It is time to end the patenting of software

One of the most significant outcomes of genomics has been arapid increase in the rate that we as a community can generatedata on interesting biological systems. Rapid improvements intechnologies such as DNA microarrays and proteomics applicationshave produced a climate where the challenge is no longer collectinghigh quality data but rather managing and analyzing it. As wein the bioinformatics community have addressed this challenge,we have had to carefully consider the way in which the resultsof our intellectual efforts—the software tools that wedevelop—are made available to the wider research community.Increasingly, bioinformatics scientists are coming to call fordevelopment in an open source environment in which softwareis distributed with its underlying source code     

MetaQuant: a tool for the automatic quantification of GC/MS-based metabolome data

MetaQuant is a Java-based program for the automatic and accurate quantification of GC/MS-based metabolome data. In contrast to other programs MetaQuant is able to quantify hundreds of substances simultaneously with minimal manual intervention. The integration of a self-acting calibration function allows the parallel and fast calibration for several metabolites simultaneously. Finally, MetaQuant is able to import GC/MS data in the common NetCDF format and to export the results of the quantification into Systems Biology Markup Language (SBML), Comma Separated Values (CSV) or Microsoft Excel (XLS) format. AVAILABILITY: MetaQuant is written in Java and is available under an open source license. Precompiled packages for the installation on Windows or Linux operating systems are freely available for download. The source code as well as the installation packages are available at http://bioinformatics.org/metaquant     

phastSim: Efficient simulation of sequence evolution for pandemic-scale datasets

Sequence simulators are fundamental tools in bioinformatics, as they allow us to test data processing and inference tools, and are an essential component of some inference methods. The ongoing surge in available sequence data is however testing the limits of our bioinformatics software. One example is the large number of SARS-CoV-2 genomes available, which are beyond the processing power of many methods, and simulating such large datasets is also proving difficult. Here, we present a new algorithm and software for efficiently simulating sequence evolution along extremely large trees (e.g. > 100, 000 tips) when the branches of the tree are short, as is typical in genomic epidemiology. Our algorithm is based on the Gillespie approach, and it implements an efficient multi-layered search tree structure that provides high computational efficiency by taking advantage of the fact that only a small proportion of the genome is likely to mutate at each branch of the considered phylogeny. Our open source software allows easy integration with other Python packages as well as a variety of evolutionary models, including indel models and new hypermutability models that we developed to more realistically represent SARS-CoV-2 genome evolution.     

TigrScan and GlimmerHMM: two open source ab initio eukaryotic gene-finders

We describe two new Generalized Hidden Markov Model implementations for ab initio eukaryotic gene prediction. The C/C++ source code for both is available as open source and is highly reusable due to their modular and extensible architectures. Unlike most of the currently available gene-finders, the programs are re-trainable by the end user. They are also re-configurable and include several types of probabilistic submodels which can be independently combined, such as Maximal Dependence Decomposition trees and interpolated Markov models. Both programs have been used at TIGR for the annotation of the Aspergillus fumigatus and Toxoplasma gondii genomes. AVAILABILITY: Source code and documentation are available under the open source Artistic License from http://www.tigr.org/software/pirate     

G-language Genome Analysis Environment: a workbench for nucleotide sequence data mining

SUMMARY: G-language Genome Analysis Environment (G-language GAE) is an open source generic software package aimed for higher efficiency in bioinformatics analysis. G-language GAE has an interface as a set of Perl libraries for software development, and a graphical user interface for easy manipulation. Both Windows and Linux versions are available. AVAILABILITY: From http://www.g-language.org/ under GNU General Public License. CD-ROMs are distributed freely in major conferences.     

ETE: a python Environment for Tree Exploration

Background  

Many bioinformatics analyses, ranging from gene clustering to phylogenetics, produce hierarchical trees as their main result. These are used to represent the relationships among different biological entities, thus facilitating their analysis and interpretation. A number of standalone programs are available that focus on tree visualization or that perform specific analyses on them. However, such applications are rarely suitable for large-scale surveys, in which a higher level of automation is required. Currently, many genome-wide analyses rely on tree-like data representation and hence there is a growing need for scalable tools to handle tree structures at large scale.     

