Open Source Bio-Informatics Software
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Bio-Informatics Software
View 53 business solutionsBrowse free open source Bio-Informatics software and projects below. Use the toggles on the left to filter open source Bio-Informatics software by OS, license, language, programming language, and project status.
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Top-Rated Free CRM SoftwareHubSpot is an AI-powered customer platform with all the software, integrations, and resources you need to connect your marketing, sales, and customer service. HubSpot's connected platform enables you to grow your business faster by focusing on what matters most: your customers.
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Jmol
An interactive viewer for three-dimensional chemical structures.
Over 1,000,000 page views per month. Jmol/JSmol is a molecular viewer for 3D chemical structures that runs in four independent modes: an HTML5-only web application utilizing jQuery, a Java applet, a stand-alone Java program (Jmol.jar), and a "headless" server-side component (JmolData.jar). Jmol can read many file types, including PDB, CIF, SDF, MOL, PyMOL PSE files, and Spartan files, as well as output from Gaussian, GAMESS, MOPAC, VASP, CRYSTAL, CASTEP, QuantumEspresso, VMD, and many other quantum chemistry programs. Files can be transferred directly from several databases, including RCSB, EDS, NCI, PubChem, and MaterialsProject. Multiple files can be loaded and compared. A rich scripting language and a well-developed web API allow easy customization of the user interface. Features include interactive animation and linear morphing. Jmol interfaces well with JSpecView for spectroscopy, JSME for 2D->3D conversion, POV-Ray for images, and CAD programs for 3D printing (VRML export). -
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Gwyddion
Scanning probe microscopy data visualisation and analysis
A data visualization and processing tool for scanning probe microscopy (SPM, i.e. AFM, STM, MFM, SNOM/NSOM, ...) and profilometry data, useful also for general image and 2D data analysis. -
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Bowtie, an ultrafast, memory-efficient short read aligner for short DNA sequences (reads) from next-gen sequencers. Please cite: Langmead B, et al. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol 10:R25.
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SAM (Sequence Alignment/Map) is a flexible generic format for storing nucleotide sequence alignment. SAMtools provide efficient utilities on manipulating alignments in the SAM format. The main samtools source code repository moved to GitHub in March 2012. For ongoing development since then, see http://github.com/samtools/samtools
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OpenClinic GA
Open Source Integrated Hospital Information Management System
OpenClinic GA is an open source integrated hospital information management system covering management of administrative, financial, clinical, lab, x-ray, pharmacy, meals distribution and other data. Extensive statistical and reporting capabilities. -
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BWA is a program for aligning sequencing reads against a large reference genome (e.g. human genome). It has two major components, one for read shorter than 150bp and the other for longer reads.
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PyRx - Virtual Screening Tool
Virtual Screening software for Computational Drug Discovery
PyRx is a Virtual Screening software for Computational Drug Discovery that can be used to screen libraries of compounds against potential drug targets. PyRx enables Medicinal Chemists to run Virtual Screening from any platform and helps users in every step of this process - from data preparation to job submission and analysis of the results. While it is true that there is no magic button in the drug discovery process, PyRx includes docking wizard with easy-to-use user interface which makes it a valuable tool for Computer-Aided Drug Design. PyRx also includes chemical spreadsheet-like functionality and powerful visualization engine that are essential for Rational Drug Design. Please visits PyRx home page to learn more about PyRx and watch videos on how to use it. -
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BBMap
BBMap short read aligner, and other bioinformatic tools.
