Best Free Bioinformatics Software

Ruffus is a computation pipeline library for python. It is open-sourced, powerful and user-friendly, and widely used in science and bioinformatics. Ruffus is designed to allow scientific and other analyses to be automated with the minimum of fuss and the least effort. Suitable for the simplest of tasks. Handles even fiendishly complicated pipelines which would cause make or scons to go cross-eyed and recursive. No "clever magic", no pre-processing. Unambitious, the lightweight syntax which tries to do this one small thing well. Ruffus is available under the permissive MIT free software license. This permits free use and inclusion even within proprietary software. It is good practice to run your pipeline in a temporary, “working” directory away from your original data. Ruffus is a lightweight python module for building computational pipelines. Ruffus requires Python 2.6 or higher or Python 3.0 or higher.

Developed in the Data Sciences Platform at the Broad Institute, the toolkit offers a wide variety of tools with a primary focus on variant discovery and genotyping. Its powerful processing engine and high-performance computing features make it capable of taking on projects of any size. The GATK is the industry standard for identifying SNPs and indels in germline DNA and RNAseq data. Its scope is now expanding to include somatic short variant calling and to tackle copy number (CNV) and structural variation (SV). In addition to the variant callers themselves, the GATK also includes many utilities to perform related tasks such as processing and quality control of high-throughput sequencing data and bundles the popular Picard toolkit. These tools were primarily designed to process exomes and whole genomes generated with Illumina sequencing technology, but they can be adapted to handle a variety of other technologies and experimental designs.

Galaxy is an open source, web-based platform for data-intensive biomedical research. If you are new to Galaxy start here or consult our help resources. You can install your own Galaxy by following the tutorial and choosing from thousands of tools from the tool shed. This instance of Galaxy is utilizing infrastructure generously provided by the Texas Advanced Computing Center. Additional resources are provided primarily on the Jetstream2 cloud via ACCESS, and with support from the National Science Foundation. Quantify, visualize, and summarize mismatches in deep sequencing data. Build maximum-likelihood phylogenetic trees. Phylogenomic/evolutionary tree construction from multiple sequences. Merge matching reads into clusters with TN-93. Remove sequences from a reference that are within a given distance of a cluster. Perform maximum-likelihood estimation of gene essentiality scores.

Explore hundreds of curated single-cell transcriptome datasets, along with your own data, through interactive visualizations and analytics. The software also supports multimodal omics, CITE-seq, TCR-seq, and spatial transcriptomic. Interactively explore the world's largest single-cell expression database. Access and query insights from a single-cell database of millions of cells, fully annotated with cell type labels and experimental metadata. Not just creating a gateway to published works, BioTuring Browser is an end-to-end solution for your own single-cell data. Import your fastq files, count matrices, Seurat, or Scanpy objects, and reveal the biological stories inside them. Get a rich package of visualizations and analyses in an intuitive interface, making insight mining from any curated or in-house single-cell dataset become such a breeze. Import single-cell CRISPR screening or Perturb-seq data, and query guide RNA sequences.

This free tool delivers stunning visualizations of your genomic data that give you the power to see what is occurring at each base pair in your samples. GenomeBrowse runs as a native desktop application on your computer. No longer do you have to sacrifice speed and interface quality to obtain a consistent cross-platform experience. It was developed with performance in mind to deliver a faster and more fluid browsing experience than any other genome browser available. GenomeBrowse is also integrated into the powerful Golden Helix VarSeq variant annotation and interpretation platform. If you love the visualization experience of GenomeBrowse, check out VarSeq for filtering, annotating, and analyzing your data before utilizing the same visualization interface. GB can display all your alignment data. Looking at all your samples in one view can help you spot contextually relevant findings.

MEGA (Molecular Evolutionary Genetics Analysis) is a powerful and user-friendly software suite designed for analyzing DNA and protein sequence data from species and populations. It facilitates both automatic and manual sequence alignment, phylogenetic tree inference, and evolutionary hypothesis testing. MEGA supports a variety of statistical methods including maximum likelihood, Bayesian inference, and ordinary least squares, making it an essential tool for comparative sequence analysis and understanding molecular evolution. MEGA offers advanced features such as real-time caption generation to help explain the results and methods used in analysis and the maximum composite likelihood method for estimating evolutionary distances. The software is equipped with robust visual tools like the alignment/trace editor and tree explorer and supports multi-threading for efficient processing. MEGA can be run on multiple operating systems, including Windows, Linux, and macOS.

Cufflinks assemble transcripts, estimate their abundances and test for differential expression and regulation in RNA-Seq samples. It accepts aligned RNA-Seq reads and assembles the alignments into a parsimonious set of transcripts. Cufflinks then estimates the relative abundances of these transcripts based on how many reads support each one, taking into account biases in library preparation protocols. Cufflinks was originally developed as part of a collaborative effort between the Laboratory for Mathematical and Computational Biology. In order to make it easy to install Cufflinks, we provide a few binary packages to save users from the occasionally frustrating process of building Cufflinks, which requires that you install the libraries. Cufflinks includes a number of tools for analyzing RNA-Seq experiments. Some of these tools can be run on their own, while others are pieces of a larger workflow.

The Bioconductor project aims to develop and share open source software for precise and repeatable analysis of biological data. We foster an inclusive and collaborative community of developers and data scientists. Resources to maximize the potential of Bioconductor. From basic functionalities to advanced features, our tutorials, guides, and documentation have you covered. Bioconductor uses the R statistical programming language and is open source and open development. It has two releases each year and an active user community. Bioconductor provides Docker images for every release and provides support for Bioconductor use in AnVIL. Founded in 2001, Bioconductor is an open-source software project widely used in bioinformatics and biomedical research. It hosts over 2,000 R packages contributed by over 1,000 developers, with over 40 million downloads per year. Bioconductor has been cited in more than 60,000 scientific publications.

Turn your single-cell RNA sequencing data into meaningful insight with Cellenics software. Biomage hosts a community instance of Cellenics, an open source analytics tool for single-cell RNA sequencing data that has been developed at Harvard Medical School. It enables biologists to explore single-cell datasets without writing code and helps scientists and bioinformaticians to work together more effectively. It takes you from count matrices to publication-ready figures in just a few hours and can be integrated seamlessly with your workflow. It’s fast, interactive, and user-friendly. And it’s cloud-based, secure, and scaleable. The Biomage-hosted community instance of Cellenics is free for academic researchers with small/medium-sized datasets (up to 500,000 cells). It’s used by 3000+ academic researchers studying cancer, cardiovascular health, and developmental biology.

Legacy R&D software is a drain on scientific potential. It slows down R&D progress, scatters data across silos, and wipes out institutional knowledge. Benchling is the industry’s leading life sciences R&D cloud. Accelerate, measure, and forecast R&D – from discovery through bioprocessing – all in one place. A suite of seven natively unified applications that accelerate R&D at all levels. Codeless configuration, open integration, and dashboards tailored to your needs. Deep life science R&D and consulting expertise ensure ongoing success. Benchling is a unified R&D platform, so you spend less time entering and hunting for data, and more time working together to move your research forward. Scientists, managers, and executives can optimize R&D output with complete visibility into experimental context, program performance, and resource utilization.