BioManager: the use of a bioinformatics web application as a teaching tool in undergraduate bioinformatics training

The completion of the human genome project, and other genome sequencing projects, has spearheaded the emergence of the field of bioinformatics. Using computer programs to analyse DNA and protein information has become an important area of life science research and development. While it is not necessary for most life science researchers to develop specialist bioinformatic skills (including software development), basic skills in the application of common bioinformatics software and the effective interpretation of results are increasingly required by all life science researchers. Training in bioinformatics is increasingly occurring within the university system as part of existing undergraduate science and specialist degrees. One difficulty in bioinformatics education is the sheer number of software programs required in order to provide a thorough grounding in the subject to the student. Teaching requires either a well-maintained internal server with all the required software, properly interfacing with student terminals, and with sufficient capacity to handle multiple simultaneous requests, or it requires the individual installation and maintenance of every piece of software on each computer. In both cases, there are difficult issues regarding site maintenance and accessibility. In this article, we discuss the use of BioManager, a web-based bioinformatics application integrating a variety of common bioinformatics tools, for teaching, including its role as the main bioinformatics training tool in some Australian and international universities. We discuss some of the issues with using a bioinformatics resource primarily created for research in an undergraduate teaching environment.     

Bioinformatics software resources

This review looks at internet archives, repositories and lists for obtaining popular and useful biology and bioinformatics software. Resources include collections of free software, services for the collaborative development of new programs, software news media and catalogues of links to bioinformatics software and web tools. Problems with such resources arise from needs for continued curator effort to collect and update these, combined with less than optimal community support, funding and collaboration. Despite some problems, the available software repositories provide needed public access to many tools that are a foundation for analyses in bioscience research efforts.     

Bacterial genome mapper: A comparative bacterial genome mapping tool

Recently, next generation sequencing (NGS) technologies have led to a revolutionary increase in sequencing speed and costefficacy. Consequently, a vast number of contigs from many recently sequenced bacterial genomes remain to be accurately mapped and annotated, requiring the development of more convenient bioinformatics programs. In this paper, we present a newly developed web-based bioinformatics program, Bacterial Genome Mapper, which is suitable for mapping and annotating contigs that have been assembled from bacterial genome sequence raw data. By constructing a multiple alignment map between target contig sequences and two reference bacterial genome sequences, this program also provides very useful comparative genomics analysis of draft bacterial genomes. AVAILABILITY: The database is available for free at http://mbgm.kribb.re.kr.     

CDK-Taverna: an open workflow environment for cheminformatics

Background  

Small molecules are of increasing interest for bioinformatics in areas such as metabolomics and drug discovery. The recent release of large open access chemistry databases generates a demand for flexible tools to process them and discover new knowledge. To freely support open science based on these data resources, it is desirable for the processing tools to be open source and available for everyone.     

不同压缩程序对海量生物信息数据压缩效率的比较分析

海量生物信息数据的不断涌现迫切需要在数据压缩技术方面进行更多研究,以减轻服务器存储压力和提高网络传输及数据分析的效率。目前虽然已开发出大量数据压缩软件,但对于海量生物信息数据而言,应该选用何种软件和方法进行数据压缩,尚缺乏详细的综合比较分析。本文选择生物信息学领域中GenBank数据库中的典型核酸和蛋白质序列数据库以及典型生物信息软件Blast和EMBOSS为例,采用不同数据压缩软件进行综合比较分析,结果发现经典压缩软件compress的总体压缩效率很高,除压缩比率可接受之外,其压缩时间相对其他软件而言显著减少,甚至比并行化的hzip2（pbzip2）和gzip（pigz）软件的时间还少很多,故可优先考虑使用。7-Zip软件虽然具有最高的压缩比率,但压缩过程十分耗时,可用于数据的长期储存;而采用bzip2、rar以及gzip等软件压缩的文件,虽然压缩比率较7-Zip的偏低,但压缩过程相对而言还比较快速。具体应用中推荐使用经典压缩软件compress以及并行化运行的pbzip2和pigz软件,三者可作为同时兼顾压缩比率和压缩时间的优选。     

