecular sequence data management and evolutionary phylogenetics studies. It uses a plugin‐based system that integrates several phylogenetic and bioinformatic tools, thereby streamlining the entire procedure, from data acquisition to phylogenetic tree annotation (in combination with iTOL). It has the following features: (a) point‐and‐click and drag‐and‐drop graphical user interface; (b) a workplace to manage and organize molecular sequence data and results of analyses; (c) GenBank entry extraction and comparative statistics; and (d) a phylogenetic workflow with batch processing capability, comprising sequence alignment (mafft and macse ), alignment optimization (trimAl, HmmCleaner and Gblocks), data set concatenation, best partitioning scheme and best evolutionary model selection (PartitionFinder and modelfinder ), and phylogenetic inference (MrBayes and iq‐tree ). PhyloSuite is designed for both beginners and experienced researchers, allowing the former to quick‐start their way into phylogenetic analysis, and the latter to conduct, store and manage their work in a streamlined way, and spend more time investigating scientific questions instead of wasting it on transferring files from one software program to another.     

Cover Picture: Biotechnology Journal 2/2017

The cover of this regular issue of BTJ shows fluorescence microscopy images of bacteria along with their processed counterparts after CellShape analysis. While the former are simple raw images, the latter reveal important quantitative information e.g. intensity contours and spots. Altogether, they form a complete dataset from which we can accurately interpret cellular fluorescent signals. The cover is prepared by ρngel Goñi‐Moreno, Juhyun Kim and Víctor de Lorenzo authors of the article ”CellShape: A user‐friendly image analysis tool for quantitative visualization of bacterial cell factories inside“. ( http://dx.doi.org/10.1002/biot.201600323 ).     

The ImageJ ecosystem: Open‐source software for image visualization,processing, and analysis

For decades, biologists have relied on software to visualize and interpret imaging data. As techniques for acquiring images increase in complexity, resulting in larger multidimensional datasets, imaging software must adapt. ImageJ is an open‐source image analysis software platform that has aided researchers with a variety of image analysis applications, driven mainly by engaged and collaborative user and developer communities. The close collaboration between programmers and users has resulted in adaptations to accommodate new challenges in image analysis that address the needs of ImageJ's diverse user base. ImageJ consists of many components, some relevant primarily for developers and a vast collection of user‐centric plugins. It is available in many forms, including the widely used Fiji distribution. We refer to this entire ImageJ codebase and community as the ImageJ ecosystem. Here we review the core features of this ecosystem and highlight how ImageJ has responded to imaging technology advancements with new plugins and tools in recent years. These plugins and tools have been developed to address user needs in several areas such as visualization, segmentation, and tracking of biological entities in large, complex datasets. Moreover, new capabilities for deep learning are being added to ImageJ, reflecting a shift in the bioimage analysis community towards exploiting artificial intelligence. These new tools have been facilitated by profound architectural changes to the ImageJ core brought about by the ImageJ2 project. Therefore, we also discuss the contributions of ImageJ2 to enhancing multidimensional image processing and interoperability in the ImageJ ecosystem.     

Programming biological models in Python using PySB

Mathematical equations are fundamental to modeling biological networks, but as networks get large and revisions frequent, it becomes difficult to manage equations directly or to combine previously developed models. Multiple simultaneous efforts to create graphical standards, rule‐based languages, and integrated software workbenches aim to simplify biological modeling but none fully meets the need for transparent, extensible, and reusable models. In this paper we describe PySB, an approach in which models are not only created using programs, they are programs. PySB draws on programmatic modeling concepts from little b and ProMot, the rule‐based languages BioNetGen and Kappa and the growing library of Python numerical tools. Central to PySB is a library of macros encoding familiar biochemical actions such as binding, catalysis, and polymerization, making it possible to use a high‐level, action‐oriented vocabulary to construct detailed models. As Python programs, PySB models leverage tools and practices from the open‐source software community, substantially advancing our ability to distribute and manage the work of testing biochemical hypotheses. We illustrate these ideas using new and previously published models of apoptosis.     