This package includes BBMap, a short read aligner, as well as various other bioinformatic tools. It is written in pure Java, can run on any platform, and has no dependencies other than Java being installed (compiled for Java 6 and higher). All tools are efficient and multithreaded. BBMap: Short read aligner for DNA and RNA-seq data. Capable of handling arbitrarily large genomes with millions of scaffolds. Handles Illumina, PacBio, 454, and other reads; very high sensitivity and tolerant of errors and numerous large indels. Very fast. BBNorm: Kmer-based error-correction and normalization tool. Dedupe: Simplifies assemblies by removing duplicate or contained subsequences that share a target percent identity. Reformat: Reformats reads between fasta/fastq/scarf/fasta+qual/sam, interleaved/paired, and ASCII-33/64, at over 500 MB/s. BBDuk: Filters, trims, or masks reads with kmer matches to an artifact/contaminant file. ...and more! -
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smina
Scoring and Minimization with AutoDock Vina
A fork of AutoDock Vina that is customized to better support scoring function development and high-performance energy minimization. smina is maintained by David Koes at the University of Pittsburgh and is not directly affiliated with the AutoDock project. -
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Systems Biology Markup Language (SBML)
A file format for exchanging computational models in systems biology
The Systems Biology Markup Language (SBML) is an XML-based description language for representing computational models in systems biology. Visit the project web site to learn more. -
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VarScan
Variant detection in next-generation sequencing data
Variant detection in massively parallel sequencing. For one sample, calls SNPs, indels, and consensus genotypes. For tumor-normal pairs, further classifies each variant as Germline, Somatic, or LOH, and also detects somatic copy number changes. THE LATEST VERSION IS AVAILABLE ON GITHUB -
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Toxtree: Toxic Hazard Estimation
Toxicity prediction for chemical compounds
A GUI application which estimates toxic hazard of chemical compounds. The latest version includes the following toxicity prediction modules: -Cramer rules (oral toxicity) -Toxicity mode of action via Verhaar scheme -Skin irritation and Eye irritation prediction -Benigni / Bossa rulebase for mutagenicity and carcinogenicity prediction -START biodegradation and persistence prediction -Skin sensitisation reactivity domain -Kroes TTC Decision tree -SMARTCyp - Cytochrome P450-Mediated Drug Metabolism and metabolites prediction -Structure Alerts for the in vivo micronucleus assay in rodents (ISSMIC) -Structural Alerts for Functional Group Identification (ISSFUNC) -Structural alerts associated with covalent protein binding and DNA binding. - Ames mutagenicity Toxtree provides a plugin framework to incorporate different approaches to the estimation. Platform independent (written in Java), with the use of The Chemistry Development Kit. -
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Subread
High-performance read alignment, quantification and mutation discovery
The Subread software package is a tool kit for processing next-gen sequencing data. It includes Subread aligner, Subjunc exon-exon junction detector and featureCounts read summarization program. Subread aligner can be used to align both gDNA-seq and RNA-seq reads. Subjunc aligner was specified designed for the detection of exon-exon junction. For the mapping of RNA-seq reads, Subread performs local alignments and Subjunc performs global alignments. Subread and Subjunc were published in the following paper: Yang Liao, Gordon K Smyth and Wei Shi. "The Subread aligner: fast, accurate and scalable read mapping by seed-and-vote", Nucleic Acids Research, 2013, 41(10):e108 -
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Open Information Systems for Emergency Services (Open ISES) is a community of software developers, paramedics, EMTs, law enforcement & fire fighters working together to create open source software & training materials for the emergency service community.
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MUMmer is a modular system for the rapid whole genome alignment of finished or draft sequence. This package provides an efficient suffix tree library, seed-and-extend alignment, SNP detection, repeat detection, and visualization tools.
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A set of tools for working with VCF files, such as those generated by the 1000 Genomes Project. This project is migrating to github: https://vcftools.github.io/
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kallisto
Near-optimal RNA-Seq quantification
kallisto is a program for near-optimal quantification of transcript abundances from RNA-Seq data, or more generally of target sequences using high-throughput sequencing reads. It is based on the idea of using pseudoalignment to quickly determine reads and targets’ compatibility, with no need for alignment. According to benchmarks done on a Mac desktop computer, kallisto can quantify 30 million human bulk RNA-seq reads in less than 3 minutes with just the read sequences and a transcriptome index, that in itself can take more than 10 minutes to build. And since it uses pseudoalignment, it is robust to errors in the reads and preserves the key information needed for quantification. This makes kallisto not only fast but highly accurate as well. In many benchmarks, it even greatly outperforms existing tools. -
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Design PCR primers from DNA sequence. Widely used (190k Google hits for "primer3"). From mispriming libraries to sequence quality data to the generation of internal oligos, primer3 does it. C&perl. Developers/testers/documenters needed.