Shedding light on black boxes in protein identification

Performing a well thought‐out proteomics data analysis can be a daunting task, especially for newcomers to the field. Even researchers experienced in the proteomics field can find it challenging to follow existing publication guidelines for MS‐based protein identification and characterization in detail. One of the primary goals of bioinformatics is to enable any researcher to interpret the vast amounts of data generated in modern biology, by providing user‐friendly and robust end‐user applications, clear documentation, and corresponding teaching materials. In that spirit, we here present an extensive tutorial for peptide and protein identification, available at http://compomics.com/bioinformatics‐for‐proteomics . The material is completely based on freely available and open‐source tools, and has already been used and refined at numerous international courses over the past 3 years. During this time, it has demonstrated its ability to allow even complete beginners to intuitively conduct advanced bioinformatics workflows, interpret the results, and understand their context. This tutorial is thus aimed at fully empowering users, by removing black boxes in the proteomics informatics pipeline.     

Automated programming for bioinformatics algorithm deployment

Many bioinformatics solutions suffer from the lack of usable interface/platform from which results can be analyzed and visualized. Overcoming this hurdle would allow for more widespread dissemination of bioinformatics algorithms within the biological and medical communities. The algorithms should be accessible without extensive technical support or programming knowledge. Here, we propose a dynamic wizard platform that provides users with a Graphical User Interface (GUI) for most Java bioinformatics library toolkits. The application interface is generated in real-time based on the original source code. This platform lets developers focus on designing algorithms and biologists/physicians on testing hypotheses and analyzing results. AVAILABILITY: The open source code can be downloaded from: http://bcl.med.harvard.edu/proteomics/proj/APBA/.     

The role of bioinformatics in pathway curation

Diagrams and models of biological pathways are useful tools in biology. Pathway diagrams are mainly used for illustrative purposes for instance in textbooks and in presentations. Pathway models are used in the analysis of genomic data. Bridging the gap between diagrams and models allows not only the analysis of genomics data and interactions but also the visualisation of the results in a variety of different ways. The knowledge needed for pathway creation and curation is available from three distinct sources: databases, literature and experts. We describe the role of bioinformatics in facilitating the creation and curation of pathway.     

Open source tools and toolkits for bioinformatics: significance, and where are we?

This review summarizes important work in open-source bioinformatics software that has occurred over the past couple of years. The survey is intended to illustrate how programs and toolkits whose source code has been developed or released under an Open Source license have changed informatics-heavy areas of life science research. Rather than creating a comprehensive list of all tools developed over the last 2-3 years, we use a few selected projects encompassing toolkit libraries, analysis tools, data analysis environments and interoperability standards to show how freely available and modifiable open-source software can serve as the foundation for building important applications, analysis workflows and resources.     

Microarray analysis using bioinformatics analysis audit trails (BAATs)

Bioinformatics analysis plays an integrative role in genomics and functional genomics. The ability to conduct quality managed, hypothesis-driven bioinformatics analysis with the plethora of data available is mandatory. Biological interpretation of this data is dependent on versions of databases, programs and the parameters used. Thus, tracking and auditing the analyses process is important. This paper outlines what we term Bioinformatics Analysis Audit Trails (BAATs) and describes YABI, a bioinformatics environment that implements BAATs. YABI can incorporate most bioinformatics tools within the same environment, making it a valuable resource.     

CINEMA-MX: a modular multiple alignment editor

Analyzing and visualizing multiple sequence alignments is a common task in many areas of molecular biology and bioinformatics. Many tools exist for this purpose, but are not easily customizable for specific in-house uses. Here we report the development of an editor, CINEMA-MX, that addresses these issues. CINEMA-MX is highly modular and configurable, and we present examples to illustrate its extensibility. AVAILABILITY: The program and full source code, which are available from http://www.bioinf.man.ac.uk/dbbrowser/cinema-mx, are being released under a combination of the LGPL and GPL, for Unix or Windows platforms.