Displaying 1 to 20 from 21 results

dash-cytoscape - Interactive network visualization in Python and Dash, powered by Cytoscape.js

  •    Python

A Dash component library for creating interactive and customizable networks in Python, wrapped around Cytoscape.js. If you want to install the latest versions, check out the Dash docs on installation.

bistro - A library to build and execute typed scientific workflows

  •    OCaml

bistro is an OCaml library to build and run computations represented by a collection of interdependent scripts, as is often found in applied research (especially computational biology). The library provides a datatype to represent scripts (including metadata and dependencies), an engine to run workflows and a standard library providing components for popular tools (although mostly related to computational biology and unix for now).

dna2vec - dna2vec: Consistent vector representations of variable-length k-mers

  •    Python

Dna2vec is an open-source library to train distributed representations of variable-length k-mers. Note that this implementation has only been tested on Python 3.5.3, but we welcome any contributions or bug reporting to make it more accessible.

open-computational-neuroscience-resources - A publicly-editable collection of open computational neuroscience resources


Computational neuroscience means one of two things: 1. analysis of neuroscientific data, whether it be fMRI imaging data, electrode recordings from a mouse running in a maze, statistical modeling of that data, or something else, and 2. simulation of neural systems, including modeling many compartments of a single neuron, or large networks of model neurons with simple individual behavior. These endeavors require expensive data from wet-lab experiments, but much of the work can be accomplished using everyday, consumer-grade laptop and desktop computers! Indeed, the biggest barrier to entry is not hardware, data, or expense, but rather time and passion to learn the tools needed for such computational science. Coupled with the great tools coming out of the modern Data Science movement, open data, open simulation models, and open analysis and simulation tools for computational neuroscience make it easier than ever to learn or even contribute to the study of the brain! The resources below should be more than enough to provide anyone with the means to begin learning or working in computational neuroscience, at no cost other than time and a modern computer. Note: This is intended as a list of resources to help with neuroscientific pursuits, as opposed to artificial intelligence pursuits. More broadly, I've made a similar repo-list of general open science resources here.

biopandas - Working with molecular structures in pandas DataFrames

  •    Python

If you are a computational biologist, chances are that you cursed one too many times about protein structure files. Yes, I am talking about ye Goode Olde Protein Data Bank format, aka "PDB files." Nothing against PDB, it's a neatly structured format (if deployed correctly); yet, it is a bit cumbersome to work with PDB files in "modern" programming languages -- I am pretty sure we all agree on this.

screenlamp - screenlamp is a Python toolkit for hypothesis-driven virtual screening

  •    Python

Screenlamp is a Python package for facilitating ligand-based virtual screening workflows and toolkits with hypothesis-driven filtering steps. The official documentation is available at https://psa-lab.github.io/screenlamp.

regulatory-prediction - Code and Data to accompany "Dilated Convolutions for Modeling Long-Distance Genomic Dependencies", presented at the ICML 2017 Workshop on Computational Biology

  •    Python

Code and Data to accompany "Dilated Convolutions for Modeling Long-Distance Genomic Dependencies", presented at the ICML 2017 Workshop on Computational Biology, by Ankit Gupta and Alexander Rush. Data forthcoming. Please email ankitgupta@college.harvard.edu if you have any questions in the meantime. Ankit is current working on a unified set of deep learning benchmarks for genomics tasks. All of these results and a cleaned up version of the code will be included with that repository, and it will be linked here. In the meantime, feel free to email us if you have any questions.

BioSymbols.jl - Nucleic and amino acid primitive types and alphabets

  •    Julia

BioSymbols defines the primitive type for nucleic acids and amino acids that are used for many other packages in the BioJulia software ecosystem. If you are interested in the cutting edge of the development, please check out the master branch to try new features before release.

idseq-web - Infectious Disease Sequencing Platform

  •    Javascript

IDseq is an unbiased global software platform that helps scientists identify pathogens in metagenomic sequencing data. A collaborative open project of Chan Zuckerberg Initiative and Chan Zuckerberg Biohub.

Biochat - Collective intelligence-driven decentralization of biological data on the blockchain

  •    Common

Currently, there exist thousands of biological databases (e.g., Gene Expression Omnibus (GEO), miRBase, TCGA, Human Epigenome Atlas, etc.) containing terabytes of publicly available data. With so much data scattered in so many centralized locations, it is virtually impossible to facilitate database interoperability at scale, making the computational process of unified and comprehensive data-driven biological discovery difficult. We consider the impact of decentralizing the metadata describing the contents of these respective data files, and computing on these metadata descriptions through a combination of human-based and machine learning-based approaches within the context of a blockchain infrastructure. Specifically, we seek to test the hypothesis that collective intelligence (as quantified by observing and learning from multiple users' behavior/interactions with metadata recorded on the blockchain), in combination with training sources such as literature citations and natural language processing techniques, is better and more effective in finding natural groupings and emergent structure within large volumes of biological data compared to arbitrarily depositing/storing data files in an ad-hoc manner in a centralized database(s).