Strategy and software for the statistical spatial analysis of 3D intracellular distributions

The localization of proteins in specific domains or compartments in the 3D cellular space is essential for many fundamental processes in eukaryotic cells. Deciphering spatial organization principles within cells is a challenging task, in particular because of the large morphological variations between individual cells. We present here an approach for normalizing variations in cell morphology and for statistically analyzing spatial distributions of intracellular compartments from collections of 3D images. The method relies on the processing and analysis of 3D geometrical models that are generated from image stacks and that are used to build representations at progressively increasing levels of integration, ultimately revealing statistical significant traits of spatial distributions. To make this methodology widely available to end‐users, we implemented our algorithmic pipeline into a user‐friendly, multi‐platform, and freely available software. To validate our approach, we generated 3D statistical maps of endomembrane compartments at subcellular resolution within an average epidermal root cell from collections of image stacks. This revealed unsuspected polar distribution patterns of organelles that were not detectable in individual images. By reversing the classical ‘measure‐then‐average’ paradigm, one major benefit of the proposed strategy is the production and display of statistical 3D representations of spatial organizations, thus fully preserving the spatial dimension of image data and at the same time allowing their integration over individual observations. The approach and software are generic and should be of general interest for experimental and modeling studies of spatial organizations at multiple scales (subcellular, cellular, tissular) in biological systems.     

Use of the ecological information system SynBioSys for the analysis of large datasets

The rapid developments in computer techniques and the availability of large datasets open new perspectives for vegetation analysis aiming at better understanding of the ecology and functioning of ecosystems and underlying mechanisms. Information systems prove to be helpful tools in this new field. Such information systems may integrate different biological levels, viz. species, community and landscape. They incorporate a GIS platform for the visualization of the various layers of information, enabling the analysis of patterns and processes which relate the individual levels. An example of a newly developed information system is SynBioSys Europe, an initiative of the European Vegetation Survey (EVS). For the individual levels of the system, specific sources are available, notably national and regional Turboveg databases for the community level and data from the recently published European Map of Natural Vegetation for the landscape level. The structure of the system and its underlying databases allow user‐defined queries. With regard to its application, such information systems may play a vital role in European nature planning, such as the implementation the EU‐program Natura 2000. To illustrate the scope and perspectives of the program, some examples from The Netherlands are presented. They are dealing with long‐term changes in grassland ecosystems, including shifts in distribution, floristic composition, and ecological indicator values.     

pyOpenMS: A Python‐based interface to the OpenMS mass‐spectrometry algorithm library

pyOpenMS is an open‐source, Python‐based interface to the C++ OpenMS library, providing facile access to a feature‐rich, open‐source algorithm library for MS‐based proteomics analysis. It contains Python bindings that allow raw access to the data structures and algorithms implemented in OpenMS, specifically those for file access (mzXML, mzML, TraML, mzIdentML among others), basic signal processing (smoothing, filtering, de‐isotoping, and peak‐picking) and complex data analysis (including label‐free, SILAC, iTRAQ, and SWATH analysis tools). pyOpenMS thus allows fast prototyping and efficient workflow development in a fully interactive manner (using the interactive Python interpreter) and is also ideally suited for researchers not proficient in C++. In addition, our code to wrap a complex C++ library is completely open‐source, allowing other projects to create similar bindings with ease. The pyOpenMS framework is freely available at https://pypi.python.org/pypi/pyopenms while the autowrap tool to create Cython code automatically is available at https://pypi.python.org/pypi/autowrap (both released under the 3‐clause BSD licence).     

Free and open source geographic information tools for landscape ecology

Geographic Information tools (GI tools) have become an essential component of research in landscape ecology. In this article we review the use of GIS (Geographic Information Systems) and GI tools in landscape ecology, with an emphasis on free and open source software (FOSS) projects. Specifically, we introduce the background and terms related to the free and open source software movement, then compare eight FOSS desktop GIS with proprietary GIS to analyse their utility for landscape ecology research. We also provide a summary of related landscape analysis FOSS applications, and extensions. Our results indicate that (i) all eight GIS provide the basic GIS functionality needed in landscape ecology, (ii) they all facilitate customisation, and (iii) they all provide good support via forums and email lists. Drawbacks that have been identified are related to the fact that most projects are relatively young. This currently affects the size of their user and developer communities, and their ability to include advanced spatial analysis functions and up-to-date documentation. However, we expect these drawbacks to be addressed over time, as systems mature. In general, we see great potential for the use of free and open source desktop GIS in landscape ecology research and advocate concentrated efforts by the landscape ecology community towards a common, customisable and free research platform.     

POSTMan (POST‐translational modification analysis), a software application for PTM discovery

Post‐translationally modified peptides present in low concentrations are often not selected for CID, resulting in no sequence information for these peptides. We have developed a software POSTMan (POST‐translational Modification analysis) allowing post‐translationally modified peptides to be targeted for fragmentation. The software aligns LC‐MS runs (MS₁ data) between individual runs or within a single run and isolates pairs of peptides which differ by a user defined mass difference (post‐translationally modified peptides). The method was validated for acetylated peptides and allowed an assessment of even the basal protein phosphorylation of phenylalanine hydroxylase (PHA) in intact cells.     