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miRprimer
Automatic design of primers for miR-specific RT-qPCR
miRprimer designs primers for PCR amplification of microRNAs as described (Busk (2014). A tool for design of primers for microRNA-specific quantitative RT-qPCR. BMC Bioinformatics. 15, 29) for use with the method miR-specific RT-qPCR (Cirera, S., and Busk, P.K. (2014). Quantification of miRNAs by a simple and specific qPCR method. Methods in Molecular Biology. 1182, 73-81.). The program was written in Ruby and is available as source code for developers and as an .exe file for easy use. -
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FusionCatcher
Somatic fusion-genes finder for RNA-seq data
FusionCatcher searches for novel/known somatic fusion genes, translocations, and chimeras in RNA-seq data (paired-end reads from Illumina NGS platforms like Solexa and HiSeq) from diseased samples. The aims of FusionCatcher are: - very good detection rate for finding candidate fusion genes, - very easy to use (i.e. no a priori knowledge of databases and bioinformatics is needed in order to run FusionCatcher), - very good detection of challenging fusion genes, like for example IGH fusions, CIC fusions, DUX4 fusions, CRLF2 fusions, TCF3 fusions, etc. - to be as automatic as possible (i.e. the FusionCatcher will choose automatically the best parameters in order to find candidate fusion genes, e.g. finding automatically the adapters, building the exon-exon junctions automatically based on the length of the input reads, etc.) while providing the best possible detection rate for finding fusion genes. -
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HaploPainter - a pedigree and haplotypes drawing tool written in Perl/Tk. The software processes pedigree information in standard linkage formats combining haplotype information outputs from Simwalk, Genehunter, Allegro and Merlin.
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CodonW is a programme designed to simplify the Multivariate analysis (correspondence analysis) of codon and amino acid usage. It was written in ANSI compliant C. See the README file for more information.
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HaploView is a Java based tool for use by biologists in the study of genetic haplotype data. It provides a quick, easy interface to many common tasks involved in such analyses. Please go to the homepage below for the latest version!
Guide to Open Source Bio-Informatics Software
Open source bioinformatics software is a type ofsoftware that can be used for analyzing biological data. It is often used in research and development to understand complex biological phenomena, such as genetic mutations or protein folding. Open source bioinformatics software makes it possible to process large amounts of data quickly and efficiently. This type of software has been developed to provide easy access for researchers who are working on challenging problems related to genetics, genomics, proteomics, systems biology, and other areas of biotechnology.
The main purpose of open-source bio-informatics software is to help analyze large sets of data from various sources such as DNA sequencing, microarray analysis, imaging technologies or gene expression studies. These programs allow biologists and other researchers the opportunity to explore their results through powerful visualization tools like 3D models or maps. The accessibility provided by these programs can lead to profound discoveries that may otherwise have remained hidden due to the difficulty in processing information manually. Additionally, open source bio-informatics provides an avenue for collaborative work between multiple disciplines which has allowed researchers from around the world to collaborate more effectively and share discoveries more easily than ever before.
Open source bioinformatics tools are becoming increasingly popular with scientists because they are typically free and highly customizable allowing them flexibility when addressing their own research questions rather than being confined by a tool’s limited capabilities although they do require coding knowledge on the part of users who wish to customize them beyond what is available in the standard packages provided by vendors that produce closed source platforms which have commercial viability but lack the flexibility afforded by open source platforms. A wide variety of open-source projects exist within the sphere ranging from applications specific tasks like gene sequence alignment all way up to more expansive projects such as Galaxy—a cloud-based system for reproducible scientific computing –and CyVerse —an online environment specifically designed for advanced data analysis. These systems enable biologists and students alike to access powerful tools so as realize greater scientific understanding as well as capacity build with respect developing knowledge graphs which are essential components powering some AI initiatives aimed at drug discovery.
Features Offered by Open Source Bio-Informatics Software
- Sequence Analysis: This feature allows for the analysis of genetic sequences, both DNA and RNA. It can be used to identify genes, compare sequences between different organisms, and assemble entire genomes from smaller pieces of data.
- Data Visualization: Open source bio-informatics software provides powerful tools for visualizing complex data sets. It can be used to explore relationships between genes and proteins in ways that are not possible with simple text-based files.
- Pattern Recognition: Bio-informatics software is capable of automatically recognizing patterns in large amounts of biological data. This can help scientists better understand how biological systems work by finding correlations among variables that were previously unknown or difficult to detect.