biosemble - AI assembly of biological wordnets

  •    Python

biosemble is a Python natural language processing (NLP) software program for assembling biological wordnets from structured and unstructured biological text. Structured text includes resources like biologically relevant dictionaries and encyclopedias, while unstructured text includes biologically relevant textbooks. biosemble uses part-of-speech (POS) tagging to assemble similar words across a wide array of biologically relevant dictionaries and encyclopedias.

fastheatmap - Super high performance interactive heatmap software

  •    Javascript

This software is thanks to the amazing work done by MANY people in the open source community of plotly. The work on fastheatmap was done by Bohdan B. Khomtchouk, Ph.D. and James R. Hennessy.

HeatmapGenerator - Cross-platform GUI binary executable for making biological heatmaps

  •    C++

HeatmapGenerator is a graphical user interface software program written in C++, R, and OpenGL to create customized gene expression heatmaps from RNA-seq and microarray data in medical research. HeatmapGenerator can also be used to make heatmaps in a variety of other non-medical fields. HeatmapGenerator is peer-reviewed published software (http://www.scfbm.org/content/9/1/30). When using this software, please cite: [Khomtchouk et al.: “HeatmapGenerator: High performance RNAseq and microarray visualization software suite to examine differential gene expression levels using an R and C++ hybrid computational pipeline.” Source Code for Biology and Medicine, 2014 9:30].

Microscope - ChIP-seq/RNA-seq analysis software suite for gene expression heatmaps

  •    R

We propose a user-friendly ChIP-seq and RNA-seq software suite for the interactive visualization and analysis of genomic data, including integrated features to support differential expression analysis, interactive heatmap production, principal component analysis, gene ontology analysis, and dynamic network visualization. MicroScope is financially supported by the United States Department of Defense (DoD) through the National Defense Science and Engineering Graduate Fellowship (NDSEG) Program. This research was conducted with Government support under and awarded by DoD, Army Research Office (ARO), National Defense Science and Engineering Graduate (NDSEG) Fellowship, 32 CFR 168a.

PubData - Smart search engine for all bioinformatics databases worldwide

  •    Python

PubData is a search engine and file retrieval system for all bioinformatics databases worldwide. PubData searches biomedical FTP data in a user-friendly fashion similar to how PubMed searches biomedical literature. PubData is hosted as both a web application and a standalone graphical user interface (GUI) software program, while PubMed is hosted as an online web server. PubData is built on novel network programming and natural language processing algorithms that can patch into the FTP servers of any user-specified bioinformatics database, query its contents, and retrieve files for download. PubData is written in the Python programming language (specifically, Django and PyQt4). PubData can remotely search, access, view, and retrieve files from the deeply nested directory trees of any major bioinformatics database via a local computer network. By assembling all major bioinformatics databases under the roof of one software program, PubData allows the user to avoid the unnecessary hassle and non-standardized complexities inherent to accessing databases one-by-one using an Internet browser. More importantly, it allows a user to query multiple databases simultaneously for user-specified keywords (e.g., human, cancer, transcriptome). As such, PubData allows researchers to search, access, view, and download files from the FTP servers of any major bioinformatics database directly from one centralized location. By using only a GUI or web application, PubData allows the user to simultaneously surf multiple bioinformatics FTP servers directly from the comfort of their local computer.

shinyheatmap - Web application for enormous biological heatmaps

  •    R

Purpose: To create user-friendly, highly customizable static and interactive biological heatmaps of big datasets in a web browser. If you are using shinyheatmap in your work, please cite the paper (http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0176334).

python_biologist - Python Programming for Biologists

  •    Jupyter

These seminars are presented to researchers on the National Institutes of Health (NIH) campus in Bethesda, Maryland. This seminar series is brought to you, at no cost, by the NIAID Bioinformatics and Computational Biosciences Branch (BCBB).

CellO - CellO: Gene expression-based hierarchical cell type classification using the Cell Ontology

  •    Jupyter

CellO (Cell Ontology-based classification) is a Python package for performing cell type classification of human RNA-seq data. CellO makes hierarchical predictions against the Cell Ontology. These classifiers were trained on nearly all of the human primary cell, bulk RNA-seq data in the Sequence Read Archive. For more details regarding the underlying method, see the paper: Bernstein, M.N., Ma, J., Gleicher, M., Dewey, C.N. (2020). CellO: Comprehensive and hierarchical cell type classification of human cellswith the Cell Ontology. iScience, 24(1), 101913.

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