A user‐friendly means to scale from the biochemistry of photosynthesis to whole crop canopies and production in time and space – development of Java WIMOVAC

Windows Intuitive Model of Vegetation response to Atmosphere and Climate Change (WIMOVAC) has been used widely as a generic modular mechanistically rich model of plant production. It can predict the responses of leaf and canopy carbon balance, as well as production in different environmental conditions, in particular those relevant to global change. Here, we introduce an open source Java user‐friendly version of WIMOVAC. This software is platform independent and can be easily downloaded to a laptop and used without any prior programming skills. In this article, we describe the structure, equations and user guide and illustrate some potential applications of WIMOVAC.     

HTAPP: High‐throughput autonomous proteomic pipeline

Recent advances in the speed and sensitivity of mass spectrometers and in analytical methods, the exponential acceleration of computer processing speeds, and the availability of genomic databases from an array of species and protein information databases have led to a deluge of proteomic data. The development of a lab‐based automated proteomic software platform for the automated collection, processing, storage, and visualization of expansive proteomic data sets is critically important. The high‐throughput autonomous proteomic pipeline described here is designed from the ground up to provide critically important flexibility for diverse proteomic workflows and to streamline the total analysis of a complex proteomic sample. This tool is composed of a software that controls the acquisition of mass spectral data along with automation of post‐acquisition tasks such as peptide quantification, clustered MS/MS spectral database searching, statistical validation, and data exploration within a user‐configurable lab‐based relational database. The software design of high‐throughput autonomous proteomic pipeline focuses on accommodating diverse workflows and providing missing software functionality to a wide range of proteomic researchers to accelerate the extraction of biological meaning from immense proteomic data sets. Although individual software modules in our integrated technology platform may have some similarities to existing tools, the true novelty of the approach described here is in the synergistic and flexible combination of these tools to provide an integrated and efficient analysis of proteomic samples.     

IPeak: An open source tool to combine results from multiple MS/MS search engines

Liquid chromatography coupled tandem mass spectrometry (LC‐MS/MS) is an important technique for detecting peptides in proteomics studies. Here, we present an open source software tool, termed IPeak, a peptide identification pipeline that is designed to combine the Percolator post‐processing algorithm and multi‐search strategy to enhance the sensitivity of peptide identifications without compromising accuracy. IPeak provides a graphical user interface (GUI) as well as a command‐line interface, which is implemented in JAVA and can work on all three major operating system platforms: Windows, Linux/Unix and OS X. IPeak has been designed to work with the mzIdentML standard from the Proteomics Standards Initiative (PSI) as an input and output, and also been fully integrated into the associated mzidLibrary project, providing access to the overall pipeline, as well as modules for calling Percolator on individual search engine result files. The integration thus enables IPeak (and Percolator) to be used in conjunction with any software packages implementing the mzIdentML data standard. IPeak is freely available and can be downloaded under an Apache 2.0 license at https://code.google.com/p/mzidentml‐lib/ .     

Scavager: A Versatile Postsearch Validation Algorithm for Shotgun Proteomics Based on Gradient Boosting

Shotgun proteomics workflows for database protein identification typically include a combination of search engines and postsearch validation software based mostly on machine learning algorithms. Here, a new postsearch validation tool called Scavager employing CatBoost, an open‐source gradient boosting library, which shows improved efficiency compared with the other popular algorithms, such as Percolator, PeptideProphet, and Q‐ranker, is presented. The comparison is done using multiple data sets and search engines, including MSGF+, MSFragger, X!Tandem, Comet, and recently introduced IdentiPy. Implemented in Python programming language, Scavager is open‐source and freely available at https://bitbucket.org/markmipt/scavager .     

Excel2SVM: a stand-alone Python tool for data analysis via support vector machines

The creation of classification kernel models to categorize unknown data samples of massive magnitude is an extremely advantageous tool for the scientific community. Excel2SVM, a stand-alone Python mathematical analysis tool, bridges the gap between researchers and computer science to create a simple graphical user interface that allows users to examine data and perform maximal margin classification. This valuable ability to train support vector machines and classify unknown data files is harnessed in this fast and efficient software, granting researchers full access to this complicated, high-level algorithm. Excel2SVM offers the ability to convert data to the proper sparse format while performing a variety of kernel functions along with cost factors/modes, grids, crossvalidation, and several other functions. This program functions with any type of quantitative data making Excel2SVM the ideal tool for analyzing a wide variety of input. The software is free and available at www.bioinformatics.org/excel2svm. A link to the software may also be found at www.kernel-machines.org. This software provides a useful graphical user interface that has proven to provide kernel models with accurate results and data classification through a decision boundary.