- Software Libraries: Many bio-informatics packages come with an extensive library of pre-configured programs which allow users to quickly analyze particular types of data without having to write their own code from scratch. These libraries often provide access to a range of popular algorithms and routines that enable sophisticated research tasks like gene expression profiling or predictive modeling.
- Database Connectivity: Modern open source bio-informatics software makes it easy for researchers to connect their analysis applications directly with online databases such as GenBank or Ensembl for quick access to additional information about genes and proteins. This speeds up research and streamlines the process of exploring new lines of inquiry into biology problems.
What Are the Different Types of Open Source Bio-Informatics Software?
- Genome Analysis: Genome analysis software helps scientists to study the structure, composition, and evolution of the genetic information within a species. This type of software helps researchers understand the functionality of DNA molecules and analyze different genetic elements within a genome.
- Sequence Alignment: Sequence alignment software is used to compare biological sequences from different species. It can help assess similarities between two or more sequences while allowing scientists to look for evolutionary markers in different organisms.
- Structural Biology: Structural biology software tools are used to predict and analyze three-dimensional structure data of proteins, nucleic acids and other macromolecular assemblies. This type of software can be useful in understanding how certain proteins interact or activating enzymes based on their 3D shape.
- Data Visualization: Data visualization software helps researchers visualize large datasets generated by bioinformatics experiments such as gene expression data or microarray data. These tools allow researchers to quickly identify patterns or correlations between datasets that could lead to new discoveries about an organism’s genetics or behavior.
- High-Throughput Sequencing Analysis: High-throughput sequencing analysis (HTSA) technology helps biologists analyze high volumes of sequence data quickly and accurately. HTSA technology allows scientists to gain insight into genomes from vastly larger amounts of data than was previously possible with traditional methods, providing powerful insights into genetics research projects.
- Bioinformatics Workflow Engines: Bioinformatics workflow engines provide a platform for automating operations related to bioinformatics pipelines such as sequence assembly, annotation, and comparative genomics workflows. Data flow through these automated workflows allows researchers to perform multiple tasks at once without manually running each task separately which saves time and resources while improving accuracy.
Benefits Provided by Open Source Bio-Informatics Software
- Cost Efficiency: Open source bio-informatics software is free to download and use, eliminating the need for expensive licenses. This makes it an attractive option for researchers on a budget who would otherwise not be able to afford such advanced tools.
- Continuous Updates: Since open source software is developed by a large community of volunteers and developers, users can benefit from continuous updates and bug fixes. This ensures that they always have access to the latest version with enhanced features and improved usability.
- Flexibility: As open source software is largely customizable, users can modify the code to fit their own specific needs or modify existing algorithms in order to gain better performance. This allows them to tailor the software’s functionality for their particular project requirements without relying on complex programming skills or hefty budgets.
- Security: As open source projects are typically backed by the community, any security issues are usually addressed quickly ensuring that data remains safe from malicious actors or other vulnerabilities.
- Global Network of Users: By using open source bio-informatics software, users gain access to a global network of peers where they can share ideas, collaborate on projects and ask questions about problems they're facing. Not only does this help speed up development time but it also gives them access to a wealth of knowledge previously unavailable through proprietary solutions.
Types of Users That Use Open Source Bio-Informatics Software
- Bioinformatics Researchers: Bioinformatics researchers are typically biologists, chemists, computer scientists, and engineers looking to understand and explore the world of bioinformatics. By using open source software to create models and simulations of biological processes, they can gain insight into things such as disease pathways and biomarkers.
- Biomedical Professionals: Biomedical professionals such as doctors, nurses, medical technicians, etc., also make use of open source bio-informatics software in order to diagnose illnesses more accurately. They may use data collected from these programs to analyze patient information or look for trends in treatment response.
- Business Users: Business users may not necessarily be directly involved with biology or healthcare but they often need quick access to powerful software solutions that can process large amounts of data quickly. Open source bio-informatics tools provide an efficient way for business users to access the data they need without having to invest time or money in costly commercial products.
- Scientists/Academicians: Academicians have long been among the most frequent adopters of open source software. Scientists from many different fields often build on each other's work by using open source programs, allowing them to stay ahead of their peers when it comes to groundbreaking research results. This is especially true for those researching topics related to the life sciences such as genetics or genomics which are heavily reliant on accurate computations made possible through bio-informatics software tools like NCBI GenBank.
- Educators & Students: Many educators within the field of biology will make use of open source bio-informatics tools when conducting class projects or assignments related topics in biology since these tools offer great ways for students learn how real science is conducted on a day-to-day basis while also introducing them to new concepts such as gene sequencing and pathology analysis in a safe environment with minimal risk associated with experimentation failures due its cost effectiveness compared with traditional learning techniques involving lab materials and equipment. Additionally many students utilize this type of free technology combined with freely available datasets found online so that they can undertake their own personal research projects which would usually require expensive laboratory equipment otherwise
How Much Does Open Source Bio-Informatics Software Cost?
Open source bio-informatics software is typically free to use and does not cost anything. The developers of these types of software are often volunteers, meaning that they work on it as a passion project or out of the goodness of their heart instead of for monetary gain. Of course, you may be able to find some commercial software options that include extra features, but the majority of open source bio-informatics tools are no-cost.
In addition to these fully featured programs, there are many other specific components related to bio-informatics such as data analysis algorithms, databases and visualizations which can be found for free on sites like GitHub and SourceForge or from individual developers looking to share their work as Open Source contributions without any fees attached. Many research labs also have resources available that provide open access data which can significantly reduce the costs associated with obtaining necessary information for a particular project or study since it’s already been collected by somebody else who has made it available online publicly at no charge.
What Software Does Open Source Bio-Informatics Software Integrate With?
Open source bio-informatics software can be integrated with various types of software, such as open source statistical packages like R and Python, databases like MySQL, MongoDB and even graphical user interfaces (GUI) development tools like Qt. This allows users to leverage the power of open source bio-informatics software by combining it with other types of software that may have different strengths or provide different functionality. For example, a GUI development tool like Qt can allow developers to create custom visualizations or interactive displays for data gathered through an open source bio-informatics program like BLAST. Additionally, the use of databases like MySQL or MongoDB can help store and manage large amounts of biological data in a way that is easy for scientists to access and analyze. With this remarkable integration capability, open source bio-informatics programs are becoming more widely used because they provide an easy way to combine programming libraries with powerful scientific analysis tools.
Recent Trends Related to Open Source Bio-Informatics Software
- Increased Use of Open Source Software: Open source bioinformatics software has seen an increase in usage in recent years due to its ability to provide free access to high-quality software. This has enabled researchers and students with limited resources to easily access the tools they need for their work.
- Expansion of Open Source Software: The number of open source bioinformatics tools available is increasing rapidly. This expansion is largely due to developers from around the world collaborating to create new software solutions and improve existing ones.
- Growing Interest in Data Science: As bioinformatics is closely linked to data science, there has been a growing interest in open source bioinformatics software due to its ability to easily visualize and analyze large datasets.
- Increasing Ease of Use: Open source bioinformatics software has become increasingly user-friendly over time, making it easier for non-programmers to use and understand. This makes it easier for scientists, students, and other professionals to leverage the power of these tools without needing extensive programming knowledge.
- Improved Collaboration: The availability of open source bioinformatics software has made it easier for researchers from different disciplines and locations to collaborate on projects more efficiently than ever before.
How Users Can Get Started With Open Source Bio-Informatics Software
The first step is to determine what kind of data you will be analyzing. Bio-informatics software often works best on datasets that are organized in certain ways, and you’ll want to make sure the program you choose can work with the kind of data you have. You may need to adjust or reformat your dataset for optimal results.
Once you have determined the type of data you will analyze, it's time to start looking at available options for open source bio-informatics software. There are plenty out there, ranging from basic programs like MUSCLE (Multiple Sequence Alignment) and BLAST (Basic Local Alignment Search Tool) to more complex programs like Cytoscape (platform for biological network analysis). Each program has its own strengths and weaknesses; look into which one fits best for your needs.
Once you decide on a specific program, download or install it onto your computer as needed - most programs provide easy methods for installation and setup instructions. Then explore the documentation accompanying the program; this should give details about how to use all of its features, as well as any potential problems that might arise during the process.
Finally, be sure to read up on any relevant tutorials or guides related to using your chosen program; these can help jumpstart your project by teaching basics of usage, common pitfalls when using the software, and other tips and tricks that could come in handy down the line. Much like determining types of datasets beforehand – investing time upfront here often makes things smoother in handling large datasets later on.